Organic Xenobiotics and Plants
Plant Ecophysiology Volume 8
Series Editors:
Luit J. De Kok University of Groningen, The Netherlands
Malcolm J. Hawkesford Rothamsted Research, United Kingdom
Ineke Stulen University of Groningen, The Netherlands
Aims & Scope: The Springer Series in Plant Ecophysiology comprises a series of volumes that deals with the impact of biotic and abiotic factors on plant functioning and physiological adaptation to the environment. The aim of the Plant Ecophysiology series is to review and integrate the present knowledge on the impact of the environment on plant functioning and adaptation at various levels: from the molecular, biochemical and physiological to a whole plant level. This series is of interest to scientists who like to be informed of new developments and insights in plant ecophysiology, and can be used as advanced textbooks for biology students.
The titles published in this series are listed at the end of this volume.
Organic Xenobiotics and Plants From Mode of Action to Ecophysiology
Edited by
Peter schröder Helmholtz Zentrum München Department of Microbe Plant Interactions Neuherberg Germany and
christopher d. collins University of Reading School of Human and Environmental Science Department of Soil Science, Reading, UK
Editors Peter Schröder Helmholtz Zentrum München Department of Microbe Plant Interactions Neuherberg Germany
Christopher D. Collins University of Reading School of Human and Environmental Science Department of Soil Science, Reading, UK
ISSN 1572-5561 e-ISSN 1572-5561 ISBN 978-90-481-9851-1 e-ISBN 978-90-481-9852-8 DOI 10.1007/978-90-481-9852-8 Springer Dordrecht Heidelberg London New York © Springer Science+Business Media B.V. 2011 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology is the eighth volume in the Plant Ecophysiology series. It presents an overview of the impact of organic xenobiotics on plants. Natural as well as agro-ecosystems are frequently exposed to a huge range of natural or synthetic substances. These, xenobiotics, may originate from both natural (fires, soil and microbes) and anthropogenic (air, water and soil pollution) sources. The latter, alone, represents an unmanageable plethora of compounds fed by the demands of our industry and daily life. In 2009 the 50 millionth synthetic organic compound was registered with Chemical Abstract Services. While many of these synthetic chemicals have no direct nutritional value or significance in metabolism, once released into the environment, they may negatively affect plant function, ecosystems and consequently human health. Divided into three sections; Principles of transport, deposition and uptake, Pollutant degradation and ecosystem remediation from enzymes to whole plants and Tools and novel applications, the volume has a broad coverage ranging from the cellular to the ecosystem and continental scale which has not been previously collated in this area of science. Principles of transport, deposition and uptake – Transport of organic xenobiotics is regularly described by the physico-chemical characteristics of the chemical of concern, however recent research indicates some of the current models may be incorrect, particularly for the more soluble compounds. Within a forest ecosystem there may be numerous anthropogenic and biogenic sources of chlorinated compounds which can induce negative impacts on the vegetation. Volatile organic compounds and semi-volatile organic compounds are both of interest because of their transport in the atmosphere away from point sources, and therefore the potential for negative impacts at significant distances from the origin of emission. The subsequent effects maybe either direct on the vegetation or indirect via accumulation of these pollutants in the food chain. Several case studies are provided to illustrate these processes and provide a deeper understanding of their underlying principles. Pollutant degradation and ecosystem remediation from enzymes to whole plants – Plant detoxification mechanisms include well developed enzymatic detoxification cascades, characterized by P450 monooxygenases, glutathione and glucosyl transferases, v
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peroxidases and ABC-transporters, plus a plethora of less known enzymatic reactions, the regulation of which may be altered in response to foreign chemicals. These enzymatic reactions may be characterised on the metabolic and the transcriptional level. Detoxification enzymes play a critical role in plant tolerance and degradation of a wide range of industrial pollutants and particular success has been observed in the degradation of industrial dyes in wastewaters. New remediation systems are also emerging where phytoremediation is part of an integrated approach to clean up contaminated soils. The emerging recognition of the roles of the rhizosphere as both a sink for xenobiotic metabolites and as an active zone for the degradation unwanted constituents of contaminated soils are evaluated in this volume. Tools and novel applications – When knowledge is accumulated it is vital that it is translated and used to develop new tools and applications relevant to the problems outlined above. This may be achieved by developing systems which use plants to determine the toxic loads in ecosystems or when developing computer programmes to determine the toxic potential of novel chemicals or those which have not been subject to detailed experimental analysis. Finally it is important that new technologies developed in other fields, such as high resolution metabolomic and proteomic analysis, are appied to the fields of environmental pollution, plant tolerance and metabolism of xenobiotics. Therefore we believe this volume will provide an excellent overview regarding the latest developments in research on plant performance under stress conditions induced by organic xenobiotics, from the cellular to the system level. The book will be of interest to a wide audience, from graduate students to senior researchers in a wide range of disciplines including plant ecology, plant biochemistry, agriculture and environmental assessment. It will also be of practical interest to environmentalists, policy makers and resource managers. Peter Schröder Chris Collins
Contents
Part I Principles of Transport, Deposition and Uptake Plant Uptake of Xenobiotics............................................................................ Chris D. Collins, Ian Martin, and William Doucette Haloorganics in Temperate Forest Ecosystems: Sources, Transport and Degradation............................................................. Nicholas Clarke, Milan Gryndler, Hans-Holger Liste, Reiner Schroll, Peter Schröder, and Miroslav Matucha Semivolatiles in the Forest Environment: The Case of PAHs...................... Claudio A. Belis, Ivo Offenthaler, and Peter Weiss
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Part II Case Studies A Case Study: Uptake and Accumulation of Persistent Organic Pollutants in Cucurbitaceae Species................................................. András Bittsánszky, Gábor Gullner, Gábor Gyulai, and Tamas Komives Trichloroacetic Acid in the Forest Ecosystem............................................... Miroslav Matucha and Peter Schröder
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Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transboundary Transport – Results of a Case Study with Special Emphasis on the Spatial Distribution of Polycyclic Aromatic and Chlorinated Air Borne Pollutants................... 105 Rolf Herzig, Christoph Bieri, Andreas Weber, and Peter Straehl
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art III Pollutant Degradation and Ecosystem Remediation P from Enzymes to Whole Plants New Perspectives on the Metabolism and Detoxification of Synthetic Compounds in Plants................................................................. 125 Robert Edwards, David P. Dixon, Ian Cummins, Melissa Brazier-Hicks, and Mark Skipsey Using Plants to Remove Foreign Compounds from Contaminated Water and Soil.................................................................................................. 149 Jean-Paul Schwitzguébel, Valérie Page, Susete Martins-Dias, Luísa C. Davies, Galina Vasilyeva, and Elena Strijakova Biodegradation of Organic Xenobiotic Pollutants in the Rhizosphere....... 191 Hassan Azaizeh, Paula M.L. Castro, and Petra Kidd Bioindicators and Biomonitors: Use of Organisms to Observe the Influence of Chemicals on the Environment........................ 217 Bernd Markert and Simone Wünschmann SAR Based Computational Models as Decision Making Tools in Bioremediation................................................................................... 237 Nick Price and Qasim Chaudhry State-of-the-Art Chemical Analyses: Xenobiotics, Plant Proteomics, and Residues in Plant Based Products........................................................... 261 Touradj Solouki, Mohammad Ali Khalvati, Mahsan Miladi, and Behrooz Zekavat Index.................................................................................................................. 307
Part I
Principles of Transport, Deposition and Uptake
Plant Uptake of Xenobiotics Chris D. Collins, Ian Martin, and William Doucette
Abstract Plant uptake of organic chemicals is an important process when considering the risks associated with land contamination, the role of vegetation in the global cycling of persistent organic pollutants, the potential for contamination of the food chain and the design of pesticides. There have been some significant advances in our understanding of the processes of plant uptake of organic chemicals in recent years; most notably there is now a better understanding of the air to plant transfer pathway, which may be significant for a number of chemicals. This chapter identifies the key processes involved in the plant uptake of organic chemicals and also identifies other important factors in the uptake process e.g., plant lipid content, growth dilution and plant metabolism.
Introduction This chapter provides an introduction to the factors that influence plant uptake and accumulation of organic chemicals. There are four principal uptake pathways, as illustrated in Fig. 1. These pathways include passive and active uptake through the root system, gaseous and particulate deposition to above-ground shoots, and direct contact between soil and plant tissues. In addition to describing each pathway, this chapter also considers the controlling factors for uptake and the ways these can be used to model these processes.
C.D. Collins (*) Department of Soil Science, University of Reading, Reading RG6 6DW, United Kingdom e-mail:
[email protected] I. Martin Human Health Division, Environment Agency, Olton Court, Solihull B92 7HX, United Kingdom W. Doucette Utah Water Research Laboratory, Utah State University, Logan, UT 84322-8200, USA
P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_1, © Springer Science+Business Media B.V. 2011
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C.D. Collins et al. Evaporation from leaf Deposition of particles followed by desorption into leaf
Gaseous deposition to leaf Transport in the phloem
Transport in the transpiration stream Volatilisation from soil
Suspension of soil particles by wind and rain
Desorption from soil followed by root uptake from soil solution
Fig. 1 Principal pathways for plant uptake of organic chemicals
Root Uptake Soil-Root Interactions In general, plant roots are the most important site for uptake of chemicals from soil (Bell 1992). Root systems have been shown to take up organic chemicals from both water and air. In general, this uptake process has been shown to involve passive and diffusive transport, with chemicals carried into the plant during the natural transpiration cycle. Active uptake has been shown for a few organic chemicals including the phenoxy acid herbicides (Bromilow and Chamberlain 1995). Plants may actively transport biological organic compounds such amino acids (El-Naggar et al. 2009), but they cannot actively import more complex organic forms such as proteins from the soil (Rentsch et al. 2007). Experiments on the uptake of non-ionised chemicals from a hydroponic solution showed that uptake consisted of two stages (Briggs et al. 1983; Cousins and Mackay 2001). Firstly, equilibration between the chemical concentration in the aqueous phase within the plant root and the external solution, and secondly, chemical sorption on to lipophilic root solids. These solids include lipids in membranes and cell walls (Cousins and Mackay 2001). Uptake form the external solution is often expressed as a root concentration factor (RCF) which is the ratio of chemical
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concentration in the root to the concentration found in an external solution (Briggs et al. 1983; Shone and Wood 1977). Lipophilic organic chemicals possess a greater tendency to partition into plant root lipids than hydrophilic chemicals. Polycyclic aromatic hydrocarbons (PAHs), chlorobenzenes, polychlorinated biphenyl (PCBs), and polychlorinated dibenzop-dioxins/dibenzofurans (PCDD/Fs) have all been found at elevated levels in plant roots (Duarte-Davidson and Jones 1996; Wild et al. 1992). Briggs et al. (1983) found a linear relationship between the octanol-water partition coefficient (KOW) of non-ionised chemicals and the observed root concentration factor, based on experiments using O-methylcarbamoyloximes and substituted phenylureas and their uptake by barley plants. Environmental scientists often use the octanol-water partition coefficient (KOW) as a surrogate for chemical lipophilic tendency (Schröder and Collins 2002). Wild et al. (1992) categorised non-ionised organic chemicals with log KOW > 4 as having a high potential for retention in plant roots. While, Cousins and Mackay (2001) suggested that for organic chemicals with log KOW < 2 and a Henry’s Law constant (H) of less than 100 cm3 cm−3, the plant aqueous phase was the most important storage compartment. Although chemical properties are important predictors of uptake potential, the physiology and composition of the plant root itself is also a significant influence. Trapp and Pussemeir (1991) found that the relationship derived by Briggs et al. (1983) appeared to overestimate uptake of organic carbamates by the common bean plant. One explanation for such differences in uptake potential is the varying types and amounts of lipids in root cells (Bromilow and Chamberlain 1995). However, the available data across a range of plant species is limited (Collins et al. 2006a). Chemical transfers from the soil into the root are primarily mediated by the uptake of soil pore water during plant transpiration. Therefore, the factors that influence the chemical concentration in pore water also exert control over the passive uptake process. Organic chemicals can be sorbed or bound to several components in soil including clays, iron oxides, and organic matter, although it is the latter that usually exerts the strongest influence on the pore water concentration. The chemical transfers from the soil into plants are strongly influenced by chemical concentration in the soil porewater. As the organic matter content of a soil increases (typically measured using the weight fraction of organic carbon present), so the proportion of the chemical in the porewater decreases and consequently the total amount of chemical taken up by the shoot decreases. In addition, the optimum chemical lipophilicity for uptake decreases, reflecting the increase in competitive sorption between plant lipids and soil organic carbon. Several researchers including Karickhoff (1981) have found empirical relationships between a chemical’s lipophilicity and its affinity to sorb to soil organic matter. These QSAR relationships have been described as over simplistic (Doucette 2003).
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Fig. 2 Variation in predicted TSCF with chemical octanol-water partition coefficient (Briggs et al. 1983; Hsu et al. 1990; Dettenmaier et al. 2009; Burken and Schnoor 1998)
Transfer from Roots to Other Plant Parts Although the root is the most important site for chemical entry from soil, the edible fractions for many plants are the stems, leaves, tubers and fruits, and only if a chemical accumulates in these parts is there a potential risk to human health. Water and solutes are transported upward from the root into other plant parts through the xylem by mass flow resulting from a pressure gradient. This driving force is created during transpiration, where water is drawn in through the root system to replace evaporative losses from stomata within the leaves, and is supplemented by capillary action (McFarlane 1995). In order for chemicals taken up into plant roots to reach the xylem, they must penetrate a number of plant tissues: the epidermis, cortex, endodermis and pericycle. At the endodermis the chemical must pass through at least one cell membrane, and it is the combination of their aqueous solubility and their solubility in the lipid-rich cell membrane that determines their potential movement into roots from the soil pore water and subsequent transport to other plant parts via the xylem. This process is often described by a transpiration stream concentration factor (TSCF). Three of these TSCF relationships all suggest an uptake maximum between high and low log KOW values (in the range two to four depending on the individual relationship). The uptake potential is proposed to be a balance between reduced potential for the less lipophilic or polar chemicals to cross cell membranes on the one side, and by the more lipophilic chemicals being retained by lipids in the endodermis on the other. However, the exact reasons are not fully understood. More recent work with a significantly wider range and number of compounds, particularly
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where log KOW < 1, has proposed a sigmoidal relationship with a maximum TSCF at Log KOW of 1 (Dettenmaier et al. 2009). While the direction of flow within the xylem is generally from the root to the shoot, movement within the phloem can be bi-directional. The primary role of the phloem is to carry sucrose formed by photosynthesis in the leaves to storage areas including fruits and tubers, where it is converted to starches and complex sugars After transport in the stem, water and solutes diffuse laterally into adjacent tissues and may become concentrated in plant shoots, tubers and fruits (McFarlane 1995) This is a two-step process beginning with equilibrium partitioning between water in the vascular system and the aqueous solution in cell tissues, followed by sorption to cell walls. Briggs et al. (1983) and Barak et al. (1983) showed that partitioning to plant stems is linearly proportional to chemical lipophilicity for non-ionised organic chemicals. Therefore, the lipid composition of above-ground plant tissues is likely to be an important factor determining chemical retention and accumulation.
Soil-Root Interactions for Ionic Chemicals The previous discussion has focused on neutral organic chemicals, that is, compounds that do not appreciably ionise under ambient soil and plant solution conditions. Most industrial chemicals do not ionise readily in the subsurface. However, chemicals such as amines, carboxylic acids, phenols (including chloro- and nitrophenols), and some pesticides may appreciably ionise under ambient soil conditions (USEPA 1996). Although uptake of electrolytes into plants has been extensively studied, there are few predictive models available because of the additional complexity required to understand such systems (Trapp 2000, 2004). Most ionisable organic chemicals are described as weak acids or weak bases by their potential to dissociate into ions in aqueous solution (McFarlane 1995). The extent of ionisation of a weak acid can be estimated using Equation 1, where both the solution pH and the acid dissociation constant (pKa) of the chemical are known (USEPA 1996). For example, at a solution pH of 7, phenol (pKa of 9.9) is only 0.2% ionised while pentachlorophenol (pKa of 4.7) is over 99% ionised.
Φ n,acid =
[HA ] = 1 + 10 pH − pKa −1 ( ) [HA ][A − ]
Where: Fn, acid is the fraction of neutral species present for organic acids (dimensionless)[HA] is the equilibrium concentration of organic acid (mol L−1)[A-] is the equilibrium concentration of the organic anion (mol L−1)pH is the acidity of the aqueous solution (dimensionless)pKa is the acid dissociation constant of the chemical (dimensionless) As noted previously, the xylem is the principal plant structure for the transport of water and chemicals to the shoot. Chemicals penetrating into the plant root system and the xylem must pass through one or more cell membranes.
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C.D. Collins et al. Table 1 Relationship between the acid dissociation coefficient (pKa), lipophilicity (log KOW) and phloem and xylem mobility (After Trapp 2004)
Log Kow pKa
Phloem and xylem mobile −3 to 0 0 – non-ionised
Xylem mobile only 0–4 6 – non-ionised
Non-sytemic 4–7 0 – non-ionised
Cell membranes contain proteins known as “proton pumps” that regulate the flow of charged ions from inside to outside the cell (and vice versa). This creates an electrochemical gradient and the movement of ions across an electrically charged membrane is driven by chemical and electrical potential and is generally described by the Nernst-Planck equation (Trapp 2004). Since the chemical environment within a cell may vary from the external solution, the degree of dissociation may increase or decrease within plant compartments. For example, a compound that is predominantly in a neutral form in a slightly acidic soil solution may dissociate in the mildly alkaline conditions of the cell cytoplasm. These ionised molecules may subsequently be unable to pass through the charged cell membrane and become trapped within the cell (the “ion trap effect”). This can lead to the strong phloem transport of weak acids (Trapp 2004). It is therefore critical that in assessing uptake of weak organic acids and bases that models consider not only the partitioning behaviour of lipophilic compounds between water and cellular lipids, but also the degree to which they are partitioned into ionic and non-ionic forms within cell compartments. Bromilow and Chamberlain (1995) attempted to classify uptake potential for organic chemicals based on both their ionisation potential (as defined by pKa) and their lipophilic tendency (as defined by KOW; Table 1). Chemicals were both phloem and xylem mobile. Most industrial chemicals have a log KOW between two and six and would therefore only be xylem mobile. Phloem mobility is unlikely to be significant for root uptake of most industrial chemicals, but will be important for the foliar uptake of hydrophilic and readily ionised chemicals such as certain amines, phenols, pesticides and herbicides. Trapp (2004) concluded that there remain considerable gaps in our knowledge about the uptake of ionic organic compounds, including the measurement and prediction of their cell membrane permeabilities and the effect of ionic complexation processes.
Leaf Uptake Vapour or Gas Uptake from Ambient Air In addition to the root system, another potential pathway for plant uptake is the absorption of chemical vapour from ambient air by shoots. This pathway differs from root uptake because it is mediated via gaseous exchange rather than through aqueous solution. As a result, this pathway is likely to be important not just for
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highly volatile pollutants but those with a strong preference to partition to air rather than water Bell (1992). This has been shown to be the main uptake pathway into above ground plant parts for a variety of organic chemicals, including PAHs (Nakajima et al. 1995), PCBs (Bohme et al. 1999) and tetra- and hexa-chlorinated PCDD/Fs (Welsch-Paulsch et al. 1995; Meneses et al. 2002). Chemicals enter the leaf by either crossing the cuticle or by entering directly through the stomata, which regulate the exchange of carbon dioxide and oxygen (McCrady 1994; Bacci et al. 1992). It is also the principal route by which water vapour from the transpiration stream is lost by the plant. After entering the leaf, chemicals diffuse into intercellular air spaces and partition to the aqueous and lipophilic phases of adjacent plant tissues. Lipophilic leaf tissues include interior lipids, such as membrane and storage lipids, resins and essential oils, and surface lipids, such as cutin and cuticular waxes (Riederer 1995). Topp et al. (1986) concluded that since substances absorbed by leaves must have passed through a cell wall covered by a cuticle, the process of penetration may be somewhat different from that of absorption by root cells. For cuticle absorption, cutin and wax composition are probably more important than thickness, with surface wax concentration correlated with resistance to foliar penetration (Topp et al. 1986). Tolls and McLachlan (1994) and Hauk et al. (1994) have suggested that, in terms of the storage of organic chemicals, plant leaves can be divided into two compartments: a relatively small surface compartment with rapid uptake and clearance kinetics, and a larger reservoir compartment with relatively slow chemical migration. The composition of the larger reservoir is believed to influence the retention of organic chemicals, but the physiological relationship between the two compartments has not been established. Air-to-plant concentration factors for a variety of organic chemicals have been estimated by empirical studies (Bacci and Gaggi 1986, 1987; Bacci et al. 1990a, b). Various studies have reported a good correlation between shoot uptake and chemical properties including the octanol-water partition coefficient (KOW), Henry’s Law constant, molecular weight and the octanol-air partition coefficient (Topp et al. 1986; Tolls and McLachlan 1994; Paterson et al. 1991; Ryan et al. 1988; Komp and McLachlan 1997). McLachlan (1999) used knowledge of the partitioning behaviour of organic chemicals to build an interpretative framework to identify those compounds most likely to be dominated by foliar uptake. Gaseous uptake is the primary pathway for chemicals with an octanol-air partition coefficient (log KOA) less than 11, although between log KOA of 8.5–11 this process will be kinetically limited as the plant will not come into contact with enough air for it to become saturated with the chemical and will not approach equilibrium.
Particulate Deposition on Plant Surfaces Organic chemicals bound to soil particles may be deposited on above-ground leaves and shoots as a result of wind resuspension or rain splash, and is a well-recognised
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phenomenon for metals and radionuclides. Kao and Venkataraman (1995) found that after taking into account the relatively high deposition velocity of soil particles, local soils accounted for 70–90% and 20–40% of total PCDD/F deposition in urban and rural areas respectively. Hulster and Marschner (1993) found that direct contact between soil particles and plant surfaces was a significant uptake pathway for PCDD/Fs in hay. Nakajima et al. (1995) investigated the uptake of PAHs from the atmosphere into Azalea leaves and suggested that for benzo(a)pyrene and perylene, dry deposition of suspended particles with subsequent permeation into the cuticle represents the major pathway of contamination. Welsch-Paulsch et al. (1995) also determined that the dry deposition of large particles represented an important uptake pathway for hepta- and octo-chlorinated PCDDs into grass leaves. Wet deposition is the process of gravitational coagulation of solid particles with water droplets. It is thought to be the dominant deposition mechanism for organic chemicals with Henry’s Law constant of less than 1 × 10−6, although the majority of particles will not be intercepted by vegetation and will return directly to the soil Cousins and Mackay (2001). Relatively little information is available on the wet deposition of particle-bound and gaseous phase organic chemicals to plant surfaces, and this pathway is seldom considered when looking at the contamination of vegetation (Welsch-Paulsch et al. 1995). Dry deposition of particles to plant surfaces involves diffusion, interception, impaction and sedimentation processes (Chamberlain 1991). Once particles have been deposited on plant foliage they are subject to removal and degradation. There is currently insufficient understanding of the transfer of organic chemicals from particles into the leaf cuticle, and in particular, of how the physicochemical properties of organic compounds influence transfer from particles into the leaf. Dreicer et al. (1984) investigated the rain splash pathway for tomato plants and found that only finer soil particles were retained on plant surfaces. The octanol-air partition coefficient (KOA) has been suggested as a suitable indicator of the significance of the dry deposition contamination pathway for semi-volatile organic chemicals (McLachlan 1999). Cousins and Mackay (2001) suggested that for chemicals with a log KOA greater than nine, particle-bound deposition becomes relatively more important than gaseous deposition. Rikken et al. (2001) observed that differences between predicted uptake using the Trapp and Matthies (1995) model and experimental data were reduced when soil resuspension was accounted for in the comparison.
Other Factors Controlling Plant Uptake of Organic Chemicals Not surprisingly, plant uptake varies considerably between types of plant and individual species (Bell 1992). Factors likely to influence uptake include root growth and depth, transpiration rate, active uptake mechanisms, growth period, location of fruits and tubers, and the size and shape of leafy foliage (Buckley 1982).
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Accumulating Species Hulster et al. (1994) found that courgette and pumpkin plants accumulated and translocated higher concentrations of PCDD/Fs from (Simonich and Hites 1995) contaminated soil than other fruits and vegetables, and that this was the main contamination pathway for these species. Higher accumulation in the courgette and pumpkin family has been reported by other workers (Mattina et al. 2002; White 2002). The authors postulated that this was due to root exudates, unique to these species, which actively mobilise PCDD/Fs from the soil and make these compounds available for uptake and translocation. Lipid Composition Certain plant species, including carrots, have high lipid contents along with the presence of oil channels (Bell 1992; Topp et al. 1986). Experimental results have shown that carrots possess a greater potential to take up non-ionised chemicals than other crop species (Smelt and Leistra 1974; O’Connor et al. 1990; Schroll et al. 1992). Since lipophilic chemicals will tend to partition to leaf lipids, differences in the lipid content of plant foliage would seem to explain interspecies variability in organic chemical concentrations in plant foliage (Buckley 1982). Simonich and Hites (1995) found that interspecies variability was reduced in an experimental study of PAH uptake by normalising data to leaf lipid contents. In contrast, Bohme et al. (1999) found that interspecies variability in leaf/air bioconcentration factors could not be explained by variability in extractable lipid contents, and proposed that lipid composition was an important factor. In foliar uptake models, such as those of Muller et al. (1994) and Trapp and Matthies (1995), the leaf lipid content represents the most sensitive input parameter for lipophilic chemicals (Collins et al. 2006b). Foliage Several researchers have also found that the exposed surface area of the plant foliage influences the foliar uptake rate for a variety of species (McCrady and Maggard 1993; Schreiber and Schonherr 1992). Simonich and Hites (1995), suggested that this plant characteristic would be particularly significant if chemical partitioning to foliage did not approach equilibrium. Riederer (1995) reported two orders of magnitude difference in the permeability of plant cuticles for different species. Significant differences between plant species have been observed in mass loadings of soil-to-plant surfaces, as a result of contact with soil particles (Smith et al. 2000). Plant characteristics that affect the rate of particle deposition and retention include the exposed surface area of the foliage and the presence or absence of leaf hairs. Little and Wiffen (1977) observed that rough or hairy leaf surfaces were more efficient at collecting aerosols than smooth surfaces, explained by the increased surface area and projection of roughness elements through the leaf-air boundary
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layer. Pinder et al. (1991) found that the largest soil loadings occurred on broad-leaved species growing close to the ground. Plant transpiration will move some chemicals to sub-stomatal tissues within leaves, from which they will subsequently be lost by volatilisation (McFarlane et al. 1990). Volatilisation is likely to be a significant transport pathway for chemicals with high water solubility and vapour pressure. This pathway was found to be significant for benzene by Collins and Bell (1997), but has not been widely reported. Leaf loss and senescence can lead to the transfer of chemicals from plant foliage to the surrounding soil, thus reducing the chemical concentration in foliage. However, edible plant parts are likely to be harvested before leaf loss and senescence become significant chemical transport pathways. Plant Metabolism Chemicals accumulated in plants may be metabolised, thus reducing their concentration within the plant’s tissues (Schröder and Collins 2002). Although considerable research has been carried out on the metabolism of pesticides, only in recent years has there been any interest in industrial chemicals (Bell 1992). Plant metabolism has been reported for a small number of such chemicals, including benzo[a]pyrene, trichloroethylene and benzene (Brady et al. 2003; Shang and Gordon 2002; Ugrekhelidze and Phiriashvili 2000). Metabolic processes and rates will be specific to particular chemicals and possibly plant species. There have been a number of attempts to use genetically modified organisms to improve the metabolism of plants. Both by modifying the plants themselves (Rylott and Bruce 2009) and by adding modified endophytic bacteria (Weyens et al. 2009). Growth Dilution Plant growth has been recognised as an important variable in the plant uptake of radionuclides by root uptake and aerial deposition (Thorne et al. 2004). Over the growing season the biomass of plant increases, potentially diluting the chemical concentration within plant tissues relative to the flux of chemical uptake, and increasing the above-ground canopy for interception of aerial deposition. Plant growth has been simulated using models based on simple first-order exponential increases (assuming a doubling of biomass per unit time) or the Richard’s equation (Collins and Cunningham 2005). Collins and Finnegan (2010) noted the importance of growth dilution in predicting foliar uptake of organic chemicals. Using growth dilution, the Trapp and Matthies (1995) model predicted a plateau region in uptake above octanol-air partition coefficients of greater than seven, in the region predicted by McLachlan (1999) to be kinetically limited. This is consistent with a scenario where the uptake of chemical per unit mass is slower than the accumulation of dry matter per unit mass, an effect observed by Schwab et al. (1998) for uptake of naphthalene by older roots of tall fescue and alfalfa and the kinetically limited deposition described by McLachlan (1999).
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Conclusion There is sufficient known about the plant uptake of organic chemicals for simple predictions of the likely accumulation behaviour to be made for uptake from both soils and the atmosphere. However, as has been described in the proceding chapters a number of the controlling processes e.g. phloem transport, uptake differences between species, our knowledge is insufficient for us to be predict precisely the behaviour of individual chemicals. New techniques such as that of Two-photon excitation microscopy recently used by Wild et al. (1992) allow us to monitor chemicals in situ and will significantly improve our understanding of the behaviour of individual chemicals within particular plant species.
References Bacci E et al (1990a) Bioconcentration of organic-chemical vapors in plant-leaves – the azalea model. Chemosphere 21(4–5):525–535 Bacci E, Gaggi C (1987) Chlorinated-hydrocarbon vapors and plant foliage – kinetics and applications. Chemosphere 16(10–12):2515–2522 Bacci E, Gaggi C (1986) Chlorinated pesticides and plant foliage – translocation experiments. Bull Environ Contam Toxicol 37(6):850–857 Bacci E et al (1990b) Bioconcentration of organic-chemical vapors in plant-leaves – experimental measurements and correlation. Environ Sci Technol 24(6):885–889 Bacci E et al (1992) Chlorinated dioxins – volatilization from soils and bioconcentration in plantleaves. Bull Environ Contam Toxicol 48(3):401–408 Barak E, Jacoby B, Dinoor A (1983) Adsorption of systemic pesticides on ground stems and in the apoplastic pathway of stems, as related to lignification and lipophilicity of the pesticides. Pesticide Biochem Physiol 20(2):194–202 Bell RM (1992) Higher plant accumulation of organic pollutants from soils. US EPA, Washington, DC Bohme F, Welsch-Paulsch K, McLachlan MS (1999) Uptake of airborne semivolatile organic compounds in agricultural plants: Field measurements of interspecies variability. Environ Sci Technol 33(11):1805–1813 Brady CAL, Gill RA, Lynch PT (2003) Preliminary evidence for the metabolism of benzo(a) pyrene by Plantago lanceolata. Environ Geochem Health 25(1):131–137 Briggs GG et al (1983) Relationships between lipophilicity and the distribution of non-ionized chemicals in barley shoots following uptake by the roots. Pestic Sci 14(5):492–500 Bromilow RH, Chamberlain K (1995) Principles governing uptake and transport of chemicals. In: Trapp S, McFarlane JC (eds) Plant contamination: modelling and simulation of organic chemical processes. Lewis Publishers, London, pp 38–64 Buckley EH (1982) Accumulation of airborne polychlorinated-biphenyls in foliage. Science 216(4545):520–522 Burken JG, Schnoor JL (1998) Predictive relationships for uptake of organic contaminants by hybrid poplar trees. Environ Sci Technol 32(21):3379–3385 Chamberlain AC (1991) Radioactive aerosols, vol 3, Cambrige Environmental Chemistry Series. Cambridge University Press, Cambridge, p 255 Collins CD, Cunningham N (2005) Modelling the fate of sulphur35 in crops. 2. Development and validation of the CROPS-35 model. Environ Pollut 133(3):439–445 Collins CD, Fryer M, Grosso A (2006a) Plant uptake of non-ionic organic chemicals. Environ Sci Technol 40(1):45–52
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Nakajima D et al (1995) Seasonal-changes in the concentration of polycyclic aromatic- hydrocarbons in azalea leaves and relationship to atmospheric concentration. Chemosphere 30(3):409–418 O’Connor GA et al (1990) Plant uptake of sludge-borne PCBs. J Environ Qual 19(1):113–118 Paterson S, Mackay D, Gladman A (1991) A fugacity model of chemical uptake by plants from soil and air. Chemosphere 23(4):539–565 Pinder JE III et al (1991) Mass loading of soil particles on a pasture grass. J Environ Radioactiv 13:341–354 Rentsch D, Schmidt S, Tegeder M (2007) Transporters for uptake and allocation of organic nitrogen compounds in plants. FEBS Lett 581(12):2281–2289 Riederer M (1995) Partitioning and transport of organic chemicals in the foliage/atmosphere system. In: Trapp S, McFarlane JC (eds) Plant contamination – modelling and simulation of organic chemical processes. Lewis Pub, Boca Raton, FL Rikken MGJ, Lijzen JPA, Cornelese AA (2001) Evaluation of model concepts on human exposure. RIVM, Bilthoven, p 138 Ryan JA et al (1988) Plant ptake of non-ionic organic-chemicals from soils. Chemosphere 17(12):2299–2323 Rylott EL, Bruce NC (2009) Plants disarm soil: engineering plants for the phytoremediation of explosives. Trends Biotechnol 27(2):73–81 Schreiber L, Schonherr J (1992) Uptake of organic-chemicals in conifer needles – surface-adsorption and permeability of cuticles. Environ Sci Technol 26(1):153–159 Schröder P, Collins CJ (2002) Conjugating enzymes involved in xenobiotic metabolism of organic xenobiotics in plants. Int J Phytorem 4(4):247–265 14 Schroll R et al. (1992) Fate of C Terbutylazine in soil-plant systems. Sci Total Environ 123:377–389 Schwab AP, Al-Assi AA, Banks MK (1998) Adsorption of naphthalene onto plant roots. J Environ Qual 27(1):220–224 Shang TQ, Gordon MP (2002) Transformation of C14 trichloroethylene by poplar suspension cells. Chemosphere 47(9):957–962 Shone MGT, Wood AV (1977) Longitudinal movement and loss of nutrients, pesticides, and water in barley roots. J Exp Bot 28(105):872–885 Simonich SL, Hites RA (1995) Organic pollutant accumulation in vegetation. Environ Sci Technol 29(12):2905–2914 Smelt JH, Leistra MAE (1974) Hexachlorobenzene in soils and crops after soil treatment with pentachloronitrobenzene. Agric Environ 1:65–71 Smith JA et al (2000) Occurrence and phase distribution of polycyclic aromatic hydrocarbons in urban storm-water runoff. Water Sci Technol 42(3–4):383–388 Thorne M, Maul P, Robinson P (2004) The PRISM foodchain modelling software: model structures for PRISM 2.0. Report QRS-1198A-1. Food Standards Agency, London Tolls J, McLachlan MS (1994) Partitioning of semivolatile organic-compounds between Air and Lolium multiflorum (Welsh Ray Grass). Environ Sci Technol 28(1):159–166 Topp E et al (1986) Factors affecting the uptake of C14-labeled organic-chemicals by plants from soil. Ecotoxicol Environ Saf 11(2):219–228 Trapp S (2000) Modelling uptake into roots and subsequent translocation of neutral and ionisable organic compounds. Pest Manage Sci 56(9):767–778 Trapp S (2004) Plant uptake and transport models for neutral and ionic chemicals. Environ Sci Pollut Res 11(1):33–39 Trapp S, Matthies M (1995) Generic one compartment model for the uptake of organic chemicals by foliar vegetation. Environ Sci Technol 29:2333–2338 Trapp S, Pussemeir L (1991) Model calculations and measurements of uptake and translocation of carbamates by bean plants. Chemosphere 22:327–345 Ugrekhelidze V, Phiriashvili V (2000) Uptake and transformation of some water phenolic pollutants by common duckweed (Lemna minor L.). Fresenius Environ Bull 9(7–8):483–488 USEPA (1996) Soil screening guidance: technical background document, EPA/540/R95/128. United States Environmental Protection Agency, Washington, DC
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Welsch-Paulsch K, McLachlan MS, Umlauf G (1995) Determination of the principal pathways of polychlorinated Dibenzo-P-Dioxins and dibenzofurans to Lolium-Multiflorum (Welsh Ray Grass). Environ Sci Technol 29(4):1090–1098 Weyens N et al (2009) Bioaugmentation with engineered endophytic bacteria improves contaminant fate in phytoremediation. Environ Sci Technol 43(24):9413–9418 White JC (2002) Differential bioavailability of field-weathered p, p’-DDE to plants of the Cucurbita and Cucumis genera. Chemosphere 49(2):143–152 Wild SR et al (1992) Polynuclear aromatic-hydrocarbons in crops from long-term field experiments amended with sewage-sludge. Environ Pollut 76(1):25–32
Haloorganics in Temperate Forest Ecosystems: Sources, Transport and Degradation Nicholas Clarke, Milan Gryndler, Hans-Holger Liste, Reiner Schroll, Peter Schröder, and Miroslav Matucha
Abstract The halogens, most importantly fluorine, chlorine, bromine, and iodine, occur in nature as ions and compounds, including organic compounds. Halogenated organic substances (haloorganics) were long considered purely anthropogenic products; however, they are in addition a commonly occurring and important part of natural ecosystems. Natural haloorganics are produced largely by living organisms, although abiotic production occurs as well. A survey is given of processes of formation, transport, and degradation of haloorganics in temperate and boreal forests, predominantly in Europe. More work is necessary in order to understand the environmental impact of haloorganics in temperate and boreal forest soils. This includes both further research, especially to understand the key processes of formation and degradation of halogenated compounds, and monitoring of the substances in question in forest ecosystems. It is also important to understand the effect of various forest management techniques on haloorganics, as management can be used to produce desired effects. N. Clarke () Norwegian Forest and Landscape Institute, N-1431 Ås, Norway e-mail:
[email protected] M. Gryndler Czech Academy of Sciences, Institute of Microbiology, Vídeňská 1083, CZ-142 20 Prague, Czech Republic H.-H. Liste Julius Kühn-Institute (JKI) Federal Research, Centre for Cultivated Crops, Königin-Luise-Strasse 19, D-14195 Berlin, Germany R. Schroll Institute of Soil Ecology, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany P. Schröder Department of Microbe-Plant Interactions, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany M. Matucha Czech Academy of Sciences, Institute of Experimental Botany, Vídeňská 1083, CZ-142 20 Prague 4, Czech Republic P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_2, © Springer Science+Business Media B.V. 2011
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Introduction As a prerequisite to quantify the impact of halogenated pollutants, it is important to understand their sources, transport and fate in ecosystems, especially forests. The halogens are the elements fluorine, chlorine, bromine, iodine and the rare element astatine. They are found in nature in compounds, including organic compounds, or as ions. Halide ions and oxoanions are found in a number of minerals and in water, especially sea water. Halogenated organic compounds (haloorganics) were long considered to be almost entirely anthropogenic products, and there has been a lot of concern about the toxicity and environmental impacts of many of these, for example the effect of CFCs on stratospheric ozone, PCB concentrations in Arctic and sub-Arctic vertebrates, and the effect of DDT and other pesticides on non-target organisms. As a result, much work has been done to understand the cycling of these compounds in ecosystems, including forest ecosystems. From the 1970s on, it has been known that haloorganics are not only of anthropogenic origin but in addition a commonly occurring part of natural ecosystems (e.g. Lovelock 1975; Harper 1985; Wigilius et al. 1988). More than 3,800 haloorganics are known to be produced by living organisms (Gribble 2003). Most contain chlorine (2,200) or bromine (1,950), while a few contain iodine (95) or fluorine (100): a few hundred contain both chlorine and bromine (Gribble 2003). Although most are of marine origin, many are found in terrestrial ecosystems, produced by a wide variety of organisms, including humans (Gribble 2003). Some haloorganics can be of both natural and anthropogenic origin: examples include chloroacetic acids (Hoekstra et al. 1999a; Matucha et al. 2003a; Laturnus et al. 2005), chloroform (Hoekstra et al. 1998a, b; Haselmann et al. 2000a; Laturnus et al. 2002; McCulloch 2003), chlorinated phenols (Verhagen et al. 1998a, b; Winterton 2000), polychlorinated dibenzofurans (PCDFs) and dibenzodioxins (PCDDs) (Silk et al. 1997; Hoekstra et al. 1999b), methyl chloride (Lovelock 1975; Harper 1985; de Jong and Field 1997; Cox et al. 2003), and brominated trihalomethanes (Hoekstra et al. 1998b). In addition to biological formation, natural haloorganics can be produced abiotically, for example by volcanoes, biomass burning and diagenesis (Schöler and Keppler 2003). Examples include CF4 and other fluorinated organics that have been found in rocks (Harnisch et al. 2000) and PCDDs and PCDFs produced in forest fires (Kim et al. 2003). Halogen cycling in forest ecosystems as shown schematically in Fig. 1 is closely linked with the main halogen reservoirs in the hydrosphere, atmosphere and biosphere. Some haloorganics, both natural and anthropogenic, occurring in forest ecosystems have biocidal properties, e.g. trichloroacetic acid is phytotoxic, chlorinated dibenzodioxins strongly toxic and chloroform carcinogenic. In addition, some are persistent: large amounts of DDT have been found in soil samples from mountains in the Czech Republic more than 20 years after its application was banned
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Atmosphere
Hydrosphere
Biosphere
Soil
Fig. 1 Halogen cycling in forest ecosystems (bold arrows) is closely connected with the main halogen cycles and reservoirs shown (mainly with oceans)
(Kosubová et al. 2005). Thus, it remains important to understand their cycling in temperate forest ecosystems. This chapter will discuss the sources, transport and degradation of haloorganics, both natural and anthropogenic, in these ecosystems.
Sources Sea Salt Chloride has been suggested as a precursor to the formation of volatile chloroorganics by plants (Laturnus and Matucha 2008), so sources of chloride are important in understanding the cycling of chloroorganics. In coastal areas, the most important source of chloride is sea salt deposition, which decreases exponentially with distance from the coastal zone. There is also smaller scale variation: in Norway, the chloride concentrations in conifer needles have been related to distance from the nearest fjord (r2 = 0.34; Aamlid and Horntvedt 2002). The occurrence of organic chlorine in Swedish forest soil has been shown to be influenced by chloride deposition, which in turn is affected by wind direction and precipitation amount, as well as distance from the sea (Johansson et al. 2003a). Seasonal chloride deposition patterns can be traced in the concentrations of organic chlorine in the soil (Johansson et al. 2003b). The concentration of organic chlorine found in coniferous forest soils was significantly greater than that in deciduous forest soils (Johansson et al. 2003b). This was interpreted as being largely due to higher chloride deposition in the coniferous forests than in the deciduous forests, because of the higher scavenging effect of needles compared with leaves with respect to dry deposition. As tree canopies act as particle filters, dry deposition of chloride in forests will be affected by all factors influencing canopy size and structure, such as tree species and stand age (Clarke et al. 2009), and also crown condition. Tree felling is likely to affect chloride deposition: removal of the canopy may enhance wet deposition to the forest floor but will reduce dry deposition, while edge effects will increase it, so that the total effect on chloride deposition may be variable (Clarke et al. 2009).
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Erosion and Weathering Erosion and weathering are important sources of chlorine. An estimated 80% of riverine chlorine derives from weathering of Cl-containing minerals in sandstone and shales, and from thermal and mineral springs in volcanic areas (Graedel and Keene 1996). Locally, evaporites may be a source of chlorine. Although most chlorine input from weathering is likely to occur in the form of chloride, organic matter in both bedrock and quaternary deposits is a direct source of chloroorganics (Müller et al. 1996; Gribble 1998). Chlorine input to soils through weathering does not appear to have been quantified, and will in any case vary greatly, depending for example on local geology and climate. The fluoroorganic and chlorofluoroorganic compounds CF4, CF3Cl, CF2Cl2, CFCl3, CHF3, SF6 and NF3 have been found in fluorite, granites, basalts and other igneous and metamorphic rocks, while trifluoroacetic acid may also be geogenic (Harnisch et al. 2000).
Fires Combustion is a source of haloorganics. Prescribed burning with intense, multiple fires may result in increased activity of enzymes such as phenol oxidase in the soil, although this was not observed following a single, relatively cool fire (Boerner et al. 2005). PCDDs and PCDFs, as well as polycyclic aromatic hydrocarbons (PAHs), are produced in forest fires (Kim et al. 2003). PCDD/Fs and PAHs appear to be introduced into the soil by the deposition of wood ash, to which they have been adsorbed, which is the main agent affecting the concentrations of PCDD/Fs and PAHs in soils after fires (Kim et al. 2003). The dispersion of wood ash has a fertilizing effect which may compensate the loss of nutrients caused by the increasing biomass removal for bioenergy. This practice, however, may increase concentrations of PCDD/Fs and PAHs in forest soils to which it is applied.
Volcanoes The earth’s mantle is a major reservoir of chlorine (Graedel and Keene 1996). Jordan et al. (2000) studied samples from four volcanoes and found more than 300 organic substances. Among these were identified 5 fluorinated, 100 chlorinated, 25 brominated, and 4 iodinated compounds. The most abundant haloorganics were chlorinated methanes, unsaturated C2-chlorohydrocarbons, and chlorobenzene. Since the only CFC compound found was CFCl3, it was suggested that volcanoes are only a minor source of CFCs. In volcanic areas thermal and mineral springs are a source of riverine Cl (Graedel and Keene 1996).
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Other Natural Abiotic Sources Early diagenetic processes in soils, including peat, and sediments involving redoxsensitive elements such as iron are a source of haloorganics (Keppler et al. 2000, 2003, 2006; Schöler and Keppler 2003). Four classes of haloorganics (volatile alkyl halides, polar haloorganics such as haloacetates, PCDDs and halogenated humic substances) can be produced by geochemical processes at ambient temperatures (Field and Sierra 2003). Compounds such as CH3Cl, C2H5Cl, C3H7Cl, C4H9Cl, vinyl chloride and chloroethyne are formed during degradation of organic matter by an oxidant, e.g. Fe (III) in the presence of chloride but without microbial mediation (Keppler et al. 2000, 2002). Drainage of peatlands for forestry improves soil aeration, leading to increased potential for organic matter decomposition. This could lead to production of volatile chlorinated organic compounds during oxidation of organic matter (Keppler et al. 2000). In forest soil, spontaneous chlorination of soil organic matter (SOM) can occur abiotically as a result of the Fenton reaction (Fahimi et al. 2003). In addition, chloroorganics have been found in carbonaceous meteorites (Schöler et al. 2005).
Production by Organisms As mentioned above, more than 3,800 different haloorganics are produced by living organisms; most of which are marine. Many others are produced by terrestrial organisms, including bacteria, fungi, lichens, plants, insects, frogs, dogs, rats and humans (Gribble 2003). A large number of bacteria and fungi in soils produce chloroorganics during degradation of organic matter (Öberg et al. 1996; Myneni 2002; Leri et al. 2007). In forest soils, microorganisms appear to mediate both the formation of SOM containing Cl and the degradation of organic substances containing Cl during decomposition (Öberg 1998; Johansson et al. 2000) so both processes coexist (Asplund 1995; Öberg 2002). This section deals with the formation of haloorganics in soil: their degradation will be addressed later. Since SOM chlorination has a predominantly microbial character, it is likely to be affected by factors influencing microbial activity, such as soil temperature, moisture and chemistry (e.g. pH). Seasonal variation in the formation of organochlorine components has been observed by Svensson et al. (2007): an increase in chlorine to carbon ratios in late summer and autumn was interpreted as being due to increased microbial activity at this time, leading to increased enzymatic formation of reactive chlorine and oxidation, fragmentation and chlorination of organic matter. The effect of the soil chloride level on microbiota needs also to be investigated. Soil microbiota have been shown to play an important role during absorption and retention of chloride in solution by forest soil (Bastviken et al. 2007), where microorganisms first took up a higher amount of chloride which was then slowly released. Changes of the microbial community induced by increased inorganic chloride concentrations
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may lead to increased chlorination activity (Gryndler et al. 2008). Chloride absorption by microorganisms was also observed by Rohlenová et al. (2009). The level of nitrogen, which is often the nutrient limiting plant growth in boreal forests, is likely to affect microbial activity. Addition of ammonium nitrate to Swedish Norway spruce (Picea abies (L.) Karst.) forest soils caused a net decrease in the concentration of organic chlorine and a net increase in that of chloride (Johansson et al. 2001). This was interpreted as being mainly a result of dechlorination of the organic matter in the water leachable fraction. However, Bastviken et al. (2006) found no effect of nitrogen load on chlorine transformations. The difference in these results may be due to the lower nitrogen load used by Bastviken et al. (2006). The highest concentrations of chloroorganics are found in the surface soil and litter (Hjelm et al. 1995; McCulloch 2002; Offenthaler et al. 2008), so it is not surprising that chlorination activity was approximately 1,000-fold higher in the top organic layer than in deeper horizons of forest soils (Laturnus et al. 1995). The degree of chlorination increases in the top few dm of the soil, reaching concentrations of 2–10 mg organically bound chlorine per g organic carbon further down in the profile (Öberg 1998). In some cases, organisms produce chloroorganics for specific purposes (e.g. as antibiotics), while in others, they are produced as unintentional by-products.
Intentional Biotic Production Antibiotics Chlorinated compounds sometimes show antibiotic activity against a variety of other organisms. Various chlorinated antibiotics have been described over the decades and some are produced by fungi present in forest soils. Chloramphenicol and aureomycin (Winterton 2000) are the most well-known of them. Drosofilin A (p-methoxy-tetrachlorphenol) is a simple phenolic molecule with wide and strong antibiotic activity, produced by the basidiomycete fungus Psathyrella subatrata (Kavanagh et al. 1952). Another fungal antibiotic is strobilurin B, isolated from the fungus Strobilurus tenacellus (de Jong and Field 1997). It contains one chlorine atom per molecule and is active against a variety of phytopathogenic fungi and yeasts. Production of two other antibiotic compounds, mycorrhizin A (one chlorine atom per molecule) and chloromycorrhizin A (two chlorine atoms per molecule) (Trofast and Wickberg 1977), is a further example. These two antibiotic compounds are effective against the root rot agent Fomes annosus and are produced by some sterile mycelia forming mycorrhizae with Monotropa hypopytis, a mycotrophic inhabitant of northern temperate forests. Even though both antibiotics were isolated from laboratory culture of the said mycelia, it is likely that they are also produced in situ and protect colonised plants from potentially pathogenic fungi.
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However, not only microorganisms can produce chlorinated organic compounds to get an advantage over other organisms sharing the same locality. For example, bulbs of a higher terrestrial plant, Lilium maximowiczii, have been observed to accumulate at least seven different chlorinated derivatives of orcinol (5-methyl1,3-benzenediol), which are most probably produced from orcinol in the presence of plant chloroperoxidase. These derivatives are accumulated in plant tissues mainly if they are attacked by the pathogenic fungus Fusarium oxysporum (Monde et al. 1998). Because these derivatives are fungitoxic, they are probably produced by the plants to protect themselves from the fungal pest and may serve as examples of intentionally produced natural chloroorganics. It seems likely that increased concentrations of available chloroorganics may favour the development of some taxa of specialized bacteria (McRae et al. 2004), but the data on this topic are rather unique. This is most probably due to the methodological difficulties in the evaluation of the composition and state of soil microbial communities: molecular genetic methods such as gradient gel electrophoresis or restriction fragments based methods may help to solve this problem (Anderson and Cairney 2004). The Role of Chlorinated Organic Compounds in Microbial Metabolism As common natural components of forest soils, chlorinated organic compounds may be useful for soil biota in at least two other cases: as compounds providing an oxidation equivalent in anaerobic environments (e.g. anaerobic halorespiration) and as a source of energy in aerobic respiration processes. The first case is important mainly in wet soils with poor structure where anoxic environments may develop (moorlands, wetlands). Anaerobic respiration is common in some taxa of bacteria and chlorination is involved in several described cases of anaerobic halorespiration (based on reductive dehalogenation of a chlorinated substrate), some of them being further mentioned. Dechlorination of trichloroacetic acid (TCA) to dichloroacetic acid (DCA) is a source of oxidation equivalents in the production of elemental sulphur from sulphide. Sulphur can then be used by Trichlorobacter thiogenes in anaerobic oxidation (halorespiration) of acetate as an oxidation equivalent (De Wever et al. 2000). A similar role of 3-chlorobenzoate was observed in anaerobic metabolism of Desulfomonile tiedjei (Louie and Mohn 1999). Further, the use of 3-chloro-4hydroxyphenyl acetate as a source of an oxidation equivalent for Desulfitobacterium dehalogenans was observed by van de Pas et al. (2001). Organic compounds present in large quantities in temperate and boreal forest floor litter and the soil humic horizons are a relatively stable form of organic matter, which can be biodegraded slowly by some microorganisms. This is because of the aromatic chemical nature of many litter and humus components. It is probable that chlorination processes enhance cleavage of resistant aromatic rings in these components (Matucha et al. 2007), producing various chlorinated organics which are then accessible to utilization by biota. TCA and DCA obviously play an important role among these chlorinated products of aromatic ring cleavage. They can be utilized by
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microbial consortia as a source of energy (McRae et al. 2004) but are probably used preferentially in a cometabolic manner, not as a sole carbon source (Forczek and Gryndler, unpublished). TCA is formed in forest soil together with DCA and chloroform by microbial chlorination of organic matter. It is caused by hypochlorous acid (i.e. by the chlorine radical) formed by peroxidase-mediated oxidation of chloride (Hoekstra et al. 1999a) under normal forest soil moisture conditions. This chlorination mechanism resembles the mechanism of chlorination of dissolved humic substances in water, affording the same products. A minor part of SOM is chlorinated abiotically by the Fenton reaction (Fahimi et al. 2003; Matucha et al. 2007).
Unintentional Biotic Production Biodegradation and formation of chlorinated organic compounds is mediated by soil enzymes (peroxidases) of different specificity (Morrison and Schonbaum 1976; Osborne et al. 2006). If the specificity is low, the oxidative enzyme peroxidase might effectively mediate a reaction for which it is not produced. For example, the fully artificial chemical warfare agent adamsite (phenarsarzin chloride) was found to be converted by fungal manganese peroxidase in a cell-free reaction mixture (Haas et al. 2004). This organo-arsenic compound, releasing chloride, disappeared completely within 48 h. The fact that such an unnatural compound is degraded via the Mn peroxidase produced by an organism isolated from nature suggests a low substrate specificity of peroxidases produced by at least some soil organisms, including fungi. Chloroperoxidase produced by the marine fungus Caldariomyces fumago (the best known Mn2+-containing chloroperoxidase) has, besides its wide chlorination activity, also halophenol H2O2-dependent dehalogenation (dechlorination, defluorination, debromination and deiodination) activity (Osborne et al. 2006). Other studies provide a detailed analysis of the oxidative dechlorination of polychlorinated phenols by horseradish peroxidase (Ferrari et al. 1999) and lignin peroxidase (Hammel and Tardone 1988). Similarly, dehalogenation activity of fungal laccase has also been observed (Schultz et al. 2001). A bark mulch- and wood-colonising basidiomycete, Agrocybe aegerita, produces a haloperoxidase that oxidizes aryl alcohols into the corresponding aldehydes and then into benzoic acids (Ullrich et al. 2004). Halogenating activity of oxidative enzymes has been observed as well. For example, veratryl alcohol-halogenating activity has been observed for H2O2dependent soybean peroxidase (Munir and Dordick 2000) and horseradish peroxidase (Hewson and Hager 1979). Probably because basidiomycetoid fungi possess a complex palette of relatively non-specific oxidative enzymes, they produce a corresponding number of halogenated compounds and are thus the most important source of these compounds in forest litter (de Jong and Field 1997). The above facts suggest that at least a part of the naturally produced chlorinated organic compounds may appear in the soil unintentionally, as a by-product of low
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specificity of corresponding peroxidase enzymes, which process a wide variety of substrates.
De-icing Salt Near major roads, chloride enters the soil from salt used in the winter to keep the road free of snow and ice. The amounts involved can be considerable: roads may receive over 28 t/km annually (Ramakrishna and Viraraghavan 2005). Howard and Haynes (1993) estimated that roads and highways in metropolitan Toronto received over 100 000 tonnes of de-icing salt annually, of which 45% was removed while the rest was temporarily stored in shallow sub-surface waters. It has been estimated that 75–90% of added salt enters the roadside environment in runoff or by splashing (Norrström and Bergstedt 2001). Most de-icing salt is deposited within about 10–150 m of the road, but groundwater contamination has also been observed (Norrström and Bergstedt 2001).
Other Anthropogenic Sources Haloorganic compounds have many uses, and are therefore widely produced. Common uses include in solvents, pesticides, refrigerants and plastics. Paths of release of these anthropogenic compounds to the atmosphere include combustion of biomass and fossil fuels, incineration and industrial release (e.g. chloroorganics in pulp and paper manufacture and water treatment) (Laturnus et al. 2005). Forests affect the deposition of some airborne organic pollutants, including dioxins, furans, biphenyls and pesticides, by filtering them from the atmosphere (McLachlan and Horstmann 1998; Su et al. 2007a). In addition, some forest management practices, such as use of pesticides, might even contribute directly to the pool of chlorine in the forest ecosystem (Likens et al. 1970). Chloride present in depositions can derive from pollutant sources such as coal burning. It has been estimated that (4.6 ± 4.3) × 106 t HCl was produced worldwide from coal combustion in 1990 (Winterton 2000). In some situations, pollution may be a major input of chloride e.g. at Hubbard Brook in the U.S.A. in the 1970s (Lovett et al. 2005). The above-mentioned function of tree canopies as particle filters is important not just for chloride but also for chloroorganics (McLachlan and Horstmann 1998; Su et al. 2007a). Dry gaseous deposition is far more important than wet deposition (Nizzetto et al. 2007). The filtration effect varies between different compounds. Modelling of the filtration factor showed that little filter effect could be expected for volatile compounds and hydrophilic substances, or for compounds that were predominantly present in particles, while a marked filtration effect was found for semivolatile compounds such as chlorinated dioxins, furans, biphenyls and
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pesticides (McLachlan and Horstmann 1998). Gaseous deposition velocities of semivolatile organic compounds to a deciduous forest canopy have been determined as 2.7 ± 0.5 cm/s in Canada (Su et al. 2007a) and 3.6 ± 0.7 cm/s in Germany (Horstmann and McLachlan 1998). A coniferous forest canopy in Germany had a gaseous deposition velocity of 0.78 ± 0.15 cm/s (Horstmann and McLachlan 1998). These high values suggest that such forests may exert a significant filter effect for semivolatile organic compounds on a regional and global scale (Su et al. 2007a). However, photodegradation in the canopy may lead to the deposition flux under the canopy being lower than that in the canopy (Su et al. 2007a). An indication of the importance of long-range transport comes from a recent European study on POPs (including organochloropesticides, PCBs, PCDD/Fs, and chloroparaffins) in remote forest sites (MONARPOP). This research concludes that advection is the main source of semi-volatile organic compounds (SVOCs) in the Alps, as indicated by both the higher levels of these pollutants in the peripheral zones of the Alpine area and the mass balance between local emissions and forest loads (Offenthaler et al. 2008; Belis et al. 2009) Various factors affect the accumulation of haloorganics in canopies; among these are species composition and meteorological factors such as temperature, occurrence and intensity of precipitation events, and wind speed (Di Guardo et al. 2008). Nizzetto and co-workers (2006) compared the effect of tree species and temperature on the canopy accumulation of hexachlorocyclohexanes (HCHs), hexachlorobenzene and PCBs along an altitudinal gradient in Italy. For the more volatile compounds, species composition had a larger effect on concentration variation than temperature. A study on the fate of PCBs in these mountain forest ecosystems showed that changes in leaf concentration with time were affected by canopy biomass development (Nizzetto et al. 2007). Deposition fluxes were higher in the spring (with a maximum value of 0.5–1.5 ng/m2/d) than the summer, possibly because of revolatilisation of the more volatile compounds during the warmest period (Nizzetto et al. 2007). Also, the relatively high specific leaf area in the early stages of leaf development tends to enhance the accumulation rate of organic pollutants (Nizzetto et al. 2008). It was predicted that up to 60 ng PCB/m2 ground surface could be stored in the canopy when fully developed (Nizzetto et al. 2007). Response time varies with the type of PCB: leaf concentrations of tri- and tetra- chlorinated biphenyls responded rapidly (i.e. within a few days) to changes in temperature or gas-phase concentration, while heavier compounds did not reach equilibrium during the growing season (Nizzetto et al. 2008).
Transport Within the Ecosystem There exist two main pathways of transport or loss of organohalogens from forest soils: volatilization of gaseous compounds and leaching of chlorinated organics to deeper soil horizons. Although much is known e.g. on the environmental behaviour
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of chlorinated pesticides in agricultural soils, less is known on the mobility of chlorinated chemicals in forest soils. One of the reasons might be that studying transport processes in forest soils is a question of the experimental design: in contrast to many laboratory studies e.g. on the degradation of chlorinated pesticides in agricultural soils, a forest ecosystem is more complex and much larger. It is not feasible to study these transport processes under laboratory conditions, and therefore the experimental design has to be adapted to the setting in a forest. This causes real problems concerning the representativity of the experiment. In addition, several haloorganics are formed by various processes in forest soils. All these facts hamper reliable estimation of haloorganic transport processes. The transport and degradation of haloorganics is affected not only by their properties (e.g. water solubility) but also by the quality and amount of SOM (Gawlik et al. 1997). Soil pH will affect sorption of many chlorinated compounds in soil as well (Persson et al. 2007). Volatilization, degradation, formation of aged residues resulting in a reduced extractability of these chemicals from soils as well as wind and water erosion and probably anaerobic dechlorination of higher molecular weight congeners occurring in soil microsites (Grimalt et al. 2004) can all affect changes in the concentrations of chlorinated organic chemicals in forest soils.
Leaching Rodstedth et al. (2003) gave an account of an amazing phenomenon regarding chloride transport in soil. “Identical undisturbed soil cores” were collected at the Stubbetorp catchment in Sweden; all soil samples were irrigated twice a week and chloride in the leachate was analysed. The result was that some of the “identical” soil cores acted as a chloride sink and others acted as a source of chloride. Reasons for the different behaviour were not found. This result emphasizes the complexity of chlorination and de-chlorination processes in soils and the difficulty of understanding the transport of the various chlorinated compounds in soils. Forest management is likely to affect chloride leaching, for example by tree felling which increases chloride runoff (Haveraaen 1981; Kauffman et al. 2003). Leaching of other halides has not been much studied. For haloorganics, the situation is complex. Partitioning of hydrophobic organic pollutants into the soil’s humic substances will clearly affect their transport. However, both haloorganics and other organic substances in soil are very variable: for example, soil-water partition coefficients for 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin have been found to vary in a manner related to the polarity/ hydrophobicity of the humic substances studied (Tanaka et al. 2005). Less volatile organic pollutants such as hexa- and higher chlorinated biphenyls and polybrominated diphenyl ethers are very stable in soils, showing little translocation
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or transfer to other ecosystem compartments, while more volatile compounds such as tri- and tetra-chlorinated biphenyls appear to be transported downwards through the soil profile (Moeckel et al. 2008). Persson et al. (2007) studied soil contamination by several levels of chlorinated compounds (substances studied were chlorophenols, chlorinated phenoxy phenols, chlorinated diphenyl ethers, chlorinated dibenzofurans and chlorinated dibenzodioxins) near contaminated sawmill sites in Sweden and the result was that the relative abundance of the chlorinated compounds varied greatly between the five sites studied, suggesting that their transport parameters differed substantially. In Sweden alone there are between 400 and 500 wood impregnation sites located and this type of contamination could constitute a major problem. As the authors conclude, “since chlorophenols have been used extensively worldwide, it is likely that similar highly contaminated sites may be present in other countries.” Nevertheless, studies on the transport of these compounds seem to be lacking. The environmental fate of TCA in forest soils has been reviewed by Schöler et al. (2003): it can be both formed and degraded in forest soils. Degradation seems to be low especially in forest soils with a low pH (Hoekstra 2003). Thus, soils can probably be both sources and sinks of TCA, and these opposed processes complicate any calculation and/or study on the transport of this compound; steady-state concentrations in soils have been suggested (Schöler et al. 2003; Matucha et al. 2003b). Transport of TCA to deeper soil horizons is possible and may be affected mainly by environmental parameters like precipitation.
Volatilisation Volatile haloorganics are produced naturally in forest soils, e.g. methyl chloride, bromomethane and iodomethane, the production of which were described by Harper (1985). One of the most studied of the volatile haloorganics is chloroform (Haselmann et al. 2000b, 2002; Hoekstra et al. 2001; Hellén et al. 2006). The chloroform emission flux was measured and calculated from the concentration gradient in soil air and atmospheric air. The highest emission fluxes (up to 1 mg m−2 h−1) were found in wood-degrading areas and in soils with a humic layer. Additionally, the authors reported on rather unexpected emissions of 1,1,1-trichloroethane, tetrachloromethane and tetrachloroethene in one of the sampling areas as well. By contrast, the forest canopy is a sink for chloroform and thus, “it is not yet clear whether or not soils in temperate zones contribute significantly to the atmospheric chloroform burden” (Hoekstra et al. 2001). The results of Haselmann et al. (2000b) also indicate a natural production of chloroform in forest soils. Compared to ambient air, the chloroform concentration in topsoil was 6.7 and 4.3 times higher for spruce and beech (Fagus sylvatica L.)
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forest, respectively. There are more hints available that this substance is synthesized in forest soils: Chloroform concentrations in soil air from top soil of a Danish spruce forest increased in spring and autumn (Haselmann et al. 2002) while other chlorinated chemicals like 1,1,1-trichloroethane and tetrachloromethane peaked during mid winter. In contrast to the other chlorinated chemicals, the seasonal variation of chloroform concentration suggests a microbial production, as high chloroform concentrations were found in soil in warm and humid periods of the year with high microbial activity. Moreover, the authors concluded that the relative ratios of soil air concentrations to ambient air concentrations indicate a natural production of chloroform, while the other chlorinated compounds investigated probably originate from non-point source pollution. When Haselmann et al. (2000a) studied the release of chlorinated compounds from forest soil in laboratory experiments they could also show that the addition of TCA to the soil increased the release of chloroform. The authors therefore suggested that this contaminant can also contribute to the formation of chloroform in soils. Laturnus et al. (2000) monitored ambient air, soil air and groundwater for volatile halogenated organic compounds in a pristine spruce forest. Although only low chloroform concentrations were found in ambient air (0.02 ng L−1), higher air concentrations in the upper soil layer of 9.6 ng L−1 and a decrease with increasing soil depth down to 1.5 ng L−1 at a soil depth of 7.5 m (i.e. just above the groundwater table) were detected. Thus, the chloroform concentration profile clearly indicated a formation of this chemical in soil. Moreover, high chloroform concentrations (up to 1.6 mg L−1) were detected in the phreatic groundwater. Although there are clear hints that forest soils contribute to the natural formation of chloroform, Laturnus et al. (2002) concluded that forest soils are only a minor source in the total biogenic flux of chloroform, contributing less than 1% to the annual global atmospheric input when only calculating data available for northern temperate forests. In peatlands, not only chloroform but also bromoform can be emitted (Carpenter et al. 2005). Although their peat bogs were not situated in forest, it is reasonable to suppose that the same may be true of forested peatlands. In a study by Krauss et al. (2000) the concentration of PCBs in soil horizons of 16 Norway spruce stands in north Bavaria (Germany) were determined. In parallel, the behaviour of polycyclic aromatic hydrocarbons (PAHs) in these soils was determined. The sum of PCBs was lowest in the upper horizon Oi (12.1 ± 6.5 mg kg−1) but higher in the following horizons, Oe and Oa (39.1 ± 19.4 and 46.0 ± 39.4), and concentrations were much lower in the mineral horizons (1.7 ± 1.5 mg kg−1). The enrichment factors for the organic soil horizons (eOe/Oi) of PCBs were consistently lower than those of PAHs with comparable Kow. The authors suggest that this could be explained with higher PCB volatility or higher PAH sorption. In the mineral soil, the concentrations of most PCBs decreased with increasing depth, except those of PCBs 8, 20, 28 and 52. It is noteworthy that in the mineral soil horizons the enrichment factor of PCBs 8, 20, 28 and 52 were around 10 times higher than those of
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PAHs with similar Kow values giving hints that leaching of these PCBs was higher than that of the PAHs. Volatilisation of semi-volatile compounds, such as PCBs, has been identified as a possible reason for the decline of these chemicals in soils (Lead et al. 1997; Su et al. 2007b). However, Lead et al. (1997) pointed out that biodegradation of these compounds, particularly of lower chlorinated compounds, will occur as well. Thus, re-evaporative emission is likely to be more important for highly chlorinated PCBs than for less chlorinated PCBs (Su et al. 2007b). Concentrations of PCBs and polybrominated diphenyl ethers in air in forests show temperature dependence, probably due to the importance of re-evaporation (Su et al. 2007b). Another fact has also to be considered: bioavailability of compounds such as PCBs is reduced with time and thus, extractability from soil is reduced as well, resulting in an overestimation of their microbial degradability (Pignatello and Xing 1996; Alexander 1995).
Role of Fire Beside leaching and volatilization of chlorinated organic compounds, Kim et al. (2003) identified another transport mechanism of these chemicals from soil surfaces: while PCDDs and PCDFs are formed during forest fires, the concentrations of these very persistent compounds decreased significantly in the first five months following the forest fire. The data presented suggest that these chemicals are formed by the forest fire and are introduced into the soil. However, this increase of PCDD/Fs is only temporary because wind and rain erosion remove ash from the soil.
Plant Litter Plant litter, both above- and below-ground, is a major part of the internal cycling of organic matter, including haloorganics, in terrestrial ecosystems. Litter greatly contributes to the high chloride and organic chlorine inventories in forest soils (Johansson et al. 2003a; Romanič and Krauthacker 2006) and is a major method of transport to the forest floor for haloorganics deposited in the canopy (Su et al. 2007a). At Klosterhede in Denmark, total input of organic Cl in needle litter was 0.035 g Cl/m2/year in 1993–94 (Öberg and Grøn 1998). The contributions from below-ground litter and other types of above-ground litter (i.e. twigs, cones, and litter from ground vegetation) were not included in this figure. Assuming the quantity of below-ground litter to be of approximately equal magnitude to that of above-ground litter (Kleja et al. 2008), and assuming non-needle above-ground litter to be negligible, gives an estimate of total input in litter at Klosterhede of about 0.07 g Cl/m2/year in 1993–94.
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Degradation Abiotic Degradation Processes leading to the degradation of haloorganics include hydrolysis, mineral surface reactions, photolysis and oxidation by molecular oxygen (Bailey et al. 2002). In hydrolysis, water reacts with functional groups (such as chloro groups) attached to the hydrocarbon backbone. This tends to make the molecule more water-soluble and thus easier for microorganisms to degrade. In mineral surface reactions, certain minerals catalyse the decomposition of bound substituted organics. An example is sedimentary clay minerals, which can catalyse the hydrolysis of some chloroorganics (Bailey et al. 2002). Photolysis can degrade volatile organics (VOCs) and occasionally solids as well. In addition, radicals produced by photolysis of other atmospheric gases may decompose VOCs. Oxidation by molecular oxygen, especially when activated by UV radiation, can affect unsaturated compounds in particular. It is, however, rare for these abiotic processes to convert a nonbiodegradable compound into one that is part of the natural environment; instead, they tend to convert nonbiodegradable compounds into ones that can be degraded by microorganisms (Bailey et al. 2002).
Biotic Degradation Even though chlorination may enhance cleavage of biodegradation-resistant aromatic rings in components of humic substances (Matucha et al. 2007), halogens generally possess great electronegativity that confers remarkable chemical stability on many haloorganics, thereby making them relatively recalcitrant to biodegradation (Mohn 2004). Chlorine, a strong oxidizing agent, forms chlorinated oxidation products with aliphatic and aromatic organic matter that are difficult to biodegrade (Crawford et al. 2004). However, halogens not only increase resistance of halogenated organic compounds (OX) to biodegradation per se. Halogens can also enhance hydrophobicity, sorption, and sequestration of OX in soil so that these compounds become less bioavailable to microbial attack, which makes them even more difficult to biodegrade in soils (Mohn 2004). Mohn (2004) reviewed the current knowledge on the principles and mechanisms of OX biodegradation, which is almost entirely based on synthetic organohalogens. In brief, microorganisms metabolize haloorganics either purposely as substrates for growth or fortuitously (cometabolically) via oxidative and reductive processes. The general strategies for biodegrading chlorinated aliphatic (e.g. alkanes, alkenes) and aromatic (e.g. phenols, benzene) compounds include partial dechlorination followed by oxidation, complete reductive dechlorination, and oxidation. Oxidation alone cannot extensively degrade chloroorganics due to insufficient dechlorination. Anoxic reductive pathways alone can extensively dechlorinate
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but may lead to the accumulation of toxic and carcinogenic metabolites, including vinyl chloride. Alternating anaerobic-aerobic conditions have accomplished dechlorination and degradation of chloroorganics most efficiently without the build-up of harmful less-chlorinated metabolites. Overall, dechlorination is a prerequisite for complete biodegradation (mineralization) but becomes more difficult with an increasing number of chlorine substituents in key positions such as in the meta, para, and ortho positions of chlorinated aromatics (Fritsche 1998; Mohn 2004). Basic principles of the mechanisms of bacterial dechlorination of chloroorganics are shown schematically in Fig. 2. The five principle mechanisms used by microorganisms to dechlorinate chloroorganics are (a) reductive, (b) oxidative, (c) hydrolytic dechlorination, (d) dehydrodechlorination, and (e) dechlorination after ring cleavage (Fritsche 1998; Alexander 1999). Other halogens such as bromine, iodine, and fluorine are also displaced from OX by these processes. Of all the processes, reductive dehalogenation (halorespiration) by anaerobic microorganisms appears most important for the dechlorination of many chloroorganics by replacing the chlorine through hydrogen. Aromatic and aliphatic chloroorganics serve as terminal electron acceptors during oxidation of the electron rich hydrogen or organic acids (van Pée and Unversucht 2003). The driving force behind reductive dechlorination is the generation of energy for anaerobic microbes since the process is thermodynamically very favourable (Mohn 2004). Therefore, microorganisms perform halorespiration purposely; however, cometabolic replacement of chlorine by hydrogen was also observed. Even highly chlorinated PCBs can be effectively dechlorinated by microbial consortia facilitating chlororespiration followed by aerobic biodegradation of the organic parent compound. In fact, heavily chlorinated compounds have the potential to serve as more thermodynamically effective electron acceptors than less chlorinated ones (Milliken et al. 2004). For example, tetrachloroethylene was more effectively dechlorinated than trichloroethylene by microbial reductive dehalogenation (Fritsche 1998). Most reductive dehalogenations are anaerobic, but the process may sometimes be aerobic (Alexander 1999; van Pée and Unversucht 2003). Oxidative dehalogenation also participates in pathways for the degradation of chlorinated aliphatic and aromatic compounds (Neilson 1994; van Pée and Unversucht 2003). Bacterial mono- or dioxygenases catalyze these reactions with oxygen replacing the chlorine and sometimes chloride being released spontaneously (Fritsche 1998). Fungal peroxidases from the lignin-degrading basidiomycete Phanerochaete chrysosporium also catalyzed oxidative dechlorination of pentachlorophenol (Reddy and Gold 2000). Hydrolytic dehalogenation is the process conducted by some microbes to replace chlorine or other halogens in aromatic or aliphatic molecules by the hydroxyl group of water (Neilson 1994; Fritsche 1998; Alexander 1999). The best studied hydrolytic dehalogenase is from the 1,2-dichloroethane-degrading bacterium Xanthobacter autrophicus (van Pée and Unversucht 2003). Actinomycetes and fungi also use hydrolytic dechlorination to biodegrade chloroorganics (Klages and Lingens 1979; Couch et al. 1965).
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Fig. 2 Examples of bacterial dehalogenation of chlorocarbons using different mechanisms. (a) Reductive dechlorination of CCl4; (b) oxidative dehalogenation of pentachlorophenol; (c) dehydrodehalogenation of HCB; (d) reductive dehalogenation of PCB after ring cleavage (Modified from Fish and Principe 1994 and www.chemistrymag.org)
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Dehydrodehalogenation provides another means of transforming chlorinated substrates by simultaneous removal of chlorine and hydrogen (Alexander 1999). Such reactions may occur with carbon atoms possessing three, two, or one chlorines such as in the metabolism of DDT. The first two steps of removing the six chlorine substituents from HCH prior to ring cleavage involve dehydrodechlorination (Nagata et al. 1999). Dechlorination after ring cleavage converts aromatic chloroorganics via microbial enzymes (e.g. oxygenases) of low substrate specificity that usually degrade non-chlorinated analogues but cometabolically cleave chlorinated rings prior to chlorine release (Fritsche 1998). In natural environments, the various chloroorganic dehalogenation reactions may occur concurrently or successively depending on the kind of chlorinated compounds present and the environmental conditions that are prevalent (Jaspers et al. 2002). Interacting microbial communities with various synergistic metabolic capabilities are most important for the degradation of halogenated compounds (Lappin et al. 1985). In particular, such mixtures of chlorinated hydrocarbons as are usually found in nature are degraded more efficiently by consortia of microbes and pathways (Malachowsky et al. 1994). Yet, typical aerobic and anaerobic chloroorganic biodegradation pathways may sometimes coexist within a single organism (Jaspers et al. 2002). A great wealth of published data exists on the biodegradation of chlorinated pesticides in arable soils and synthetic chlorinated contaminants in waste soils, but little is known about the biodegradation and mineralization of chlorinated organic materials in forest soils (Öberg 2002; Rodstedth et al. 2003). Yet, twofold higher concentrations of chlorinated pollutants such as PCBs (Meijer et al. 2003) and a significant natural production of organically bound halogens such as chlorinated dioxins and dibenzofurans in forest soils (Gribble 2003) suggest that microorganisms must exist in forest soils that can biodegrade organohalogens efficiently (Hjelm et al. 1995; Mohn 2004). But where in forest soils is what biodegraded when and how by whom? As mentioned above, chlorination activity was approximately 1,000-fold higher in the top organic layer than in deeper horizons of forest soils (Laturnus et al. 1995). Since chlorination and dechlorination are two coexisting transformation processes for organic chlorine (Asplund 1995; Rodstedth et al. 2003), biodegradation of chloroorganics should also be most intense in forest topsoil and the litter layer. Organohalogens in spruce litter were biodegraded rapidly and extensively at a Norway spruce forest site (Hjelm et al. 1995). In coniferous forest soils, the rate of biodegradation and mineralization of organic acids was greatest in the surface organic horizons (van Hees et al. 2002). Phenol oxidase activity, which relates to oxidative degradation activity, was greater in the top 1 cm of the soil (Meade and D’Angelo 2005). Burning in oak-hickory forests tended to stimulate phenol oxidase activity, especially after multiple fires (Boerner and Brinkman 2003). Both natural and anthropogenic chloroorganics are biodegraded in forest soils. TCA was biodegraded rapidly in soil under Norway spruce (Matucha et al. 2003b;
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Schröder et al. 2003). SOM-bound chlorine is easily and continuously degraded in forest soil (Rodstedth et al. 2003). An estimated 50% or more of Cl– export in stream flow from a forest area results probably from the mineralization of Cl from SOM (Lovett et al. 2005). Litter degrading fungi formed chlorinated metabolites during SOM biodegradation (Hjelm et al. 1996). Fungi associated with the aerobic decomposition of lignin also degraded chloroorganics, including dieldrin, heptachlor, chlordane, lindane, mirex, atrazine, PCBs, and pentachlorophenols (PCP) (Mohn 2004). Fungal ligninase degradation of contaminants was reviewed by Pointing (2001). Pure culture experiments have indicated that the ability to transform or mineralize chlorinated pesticides is commoner among bacteria than among fungi (Bollag 1972). Under field conditions, however, fungal and bacterial enzymes act simultaneously in the biodegradation of chlorinated pesticides (Levanon 1993). Generally, the chloroorganic-producing organisms listed by Öberg (2002), several of them being soil bacteria, fungi, and actinomycetes, may also possess the metabolic tools to dechlorinate and mineralize organochlorines in forest soils. A number of environmental factors such as texture, temperature, moisture, redox potential, salinity, and pH can influence the biodegradation of chloroorganics by microorganisms in soils (Spain and Van Veld 1983). The activity of atrazinedegrading microorganisms in arable soil was highly sensitive to changes in soil conditions, including low temperatures and high humidity (Barriuso and Houot 1996). High clay content may prevent rapid mineralization of atrazine because of sorptive retention to soil that lowers the bioavailability of the chemical (Scow and Hutson 1992; Houot et al. 2000). Trees and forest ground vegetation may influence the biodegradation of chloroorganics. Trichloroethylene (TCE) was mineralized more rapidly in the rhizosphere of Loblolly pine (Pinus taeda L.) (Walton and Anderson 1990; Anderson and Walton 1995). Austrian pine (Pinus nigra Arn.) and goat willow (Salix caprea L.) increased the numbers of PCB-metabolizing bacteria, predominantly rhodococci, in the root zone and even in the soil beyond the immediate vicinity of the roots (Leigh et al. 2006). P. nigra produces terpenoids (Bardyshev et al. 1970) and some terpenoids are known to support the growth of PCB-degrading bacteria and induce the PCB degradation pathway (Singer et al. 2003). Austrian pine and goat willow were associated with higher numbers of PCB degraders than ash (Fraxinus excelsior L.), silver birch (Betula pendula Roth) and black locust (Robinia pseudoacacia L.), at least for certain seasons and soil depths (Leigh et al. 2006). Legumes are common plants in forests and some of the associated rhizobial species were effective degraders of chlorinated aromatic and aliphatic substrates (Vela et al. 2002). Root activities trigger sequential anaerobic and aerobic processes in the rhizosphere that could affect the fate of chlorinated organics such as chloroacetic acids, volatile organochlorines, and PCPs in forest soil (Laturnus et al. 2005; Meade and D’Angelo 2005). In general, little is known about the chemical nature and biological degradation of halogenated organic matter in forest soils under varying environmental conditions (Asplund 1995). It was reported, however, that high-molecular-weight
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organohalogens are predominant while organohalogens of low molecular weight are rarely found in forest soils (Hjelm et al. 1996). Given the lack of information on halogenated organic matter in forest soils in general and the biodegradation of organically bound halogens in particular, this represents an important area of future research (Öberg 2002; Lovett et al. 2005). The ability of microorganisms to mineralise xenobiotics appears to be present even in pristine soils that have not been directly exposed to them (Fulthorpe et al. 1996), suggesting that this ability is not recently evolved. Different microbial populations appear to be responsible for the degradation of different substances (Fulthorpe et al. 1996). Differences exist between different forest and agricultural soils in the ability to mineralise different xenobiotics, and seasonal differences exist as well (Entry and Emmingham 1996; Fulthorpe and Schofield 1999). Mineralisation of atrazine in the organic layer was higher in coniferous forest than in deciduous forest and highest in coniferous forest in the spring, while mineralisation of 2,4-dichlorophenoxyacetic acid in the organic layer was higher in coniferous than deciduous forest in the spring (Entry and Emmingham 1996). In mineral soil, mineralisation of atrazine tended to be higher in coniferous forests than in deciduous forests and grasslands, while mineralisation of 2,4-dichlorophenoxyacetic acid was higher in deciduous and coniferous forest than in grassland in all seasons (Entry and Emmingham 1996). Coniferous forest strips were therefore suggested as a way of minimising the input of agricultural herbicides to lakes and streams (Entry and Emmingham 1996). However, in some cases pre-exposure appears to be necessary before mineralisation takes place, as found for atrazine by Fulthorpe and Schofield (1999).
Site Budgets There have been very few attempts to produce budgets for halogens, including haloorganics, at site level. At the Stubbetorp catchment in Sweden, most chlorine input was found to be inorganic, i.e. as chloride (Öberg et al. 2005). Wet and dry deposition of chloride at Stubbetorp were estimated by Maxe (1995) at 0.40 g Cl−/m2/ year and 0.31 g Cl−/m2/year, respectively, based on the assumption that wet and dry deposition of chloride equalled stream outflow. However, this assumption may have been false, and could have led to underestimation of chloride in dry deposition by 25% (Rodstedth et al. 2003). Wet deposition of organic Cl was estimated at 0.007 g Cl/m2/year (Öberg et al. 2005). Deposition of organic Cl in throughfall was not measured at Stubbetorp, but in a Norway spruce forest at Klosterhede in Denmark it was estimated to be 0.006–0.09 g Cl/m2/year, with a median value of 0.037 g Cl/ m2/year (Öberg et al. 1998). This was believed to originate to a large extent within the forest, i.e. from canopy leachates and other internal sources (Asplund and Grimvall 1991).
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Gaps in Knowledge Despite a great increase in knowledge in the last few decades, we are still far from being able to predict the fate of haloorganics in terrestrial ecosystems. Although many highly sophisticated studies have been carried out in certain regions and reasonable data sets are available for certain topics, the systematic wide range studies which would be essential to model the environmental fate of chlorinated compounds on a landscape scale have not been made. Monitoring is needed to obtain long time series of data covering a large area, in order to predict the behaviour of haloorganics on scales ranging from landscape to continental. Haloorganics are not widely monitored at present. One interesting effort in this sense is the monitoring network for POPs in the Alps set up within the framework of the project MONARPOP (Offenthaler et al. 2008). Halides, with the exception of chloride, are not widely monitored either. It is essential that sampling and analytical methodology is of good quality and harmonised. Sampling methodology for forest soil, water and air is described for example in the manual of ICP Forests (UN-ECE 2006). Inaccuracy in measurements appears most often to be due to deficiencies in the sampling systems used (e.g. Bleeker et al. 2003; Erisman et al. 2003) rather than in the laboratory analysis. Sampling procedures for deposition, forest soils and soil waters vary (e.g. there are different types of lysimeter and throughfall sampler) and, as different samplers may produce different results, a greater degree of harmonisation appears to be essential. On the other hand, harmonisation of analytical procedures for measuring and identifying the various chlorinated compounds in soil and water samples has been to a high degree achieved, although some problems remain to be solved in extraction procedures for chloride and chlorinated humic acids of different soils, in determination of residual chlorine in soil and chloride content in microorganisms. Using isotopic labelling techniques (14C, 36Cl), model experiments for a deeper understanding of several key processes can be conducted (e.g. on the influence of chlorination time on kind of chlorine bond, what part of SOM is degraded by chlorination or volatilization of formed methyl chloride and chloroform). The balance of radioactivity during experiments makes it possible to follow all the main processes to a high degree. As both chlorination and degradation are largely mediated by soil microflora, knowledge of factors influencing these microorganisms (e.g. soil temperature, moisture and chemical factors such as pH) is likely to be essential for understanding haloorganic cycling. The effect of soil chloride level on microbiota needs to be investigated. In addition, increased concentrations of available haloorganics may favour some taxa of specialized bacteria. Methodological difficulties to study soil microbial communities could be solved by molecular genetic methods. Little is known of the mobility of haloorganics in forest soils. Partly this is a result of the difficulty of designing experiments in the complex, very heterogeneous and large-scale (in both space and time) forest ecosystem. Forest soils are
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often characterized by large horizontal and vertical gradients in organic matter content and the permanent but inhomogeneous root systems of trees also in deeper soil horizons. As a result, it can be speculated that there exist strong gradients (e.g. for nutrients) in forest soils, and it thus becomes more difficult to predict soil processes when monitoring them on a small scale. Thus, it is most likely that soil processes in forest soils must be studied on a larger scale in comparison to other more homogeneous soil systems. Such kinds of studies are more time consuming and costly. In addition, it must be remembered that not all SOM is the same: to understand cycling of haloorganics in forest ecosystems, more basic knowledge is required of how various haloorganic fractions are related to the various fractions of SOM. Modelling might be a suitable approach in studies of haloorganic mobility. Chlorinated compounds and their degradation products are commonly considered both persistent and toxic. However, the vast majority of naturally produced chlorinated substances are neither persistent nor toxic (Öberg 2002). Still, as our understanding of the nature of organochlorines and their biodegradation in forest soils is limited, so also is our knowledge of possible toxicological risks associated with these compounds and their biodegradation products. Many studies with numerous synthetic organochlorines have proved that halogen substituents can contribute to harmful biological effects of organic compounds, increasing their toxicity, mutagenicity, and further detrimental capacities (Mohn 2004). Knowledge of how forest management affects haloorganics in forest soil and water is at present very limited, although it is clearly important for our understanding of the environmental impact of management practices. Forest management processes affecting SOM and chloride deposition are likely to affect haloorganics as well. However, although there are numerous studies on effects of management practices on SOM and some that include chloride, there are very few studies of such effects on haloorganics. As an example, the effect of compensation fertilisation using wood ash (relevant for current bioenergy policy; Stupak et al. 2007) on cycling of PCDD/Fs and PAHs has not been studied, although these may be adsorbed to wood ash (Kim et al. 2003).
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Svensson T, Sandén P, Bastviken D, Öberg G (2007) Chlorine transport in a small catchment in southeast Sweden during two years. Biogeochemistry 82:181–199 Tanaka F, Fukushima M, Kikuchi A, Yabuta H, Ichikawa H, Tatsumi K (2005) Influence of chemical characteristics of humic substances on the partition coefficient of a chlorinated dioxin. Chemosphere 58:1319–1326 Trofast J, Wickberg B (1977) Mycorrhizin A and chloromycorrhizin A, two antibiotics from a mycorrhizal fungus of Monotropa hypopytis L. Tetrahedron 33:875–879 Ullrich R, Nüske J, Scheibner K, Spantzel J, Hofrichter M (2004) Novel haloperoxidase from the agaric Basidiomycete Agrocybe aegerita oxidizes aryl alcohols and aldehydes. Appl Environ Microbiol 70:4575–4581 UN-ECE (2006) Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests, http://www.icp-forests.org/Manual van de Pas BA, Jansen S, Dijkema C, Schraa G, de Vos WM, Stams AJM (2001) Energy yield of respiration on chloroaromatic compounds in Desulfitobacterium dehalogenans. Appl Environ Microbiol 67:3958–3963 van Hees PAW, Jones DL, Godbold DL (2002) Biodegradation of low molecular weight organic acids in coniferous forest podzolic soils. Soil Biol Biochem 34:1261–1272 van Pée K-H, Unversucht S (2003) Biological dehalogenation and halogenation reactions. Chemosphere 52:299–312 Vela S, Häggblom MM, Young LY (2002) Biodegradation of aromatic and aliphatic compounds by rhizobial species. Soil Sci 167:802–810 Verhagen FJM, Swarts HJ, Wijnberg JBPA, Field JA (1998a) Organohalogen production is a ubiquitous capacity among Basidiomycetes. Chemosphere 37:2091–2104 Verhagen FJM, Swarts HJ, Wijnberg JBPA, Field JA (1998b) Biotransformation of the major fungal metabolite 3, 5-dichloro-p-anisyl alcohol under anaerobic conditions and its role in formation of bis(3, 5-dichloro-4-hydroxyphenyl)methane. Appl Environ Microbiol 64:3225–3231 Walton BT, Anderson TA (1990) Microbial degradation of trichloroethylene in the rhizosphere: potential application to biological remediation of waste sites. Appl Environ Microbiol 56:1012–1016 Wigilius B, Allard B, Borén H, Grimvall A (1988) Determination of adsorbable organic halogens and their molecular weight distribution in surface water samples. Chemosphere 17:1985–1994 Winterton N (2000) Chlorine: the only green element – towards a wider acceptance of its role in natural cycles. Green Chem 2:173–225
Semivolatiles in the Forest Environment: The Case of PAHs Claudio A. Belis, Ivo Offenthaler, and Peter Weiss
Abstract Forests are an important sink for semivolatile organic compounds (SVOCs) due to the great aerodynamic roughness of woodland landscape which enhances downward fluxes of both gaseous and particle-bound pollutants and the slow turnover of soil organic content. In particular, Polycyclic Aromatic Hydrocarbons (PAHs) are the most abundant persistent organic toxics in forests. Due to their lipophilic properties PAHs accumulate in soil, sediment and living organisms. PAHs emitted to the atmosphere by combustion processes are transported by air masses and are subject to dry or wet deposition. In forests PAHs are mainly present in the soil compartment, therefore the forest biomass can be regarded as a pump of pollutants from the atmosphere to the soil from which chemicals can return to the atmosphere only with difficulty. In the atmosphere, the main processes responsible for PAH degradation are photolysis and oxidation by gaseous pollutants, while microbial metabolism is the major process for the degradation of PAHs in soil.
Introduction to Polycyclic Aromatic Hydrocarbons Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous toxic pollutants subject to long-range transport from their sources. Concern about these substances is due to their probable carcinogenic effects on humans, but also because they represent a risk for environmental compartments such as water, soil, air, ground water and
C.A. Belis () European Commission Joint Research Centre – Institute for Environment and Sustainability, 21027 Ispra, Italy e-mail:
[email protected] I. Offenthaler and P. Weiss Umweltbundesamt GmbH, Spittelauer Lände 5, 1090 Wien, Austria
P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_3, © Springer Science+Business Media B.V. 2011
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C.A. Belis et al. Table 1 List of ATSDR and US-EPA priority PAHs (From Ravindra et al. 2008) PAHs Particle/gas phase distribution Naphtalene Gas phase Acenaphthene Gas phase Acenaphthylene Gas phase Anthracene Particle and gas phase Phenanthrene Particle and gas phase Pyrene Particle and gas phase Benz[a]anthracene Particle phase Chrysene Particle phase Benzo[b]fluoranthene Particle phase Benzo[j]fluoranthene a Particle phase Benzo[k]fluoranthene Particle phase Benzo[a]pyrene Particle phase Fluoranthene Particle and gas phase Fluorene Gas phase Dibenz[a,h]antracene Particle phase Benzo[g,h,i]perylene Particle phase Indeno[1, 2, 3-c, d]pyrene Particle phase a Not included in the 16 US-EPA priority list
sediments (RIVM 1999). PAHs include hundreds of hydrocarbons (consisting of C and H), structured in more than one ring (polycyclic) with properties similar to those of benzene (aromatic), a well-known human carcinogen. Although there are natural sources of PAHs (volcanic eruptions, diagenesis of soil organic matter, and biosynthesis), the presence of these compounds in the environment is mainly due to anthropogenic emissions, in particular incomplete combustion of organic matter associated with mobile sources, biomass burning and industrial activities. The United States Agency for Toxic Substances and Disease Registry (ATSDR 1995) and the United States Environmental Protection Agency (US EPA 1998) considered respectively 17 and 16 priority PAHs (Table 1). These PAHs were selected because they are present in higher concentrations, so that there is a greater risk of exposure to them; there is more information available on them than on other PAHs; and they are representative of the harmful effects of PAHs in general. As PAHs share most of the characteristics of Persistent Organic Pollutants (POPs) their inclusion in the Stockholm Convention, signed under the sponsorship of the United Nations Environmental Programme (UNEP), to progressively reduce emissions and phase out the use of POPs, is under discussion. Interdisciplinary groups of scientific experts working in the framework of the International Agency for Research on Cancer (IARC) draw up and periodically review reports containing the most up-to-date evaluation of carcinogenic substances and activities. So far, more than 900 agents have been assessed. Monographs 32–34, published between 1983 and 1984, report the evaluation of PAHs. These documents were revised and results were published in monograph 92 (IARC, 2010). According to IARC, many PAHs are regarded as: carcinogenic, probably carcinogenic or possibly carcinogenic to humans (Table 2).
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Table 2 Classification of some PAHs according to their carcinogenicity (http://monographs.iarc.fr) Carcinogenicity Name IARC group to humans Benzo(a)pyrene Upgraded from 2B to 1 Proven Cyclopental(c,d)pyrene Upgraded from 2B to 2A Probable Dibenzo(a,h)anthracene Upgraded from 2B to 2A Probable Dibenzo(a,l) pyrene Upgraded from 2B to 2A Probable Benz(j)aceanthrylene Upgraded from 3 to 2B Possible Benz(a)anthracene 2B Possible Benzo(b)fluoranthene 2B Possible Benzo(j)fluoranthene 2B Possible Benzo(k)fluoranthene 2B Possible Benzo(c)phenanthrene 2B Possible Dibenzo(a,h)pyrene 2B Possible Dibenzo(a,i)pyrene 2B Possible Indeno(1,2,3-c,d)pyrene 2B Possible Chrysene 2B Possible
Properties of PAHs PAHs consist of two or more rings of either five or six carbon atoms. PAHs have an aromatic structure, so that they have characteristics similar to those of the benzene molecule: double bonds conjugated into a resonance structure and planar configuration which increase chemical stability and susceptibility to electrophilic substitution. The biochemical persistence of PAHs is due to the dense clouds of p-electrons on both sides of the ring structures, making them resistant to nucleophilic attack. PAHs are also rather thermically stable. Those emitted to the atmosphere are formed at more than 500°C by pyrosynthesis of unsaturated hydrocarbons under oxygen-deficient conditions, and grow by the addition of hydrocarbon radicals to low molecular PAHs (Ravindra et al. 2008). The vapour pressure of PAHs at ambient conditions increases with their molecular weight. PAHs with two to three rings are commonly present in the gas phase, while PAHs with more than four rings are almost completely associated with particles. Also the lipophilic properties and water solubility of PAHs are associated with their molecular weight (Table 3). For example, log Kow ranges from 3.4 in naphthalene (two rings) to 6.5 in benzo(a)pyrene (five rings). The solid-gas partitioning of PAHs is controlled by the interplay of various factors, including ambient temperature, the concentration and chemical composition of the atmospheric particles, and compound volatility. Depending on whether they are present in the gas phase or bound to particles, PAHs of intermediate volatility are differently inhaled, degraded, deposited, and scavenged (Su et al. 2006). The octanol/air partitioning coefficient (log KOA) can be used to estimate the distribution of a chemical between atmospheric particulate matter and air while the distribution between liquid and solid phases can be estimated from the air/water partitioning coefficient (log KAW). The partitioning of SVOCs between solid or
Abbrev. NAPH ACNATHE ACNATHY FLENE PHEN ANT FLNTE PYR BaA CHR
BbF BkF BaP IcdP BghiP dBahA
Name Naphtalene Acenaphtylene Acenaphtene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo[a]anthracene Chrysene
Benzo[b]fluoranthene Benzo[k]fluoranthene Benzo[a]pyrene Indeno[1,2,3-c,d]pyrene Benzo[g,h,i]perilene Dibenzo[a,h]antracene
168.3 215.7 178.1 163.6 278.3 266.6
Melting point (°C) 81 92–93 95 115–116 100.5 216.4 108.8 150.4 160.7 253.8
Table 3 Some physical properties of the 16 EPA priority PAHs
481 480 496 536 545 524
Boiling point (°C) 217.9 265–275 279 295 340 342 375 393 400 448
Vapour pressure (Pa) at 25°C 10.4 8.9 × 10−1 2.9 × 10−1 8.0 × 10−2 1.6 × 10−2 8.0 × 10−4 1.2 × 10−3 6.0 × 10−4 2.8 × 10−5 8.4 × 10−5 (20°C) 6.7 × 10−5 (20 °C) 1.3 × 10−8 (20°C) 7.3 × 10−7 1.3 × 10−8 (20°C) 1.4 × 10−8 1.3 × 10−8 (20°C) 6.12 6.84 6.5 6.58 7.1 6.5
Partition coeff. octanol/ water (log Kow) 3.4 4.07 3.92 4.18 4.6 4.5 5.22 5.18 5.61 5.91
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liquid surfaces and air can be modelled as a linear function of the atmospheric temperature (Wania et al. 1998):
ln pa = mT −1 + b
(1)
where pa denotes the partial pressure of a given compound in the atmosphere and T the ambient temperature, near the air/surface interface. When pa of a chemical is in equilibrium with its concentration in the sorbing surface, the slope m is negative. In these cases an increase in T leads to outgassing of the chemical from the surface while net deposition is the result of an air cooling. In non equilibrium conditions the temperature influence is lower (m is slightly positive) and the pa is controlled by the advection from more polluted areas.
Sources of PAHs In nature, PAHs can be produced by pyrolysis of organic materials, e.g. in forest fires. Diagenetic processes of sedimentary organic material also lead also to the formation of PAHs. These compounds may also be biosynthesized by microbes and plants (Neff 1979). Most PAHs present in nature are generated from biogenic precursors which are common constituents of terrestrial higher plants. Also, components of animal membranes like cholesterol and the steroid hormones have a basic structure derived from a substituted phenanthrene cycle (perhydrocyclopentanophenanthrene). Some authors believe that PAHs can be synthesized by unicellular algae, higher plants or bacteria, while others hold that organisms accumulate PAHs rather than synthesizing them (Wilcke 2000). Emissions from vehicle exhaust (diesel, leaded and unleaded gasoline) are the largest contributors of PAHs in urban areas. The emission of PAHs from mobile sources is a function of engine type, load and age, fuel type and quality, PAH accumulation in lubricant oil, lubricant oil combustion, driving mode, and emission control (Ravindra et al. 2008). In addition to combustion-related emissions, transportation emissions of PAHs include abrasion of rubber tires, asphalt road surfaces, and brake linings (Marchesani et al. 1970; Rogge et al. 1993; Boulter 2005) and those from railway ties treated with creosote (Kohler and Kunniger 2003). The most important industrial sources of PAHs are primary aluminium production, coke production, creosote and wood preservation, waste incineration, cement manufacture, petrochemical and related industries, bitumen and asphalt industries, rubber tire manufacturing, and commercial heat/power production (PAHs Position paper 2001). Domestic emissions are predominantly associated with the burning of coal, oil, gas, garbage, or other organic substances like tobacco or char-broiled meat (Smith 1987). Furthermore, wood, dried animal-dung-cake and agricultural residues are also used extensively for cooking in developing nations (WHO 2002).
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A widespread practice for disposing of agricultural and logging residues is their combustion on-site. All of these activities involve burning organic materials under sub-optimum combustion conditions, releasing a significant amount of PAHs to the atmosphere (Hays et al. 2005).
Spatial Patterns and Trends of PAH Emissions and Advection Concentrations of PAHs in the atmosphere follow a typical seasonal trend. Winter PAH levels in the mixing layer are higher than those in summer due to the higher emissions and the lower inversion layer height during the cold season. Moreover, both the oxidation of PAHs by reaction with hydroxyl radicals (OH•) and their photolysis by ultraviolet radiation reach their maximum levels in summer. According to the official data provided by member states of the UN-ECE Convention on Long-range Transboundary Air Pollution (LRTAP), integrated with expert estimations for missing data, the overall emission trend of four PAHs, namely benzo(a)pyrene, benzo(k)fluoranthene, benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene in 44 European countries decreased between 1990 and 2004 (Denier van der Gon Hac et al. 2005). The annual pollution levels (atmospheric concentrations and deposition) of the four indicator PAH congeners in the EMEP region in 2004 were calculated for 1 year period with the regional model MSCE-POP, using a resolution of 50 × 50 km2. Reductions in the emissions of each of these four congeners ranged between 22% and 28%, and the reduction for the sum of all of them was 24% (Fig. 1). The highest reported BaP emissions in 2004 were located in northern Italy, the north-west of the Iberian peninsula, north-western Europe (i.e. Belgium, Netherlands and the Rhineland), the Visegrad area (southern Poland and the western Czech
800 B(a)P
700
B(k)F
B(b)F
I(cd)P
600 ton
500 400 300 200 100 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
year
Fig. 1 Long term trend of the emission of four PAHs (BaP,BkF, BbF, IcdP) in 44 countries (Denier van der Gon Hac et al. 2005; Gusev et al. 2006; Vestreng et al. 2006; http://emep.msce.org)
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Republic), Latvia, Ukraine (in particular in the south-east) and the Marmara region in Turkey. The estimated concentrations of BaP exceed the target level of 1 ng m−3 established in EU directive 2004/107/CE as the maximum acceptable annual mean in the north-western Iberian peninsula, the Po Valley, central and southern Poland, Latvia, and large areas of south-eastern Ukraine. The same geographical pattern is observed in the estimated deposition fluxes of BaP (Fig. 2). For BaP, calculated values were compared with available measurement data for 2004 in seven EMEP measurement sites. In general, the modelled air concentrations fall within the range of measurements multiplied by a factor of 3. The spatial distribution of the emission of six PAHs in North America was estimated using the emission processing system SMOKE (Galarneau et al. 2007). Total phenantrene emission rates, are higher in industrial districts of the centraland north-eastern U.S. (in particular the strip between New York and Chicago), Washington state in the north-west of the U.S., and the area between Montreal and Quebec in the south-east of Canada. Diffuse sources are distributed in the central- and north-east of the U.S., in the Rocky mountains between Las Vegas and Salt Lake City and in the surroundings of the two largest cities in California (Los Angeles and San Francisco). These sources present a strong seasonal excursion, with maximum levels in winter and summer emissions relevant only in Virginia, Tennessee and Georgia. Mobile sources of PAHs are associated with the urban areas and are therefore higher in the eastern half of the U.S., with maximum levels in the strip between New York and Chicago. On the west coast, hot spots coincide with the largest cities like Los Angeles, San Francisco and Seattle. Also mobile sources present a strong seasonality pattern with higher winter levels. Figure 3 illustrates the estimated phenanthrene emissions (1 h resolution) under typical winter (January) and summer (July) conditions for different kinds of sources. The transboundary transport of BaP between European countries (EMEP area) was estimated for 2004 using the output of the regional model MSCE-POP, assuming that this pollutant is, in the atmosphere, predominantly adsorbed to particulate matter (Fig. 4). The contribution of other European countries to the deposition levels of a particular country vary from 20% to 80%. The relevance of the long range transport of PAHs is reflected by the fact that in 30 out of 44 countries more than 50% of the BaP deposition is due to long range transport from other countries in the EMEP area. In conclusion, anthropogenic PAH emissions are the factor triggering the whole pollution process. Therefore, source spatial patterns and temporal trends, both in the short and the long term, determine the amount and the quality of pollutants that will reach a given ecosystem. In principle, the amount of pollutants reaching a receptor ecosystem is inversely proportional to its distance from the sources. Nevertheless, atmospheric circulation may transport PAHs over long distances and local meteorology strongly influences the deposition of PAHs. Very often large forest ecosystems are located far from urban and industrial point sources but near to
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Fig. 2 Calculated deposition fluxes (a) of BaP in 2004 in comparison with emissions (b). POPs status- EMEP Report 3/2006 (From Gusev et al. 2006)
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Fig. 3 Estimated phenantrene emission rates (g/s) in winter (right) and summer (left) in 2002 for (a) total emissions, (b) diffuse sources, and (c) mobile sources (Adapted with permission from Galarneau et al. Copyright 2007 American Chemical Society)
diffuse sources like domestic heating and open field biomass burning. On the other hand, the advection of PAHs and other persistent pollutants has been found to be relevant on the global scale and is the cause of the deposition and accumulation of
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100% 80% 60% 40% 20%
Monaco Iceland Switzerland Republic of Kazakhstan Croatia Norway The FYR of Russian Federation Austria Lithuania Luxembourg Slovakia Greece Belarus Sweden Romania Slovenia France Finland Germany Hungary Czech Republic Albania Denmark Estonia Georgia Bosnia&Herzegovi Bulgaria Serbia and United Kingdom The Netherlands Armenia Azerbaijan Portugal Cyprus Ireland Latvia Belgium Poland Turkey Italy Ukraine Spain
0%
Fig. 4 Contribution of transboundary transport to total annual depositions of BaP from European emission sources for each European country (%) (From Gusev et al. 2006)
these substances in remote areas like the polar regions (Simonich and Hites 1995) and mountain ranges like the Alps (Belis et al. 2009), the Rocky Mountains and the Andean cordillera (Daly and Wania 2005).
The Fate of PAHs in Forests The deposition of semivolatile organic compounds (SVOCs) from air to the terrestrial environment is higher in forested areas than in other natural or anthropogenic environments, making forests an important sink for atmospheric pollutants. With forests covering 80% of the earth’s surface, it can be seen that on the global scale forest ecosystems regulate the transport and atmospheric concentration of SVOCs. PAHs are the most abundant semivolatile persistent organic toxics in forests (Offenthaler et al. 2008). Their deposition onto the plants, either dry or wet, depends mainly on their concentration in the atmosphere. The canopy acts as a filter for gaseous and particle-bound PAHs, which are also subject to secondary deposition to the soil due to the rainfall and litter fall (Howsam et al. 2001b). The ability of a forest to remove pollutants from the atmosphere was quantified by McLachlan and Horstmann (1998), who defined the filter factor (F) as the quotient of the net deposition fluxes to canopy and to a bare soil:
F=
net deposition flux to vegetation canopy net deposition flux to bare soil
(2)
The prediction of F is essentially based on the partition coefficients KOA and KAW of the studied pollutant. Its full mathematical expression is the combination of a
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number of algorithms describing the wet deposition, the gaseous deposition to the canopy and to the soil, and the particle-bound deposition (wet, dry and impaction-driven). F values above unity indicate a filter effect, since bulk deposition (wet and dry) in the forest is higher than on the bare soil (non forested areas). Loss of PAHs from the litter occurs by physical processes like volatilization and leaching, and by biological processes like degradation and assimilation by microorganisms (Howsam et al. 2001a). Considering that in forests PAHs are mainly present in the soil, the forest biomass can be depicted as a mechanism pumping (or funnelling) pollutants from the atmosphere to the soil (Fig. 5), which acts as a high capacity reservoir from which chemicals can return to the atmosphere only with difficulty (Wania and Mclachlan 2001). Nevertheless, most semivolatile organic pollutants evaporate from the soil when the temperature rises, are transported with air masses towards cooler regions and are deposited when the ambient conditions favour partitioning from air to surfaces. The repetition of these “hops” results in the transport and distillation of persistent chemicals on the global scale from the tropics to the polar regions, referred to as the “Grasshopper effect” or “Global distillation”. This phenomenon originally described for organochlorine compounds (Simonich and Hites 1995), has also been observed in PAHs (Franzaring and van der Eerden 2000). In the atmosphere, the main processes responsible for PAH degradation are photolysis and oxidation by gaseous pollutants which lead to the production of oxy-, hydroxy-, nitro- and hydroxynitro-PAH derivatives (Baek et al. 1991). On the other hand, microbial metabolism is the major process for degradation of PAHs in soil environments (Sims and Overcash 1983).
Fig. 5 Schematic representation of the fate of PAHs in forests
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Interaction of Air Masses with the Forest Canopy Deposition of PAHs Semivolatile organic compounds (SVOCs) transported by air masses across forested areas strongly interact with the canopy due to the roughness of this type of land cover and to the presence of waxes and hydrophobic lipids on the leaves (Simonich and Hites 1994). The uptake rate and bioaccumulation of SVOCs is quite variable depending on (a) the properties of the plant, (b) the physicochemical properties of the considered pollutant, and (c) the environmental conditions. The parameters required to describe the deposition of a pollutant and its fate in the plant biomass depend on the complexity of the adopted model. The lipophilic nature of the cuticle facilitates the accumulation of non-polar substances, so that for a long time this was considered the preferential way for POPs to enter the plant. More recent work has provided evidence of a significantly higher uptake rate of PCB in plants with high stomatal density than in similar ones with a low number of stomata, stressing the role of stomatal uptake for POPs (Barber et al. 2002). The most commonly used vegetation parameters in models are: boundary layer thickness, cuticle-wax thickness, leaf thickness, stomatal density, depth and aperture, leaf area index, and growth rate. Also the physicochemical properties of the pollutant influence the uptake pathways into plants. Two parameters log KOA (octanol/air partition coefficient) and log KAW (air/water partition coefficient) are useful in identifying whether a chemical will be adsorbed from the atmosphere or from the soil. PAHs fall in the in interval of values (log KOA > 6 and log KAW > −6) in which adsorption from atmosphere is expected to occur (Mackay et al. 1992). Analogously, log KOA can be used to estimate the partition of an organic pollutant between the gaseous phase and the particles. Log KOA of many PAHs at 25°C ranges between 7 and 14, which means that in temperate areas heavier PAHs are predominantly in the particulate phase. The relative relevance of dry and wet deposition of a substance in the atmosphere depends partially on its air/water partition coefficient KAW. The most important environmental parameters influencing the deposition of SVOCs are temperature and wind speed. Temperature influences uptake processes in different ways. There is an inverse relationship between temperature and the concentration of POPs in plants; also the particle/gas partition of a given PAH increases when temperature decreases. Finally, diffusion coefficients increase by factors between 5 and 10 for every 10 K increase in temperature. Air masses circulating through large forested areas during the growing season may experience a significant reduction in SVOC concentrations due to the deposition of pollutants to the forest and consequently to the soil (Howsam et al. 2001a, b; Wania
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and McLachlan 2001). In particular, global scale modelling revealed that the filter effect of boreal deciduous forests may halve the long-range transport of SVOCs to the Arctic (Su and Wania 2005). Model calculations indicate that canopy interaction is stronger for SVOCs with octanol-air partition coefficient (logKOA) between 7 and 11 and an air-water partition coefficient (log KAW) above −6 (McLachlan and Horstmann 1998). Choi et al. (2008) report that concentrations of gaseous PAHs, such as phenanthrene, anthracene, and pyrene, within the canopy during the spring budburst are reduced compared to the air layers above and below the canopy. The authors found that concentrations of PAHs in the particulate phase do not present a vertical profile, and that the gas/particle partitioning behaviours above and within the canopy are statistically different. This implies a substantial canopy interaction for the gaseous PAHs. The median fluxes for the three PAHs are in the range of 35–71 pg·m −2·s−1 for phenanthrene, 3–7 pg·m−2·s−1 for anthracene, and 7–14 pg·m−2·s−1 for pyrene implying a net dry gaseous deposition of some micrograms of PAHs per square meter to the canopy during spring. Observed deposition velocities range between 3 and 12 cm s−1 (Fig. 6). Deposition values of 12 PAHs and BaP in rural areas in Europe and North America obtained with different sampling methods are reported in Table 4. Although the major PAH deposition process is particle-associated, it has been observed that spruce needles in the litterfall, present higher levels of semivolatile PAHs than those observed in open field deposition, indicating that gaseous dry deposition contributes significantly to the deposition in forest stands (Gocht et al. 2007). Wet deposition is the wash-out of both vapour phase and particulate-bound chemicals by rain, snow, dew formation, mist and fog and is dependent on the KAW and on the particle scavenging efficiency of the precipitation (Barber et al. 2004). The role of rain is also important in the removal of particles and adsorbed PAHs from the leaves of different species, in particular in the understorey (Howsam et al. 2001b) Considering that in temperate areas the highest emissions of PAHs coincide with the cold season and that many forested areas are located in mountain ranges, establishing whether snow or rain are more efficient in scavenging organic compounds from the atmosphere is crucial. To answer this question Lei and Wania (2004) devised a conceptual model of the equilibrium phase distribution of an organic chemical based on partitioning coefficients that makes it possible to estimate the behaviour of any compound for which these coefficients are known. As temperature decreases, chemicals tend to pass from the vapour phase to liquid water droplets, atmospheric particles or the snow surface. At 0°C rain is typically more effective in scavenging the vapors of small organic molecules than snow, because the capacity of the snow surface to sorb such chemicals is smaller than that of liquid water droplets. Snow is a more effective scavenger for vapors of larger, non-polar organic compounds like PAHs of more than four rings, which are less water soluble. For these substances temperature and snow
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Fig. 6 Temporal variation of the sum of 13 PAHs in the gaseous phase and in particles at different heights (A: above the canopy; B and C: within the canopy; D: below the canopy (Reproduced from Atmos. Chem. Phys. 8:4105–4113, Copyright Choi et al. 2008 under the Creative Commons Attribution 3.0 License)
properties (particle scavenging ratio and the specific snow surface area) determine the mode of scavenging (vapor vs particles) and the total scavenging efficiency (Lei and Wania 2004). The effect of temperature explains why snow is a more efficient scavenger than rain, especially below −10°C. It is also the reason why wet deposition processes become increasingly important with decreasing temperature, and even constitute the dominant deposition mechanism for some organic contaminants in cold environments (Lei and Wania 2004).
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Table 4 Bulk deposition (wet + dry) of PAHs in rural areas using different sampling methods (mg m−2 year−1) (From Gocht et al. 2007) Sampling method Study area S 12PAH BaP Reference Bergerhoff Germany (forest) 2.2–11.2 Matzner 1984 Bergerhoff Germany (forest) 142 7.4 Horstmann and McLachlan 1998 Funnel–bottle France (suburban) 46 1.5 Motelay-Massei et al. 2003 Funnel–adsorber– Europe (high altitude) 12.6–16 0.4–0.7 Fernandez et al. cartridge 2003 Funnel–adsorber– Sweden (forest) 186 9.5 Brorström Lundén cartridge and Löfgren 1998 Funnel-adsorberGermany (forest) 137–347 4.2–11.6 Gocht et al. 2007 cartridge Funnel-adsorber Germany (open field) 108–201 4.9–10.5 Gocht et al. 2007 cartridge
Levels of PAHs in Leaves and Needles While plants have been widely used as passive samplers of organic compounds in the atmosphere, it is not possible to establish a linear quantitative relationship between PAH levels in the leaves or needles and air or soil PAH concentrations (Menichini 1992; Watts et al. 2006). It is difficult to compare literature accounts of the concentration of PAHs in needles, since authors often report the sum of different number and type of PAHs (Table 5). A comparable methodological approach was adopted in two studies conducted 10 years apart at remote Alpine sites. The median of the total PAH concentrations in needles at remote Austrian sites in 2004 (18 mg kg−1 d.w.) reported by Belis et al. (2007) was less than half that observed in 1993 (48 mg kg−1 d.w.) by Weiss et al. (2000). This decreasing trend in PAH levels in the needles coincides with the findings of another study on PAH concentration in needles at some German sites (Schroter-Kermani et al. 2006). The geographic patterns of most PAHs in needles in the Alpine area reported by Belis et al. (2007) displayed a clear longitudinal gradient, with light (three to four rings) PAHs peaking in the western and heavy (five to six rings) species cumulating in the eastern part of the investigated range. This is probably associated with differences in the sources between eastern and western European countries. Atmospheric deposition of 12 PAHs was observed also in the lichen Xantoria parietina (Srogi 2007). The total PAH concentration ranged between 25 and 40 mg kg−1, and the most abundant species were dibenzo(a,h)antracene and benzo(k)fluoranthene followed by benzo(a)anthracene, chrysene and fluorene.
Spruce forest n.a n.a. nursery 1 1997 Deciduous forest (oak, ash, hazel) 40 2004 Norway spruce background forest a Sum 17 PAHs b Sum 9 PAHs c Mean for oak, ash and hazel respectively d Sum 22 PAHs e w.w.
18–77b 39–264d
7–46
24 84, 69, 92 c;d
19
8.2–61a,e
2004
Spruce forest
7
24–38
Range 28–412
Table 5 Concentrations of PAHs in forest needles (mg kg−1) d.w. Sum 16 PAHs Type of site n Sites Year Median 24 1993 48 Norway spruce background forest Spruce forest 2 1995
0.25
BaP Median
0.04–1.05
0.19–0.86e
Range
Alps
Canada
Germany
Germany
Sweden
Country Austria
Belis et al. 2007
Howsam et al. 2001b
Brorström Lundén and Löfgren 1998 Schroter-Kermani et al. 2006 L.U.B.W. 1993
Reference Weiss et al. 2000
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Considering the susceptibility of PAHs to photodegradation it is likely that photolysis of PAHs deposited on vegetation influences the fate of these pollutants in forests. Degradation of particle-bound PAHs associated with exposure to light is highly dependent on the substrate to which they are adsorbed. The more polar the solvent, the faster the degradation process. Photodegradation has been found to be relatively fast in natural waters and in organic solvents (Zepp and Schlotzhauer 1979; Low et al. 1987). In contrast, PAHs adsorbed on fly ash and black carbon atmospheric particles can better resist photolysis and be transported with air masses from their sources to the receptor (Yokley et al. 1986). Photolysis of PAHs adsorbed on the waxy surface of spruce needles follows a first order kinetics and can be faster than on fly ash and black carbon (Wild et al. 2005). The half-lives of PAHs on needles under experimental conditions range from 15 h for dibenzo(a,h)anthracene to 75 h for phenanthrene (Niu et al. 2003); much faster photodegradation (half-lives under 30 min) was observed on maize leaves although the methodological comparability of studies is limited (Wild et al. 2005). Since photolysis is slower on the needle surface than in water a stabilizing effect of needle surface waxes has been hypothesized.
PAHs in the Litter and the Soil Deposition of PAHs Deposition of atmospheric pollutants, both in gaseous phase and particulate matter, to the soil may take place directly or with the vegetation as an intermediary sink before deposition by litterfall and subsequent decay of the organic material. Although PAHs are not permanently bound in leaves, their rate of loss is very low. The low desorption is probably controlled by diffusion through the cuticular skin (Barber et al. 2004). Therefore, no significant proportion of PAHs accumulated in vegetation will exchange directly back into the atmosphere, and PAHs in the vegetation will be incorporated to the soil when the leaves senesce or when the plant dies (and/or falls). Winterly PAH burdens in the litter layer are higher than those observed after litterfall by a factor between 4 and 11. This means that fluxes of PAHs associated with litterfall during the vegetative season are lower than throughfall fluxes to the litter layer after the fall of leaves. These differences are mainly ascribed to the low atmospheric concentrations of PAHs during the warm season, compared to winter, and to PAH losses from the litter by processes which are more intense in summer, like volatilization or degradation and assimilation by decomposers (Howsam et al. 2001a). The storage quotients (ratio of stock to input) of PAHs in the litter increases, as their degradation rate slows, with their molecular weight. In a Canadian forest, the amount of PAHs between 200 and 250 u stored in the soil is seven to ten times the annual deposition to the litter layer during winter, while the storage quotient of BghiP (276 u) in the soil is equivalent to 25 years’ worth of this deposition (Howsam et al. 2001a).
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Soils are regarded as pollutant sink due to their great capacity for absorbing and retaining hydrophobic chemicals and then releasing them slowly back to the atmosphere (Wong et al. 2004). The uptake of PAHs by plant roots from the soil is not yet fully understood. Some authors consider the plant uptake of POPs from soil negligible, especially those with high lipophilicity and low water solubility (Bacci and Gaggi 1986; Kipopoulou et al. 1999). However, translocation of PAHs from contaminated soils and sediments with high concentrations of these pollutants to the roots of plants and trees has been observed (Fismes et al. 2002; Meudec et al. 2006). Due to their high hydrophobicity and high solid–water partitioning coefficients PAHs tend to interact with non-aqueous phases and organic matter in the soil, becoming virtually unavailable for microbial degradation. This is particularly true for heavier PAHs whose bioavailabilty decreases almost logarithmically with increasing molecular weight (Johnsen et al. 2005). In addition, the biodegradation rate of hydrophobic substrates is conditioned by two factors: the physical transfer rate of the chemical in the medium, and the metabolic activity of bacteria. In the soil, PAHs are heterogeneously distributed and may even be absorbed inside organic particles. Therefore PAH-degrading bacteria are physically separated from the PAH-sources and depend on diffusive transport. Since bacteria degrade PAHs with intracellular dioxygenases, PAHs have to be taken up by bacteria before degradation. One of the bacterial strategies to improve their access to PAHs is the release of biosurfactants, small detergent-like molecules, to increase water solubility of the PAHs. In addition, it was observed that the extracellular polymeric substances (EPS) secreted by bacteria in the formation of biofilms have PAH adsorbing properties (Johnsen et al. 2005). The bacterial strains able to degrade PAHs belong to a very limited number of taxonomic groups: Sphingomonas, Burkholderia, Pseudomonas and Mycobacterium (Ho et al. 2000; Bastiaens et al. 2000; Johnsen et al. 2002) Soil is considered to be a sink for organic pollutants, and its content of organic matter (OM) in particular is believed to be the principal factor controlling the sorption of these chemicals in soil and sediments (Burgess and Lohmann 2004; Cornelissen et al. 2005; Sweetman et al 2005). Understanding the influence of OM on the air/soil exchange in rural or remote areas is crucial for establishing the global budget of POPs. In recent years, one of the components of OM, namely the black carbon (BC), has received most attention due to its high sorption capacity for hydrophobic organic compounds (HOC) compared to that of amorphous organic carbon (AOC) (Nam et al. 2008). Both, organic pollutants and BC, are components of the atmospheric particulate matter that are emitted by incomplete combustion processes. However, it is not yet clear whether the association between HOC and BC in soils can be attributed to the common origin of these compounds or to processes occurring subsequently in the atmosphere or in the soils (Fig. 7). Krauss et al. (2000) found that PAHs in German forest soils are ten time less mobile than PCBs with similar Kow explaining these results as a consequence of the strong association of PAHs with soot or pyrolytic black carbon particles, which reduce PAH mobility and bioavailability compared to single PCBs with similar Kow (Gustafsson et al. 1997).
Semivolatiles in the Forest Environment: The Case of PAHs
POP Gas
Air
65
2 Atmospheric transport and deposition
Particle BC
of POP and BC
Partitioning
either: A. together, following emission together B. together, following partitioning during transport C. separately POPs could be subject to dry gaseous or wet/dry deposition in non-BC form
1
Emission of POP and BC either: A. Together (combusition) B. Separately (non-combusion or volatilization)
BC
POP
3 Re-emission of POP BC
either : A. as vapour, separate form BC B. as re-entrained soil dust with BC
POP
Sources to atmosphere 4
Soil TOC/SOM stock
Dissolved Phase
Gas
formed from vegetation/ Microbial turnover
Soil Soil C forms
Historical & natural combustion sources (eg. forest fires, domestic coal/wood burning)
Soil BC Stock
Possible relation in soil either correlation of POP with:
A. BC, following 1A, 2A, or 2B. B. BC, following association formed in the soil, i.e. following deposition/leaching/bioturbation or other forms of soil mixing C. TOC or SOM, following association formed in the soil D. No correlation, Indication 1B and 2C occured, plus lack of time, mechanisms or affinities to combine with OM
Fig. 7 Possible scenarios for the relationship between POPs and SOM (with a focus on BC) and inferred sources, fate, and transport (Reprinted from Environ Pollut (156) Nam et al., Relationships between organic matter, black carbon and persistent organic pollutants in European background soils. 809–817. Copyright (2008), with permission from Elsevier)
However, Nam et al. (2008) report no relationship either between PAHs and TOC or between PAHs and BC in remote/rural forest and grassland soils from UK and Norway. In this study, TOC is strongly correlated with HCB and PCBs, while both TOC and BC are strongly correlated with PBDEs. These data suggest that the BC pool in the soil may be the result of the accumulation of emissions over centuries and that the association between non-pyrogenic pollutants like PBDEs and BC may be the consequence of their interaction in the soil or during deposition and re-emission cycles.
Levels of PAHs in Soils The concentration of PAHs in forest soils in different studies in Europe and North America are summarized in Table 6. Levels of the sum of 20 PAHs in the Alps collected in 2004 are comparable with those observed in Germany in the 1990s. On the other hand, the sum of 16 PAHs is lower than that observed in Austria in the 1990s but higher than that measured in Canadian deciduous forests. Average BaP concentrations in the Alpine area in 2004 are lower than those reported for Swiss forests but the range is comparable with the
77–501
0–5
Entire humus 189 layer
84–542
185
6.6
15
2–19
15–22
Canada (Toronto) Alps
CH
CH
Belis et al. 2007
36–214
0–20
From urban to forest – 105 all types Deciduous forest 3 (sugar maple) 30 Norway spruce background forest
221
0.5–22
32–8465
0–20
16
CH
Coniferous forest
0–20
12
10
Deciduous forest
176
DE
11–687
16
Spruce forest
15–20
Krauss et al. 2000 Krauss et al. 2000 Desaules et al. 2008 Desaules et al. 2008 Desaules et al. 2008 Wong et al. 2004
DE
Range
Source Weiss et al. 2000
BaP Median Country A
Range 68–1,342
Table 6 Concentration of PAHs in forest soils in different studies (mg kg−1) Sum 16 PAHs Sample depth Sum 20 PAHs Kind of site n (cm) Median Range Median 210 25 Entire Norway spruce humus background layer forest Spruce forest 16 0–5 60–2,606
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one observed in the Canadian forests. These results are in agreement with the findings of the monitoring network for POPs in the Alpine region (project MONARPOP), which concludes that although the long-term trend is towards decreasing levels of PAHs, the Alps are a sink for these compounds, with the highest soil concentrations in their northern fringe and in the western part of the study area (Belis et al. 2007). These authors conclude that the stocks in the Alpine soils cannot be explained only by the accumulation of officially reported emissions within the area, suggesting that both transboundary transport and local emissions, like biomass burning, contribute significantly to the mass balance of these compounds. The existence on the global scale of two major source patterns for PAHs in soils, natural and anthropogenic, has been hypothesized by Wilcke (2007). The first of these, characterized by low molecular weight PAHs, is the result of biological activity or released by natural fires or volcanic emissions. This source of PAHs is represented by the sum of naphthalene, phenanthrene and perilene. The second source pattern is anthropogenic, and derives from the combustion of fossil fuels. Although anthropogenic emissions of light PAHs are not excluded, PAHs in this cluster are better represented by 11 of those with higher molecular weight ranging from benzo(a)anthracene (228 u) to dibenzo(a,h)anthracene (278 u) (Wilcke 2007). According to this hypothesis, local deviations from the general pattern may be associated with specific sources or changes in the relative abundance of PAHs due to transport processes. Average concentrations of the S 16 EPA PAHs in the soils of different sites of the world, including tropical and temperate areas, range from 3.6 to 170,000 mg kg−1. In the scatter plot of natural versus anthropogenic sources indicators samples from different areas of the globe arrange on a line (Fig. 8). As expected, the total amount of PAHs seems to be correlated to anthropogenic sources and decrease in areas where natural sources are predominant. However, the limitation of the study for generalization on the global scale is the small number of samples and the poor representation of the southern hemisphere and the polar regions.
Effects of PAHs on Ecosystems Once PAHs reach the forest ecosystem they can either (a) accumulate in one compartment, (b) exit the system by evaporation or leaching; (c) be degraded by physico-chemical agents (photolysis; oxidation etc.), or (d) interact with the biota. The interaction of a chemical with living organisms depends on the fraction of chemical that reaches the circulatory system or the physiologically active areas of the cells. An example is the relationship between water solubility of PAHs and their degradation by soil bacteria discussed above. The contact of the chemical with the organism does not necessarily imply it enters the active metabolising compartment of the cells. In leaves, the presence of a large volume of lipidic materials is likely to keep lipophilic PAH concentrations in the cytoplasm very low.
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C.A. Belis et al. 80
CZ ZI
70 BT
Σ11HMPAHs[%]
60
y = –0.86x + 75 r = –0.98
ST UB
50 BK
40
RU
30
GH
20 BR
10 0
PR
0
10
20
30
40
50
60
AM
CE
70
80
90
100
NAPH+PHEN+PERY[%]
Fig. 8 Regression of naphthalene, phenanthrene, and perylene (NAPH+PHEN+PERY) and the S11 high-molecular weight PAHs as percentages of S 20 PAHs at 12 locations around the world. Error bars indicate standard deviations (Reprinted from Geoderma (141) Wilke. Global patterns of polycyclic aromatic hydrocarbons in soil.157–166, Copyright (2007), with permission from Elsevier) ST = Stephanskirchen, Germany; BT = Bayreuth, Germany; CZ = Bohemia, Czech Republic; ZI = Ziar nad Hronom, Slovak Republic; UB = Uberlândia, Brazil; BK = Bangkok, Thailand; RU = Moscow Russia; GH = Accra, Ghana; PR = North American; BR = Brazil; CE = Cerrado Brazil; AM = Amazon rainforest
However, if PAHs deposited from the atmosphere are able to penetrate into the cells they are likely to be transformed into a conjugate. Nakajima et al. (1996) reported significant amounts of 1-hydroxypyrene conjugates in the leaves of four different woody species growing close to traffic congested roads. These authors found no conclusive evidence on whether the conjugates are produced in the leaves or taken up from the soil by the roots and transported to the leaves. Many PAH degradation products are more soluble and consequently more leachable and bioavailable than their precursors. For example, the mobility of nine to ten phenantrenodione was found to be higher than that of phenanthrene (Amellal et al. 2006). A biomonitoring study on background spruce forests in the Alps used the activity of the detoxifying enzyme glutathione transferase (GST) in the needles as a marker for exposure to xenobiotics (Offenthaler et al. 2008). These authors observed significant correlations between the GST activity and the concentration of PAHs (in particular benzo[g,h,i]perilene) in Norway spruce needles, and in the soil. Most of the damage observed in vegetation is associated with photo-oxidative stress. During photosynthesis the formation of active oxygen species is rapidly compensated by the anti-oxidative systems. The accumulation of active oxygen species triggers a part of the plant’s alarm-signal system that activates the scavenging
Semivolatiles in the Forest Environment: The Case of PAHs
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mechanisms. However, when the plants are under the influence of stress factors such as chilling or toxic pollutants, their response may be insufficient to eliminate the active oxygen species that can damage the cell structures. Chlorosis and reddish-brown necrosis on the needles of the Japanese red pine (Pinus densiflora) have been reported following exposure to phenanthrene and fluoranthene (Oguntimehin et al. 2008). After 3 months fumigation with a solution of these pollutants, net photosynthesis, stomatal conductance, chlorophyll content and Rubisco concentrations decreased. The authors found that the effects of the studied PAHs were similar to those observed in previous studies using OH• radicals or OH• radical generating substances that are responsible for photo-oxidative damage to photosystem II. The authors concluded that the negative effects of PAHs on pine needles are similar to those obtained using a broad-spectrum herbicide and are associated with the generation of OH• radicals. Concern about the high levels of organic pollutants in the soil and vegetation arises from their possible introduction into the food web. It is commonly accepted that predators accumulate unmetabolized pollutants and thus have higher levels than their food supply (Cripps 1992). Food also appears to be the main source of PAH intake for humans not occupationally exposed to PAHs (Grova et al. 2006). In vivo studies suggest a transfer into the intestinal epithelium by diffusion even though other mechanisms like metabolism are not excluded. A study on the oral uptake of two PAHs by goats monitored the incorporation of these chemicals into the bloodstream (Laurent et al. 2002). These authors found that light PAHs like phenanthrene are absorbed at a higher rate than heavier ones like benzo(a)pyrene. Similar mechanisms can be hypothesized for wild fauna or cattle living in forested areas or grasslands. In nature, PAHs are present in complex mixtures, the ecotoxicology of which is hardly known for the single substances and much less for the whole mixture. It is possible, however, to predict the toxicity of a substance on the basis of its physical and chemical properties using the quantitative structure-activity relationships (QSAR) approach. PAHs, like PCDD/F, PCB and other aromatic compounds, bind to the animal aryl hydrocarbon receptor (AhR). This accelerates the breakdown of aromatic compounds, the metabolites of which have been linked to immunotoxic and carcinogenic effects. PAHs are also expected to exert narcotizing effects, a toxic action which does not involve binding to specific receptors, but modification of the fluidity and function of cell membranes. The lipophilicity of a chemical is considered a good indicator of its narcotic toxicity and can be estimated from the octanol–water partition coefficient (log KOW). The bioavailability of a chemical is useful in estimating its toxic effects. In the case of PAHs, bioavailability is a function of the pore-water concentrations and can be estimated from the organic carbon–water partitioning coefficient (log KOC). Sverdrup et al. (2002) applied a model based on log KOW and log KOC to predict the effects of the total concentration of 16 PAHs on survival and reproduction of the soil-dwelling springtail Folsomia fimetaria (Collembola). The authors concluded that only PAHs with log KOW £ 5.2 (i.e. naphthalene, acenaphthene, acenaphthylene, anthracene,
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phenantherene, fluorene, pyrene and fluoranthene) significantly affected the survival or reproduction of the springtails. The lack of toxicity of the heavier PAHs has been explained by their low water solubility and consequently their limited bioavailability. Another study on the effects of PAHs on soil-dwelling organisms was carried out by Bowmer et al. (1992), who found no effects of chrysene on the survival of the earthworm Eisenia fetida. Moreover, Van Brummelen et al. (1996) report effects of benzo(a)anthracene on the growth of the isopod Oniscus asellus while no effects were observed after exposure to benzo(a)pyrene. The approach followed by Sverdrup et al. (2002) seems to be useful in interpreting the results of these previous studies, since it predicts toxic effects for benzo(a) anthracene, but not for chrysene and benzo(a)pyrene.
References Amellal S, Boivin A, Perrin Ganier C, Schiavon M (2006) Effect of ageing on mobility and sequestration of phenanthrene in an agricultural soil. Agron Sustain Dev 26:269–275 ATSDR (1995) Toxicological profile for polycyclic aromatic hydrocarbons. US Department of Health and Human Services, Public Health Service, Atlanta Bacci E, Gaggi C (1986) Chlorinated pesticides and plant foliage: translocation experiments. Bull Environ Contam Toxicol 37:850–857 Baek SO, Field RA, Goldstone ME et al (1991) A review of atmospheric polycyclic aromatic hydrocarbons: sources, fate, and behaviour. Water Air Soil Pollut 60:279–300 Barber JL, Kurt PB, Thomas GO et al (2002) Investigation into the importance of the stomatal pathway in the exchange of PCBs between air and plants. Environ Sci Technol 36:4282–4287 Barber JL, Thomas GO, Kerstiens G, Jones KC (2004) Current issues and uncertainties in the measurement and modelling of air–vegetation exchange and within-plant processing of POPs. Environ Pollut 128:99–138 Bastiaens L, Springael D, Wattiau P et al (2000) Isolation of adherent polycyclic aromatic hydrocarbon (PAH)-degrading bacteria using PAH sorbing carriers. Appl Environ Microbiol 66:1834–1843 Belis CA, Bassan R, Iozza S et al (2007) PAHs in needles and humus of Alpine ecosystems (Project Monarpop). Organohalogen Compd 69:1689–1692 Belis CA, Offenthaler I, Uhl M et al (2009) A comparison of Alpine emissions to forest soil and spruce needle loads for persistent organic pollutants (POPs)- Environ Pollut 157:3185–3191 Boulter P (2005) A review of emission factors and models for road vehicle non-exhaust particulate matter. TRL Project Report for DEFRA, PPR065. Bowmer CT, Roza P, Henzen L, Degeling C (1992) The development of chronic toxicological tests for PAH contaminated soils using the earthworm Eisenia fetida and the springtail Folsomia candida; TNO report IMW-R 92/387, The Netherlands. Brorström Lundén E, Löfgren C (1998) Atmospheric fluxes of persistent semivolatile organic pollutants to a forest ecological system at the Swedish west coast and accumulation in spruce needles. Environ Pollut 102:139–149 Burgess RM, Lohmann R (2004) Role of black carbon in the partitioning and bioavailability of organic pollutants. Environ Toxicol Chem 23:2531–2533 Choi SD, Staebler RM, Li H et al (2008) Depletion of gaseous polycyclic aromatic hydrocarbons by a forest canopy. Atmos Chem Phys 8:4105–4113 Cornelissen G, Gustafsson O, Bucheli TD et al (2005) Extensive sorption of organic compounds to black carbon, coal, and kerogen in sediments and soils: mechanisms and consequences for distribution, bioaccumulation, and biodegradation. Environ Sci Technol 39:6881–6895 Cripps GC (1992) Baseline levels of hydrocarbons in seawater of the Southern Ocean; natural variability and regional patterns. Mar Pollut Bull 24:109–114
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Simonich SL, Hites RA (1995) Organic pollutant accumulation in vegetation. Environ Sci Technol 29:2905–2914 Sims RC, Overcash MR (1983) Fate of polynuclear aromatic compounds (PNAs) in soil-plant systems. Res Rev 88:1–68 Smith KR (1987) Biofuels, air pollution, and health – a global review. Plenum Press, New York Srogi K (2007) Monitoring of environmental exposure to polycycic aromatic hydrocarbons: a review. Environ Chem Lett 5:169–195 Su Y, Lei YD, Wania F et al (2006) Regressing gas/particle partitioning data for polycyclic aromatic hydrocarbons. Environ Sci Technol 40(11):3558–3564 Su Y, Wania F (2005) Does the forest filter effect prevent semivolatile organic compounds from reaching the Arctic? Environ Sci Technol 39:7185–7193 Sverdrup LE, Nielsen T, Henning Krogh P (2002) Soil ecotoxicity of polycyclic aromatic hydrocarbons in relation to soil sorption, lipophilicity, and water solubility. Environ Sci Technol 36:2429–2435 Sweetman AJ, Valle MD, Prevedouros K, Jones KC (2005) The role of soil organic carbon in the global cycling of persistent organic (POPs): interpreting and modeling field data. Chemosphere 60:959–972 Van Brummelen TC, Verweij RA, Wedzinga SA, Gestel CAM (1996) Enrichment of polycyclic aromatic hydrocarbons in forest soils near a blast furnace plant. Chemosphere 32:293–314 Vestreng V, Rigler E, Adams M et al (2006) Inventory review 2006. Emission Data reported to LRTAP Convention and NEC Directive Stage 1, 2 and 3 review and Evaluation of inventories of HMs and POPs. Technical Reports MSC-W/1. Wania F, Haugen JE, Lei YD, Mackay D (1998) Temperature dependence of atmospheric concentrations of semivolatile organic compounds. Environ Sci Technol 32:1013–1021 Wania F, McLachlan MS (2001) Estimating the influence of forests on the overall fate of semivolatile organic compounds using a multimedia fate model. Environ Sci Technol 35:582–590 Watts WA, Ballestero TP, Gardner KH (2006) Uptake of polycyclic aromatic hydrocarbons (PAHs) in salt marsh plants Spartina alterniflora grown in contaminated sediments. Chemosphere 62:1253–1260 Weiss P, Lorbeer G, Scharf S (2000) Regional aspects and statistical characterisation of the load with semivolatile organic compounds at remote Austrian forest sites. Chemosphere 40:1159–1171 WHO (2002) World Health Report 2002: Reducing risks, promoting life. http://www.who.int/ whr/2002/en/index.html (accessed on 18/09/10) Wilcke W (2000) Polycyclic aromatic hydrocarbons (PAHs) in soil – a review. J Plant Nutr Soil Sci 163:229–248 Wilcke W (2007) Global patterns of polycyclic aromatic hydrocarbons (PAHs) in soil. Geoderma 141:157–166 Wild E, Dent J, Gareth TO, Jones KC (2005) Real-time visualization and quantification of PAH photodegradation on and within plant leaves. Environ Sci Technol 39:268–273 Wong F, Harner T, Qin-Tao L, Diamond ML (2004) Using experimental and forest soils to investigate the uptake of polycyclic aromatic hydrocarbons (PAHs) along an urban-rural gradient. Environ Pollut 129:387–398 Yokley RA, Garrison AA, Wehry EL, Mamantov G (1986) Photochemical transformation of pyrene and benzo(a)pyrene vapor-deposited on eight coal stack ashes. Environ Sci Technol 20:86–90 Zepp RG, Schlotzhauer PF (1979) Photoreactivity of selected aromatic hydrocarbons in water. In: Jones PR, Leber P (eds) Polynuclear aromatic hydrocarbons. Ann Arbor Science Publishers, Ann Arbor, MI
Part II
Case Studies
A Case Study: Uptake and Accumulation of Persistent Organic Pollutants in Cucurbitaceae Species András Bittsánszky, Gábor Gullner, Gábor Gyulai, and Tamas Komives
Abstract Persistent organic pollutants (POPs) are a group of toxic compounds with global distribution and long persistence in the environment. POPs are hydrophobic with the potential to bioaccumulate in fatty bodies of tissues and biomagnified through food webs. Remediation of sites contaminated with POPs is highly desirable. Although phytoremediation of POPs is very difficult because of their low bioavailability, recent literature indicates that some plants, especially those belonging to the Cucurbitaceae family are capable of taking up and accumulate significant amounts of POPs. In this chapter we summarize the most important findings concerning POPs in relation to Cucurbitaceae.
Introduction The first chapter outlined the general principles of organic xenobiotic uptake and transport in plants. Like hyperaccumulators for heavy metals there are also certain plants that have been observed to hyperaccumulate persistant organic pollutants, POPs. POPs are a group of toxic compounds with global distribution and long persistence in the environment. POPs are hydrophobic with the potential to bioaccumulate in fatty bodies of tissues and biomagnified through food webs. Remediation of sites contaminated with POPs is highly desirable. Although phytoremediation of POPs is very difficult because of their low bioavailability, A. Bittsánszky (), G. Gullner, and T. Komives Hungariany Academy of Sciences, Plant Protection Institute, Herman Ottó 15, 1022 Budapest, Hungary e-mail:
[email protected] G. Gyulai Szent István University, Institute of Genetics and Biotechnology, 2103 Gödöllő, Páter K. 1, Hungary P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_4, © Springer Science+Business Media B.V. 2011
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recent literature indicates that some plants, especially those belonging to the Cucurbitaceae family are capable of taking up and accumulate significant amounts of POPs. Historically, environmental research focused on contaminants that decompose in the environment, such as acids, oils, grease, and wastes from animal processing. The discovery, that there are pollutants that are highly toxic, chemically stable, non-biodegradable, and have the tendency to accumulate in living organisms opened an entirely new field in environmental sciences. For example, a group of synthetic organic chemicals characterized by multiple carbon–chlorine bonds, high chemical stability and high toxicity (persistent organic pollutants, POPs) have been clearly identified as an unusual and special class of hazard to human health and the environment. POPs are a variety of chemicals that have been produced for industrial and agricultural use or created as by-products or waste. They belong to three groups (Table 1, Fig. 1). Chlorinated insecticides (Group A) and polychlorinated biphenyls (Group B) have been developed in the first half of the twentieth century as pest-controlling agents and as additives to oils used in electrical transformers and hydraulic couplings, respectively, while polychlorinated dioxins and furans (Group C) are generated as by-products of industrial processes and incineration (Inui et al. 2008; White 2009). After decades of intensive production of POPs the damaging consequences became evident and showed that they are a global threat (Inui et al. 2008; White 2009; Oldal et al. 2006). Environmental and human health hazards demand the cleanup of sites polluted by POPs. There are various options available for remediating POPs, which may be divided into those that use either chemical or biological mechanisms to decompose them or remove them from the soils. There are abiotic and biological systems Table 1 Groups of persistent organic pollutants (POPs) Group POPs A. Chlorinated insecticides A family of seven active insecticide ingredients B. Polychlorinated biphenyls A family of 209 individual compounds used as industrial chemicals C. Polychlorinated dioxins and furans A family of 75 compounds formed as by-products of industrial processes (primarily combustion)
a Cl
Cl Cl
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c Cl
Cl Cl Cl
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Cl Cl
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2,2’,3,4,5,6’-hexachlorobiphenyl
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O
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Fig. 1 Representative chemicals belonging to POP groups (a), (b), and (c) as indicated in Table 1
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that may be classed as active (i.e., require continuous inputs of resources to sustain the process) or passive (i.e., require relatively little resource input once in operation). It is important to evaluate strategies that are currently used together with new and emerging technologies to mitigate POPs and compare their strengths and weaknesses. This paper will focus on the depollution method called phytoremediation, i.e. the use of plants for removing, sequestering, or chemically decomposing environmental pollutants. This method has become one of the most rapidly developing ones of environmental restoration. The efficiency of plants as detoxifiers, filters and traps has been proven in cleaning up soils polluted with crude oil, explosives, landfill leachates, metals, pesticides, and solvents (Kömives and Gullner 2000). Although information about the phytoremediation of POPs is rather scanty, very recent literature has indicated that some plants (primarily those belonging to the Cucurbitaceae family) are capable of taking up significant amounts of POPs and accumulate them in their tissues (Inui et al. 2008; White 2009).
The Cucurbitaceae Family The Cucurbitaceae family belongs to the order Cucurbitales, class Magnoliopsida (subclass Rosidae). Botanists have applied about 435 taxons at various taxonomic ranks to describe the extreme morphological diversity of cucurbits (Nee 1990). All of the cultivated species are found in subfamily Cucurbitoideae. The genus Cucurbita includes about 20–26 species (Cutler and Whitaker 1961). All species of Cucurbita genus contain 20 pairs of chromosomes (2n = 20). They are secondary polyploids with the base number of 10 (n = 2x = 10; Robinson and Decker-Walters 1997; Zraidi et al. 2007). The fruit shape, color and surface pattern is extremely variable. Fruits size ranges from 4–15 cm (wild species) up to 1–2 m in diameter (see Cucurbita maxima, the largest fruit known). The five cultivated species of the genus (C. pepo, C. maxima, C. moschata, C. argyrosperma and C. ficifolia) are insect-pollinatted and reproductively isolated. Other cucurbits produced for commercial fruit–vegetable production especially cucumber (Cucumis) and watermelon (Citrullus) are grafted on Cucurbita rootstocks characterized with vigorous growth and pest resistance (Doty 2008). Cucurbits are present in the archaeological records of the American continent from the earliest stages of agriculture (Whitaker 1981) and they have been a part of nearly all indigenous cultures of the New World from southernmost Canada to Argentina and Chile. Many cucurbits are important as foods and as medical plants. C. pepo was domesticated thousands of years ago in North America (Cutler and Whitaker 1961). There is evidence suggesting that several groups of edible cultivars (including zucchini) subsequently originated in Europe, perhaps by crossing of culinary forms with gourds, as early as the sixteenth century (Paris 1989). Today, C. pepo is among the economically most important fruit–vegetable crops worldwide and is grown in almost all temperate and subtropical regions (Paris 1996).
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Pumpkin seed oil and pumpkin seed extract are increasingly esteemed for their excellent nutritional quality and medicinal value (Paris 1996).
POPs and Cucurbita Species Ability of Cucurbita species to take up and accumulate unusually high levels of POPs in their tissues was first observed in 1994 when in fruits of zucchini (Cucurbita pepo L. convar. giromontiina) polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/PCDF) were detected with concentrations ca. two orders of magnitude higher than in other fruits and vegetables growing in the same polluted soil (Paris 1996). Findings on the uptake and accumulation of individual POPs in different plant species will be summarized below.
DDT/DDE/DDD DDT (2-bis(p-chlorophenyl)-1,1,1-trichloroethane) is the most infamous POP type chemical. It has been used as an inexpensive and effective insecticide worldwide. In the environment DDT can be converted microbiologically or abiotically to DDE (2,2-bis(p-chlorophenyl)-1,1-dichloroethylene) and DDD (2,2-bis(p-chlorophenyl)1,1-dichloroethane) that are also highly persistent and have similar chemical and physical properties. White (2002) assessed the bioavailability of DDE in soil to the Cucurbita (squash, pumpkin) and Cucumis (cucumber, melon) species. Bioconcentration factors were greater in Cucurbita species with ca. an order of magnitude. Based on these results a complete phytoremediation system was elaborated. The impacts of variable plant density, soil moisture content, plant age, interactions between earthworms and plants, nutrient amendments, intercropping, organic acids, etc. on the uptake of DDE were investigated. (White 2009; Wang et al 2004; Kelsey and White 2005; Kelsey et al 2006; Peters et al. 2007; White et al. 2003a, 2005a, 2006; White and Zeeb 2007). It was also shown that different subspecies of cucurbits take up different amounts of DDE (White et al. 2003b).
Dieldrin and Endrin Dieldrin and endrin are structurally similar organochlorine insecticides developed in 1940s as alternatives of DDT. Diedrin is stable in the soil, while aldrin is gradually converted to dieldrin. Otani et al. (2007) compared the uptake of dieldrin and endrin of 32 plant species belonging to 17 families and by 34 cultivars of Cucurbita
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sps. grown in contaminated soil. Uptake of cucurbits were the highest especially of zucchinis as the most efficient plants. As Cucurbita species are used as rootstock for grafted cucumbers and watermelons the selection of low uptake cucurbits is also desired for rootstock that reduce dieldrin concentration in cucumbers and watermelons (Otani and Seike 2007).
Heptachlor Heptachlor is a chlorocyclodiene insecticide that was used to control termites and crop insect pests. Campbell et al. (2009) indicated the use of Lagenaria siceraria (bottle gourd) for the uptake of heptachlor and heptachlor epoxide. Seven cultivars were screened and all the seven took up considerable amount of heptachlor epoxide into the vines. Cucumber (Cucumis sativus) was also found to be able to take up and accumulate considerable amounts of heptachlor in the fruits.
Chlordane Chlordane is another organochlorine insecticide developed for corn (Zea mays). Chlordane is efficiently taken up by Cucurbita species and here, too, zucchini was the most effective accumulator among 12 crop plants investigated (Lee et al. 2003; Mattina et al. 2002). Interestingly, structure-favorable translocation was also observed for this chemical, even between chiral components with the same physicochemical properties. Xenobiotic residues translocate enantioselectively from the soil into the plant with genera-specific patterns (Lee et al. 2003; Mattina et al 2002).
Polychlorinated Biphenyls Polychlorinated biphenyls (PCBs) are a group of synthetic halogenated aromatic hydrocarbons characterized with high chemical stability. PCBs were widely used for many industrial applications. Today, they can be found in soils, drinking water, and living organisms. Ability of nine plant species (including three varieties of C. pepo) to take up polychlorinated biphenyls (Aroclor 1260) was investigated in contaminated soils in a greenhouse experiment (Zeeb et al. 2006). The results indicated that C. pepo varieties were the most effective as regards to their PCB phytoextraction capacity. Phytoremediation efficacy depended on the soil composition and contaminant’s concentration in the soil. It was also shown that tetra- to hexachlorobiphenyls
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contribute the largest proportions to plant shoots, while heavier chlorinated congeners (especially hepta- and octa-chlorobiphenyls) were found mostly in the roots. Inui et al. (2008) also examined the correlation between chlorination level and bioconcentration factor (BCF: ratios of concentrations in the plant to those in the soil) and found that PCBs with five chlorine atoms were more bioconcentrated than tetraand hexachlorinated biphenyls. White et al. (2005b) examined the effect of citric acid and earthworm species on the uptake of PCBs in Cucurbita, Cucumis and Lupinus species. Citric acid amendment resulted higher PCB concentration in the stem and the leaf. Aslund et al. (2007, 2008) carried out a field experiment with Cucurbita pepo, greater straw sedge (Carex normalis) and tall fescue (Festuca arundinaceae) and observed that PCB concentrations in pumpkin shoots decreased as the distance above the root increased and was independent of the type of tissue. All these results confirmed that Cucurbita pepo has the highest PCB accumulation capacity of all the plants examined.
Dioxins and Furans The first data on the high uptake of dioxins into certain Cucurbita species was reported in 1994 (Hülster et al. 1994). Zucchini fruits were found to contain concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans two orders of magnitude higher than other plants examined. Further investigations showed that zucchini plants were able to take up and translocate these contaminants to the shoot. Cultivar specificity among three zucchini cultivars for uptake of 29 dioxinlike compounds was also detected, as phytoextraction capacity of cv. ‘Goldrush’ and ‘Black Beauty’ zucchini varieties were found to be greater than that of the cv. ‘Patty Green’ with at least two orders of magnitude (Inui et al. 2008). In addition, a negative correlation was shown between the extent of the chlorination of the dibenzo-p-dioxin and dibenzofuran molecule and its bioconcentration factor.
Conclusions The reviewed results raise the question why does this unique uptake and accumulation of POPs by some cucurbit plants take place? In spite of great efforts to answer this question the phenomenon is still unexplained. It is generally accepted that POPs enter the plants via pathways evolved for acquisition of soil-derived nutrients (Schröder et al. 2001): uptake by the roots, followed by translocation and sequestration along the transpiration stream (Lunney et al. 2004) (obviously, semivolatile POPs may also enter the plants via air, but this uptake mechanism is rather inefficient and nonspecific). Most plants are able to take up hydrophobic compounds from soil, e.g. significant amounts of DDE were detected in roots of zucchini, pumpkin, alfalfa (Medicago sativa), ryegrass (Lolium multiflorum) and tall fescue (Lunney et al. 2004). A possible explanation of this uptake process is that root exudates serve as
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mobilizing agents through impacting the physico-chemical structure of the soil and improving the release of contaminants into the soil solution. In regular, root exudates are produced to acquire nutrients (White et al. 2005a). Among the organic acids in root exudates citric acid content showed high correlation with phytoextraction capacity of several weathered organic contaminants (Gent et al. 2005). In addition to the efficient uptake of POPs from polluted soils some Cucurbita pepo cultivars showed significant amount of pollutants translocated into the aboveground parts of plant. Explanation for this unique process provides challenges for further research. Lunney et al. (2004) speculated that Cucurbitaceae plants might metabolize POPs with the help of endophytic bacteria living in the plant cells. Some plant species had the ability to recruit, or selectively augment, the necessary bacteria to remove pollutants, while other plants in the same area were unable to do so (Doty 2008). Grafting experiments revealed that translocation ability depends only on roots. Cucumber scion grafted on zucchini rootstock extracted considerable amount of dieldrin, endrin and chlordane in the shoot while zucchini scion on cucumber rootstock did not (Otani and Seike 2007; Mattina et al. 2007). Successful phytoremediation would include the conversion of the contaminants into less harmful derivatives. Most organics appear to undergo some degree of transformation in plant cells (Macek et al. 2000), but the metabolism of POPs is minimal and only a few enzymes are known to break these molecules down (White et al. 2006; Nash and Woolson 1967). Furthermore, microorganisms usually require longer times to degrade POPs because they are directly affected by the toxicity of these compounds. In order to develop an efficient and inexpensive phytoremediation system expression of such enzymes in Cucurbita species would be desirable, otherwise the POPs accumulated in the plant tissues needs to be degraded physicochemically (Campanella et al. 2002). High-uptake cucurbits are excellent candidates for phytoremediation of soils polluted by POPs. In addition, low-uptake cucurbits bred for substituting rootstocks used for grafting commercial Cucurbitaceae cultivars (i.e. cucumber, watermelon, melon, summer squash (Davis et al. 2008) are also desirable. In spite of continuous efforts mechanisms explaining the high POP accumulation capacity of Cucurbitaceae plants are not known. Clarification of the uptake, translocation, sequestration processes will help to select and breed new cucurbit cultivars with improved capacity for phytoremediation of POP-contaminated sites. Acknowledgement Financial support for this work came from OTKA grant PD-75169.
References Aslund MLW, Rutter A, Reimer KJ et al (2008) The effects of repeated planting, planting density, and specific transfer pathways on PCB uptake by Cucurbita pepo grown in field conditions. Sci Total Environ 405:14–25 Aslund MLW, Zeeb BA, Rutter A et al (2007) In situ phytoextraction of polychlorinated biphenyl- (PCB) contaminated soil. Sci Total Environ 374:1–12 Campanella B, Bock C, Schröder P (2002) Phytoremediation to increase the degradation of PCBs and PCDD/Fs. Environ Sci Pollut Res 9:73–85
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Campbell S, Arakaki AS, Li QX (2009) Phytoremediation of heptachlor and heptachlor epoxide in soil by Cucurbitaceae. Int J Phytoremediation 11:28–38 Cutler HC, Whitaker TW (1961) History and distribution of the cultivated cucurbits in the Americas. Am Antiquity 26:469–485 Davis AR, Perkins-Veazie P, Sakata Y et al (2008) Cucurbit grafting. Crit Rev Plant Sci 27:50–74 Doty SL (2008) Enhancing phytoremediation through the use of transgenics and endophytes. New Phytol 179:318–333 Gent MPN, Parrish ZD, White JC (2005) Nutrient uptake among subspecies of Cucurbita pepo L. is related to exudation of citric acid. J Am Soc Hort Sci 130:782–788 Hülster A, Müller JF, Marschner H (1994) Soil-plant transfer of polychlorinated dibenzop-dioxins and dibenzofurans to vegetables of the cucumber family (Cucurbitaceae). Environ Sci Technol 28:1110–1115 Inui H, Wakai T, Gion K et al (2008) Differential uptake for dioxin-like compounds by zucchini subspecies. Chemosphere 73:1602–1607 Kelsey JW, Colino A, Koberle M et al (2006) Growth conditions impact 2, 2-bis(p-chlorophenyl)-1, 1-dichloroethylene (p, p’-DDE) accumulation by Cucurbita pepo. Int J Phytoremediation 8:261–271 Kelsey JW, White JC (2005) Multi-species interactions impact the accumulation of weathered 2, 2-bis (p-chlorophenyl)-1, 1-dichloroethylene (p, p’-DDE) from soil. Environ Pollut 137:222–230 Kömives T, Gullner G (2000) Phytoremediation. In: Wilkinson RE (ed) Plant–environment interactions. Marcel Dekker, New York, pp 437–452 Lee WY, Iannucci-Berger WA, Eitzer BD et al (2003) Plant uptake and translocation of air-borne chlordane and comparison with the soil-to-plant route. Chemosphere 53:111–121 Lunney AI, Zeeb BA, Reimer KJ (2004) Uptake of weathered DDT in vascular plants: potential for phytoremediation. Environ Sci Technol 38:6147–6154 Macek T, Macková M, Kás J (2000) Exploitation of plants for the removal of organics in environmental remediation. Biotechnol Adv 18:23–34 Mattina MI, Berger WA, Eitzer BD (2007) Factors affecting the phytoaccumulation of weathered, soil-borne organic contaminants: analyses at the ex planta and in planta sides of the plant root. Plant Soil 291:143–154 Mattina MI, White J, Eitzer B et al (2002) Cycling of weathered chlordane residues in the environment: compositional and chiral profiles in contiguous soil, vegetation, and air compartments. Environ Toxicol Chem 21:281–288 Nash RG, Woolson EA (1967) Persistence of chlorinated hydrocarbon insecticides in soils. Science 157:924–927 Nee M (1990) The domestication of Cucurbita (Cucurbitaceae). Econ Bot 44:56–68 Oldal B, Maloschik E, Uzinger N et al (2006) Pesticide residues in Hungarian soils. Geoderma 135:163–178 Otani T, Seike N (2007) Rootstock control of fruit dieldrin concentration in grafted cucumber (Cucumis sativus). J Pestic Sci 32:235–242 Otani T, Seike N, Sakata Y (2007) Differential uptake of dieldrin and endrin from soil by several plant families and Cucurbita genera. Soil Sci Plant Nutr 53:86–94 Paris HS (1989) Historical records, origins, and development of the edible cultivar groups of Cucurbita pepo (Cucurbitaceae). Econ Bot 43:423–443 Paris HS (1996) Multiple allelism at the D locus in Squash. J Hered 87:391–395 Peters R, Kelsey JW, White JC (2007) Differences in p, p’-DDE bioaccumulation from compost and soil by the plants Cucurbita pepo and Cucurbita maxima and the earthworms Eisenia fetida and Lumbricus terrestris. Environ Pollut 148:539–545 Robinson RW, Decker-Walters DS (1997) Cucurbits. CAB International, New York Schröder P, Scheer C, Belford BJD (2001) Metabolism of organic xenobiotics in plants: conjugating enzymes and metabolic end points. Minerva Biotechnol 13:85–91
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Wang XP, White JC, Gent MPN et al (2004) Phytoextraction of weathered p, p’-DDE by zucchini (Cucurbita pepo) and cucumber (Cucumis sativus) under different cultivation conditions. Int J Phytoremediation 6:363–385 Whitaker T (1981) Archeological cucurbits. Econ Bot 35:460–466 White JC (2002) Differential bioavailability of field-weathered p, p’-DDE to plants of the Cucurbita and Cucumis genera. Chemosphere 49:143–152 White JC (2009) Optimizing planting density for p, p’-DDE phytoextraction by Cucurbita pepo. Environ Eng Sci 26:369–375 White JC, Mattina MI, Lee WY et al (2003a) Role of organic acids in enhancing the desorption and uptake of weathered p, p’-DDE by Cucurbita pepo. Environ Pollut 124:71–80 White JC, Parrish ZD, Gent MPN et al (2006) Soil amendments, plant age, and intercropping impact p, p’-DDE bioavailability to Cucurbita pepo. J Environ Qual 35:992–1000 White JC, Parrish ZD, Isleyen M et al (2005a) Influence of citric acid amendments on the availability of weathered PCBs to plant and earthworm species. Int J Phytoremediation 8:63–79 White JC, Parrish ZD, Isleyen M et al (2005b) Influence of nutrient amendments on the phytoextraction of weathered 2, 2-bis(p-chlorophenyl)-1, 1-dichloroethylene by cucurbits. Environ Toxicol Chem 24:987–994 White JC, Wang XP, Gent MPN et al (2003b) Subspecies-level variation in the phytoextraction of weathered p, p’-DDE by Cucurbita pepo. Environ Sci Technol 37:4368–4373 White JC, Zeeb BA (2007) Plant phylogeny and the remediation of persistent organic pollutants. In: Willey N (ed) Phytoremediaton. Humana Press, Totowa, NJ Zeeb BA, Amphlett JS, Rutter A et al (2006) Potential for phytoremediation of polychlorinated biphenyl-(PCB-) contaminated soil. Int J Phytoremediation 8:199–221 Zraidi A, Stift G, Pachner M et al (2007) A consensus map for Cucurbita pepo. Mol Breed 20:375–388
Trichloroacetic Acid in the Forest Ecosystem Miroslav Matucha and Peter Schröder
Abstract Trichloroacetic acid (TCA) is a ubiquitous phytotoxic substance that occurs at various levels in the environment. The last century, it was produced and used in agriculture as herbicide against perennial grasses for some time, before it was found as secondary atmospheric pollutant. It was considered a reason of coniferous forest decline. TCA was further found among products of disinfection of drinking water and of delignification of cellulose pulp by chlorine. In addition to these anthropogenic sources of TCA, is has been found to be formed in the forest ecosystem as a result of microbial chlorination of humic substances that subsequently yield TCA in the soil. TCA may be considered important intermediate of soil organic matter degradation and belongs thus to naturally-produced organohalogens and at the same time to relevant xenobiotics and stressors affecting plants in the forest ecosystem. Its role in the forest ecosystem is clearly shown.
Introduction Trichloroacetic acid (TCA) is a ubiquitous phytotoxic substance that presently occurs at various levels in all compartments of the environment. During the last century, it was produced and used in agriculture as herbicide against perennial grasses for some time, before it was also detected as secondary atmospheric pollutant formed by photooxidation of important solvents – methylchloroform and perchloroethylene emitted from anthropogenic sources (Frank 1984).
M. Matucha () Institute of Experimental Botany, Academy of Sciences of the Czech Republic, Vídeňská 1083, CZ-14220 Prague, Czech Republic e-mail:
[email protected] P. Schröder Helmholtz-Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, FRG P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_5, © Springer Science+Business Media B.V. 2011
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TCA was further found among products of disinfection of drinking water and of delignification of cellulose pulp by chlorine. Astonishingly, and in addition to these anthropogenic sources of TCA, is has been found to be formed in the forest ecosystem as a result of microbial chlorination of humic substances followed by cleavage of aliphatic chlorinated residues that subsequently yield TCA in the soil. Natural TCA formation in soil from humic substances was shown several times (Haiber et al. 1996; Hoekstra et al. 1999; Fahimi et al. 2003; Matucha et al. 2003b) and together with the microbial degradation, TCA as well as DCA may be considered important intermediates of soil organic matter degradation (Matucha et al. 2007a,b). TCA thus belongs to naturally-produced organohalogens and at the same time to relevant xenobiotics and stressors affecting plants (especially conifers) in forest ecosystems (Schröder 1998). Its role (sometimes together with that of the other chloroacetic acids) in the environment has been reviewed several times (McCulloch 2002; Hoekstra 2003; Lewis et al. 2004; Laturnus et al. 2005; Cape et al. 2006; Clarke et al. 2009) documenting intensive research in this field, starting with its sources (natural and anthropogenic), continuing with its influence on coniferous trees and closing with its role and effects in the environment.
Properties of Trichloroacetic Acid and Its Occurrence in the Environment TCA is a simple chlorinated organic aliphatic acid with a molecular weight of 163.4, and a boiling point of 198°C. It is polar, has a hydrophilic character and low volatility (21 Pa at 25°C), is very well soluble in water (1,200 g/L) and at the same time a strong acid with a pKa of 0.26 (Weast et al. 1987; Suntio et al. 1988; Bowden et al. 1998). TCA is thermally degraded to produce chloroform and carbon dioxide. This decarboxylation proceeds only at elevated temperatures in presence of water, in aqueous solution, see Table 1, but in the environment abiotic cleavage proceeds only at low rates (Matucha et al. 2006). Its properties – high polarity and solubility in water – determine its physico-chemical behavior in air, soil and plants. Despite its low abiotic reactivity, TCA is easily biodegradable by soil microorganisms (Lignell et al. 1984; Matucha et al. 2003a) that possess dehalogenation enzymes as shown recently by PCR techniques (Leach et al. 2009). On the other hand, its stability in higher plants has been reported several times (Blanchard 1954; Ashton and Crafts 1973; Åberg 1982), whereas microbial degradation by endophytic bacteria in spruce needles has been detected (Forczek et al. 2004). An important turning point in the understanding of the fate of TCA in the environment was reached when Schöler et al. (2003) identified a significant TCA deficit in the balance of fluxes between atmosphere, biota, soil and groundwater. Hypothesizing that TCA formation would in fact need a biotic component, they were able to set up a comprehensive mass balance for the substance, and excellently explain its production in forest soils (Schöler et al. 2003). In fact, TCA formation from resorcinolic structures of humic acids by chlorination had earlier and convincingly
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Table 1 Decarboxylation of aqueous TCA solution. Results are means of at least three replicates and duplicate LS measurements; the calculated values for room temperature are from a 24 h experiment, 37 kBq [1,2-14C]TCA per experiment. (A) Influence of pH on a decarboxylation of [1,2-14C]TCA at 70 C/90 C for 2 h; (B) Influence of temperature on decarboxylation of [1,2-14C]TCA at pH 4.6 during 2 h exposure A pH TCA decomposition (%) 3.0 4.6 6.0 7.0 8.0 9.2
4.42/100 14.8/100 16.8/100 16.6/100 7.63/92.1 7.15/72.6
B Temperature (°C)
TCA decomposition (%)
23 ± 2 35 60 70 90
0.0024 0.011 1.8 14.8 100
been shown by Hoekstra et al. (1999), who used 13C-labelled resorcinol as a substrate. TCA turnover, including formation and degradation, in forest soil was then conclusively shown using chlorine 36. In these experiments, TCA formation was accompanied by large amounts of DCA (Matucha et al. 2007a,b). Yet another TCA source is atmospheric photooxidation of widely used chlorinated solvents like perchloroethene (PER) (Gay et al. 1976; Tuazon et al.1988; Itoh et al. 1994), and 1,1,1-trichloroethane (Frank et al. 1992, 1994). This mechanism of TCA formation from anthropogenic chlorinated C2-hydrocarbons was supposed by Weissflog et al. (1999, 2001, 2004, 2005, 2006). However, the yield of TCA from these reactions was found to be low and is still a matter of controversy (Franklin 1994; Sidebottom and Franklin 1996). Nevertheless, Weissflog et al. (1999 and 2004) suggested that these compounds could contribute to desertification of dry areas or in the vicinity of saline lakes where PER is produced and absorbed by plant leaves because of their detrimental and herbicidal effects.
Plants and TCA The effect of TCA on plants has been studied in the context of its use as a pre-emergence herbicide against perennial grasses and weeds since 1947 (Martin 1972). In spite of its commercial and widespread use, interaction of TCA in plants
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is not exactly understood. On the one hand, TCA is a known inhibitor of elongation reactions during the biosynthesis of very long chain fatty acids and thereby inhibits the formation of epicuticular waxes (Franich and Wells 1980; Gullvåg et al. 1996; Macey 1974; Böger et al. 2000). On the other hand, its capability to precipitate proteins indicates an interaction with soluble enzymes or with the photosynthetic apparatus, as many effects recorded under the influence of TCA concern especially the functioning of chloroplasts (Ashton and Crafts 1973; Weissflog et al. 2007; Forczek et al. 2008). The applications of TCA as herbicide were treated in several individual articles together with its behavior and degradation in soil and plants (Blanchard 1954; Lode 1967; Smith 1974; Chow 1976; McGrath 1976; Süss and Eben 1977; Lignell et al. 1984). A possible role of aerobic microorganisms in TCA decomposition was also investigated, and Arthrobacter sp. were found to be responsible for its degradation (Lode 1967; Süss and Eben 1977). Lower (microbial) degradation rates of TCA in sites where it had been used as herbicide was accounted for by its relatively high concentration in soil (120 kg/ha), which was recognized later (Forczek et al. 2001; Matucha et al. 2003a,b; Schröder et al. 2003). Indirectly, another branch of research focusing on aliphatic solvents as micropollutants discovered chloroacetates as active substances in plant decline. Monochloroacetate (MCA), di- (DCA) and trichloroacetate (TCA) may build up as a result of the atmospheric degradation of chlorinated solvents like tri- and tetrachloroethene and 1,1,1-trichloroethane. In the 1970s, it became clear that the world production rate of these compounds exceeds the atmospheric scavenging rate, leading to an increase of the concentration of aliphatic chlorocarbons in the atmosphere of regions void of any production sources. This finding did not attract public interest until a connection to the depletion of the stratospheric ozone layer by CFC was proven (Crutzen 1996). Today the predominant source of chloroacetic acids arises from aliphatic solvents as micropollutants. Hence, the Montreal protocol (1987) and follow up meetings forced industry to discontinue the production of CFC, halons, carbon tetrachloride, methyl chloroform, HCFC, HBFC and methyl bromide in favor of partially hydroxylated substitutes. Chlorinated C1 and C2 hydrocarbons, except CCl4 and 1,1,1-trichloroethane have, due to their considerably shorter lifetimes not been banned. In general these compounds have been regarded as “micropollutants” with concentrations in the pptv range and judged to have no considerable harmful potential. It was not before 1984 that researchers voiced suspect about possible effects of chlorinated aliphatics on plants (Frank 1984; Frank and Frank 1985; Grimmer and Schmidt 1986). Tetrachloroethene (PER) is of considerable importance as a degreasing and cleaning agent in the metal-processing and textile industries because of its physicochemical properties. Approximately 300 kt were industrially produced worldwide in 1996 (McCulloch and Midgley 1996), when industrial production ceased due to concerns about the involvement of chlorohydrocarbons in climate change. Still, extensive amounts of PER are emitted in the processes of bleaching raw celulose (Juuti et al. 1995; Juuti et al. 1993), incinerating chlorinated plastics (Weissflog et al. 2004) and the combustion of coal containing chloride in power stations (Garcia et al. 1992). Among natural sources, microbial production in forest soil (Hoekstra et al. 1998;
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Hoekstra et al. 1999) and formation in biomass fires (Rudolph et al. 2000; Weissflog et al. 2004;) are the predominant producers of PER. The microbial formation of PER and other C1/C2 chlorohydrocarbons in salt lakes has been shown by Weissflog et al. (2005). Forests are a strong sink for volatile chlorohydrocarbons. As a result of the large leaf area index and the cuticular properties of many plants, high scavenging rates for atmospheric organic xenobiotics (Gaggi et al. 1985; Plümacher and Schröder 1994; Plümacher et al. 1994) may be observed. The chloroacetates are globally distributed in rain and snow and may even be present in wet precipitation in concentrations higher than any other organochlorines (Laniewski et al. 1999). Especially trichloroacetic acid, and independent of its origin was supposed to exert phytotoxic effects on coniferous trees. Hence it became a topic for the intensive research of forest decline in the central and north Europe in the last 20 years. This was supported by the finding from laboratory experiments that not only aliphatic chlorocarbons, but also the much less lipophilic TCA can enter foliage directly from wet precipitation. When tree branches were treated with TCA containing mist significant uptake was shown (Cape et al. 2003; Dickey et al. 2004), and a possible uptake of TCA from fog or cloud droplets by stomata was assumed (Römpp et al. 2001). When available in the rhizosphere, TCA was also easily taken up by tree roots and transported by transpiration stream into leaves (Fig. 1). As third exposure pathway, perchloroethylene (PE) – well known TCA precursor – that was easily taken up across needle cuticules, could be bio oxidized in chloroplasts to yield TCA (Weissflog et al. 2006). Such an enzymatic production of TCA from aliphatic chlorocarbons has been shown in a study on trichloroethene (TCE) taken up from soil in the context of plant
radioactivity (kBq/g)
10
radioactivity of current year needles (spruce A)
wood radioactivity (spruce A)
radioactivity of current year needles (spruce B)
wood radioactivity (spruce B)
8 6 4 2 0
0
4
8
11
15
time (days)
Fig. 1 Radioactivity of current needles and of wood of potted spruce nurslings A and B (exposed to 360 and 720 kBq of [1,2-14C]TCA, resp., added to the roots); radioactivity in C + 1 and C + 2 needles at the end of the experiment was 6.82 and 3.26 kBq/g for spruce A and 1.63 and 1.28 kBq/g for spruce B, resp
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utilization in phytoremediation (Newman et al. 1997). In this study, poplar hybrids were found to take up and degrade TCE to several metabolites including TCA. The trees were also shown to transpire TCE in amounts sufficient to reclaim the contaminated site. Today, clear-cut evidence is available that TCA may enter plants through foliage or the roots. Co-transport in the transpiration stream leads to the accumulation of TCA in leaves or needles as the transported water is transpired, leaving TCA in the leaf. Attempts to correlate TCA concentrations in conifer needles with the degree of defoliation of trees or damage of the surface wax layer have been pursued (Frank et al. 1992, 1994; Plümacher et al. 1993; Norokorpi and Frank 1995; Gullvåg et al. 1996), however, they failed, because the conifers in the investigated stands were simultaneously affected by other stressors (e.g. Frank 1988; Frank et al. 1991, 1994; Norokorpi and Frank 1993; Sutinen et al. 1995; Weissflog et al. 1999, 2001) see also Table 2. However, the contribution of TCA was considered in several reviews (McCulloch 2002; Hoekstra 2003; Schöler et al. 2003; Lewis et al. 2004; Laturnus et al. 2005; Cape et al. 2006; Clarke et al. 2009).
Physiological Effects The investigation of physiological effects of TCA on plants almost ceased after it stopped being applied as a herbicide, so its behavior in soil has been described only for higher concentrations (Smith 1974; McGrath 1976; Süss and Eben 1977; Lignell et al. 1984). For those investigations 14C-labelled TCA and radioanalytical methods were succesfully used. Few exceptions were the investigations of possible role of glutathione S-transferase on detoxification of TCA and chlorinated solvents in spruce needles (Plümacher and Schröder 1994; Plümacher et al. 1994) and effects of TCA on pine-needle chloroplasts (Sutinen et al. 1995). High needle VCH concentrations were measured when the detoxification enzyme GST had its lowest activity, which might indicate a direct connection between the VCH detoxification potential and the actual concentrations of the pollutants. An array of papers have been devoted to adverse effects of TCA on conifers (from Germany, Finland and Scotland: Frank 1988 and 1994; Plümacher and Schröder 1994; Sutinen et al. 1995, 1997; Juuti 1997; Schröder et al. 1997; Cape et al. 2003; Dickey et al. 2004; Reeves et al. 2000; Heal et al. 2003a, b; Stidson et al. 2004a, b) and only rarely has the role of soil been mentioned (Frank 1988; Juuti 1997). The approach using labelled [1,2-14C]TCA of high specific activity (Bubner et al. 2001) has yielded some new aspects of the mode of action of TCA on conifers. After our preliminary investigation of uptake, translocation and fate of TCA in Norway spruce (Uhlířová et al. 1996; Matucha et al. 2001), the importance of processes in the plant/soil-system as a whole was recognized (Forczek et al. 2001; Matucha et al. 2001). In spite of the potentially important uptake of TCA from the air, mostly the uptake from soil via roots and subsequent transport upwards into needles, driven by the transpiration stream is to be considered here.
Scots pine Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Scots pine
Frank et al. 1990
Matucha and Uhlířová 1999
Juuti et al. 1995 Juuti et al. 1996
Juuti et al. 1993a
Frank et al. 1994
Scots pine Norway spruce & Scots pine Norway spruce & Scots pine Norway spruce
Norway spruce Norway spruce
Frank 1991 Frank et al. 1989
Frank et al. 1992
Tree species Norway spruce
Reference Coufal et al. 2003
C C + 1 C + 2
130 2 2 11
Finland NW-Czech Republic
C + 2
130 10
C C + 2 C + 2 C
C + 2
C + 2
Needle age C C + 1 C + 2 C C + 2 C C + 2 C + 1 C, C + 2
NW-Finland W-Finland
>60
42
N-Finland E-Finland
246
n 29 29 19 15 n.d. 1 10 15 120
Black Forest
Finland Germany Finland
Black Forest S-Germany
Sample origin Czech Republic
Table 2 TCA content in conifer needles reported in literature
90–126 (108.0) 28–106 (47.0)
11–126 (68.5)
(continued)
1–180 (23) (5 > DCA)
2–50 5–135 6–276 3–14 (9)
3.3–90 (20.3) (19.7 > MCA)(DCA 0) 8–126
TCA content in needles (average) (ng/g FW) 3.4–110 (34.9) 1.5–127 (37.9) 4.2–144 (42.1) 4–67 10–300 30 33–180 (74), 20–73 (45) 4–96 3–126
Trichloroacetic Acid in the Forest Ecosystem 93
Scots pine Norway spruce Norway spruce Eur. silver fir Plümacher and Schröder Norway spruce 1994; Plümacher et al. Scots pine 1994 Reeves et al. 2000 Sitka spruce Schröder and Plümacher Norway spruce 1998 Scots pine Scots pine Sinkkonen et al. 1998 Scots pine Scots pine Weissflog et al. 1999 Scots pine
Germany Netherlands Italy Scandinavia UK Berlin, Achenkirch Hesse, Berlin
Spruce
Plümacher et al. 1993
N-Finland
Scots pine
Norokorpi and Frank 1995 Peters 2003
41 21 21 108 >150 50 55
Achenkirch, Austria Berlin Scotland Hessen Hessen Berlin S &W-Finland Caucasus Astrakhan,Volga,
10 10 10 20
n
3 3 3 3 3 56 18 1 1 48 6
NW-Czech Republic
Norway spruce
Matucha et al. 2001
Sample origin
Tree species
Reference
Table 2 (continued)
various C – C + 1 C – C + 1 C – C + 2 C – C + 2 C + 1 C + 1
n.d. n.d. n.d. n.d. n.d. C, C + 1 C, C + 2 n.d. n.d. C – C + 2 C – C + 1
C C + 1 C + 2 C + 1
Needle age
3–165 2–25 (11) 1–26 (6.6) 1–178 (2–56) n.d. (0–3.8 DCA) 3.5–5.3 3.2–68.9
(11.9) (39.2 DCA) (6.4) (4.0 DCA) (5.3) (4.9 DCA) (5.1) (2.1 DCA) (10.9) (8.7 DCA) 0.59–178 (55.6) 4–20 43 13 1–18 (7) (34)
28–110 (63.1) 34–127 (77.8) 28–144 (67.0) 8–65 (33)
TCA content in needles (average) (ng/g FW)
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The TCA level in soil (given by the difference between influx plus formation and efflux plus degradation) determines the uptake by spruce roots. Formation of TCA in the forest soil was shown by Hoekstra et al. (1999). TCA decarboxylation in needles was also considered as a possible reason for the observed slow degradation in the tissue (Matucha et al. 2006). There has been no evidence for loss of other volatile degradation products (e.g. methane, carbon monoxide, trichloromethane) from foliage, except CO2 (Schröder et al. 2003). The plausible dehalogenation of TCA, which is confirmed in microorganisms (Weightman et al. 1992), has not yet been demonstrated in plants. A dehalogenase activity that has been described for aliphatics has not been demonstrated for TCA (Schröder 1997). The lack of metabolism of TCA has been also connected with forest decline (Frank et al. 1990, 1994; Schröder and Plümacher 1998).
The Role of the Rhizosphere Natural formation of TCA and of short chain halocarbons in soil has been reported (DeJong and Field 1997; Hoekstra et al. 1998; Hoekstra et al. 1999; Keppler et al. 2000) thus elucidating the origin of TCA levels found in nature. The five principle mechanisms used by microorganisms to dechlorinate chlorinated organic compounds include (i) reductive, (ii) oxidative, and (iii) hydrolytic dehalogenation, (iv) dehydrodehalogenation, and (v) dehalogenation after ring cleavage (Fritsche 1998; Alexander 1999). Other halogens such as bromine, iodine, and fluorine are also displaced by these processes. Of all the processes, reductive dehalogenation (halorespiration) by anaerobic microorganisms appears most important for the dechlorination of many chlorinated organics by replacing the chlorine through hydrogen. Aromatic and aliphatic chlorocarbons serve as terminal electron acceptors during oxidation of the electron rich hydrogen or organic acids (van Pée and Unversucht 2003). The driving force behind reductive dechlorination is the generation of energy for anaerobic microbes since the process is thermodynamically very favourable (Mohn 2004). In addition, there has been a significant natural background level of TCA in precipitation over the past 200 years (von Sydow et al. 2000), which could be caused by natural sources (Gribble 2003). Several genera of higher fungi have a capacity for biosynthesis of organohalogens, but they can also cause their reductive dechlorination (De Jong and Field 1997). Only aerobic microbial degradation of TCA was reported (e.g. Yu and Welander 1995; Olarinan et al. 2001). Microbial degradation of TCA to CO was supposed also by Weightman et al. (1992) and its decarboxylation leading to chloroform in soil was supposed in several other papers (Frank 1988; Frank et al. 1990; Plümacher et al. 1993; Uhlířová et al. 1995) or even reported (Haselmann et al. 2000). Further, enzymatic dechlorination of TCA to oxalic acid and related short chain aliphatic acids in pond waters was reported (Ellis et al. 2001). Shortchain chlorocarbons and chloroacetic acids are thus to be regarded as important minor contaminants and, because of their natural origin, also products of the environment, which undergo complex reactions.
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M. Matucha and P. Schröder CO2 evolution
100% 90% 80% 70% 60% 50% 40%
A - O2 carrierless B - O2 isotopically diluted C - A1 carrierless D - A1 isotopically diluted
30% 20% 10% 0%
0
50
100
150
200
250
300
350
400
time [h]
Fig. 2 Microbial degradation of [1,2-14C]TCA in spruce-forest soil (horizon A and O2) amended with 396 kBq of [1,2-14C]TCA, i.e. with 17.5 (carrier-free) and 192 mg (11 times isotopically diluted) TCA, in 50 g soil
Also, dynamic equilibrium of the pollutant within the system has to be assumed. The uptake, translocation and effects of TCA on Norway spruce (Picea abies L. Karst.) has been recently examined using [1,2-14C]TCA of high specific activity (3.7 GBq. mmol−1, i.e. 22 Bq ng−1 TCA) thus enabling the following TCA levels appearing in nature, 5–200 ng g−1 TCA in needles, and 5–400 ng g−1 TCA in soil (Fig. 2). It was found that translocation of TCA occurs from the atmosphere into soil by precipitation water, followed by uptake by roots and then movement into needles via transpiration stream, where TCA interacts (at concentrations higher than 60 ng g/L) with the photosynthetic apparatus in an inhibitory manner (Uhlířová et al. 1996; Matucha et al. 2001). Our radioactivity balance studies could not fully explain the observed losses of [1,2-14C]TCA-derived radioactivity. Since the role of soil and plant in the TCA metabolism of the studied plant/soil-system was not fully understood, several studies were carried out to address these questions (Forczek et al. 2001; Matucha et al. 2001), see Fig. 3. The curves in Fig. 2 show the strong influence of soil character on the released 14 CO2 in the course of degradation at room temperature and 30% soil moisture; residual soil radioactivity corresponds mostly to microorganisms´ biomass. At the end of the experiment (after 17 days) the soil contained only 0.7 kBq [1,2-14C]-TCA from 76.4 kBq residual 14C-activity determined by combustion method (or 81.2 kBq estimated by substraction of the released 14CO2 from the sample). The major part of the residual 14C-activity of experiments is integrated into radioactive microbial biomass, less than 0.5% radioactivity (ca. 220 Bq) was found as oxalic acid, one of the major degradation products of TCA. Similarly, the available soil water has a huge impact on the degradation of TCA. Obviously, a high soil humidity inhibits aerobic processes which have a humidity
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remaining radioactivity
100%
22% 25% 29% 39% 50% 79%
80% 60% 40% 20% 0% 0
50
100
150
200
250
300
350
400
time [h]
Fig. 3 Microbial degradation of [1,2-14C]TCA in forest soil: influence of soil humidity at room temperature
optimum (Fig. 3). These observations on microbial TCA biodegradation in the forest soil are contrary to TCA degradation in digester sludge that has been demonstrated by Chen et al. (1999). The actual rates are likely to be dependent on the degree to which soils are aerobic or anaerobic, determined by changes in temperature and precipitation amounts. Whereas the influence of temperature is low, the rate of TCA degradation in detached spruce branches [C1 & C2] may be highly dependent on the presence and activity of epiphyllic microorganisms. When the branches were immersed with cut end into TCA solution in the presence or absence of antibiotics and 14CO2 radioactivity release was measured in the atmosphere, a clear inhibition of TCA metabolism was observed in the branches treated with antibiotics.
Conclusions TCA is a ubiquitous phytotoxic substance, secondary air pollutant, photooxidation product of natural as well as of anthropogenic origin. Although more information has become available recently on the sources of chlorinated hydrocarbons, their reactions in the atmosphere are also important. The role of heterogeneous processes and Cl atoms in the oxidation of chlorinated hydrocarbons is not well known. Many atmospheric degradation processes favour the formation of chloroacetic acids. Dry deposition of gaseous or particulate TCA is expected to be only a small component, with wet deposition representing the most important pathway. As such they affect health state of forests, especially of conifers, because of the high scavenging rates of these canopies. The importance of trichloroacetic acid (TCA) and related compounds (mono- and di-chloroacetates
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and other halogenoacetates) to terrestrial ecosystems has been clearly described in several recent reviews, and today, the sources of TCA in the environment are already known to a great extent. The largest source is probably forest soil containing humic acids with structures derived from degradation of lignin. TCA uptake from soil by roots and its translocation in spruce is governed by transpiration stream, the highest accumulation is then in current needles, however, the highest content in older ones. Measurements of TCA concentrations in conifer needles represent a balance between uptake rates or internal production, and decomposition or metabolic processing to less dangerous compounds. Consequently, single measurements of TCA concentrations will yield little quantitative information on the different processes. Long term investigations are needed to obtain sound data on the mode of action, detoxification and fate of TCA in plants. Chlorides have previously been considered to be chemically inert substances in the environment, and chlorinated organic compounds were assumed to be of solely anthropogenic origin. But the role of chlorine in the forest ecosystem is much more active and complex than previously thought. Adsorbable organic halogenes in the forest soil indicate their active role in a natural chlorine cycle, in which only a small fraction is absorbed by plants. Despite the large literature on the sources and fate of TCA in plants, there are still many unanswered questions worth of further research.
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Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transboundary Transport – Results of a Case Study with Special Emphasis on the Spatial Distribution of Polycyclic Aromatic and Chlorinated Air Borne Pollutants Rolf Herzig, Christoph Bieri, Andreas Weber, and Peter Straehl Abstract Representative samples of the foliose lichen Parmelia sulcata (Taylor) were collected from 33 spatially distributed measuring sites in Switzerland, representing all relevant sources of air pollution, such as industrial sites, big and mid sized cities and agglomeration, motorized traffic sites, rural and background sites, from plateau to alpine regions and were analysed for a big number of 88 individual chlorinated air pollutants and other volatile POPs from eight different chemical classes: Short chain chlorinated paraffin’ (CFC), chlorobenzenes, polychlorinated biphenyls (PCBs), hexachlorocyclohexanes (HCH), organochlorine pesticides OCPs) polychlorinated dibenzo-p-dioxins and furans (PCDDs/PCDFs) and polycyclic aromatic hydrocarbons (PAHs). Measurements were performed in order to explore patterns of long-range transport of these volatile and persistent air pollutants across Switzerland. Comparison of the air pollution with POPs in areas with different types of landuse indicates that the burden emanating from within Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the long-range transport certainly contributes to the background level of contamination.
R. Herzig (*) and C. Bieri AGB, Arbeitsgemeinschaft für Bioindikation, Umweltbeobachtung und ökologische Planung, Quartiergasse 12, CH3013 Bern, Schwitzerland e-mail:
[email protected] A. Weber and P. Straehl Bundesamt für Umwelt, Bafu, Berne, Switzerland
P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_6, © Springer Science+Business Media B.V. 2011
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Introduction The primary objective of this first country-wide and spatially differentiated biomonitoring study in Switzerland was to gain an overview of the deposition pattern by as wide a range as possible of persistent organic pollutants (POPs) and by selected volatile organic compounds (Herzig 2002). The substances were selected largely on the basis of the POPs Protocol to the UN/ECE Convention on Long-range Transboundary Air Pollution and the UNEP Convention on Persistent Organic Pollutants. This broad form of screening determined which of the more than 100 evaluated organic and airborne trace substances could be determined quantitatively in lichen samples. POPs which are particularly critical in toxicological terms and which have since long been banned in Switzerland – i.e. polychlorinated biphenyls (PCBs), lindane, DDT and other organochlorine pesticides and additional substances from the chemical classes polycyclic aromatic hydrocarbons (PAHs), chlorofluorocarbons (CFCs), dioxins and furans (PCDD/PCDFs) which are also relevant in terms of air pollution or toxicological impact – were analysed quantitatively with high resolution GC-MS and GC-ECD-techniques, followed upon substance class specific cleanup-techniques.
Material and Methods The “passive biomonitoring of lichen” method using the foliose lichen species Parmelia sulcata (Taylor), was developed in the mid Eighties within the Swiss Priority Programme 14 (Herzig et al. 1989a,b, 1990, Herzig & Urech 1991, Herzig 1993a,b). The samples were collected in the late autumn of 1995. Since monitoring organisms generally have a delayed and cumulative response to environmental deposition, the data recorded can be said to reflect airborne contamination with POPs in the period 1990–1995. Representative measurement sites comprising 33 spatially differentiated locations throughout Switzerland (NABEL monitoring network and other measurement sites) produced a conclusive data base showing the geographical distribution and the load of these POPs. An attempt was also made to identify those POPs whose burden derives predominantly from their being transported over long ranges. The sampling site “Hagen” at 910m on the top of north-eastern Jurassic Mountains, at the border to Germany and the eastern European countries, and the nearby site “Hemmental”, a small village in the valley behind “Hagen”, reflects the main comparison sites to distinguish local from long-range transport (Figs 1 and 2). Since the study locations were assigned to six different land-use categories and comprised everything from towns, agglomerations, industrial and transport sites to rural locations and villages and locations with background contamination, special evaluation methods made it possible to identify the sources of relevant individual POPs.
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Fig. 1 Spatial distribution of PCB-Congeners PCB-28, 52, 101, 153, 138 and 180. Air Pollution load and differentiation of single PCBs among the six land-use categories in Switzerland in 1995. The loss of PCBs at the recycling plant at “Thörishaus/Industry” site could be stopped due to findings of the lichen biomonitoring analysis. Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminants
Fig. 2 A special analysis of age-dependent sub samples showed that the PCB burden at the most contaminated location, the “Thörishaus/Industry” site, had declined very considerably in the past 4–8 years since work on processing transformers was stopped there. The grey bars represent the initial PCB burden during the transformer recycling (>8 year old sub samples), and the white ones after that procedure have been stopped (4–8 year old sub samples). Obviously, the PCB load quickly declined after the stop of the transformer recycling by more than 50% for each PCB congener
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Results and Discussion Distinct use-dependent and, in many cases, even source-dependent differences in impact were found for all eight of the chemical classes observed and for most of the individual 82 and additional 23 sum parameters of POPs (Table 1):
Short Chain Chlorinated Paraffin’s (CFCs) Analysis of the CFC categories clearly identified industry and small businesses as the source of input into the environment. The method used (Headspace-GC) is not that sensitive, but certainly sensitive enough to pinpoint the loss of fluorotrichloromethane (R11; Freon) solvents from industrial recycling plant at “Thörishaus/ Industry” site.
Chlorobenzenes Similar findings were recorded for chlorobenzenes, with industry and small businesses again proving to be the main source of inputs. Motor vehicles were also identified as a potential source of individual chlorobenzenes (Table 2).
Hexachlorocyclohexanes (HCH) Lindane, one of the persistent organic pollutants whose use is largely banned nowadays, is present in the highest concentrations in the centres of towns and in industrial locations as a result of its former ubiquitous use as a broad-spectrum insecticide (Table 2).
Organochlorine Pesticides (OCPs) This is the first time that the largely banned category of organochlorine pesticides (OCPs) has been investigated, and the survey produced a number of unexpected findings. Strikingly high burden gradients with a factor of 9–11 were found for DDT, it’s degradation product DDD, heptachloroepoxide and endosulphane sulphate. The heaviest burdens of DDT and DDD were found at sampling sites in cities. No adequate explanation for this has been put forward so far. However, evaluations in terms of road traffic also show that concentrations of DDT and DDD
. 33 18 8 14 56 24 26 17 8
. 3842 771 246 2270 428 359 1319 1445 246
. . 756 829 . 564 801 737 120 564 829 . 13 8 4 39 11 26 17 13 4
. . 22 28 . 9 10 17 9 9 28 . 65 33 15 79 37 32 44 24 15
. . 68 56 . 49 31 51 15 31 68 . 96 49 37 162 44 101 82 48 37
. . 86 170 . 75 37 92 56 37 170
. 8.3 . . . . . 8.3 . 8.3
13.0 6.3 5.8 17.5
. 5.8 17.5 15.7 .
(continued)
2-Agglomeration and mid sized cities Allschwil93 4749 Dübendorf-NABEL 3730 Wil-SG 3009 Gossau-SG 1460 Rorschach 1441 Wallisellen-ATAL 1162 Basel-NABEL 1143 Mean 2385 SD 1448 Min 1143
. . 22 53 . 97 103 69 38 22 103
Table 1 Sum parameters of eight POP substance classes: air pollution load, and contamination patterns among the six land-use categories in Switzerland in 1995, obtained by Lichen Biomonitoring ITE (NATO/ SDi-HexacloroSOrgano-chloroCCMS) Sampling site PAH S20 SCFCs benzene SHCHs SPCBs Pestizides Unit ng/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W mg/kg D.W
7667 7380 6839 3511 2667 2321 1649 4576 2613 1649 7667
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1-Urban sites Base-nord93 Bern-Brunnadern93 Zurich-NABEL St. Gallen-grabenhalle Chur 93 Luzern-löwenplatz Lugano-NABEL Mean SD Min Max
Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport
127 165 27 318
2828 1513 1487 5000
4-Motorized traffic outside cities Härkingen/nah-NABEL 2890 Schönbühl-N1 2643 Thörishaus-West 1797 Härkingen/fern1708 NABEL Piotta-N 2 1038 Erstfeld-N 2 946 Mean 1837 SD 801 Min 946 Max 2890
1459 . 2027 259 . . . .
318 . 27 37 . . . .
5000 2482 2343 1487 . . . .
2950 2934 508 340 273 219 1204 1350 219 2950
41 3 80 30 10 18 30 28 3 80
1248 903 259 2027
3842
56
4749
SDi-Hexaclorobenzene mg/kg D.W
Max 3-Industrial sites Thörishaus-industrie Jenaz93 KVA-Bern ZH-Nord-Hagenholz Buchs KVA Niederglatt Dornach-Industrie Rheinfelden-PCPIndustrie Mean SD Min Max
SCFCs mg/kg D.W
PAH S20 mg/kg D.W
Sampling site Unit
Table 1 (continued)
9 11 14 5 9 21
21 21 14 11
20 16 11 38
38 . 11 12 . . . .
39
SHCHs mg/kg D.W
15 22 28 18 8 57
57 38 26 8
93 99 29 207
207 . 42 29 . . . .
79
SPCBs mg/kg D.W
20 42 53 33 20 115
115 46 59 37
75 47 45 130
130 . 45 51 . . . .
162
SOrgano-chloroPestizides mg/kg D.W
6.1 1.3 4.4 7.1
4.4
7.1 7.0 5.0 7.1
20.7 12.8 5.3 42.4
5.3 . 21.3 10.3 32.2 18.2 15.0 42.4
8.3
ITE (NATO/ CCMS) ng/kg D.W
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759 565 451 592 127 22 451 759
5-Rural sites Hemmental-SH Tänikon-NABEL Payerne-NABEL Mean SD CV Min Max 10 13 49 24 18 75 10 49
SCFCs 270 538 424 411 110 27 270 538
SDi-Hexaclorobenzene
6-Backgound sites Hagenturm 858 19 593 594 24 89 Forst-NeueneggReferenz Davos-NABEL 507 17 271 Mean 653 20 318 SD 183 4 256 Min 507 17 89 Max 858 24 593 Maximum load: bold; clearly enhanced: bold italics; minimum load: italics
PAH S20
Sampling site
17 4 24 15 10 4 24
8 10 3 8 13
9 31 10 17 10 63 9 31
SPCBs
13 8
11 23 14 16 5 32 11 23
SHCHs
26 25 12 13 36
36 13
23 75 48 49 21 44 23 75
SOrgano-chloroPestizides
3.3 0.1 3.2 3.3
3.2 3.3
3.0 0.4 13.3 2.6 3.4
2.6 3.4
ITE (NATO/ CCMS) Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 111
1.1 13.7 7.1 1.2 25.0 20.3 21.1 14.6 18.8 6.6
Hexachlorcyclohexane (HCHs) (mg/kg D.W) a-HCH 1.4 g-HCH (Lindane) 15.7 b-HCH 1.5 d-HCH 0.9
Polychlorineted Biphenyls (PCBs) (mg/kg D.W) PCB 28 4.7 PCB 52 3.6 PCB 101 9.7 PCB 153 13.0 PCB 138 12.7 PCB 180 8.6 3.0 4.3 12.7 10.3 9.2 5.1
1.6 11.5 3.0 1.3 6.2 4.4 7.4 3.7 6.4 4.0
1.4 11.2 1.9 0.6
3.5 2.1 8.5 3.1 1.9 1.2
2.1 12.8 2.7 0.3
3.3 2.6 3.6 3.0 2.5 2.0
0.9 9.3 2.2 0.4
8.2 9.9 5.8 4.8 9.7 7.4
2.3 1.7 4.8 4.8
Table 2 Air pollution load and differentiation of single POPs of chlorobenzenes, HCHs, PCBs and organochlorine pesticides among the six land-use categories in Switzerland in 1995, obtained by Lichen Biomonitoring Motorized Agglomeration traffic outside Background and mid sized Industrial Gradient of air cities Rural sites sites cities Urban sites sites pollution Site category S1 S3 S2 S4 S5 S6 Max/min Chlorobenzenes (CBs) (mg/kg D.W) 1,3-Dichlorobenzene 87 125 205 251 69 133 3.7 1,4-Dichlorobenzene 295 417 625 528 171 148 4.2 1,2-Dichlorobenzene 224 178 296 219 92 71 4.1 1,3,5 -Trichlorobenzene 34 235 154 100 29 33 8.0 1,2,4 -Trichlorobenzene 37 130 39 54 16 26 8.2 1,2,3 -Trichlorobenzene 35 128 44 101 11 37 11.6 1,2,4,5-Tetrachlorobenzene 10.0 13.0 33.3 26.9 11.0 6.2 5.4 1,2,3,5-Tetrachlorobenzene 9.5 8.8 2.8 14.6 2.0 7.2 1,2,3,4-Tetrachlorobenzene 36.8 87.2 52.4 32.3 28.1 17.8 4.9 Pentachlorobenzene (QCB) 8.8 9.9 9.5 8.6 2.7 2.4 4.1 Hexachlorobenzene (HCB) 1.3 2.9 2.7 2.5 2.3 2.0 2.2
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1.7 6.9 8.1 8.0 4.9 13.3 5.3 2.9 3.0 7.5 4.9 13.2 12.2 2.8
1.9 2.0 3.6 4.6 5.7 7.1 5.7 10.5 4.5 21.9 4.5 8.3 6.1 2.9
Sum Parameters (mg/kg D.W) Di- Hexachlorobenzenes 737 1269 1319 HCHs 17.1 22.2 16.8 PCBs 51.0 103.9 43.5 organochloro-Pestizides 92.0 80.7 81.5 Maximum load: bold; clearly enhanced: bold italics; minimum load: italics
Organochloro-Pestizides (OCPs) (mg/kg D.W) Heptachlor 0.7 Aldrin 2.1 Heptachlorepoxid 2.3 Endosulfan I 6.4 p,p-DDE 5.9 Dieldrin 5.3 Endrin 3.4 p,p-DDD 12.3 Endosulfan II 2.7 p,p-DDT 36.3 Endrin Aldehyd 4.3 Endosulfan Sulfat 1.4 Endrin Keton 9.8 Methoxychlor 4.7 1204 14.2 27.6 52.9
1.7 2.4 10.0 5.8 4.7 4.7 2.6 2.3 1.8 8.4 6.1 5.7 4.6 2.1 411 15.7 16.5 48.5
0.7 1.1 11.4 6.7 3.2 3.2 10.7 3.0 2.7 4.9 3.2 7.8 3.9 1.7 324 9.8 14.1 25.0
0.4 1.9 1.2 3.7 2.1 2.1 3.6 1.4 1.6 3.3 1.0 4.4 3.6 1.4 4.1 2.3 7.4 3.7
4.7 6.2 9.7 2.1 2.8 6.2 4.1 8.8 2.8 10.9 6.3 9.2 3.4 3.4
Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 113
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are similarly high at roadsides and in inner-city locations. It is difficult to explain this pattern of distribution in terms of the analyses that have been carried out. Verification analyses of selected organochlorine pesticides in all the archived samples using HR-GCMS, a technique that gives a very high resolution, have already shown very good agreement with the GC-ECD measurements recorded in the first round of analysis (Table 2).
Polychlorinated Biphenyls (PCBs) There are also very large differences in the burdens of PCBs which correlate closely with industrial locations (Table 2, Fig. 1). A special analysis of age- dependent subsamples showed that the PCB burden at the most contaminated location, the “Thörishaus/Industry” site, had declined very considerably in the past 4–8 years since recycling work on processing of old transformers was stopped there. This novel approach to “archiving” allows more detailed conclusions about the extent of the burden over time to be drawn from recent lichen samples. This technique can be used in future for a more detailed analysis of the burden of other POPs (Fig. 2).
Polychlorinated Dibenzo-p-Dioxins and Furans (PCDDs/PCDFs) Evaluations of the polychlorinated dioxins and furans (PCDD/PCDFs) as a function of land-use category show a clear relationship between the burden and industrial sources, including waste incineration facilities (Table 3). Figure 3 shows the air pollution load and spatial distribution of the Toxicity Equivalent TEQ, obtained for the dioxins and furans congeners at the sites of the six land-use categories in Switzerland in 1995. Obviously the map shows a clear relationship between the burden and industrial sources (Rheinfelden, Niederglatt), including domestic waste incineration facilities, without the highest efficient filter systems (Buchs, Berne), and the forbidden waste disposal by cheminee-fireing. The latter is represented by a very residential quarter of city of Berne (Brunnadern). It’s well known, that up to 50% of the actual PCDD-emissions comes from that forbidden waste disposal. Maps showing the geographical distribution of the burden point to only moderate levels of contamination in typical transport-related locations outside large settlements. However, a special evaluation of transport parameters as a function of the most conclusive criterion – distance from the road – paints a rather different picture. With very few exceptions, all the maximum values for dioxins and furans were recorded at the measurement points closest to roads (1–3 m), and the values were still distinctly elevated at points between 4 and 10 m from the road. The mean burdens for these roadside locations are in most cases as high as the maximum
Dibenzo-p-Dioxins (PCDD) (ng/kg D.W) TetraCDD 32 PentaCDD 38 HexaCDD 84 HeptaCDD 200 OctaCDD 478 Tetra-OctaCDD 832 36 44 89 226 2739 3133 21 24 46 80 173 343
19 22 35 58 107 241
12 12 19 26 53 122
14 14 22 43 103 194
(continued)
3.0 3.7 4.7 8.5 51.5 25.7
Table 3 Air pollution load and differentiation of single Dibenzo-p-Dioxins and Furans and sum parameters among the 6 land-use categories in Switzerland in 1995. “Industrial sites”, clearly represents the strongest impact for most of the dioxins and furans Motorized Agglomeration traffic outside Background and mid sized Industrial Gradient of cities Rural sites sites cities Urban sites sites air pollution Site category S1 S3 S2 S4 S5 S6 Max/min Dibenzo-p-Furans (PCDF) (ng/kg D.W) TetraCDF 143 163 84 97 56 72 2.9 PentaCDF 100 139 72 51 32 34 4.3 HexaCDF 86 122 55 29 17 18 7.3 HeptaCDF 114 89 40 18 11 11 10.8 OctaCDF 163 112 45 31 21 23 7.7 Tetra-OctaCDF 605 624 296 226 137 157 4.6 2378-TetraCDF 9.0 10.3 5.5 5.4 3.3 2.5 4.1 12378-/12348-PentaCDF a 7.2 15.1 5.3 3.9 2.4 2.3 6.5 23478-PentaCDF 6.8 10.2 5.1 3.5 1.9 1.9 5.3 123478-123479-HexaCDF a 8.9 26.6 6.3 3.6 1.7 2.2 15.3 123678-HexaCDF 6.6 11.4 4.9 2.9 1.4 1.7 8.0 123789-HexaCDF 0.8 1.1 0.6 0.6 0.2 0.2 7.3 234678-HexaCDF 8.3 12.6 6.0 3.5 1.7 2.2 7.3 1234678-HexaCDF 63.2 61.2 27.7 12.3 7.1 7.0 9.0 1234789-HeptaCDF 9.1 8.0 2.6 1.6 0.7 1.0 12.6
Persistent Organic Pollutants (POPs) in Switzerland Related to Long-Range Transport 115
0.7 2.0 3.3 6.9 5.4 111 205
13.9 13.0
Site category
2378-TetraCDD 12378-PentaCDD 123478-HexaCDD 123678-HexaCDD 123789-HexaCDD 12346789-HeptaCDD Tetra-OctaCDF/D
TE (BGA) excl. Det. Lim. ITE (NATO/CCMS) excl. Det. Lim. 8.4 8.3
0.5 1.6 1.9 3.5 3.0 40 32
1.1 3.7 4.8 7.5 7.6 113 1879 21.5 21.4
Agglomeration and mid sized cities S2
Industrial sites S3
Maximum load: bold; clearly enhanced: bold italics; minimum load: italics
Urban sites S1
Table 3 (continued)
6.3 5.9
0.4 1.6 1.3 2.8 2.4 29 156
Motorized traffic outside cities S4
3.4 3.0
0.2 0.6 0.6 1.3 1.0 13 57
Rural sites S5
3.8 3.2
0.2 0.8 0.9 1.4 1.3 20 88
Background sites S6
6.2 7.1
5.4 5.9 7.5 6.0 7.8 8.8 58.8
Gradient of air pollution Max/min
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117
Fig. 3 Air pollution load and spatial distribution of the Toxicity Equivalent TEQ obtained for the dioxins and furans congeners at the sites of the six land-use categories in Switzerland in 1995. Obviously the map shows a clear relationship between the burden and industrial sources (Rheinfelden, Niederglatt), including waste incineration facilities, without the highest efficient filter systems (Buchs, Berne), and the forbidden waste disposal by cheminee-fireing. The latter is represented by a very residential quarter of city of Berne (Brunnadern). Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminant
values recorded for industrial locations. It can be concluded from these findings that motor vehicles evidently have to be considered as a potential source of dioxins and furans, certainly up to 1999 when the leaded petrol that was still in use contained chlorine-based scavengers.
Polycyclic Aromatic Hydrocarbons (PAHs) There are strikingly high impact gradients between areas of maximum and minimum burden for PAHs bound to dust particles; these are of a factor of 10–20 for several PAHs, some of which have a carcinogenic and mutagenic effect. The highest PAH burdens are found at measurement points in the centres of large towns and, more particularly, in poorly ventilated sites (Fig. 4). However, more refined evaluations taking into account the distance between the sampling point and the nearest road showed that motor vehicles accounted for a significant proportion of this PAH burden (Fig. 5). A follow-up study in the direct vicinity of the transalpine highway A2 through Switzerland already showed a very clear relationship between the density of
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Fig. 4 Air pollution load and spatial distribution of three PAHs with both, carcinogenic and mutagenic activity: Benzo(a)anthracene, Benzo(b)fluoranthene, and Benzo(a)pyrene at the sites of the six land-use categories in Switzerland in 1995. Most elevated PAH concentrations were clearly found in the direct vicinity of intense motorized traffic. Size of the circles is equivalent to concentrations; size of segments is contribution of individual contaminants
otorized traffic, and specially of heavy duty vehicles and the PAH contamination m analysed in the lichen samples at 23 different locations all along the highway between Basel and Chiasso (Herzig 2007, 08).
Conclusion Comparison of the POP burden in areas with different types of land-use shows that the burden emanating from within Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the latter category certainly contributes to the background level of contamination. Switzerland signed the Protocol on Persistent Organic Pollutants (POPs) to the UN/ECE Convention in November 2000, and in May 2001 also signed the global UNEP Convention on POPs in Stockholm. These documents place a ban on the production and use of 16 (UN/ECE) and 12 (UNEP) POPs of particular environmental relevance or subject them to limits on production, use and emissions. The first country-wide POP study established a representative and spatially differentiated biomonitoring network and a reference archive of samples in Switzerland.
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Fig. 5 Overall influence of land use and traffic on the air born contamination of PAHs in lichen samples. The upper 3D-table shows with higher accumulated PAHs, whereas the lower shows the individual PAHs with smaller concentration. Urban sites, include intense traffic, directly followed “Street Distance <3m” and “Intensive Traffic (Tra3)” are the most important sources of the air born PAH contamination in Switzerland. c: carcinogenic actitvity; m: mutagenic actitivity; c,m: both carcinogenic and mutagenic activity
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A monitoring instrument of this kind can also be used to document the success of efforts to reduce the POP burden over time. Comparison with the results of the first POP study will highlight reductions that have been achieved and any need for further action.
References Bundesamt für Strassen, ASTRA. Automatische Strassenverkehrszählung (2003) Fahrzeugklassen (LCV). Mit längenklassierten DTV und DWV-Daten 2003. Sigmaplan. Bern, Mai 2004 CARBOTECH (1995) PAK-Immissionsmessungen in den Kantonen Zürich, Schaffhausen und Luzern 1994/95. Im Auftrag des Amtes für Techn. Anlagen (ATAL) des Kt. Zürich, des kant. Laboratoriums für Lebensmittelkontrolle und Umweltschutz, Schaffhausen und des Amtes für Umweltschutz, Luzern Carlberg G, Baumann Ofstad E, Drangsholt H, Steinnes E (1983) Atmospheric depositions of organic micropollutants in Norway studied by means of mosses und lichen analysis. Chemsophere 12/3:341–356 CRM 598 EUR Report (1997) the certification of the contents of HCB, a-HCH, b-HCH, g-HCH, d-HCH, g, a-Chlordane, Dieldrin, pp-DDE, op-DDD, pp-DDD, pp-DDT in cod liver oil. European Commission BCR Information Reference Materials CRM 536 EUR Report (1997) The certification of the contents of Chlorobsssiphenyl IUPAC No 28, 52, 101, 105, 118, 138, 149, 153, 156, 163, 170, 180 in freshwater harbour sediment. European Commission BCR Information Reference Materials EPA: Environmental Protection Agency (1996) Test methods for evaluating solid waste. Physical/chemical methods, Vol. 1, Section B (US Department of Commerce, NIST, 1986) Herzig R et al (1989a) Lichens as biological indicators of air pollution in Switzerland passive biomonitoring as a part of an integrated biological system of monitoring air pollution. Int J Environ Anal Chem 35:43–57 Herzig R et al (1989b) Lichens as biological indicators of air pollution in Switzerland: passive biomonitoring as a part of an integrated measuring system for monitoring air pollution. In: Lieht H, Markert B (eds) Element concentration cadasters in ecosystems. VCHVerlagsgesellschaft, Weinheim, pp 317–332 Herzig R (1990) Entwicklung des Integrierten biologischen Messsystems der Luftverschmutzung mit Flechten in der Schweiz, seine Anwendung und erste Vergleiche mit Bodenanalysen. VDIKolloquium: Wirkungen von Luftverunreinigungen auf Böden, 15–17. Mai 1990, Lindau. VDI-Berichte 837, Seiten 937–956 Herzig R (1993a) Multi-Residue Analysis with Passive Biomonitoring: A New Approach for Volatile Multi-Element Contents, Heavy Metals and Polycyclic Aromatic Hydrocarbons with Lichens in Switzerland and the principality of Liechtenstein. In: Markert B (ed) Plants as biomonitors for heavy metal pollution in the terrestrial environment. VCH-Verlagsgesellschaft, Weinheim, pp 285–328 Herzig R (1993b) PAK-Analysen in Flechtenproben und Vergleich mit entsprechenden Immissionsmessungen an vier Standorten in der Schweiz – Als Grundlage für den OECDWorkshop on Hazardous Air Pollutants 1993, London. Int. Schlussbericht, 57 S., BUWAL, 1993 Herzig R (2002) Persistente organische Luftschadstoffe (POP) in der Schweiz. Umwelt–Materialien Nr. 146 Luft. Buwal schriftenreihe, Bern. Herzig R (2005) Erfolgskontrolle zur Luftreinhaltung in der Stadt Bern 2004 Wiederholung der Untersuchungen mit Flechten nach 14 Jahren. Schlussberichtes vom 20.10.05 Stadt Bern Amt für Umweltschutz und Lebensmittelkontrolle, beco Berner Wirtschaft und Gemeinden Köniz und Bremgarten
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Herzig R (2006) Monitoring flankierende Massnahmen Umwelt (MfMU): Luftqualitätsmonitoing mit Flechten entlang der Alpentransitautobahnen A2 und A13 und Zuweisung relevanter Luftschadstoffe zum Schwerverkehr. Kurzfassung 21.9.06. Schweiz. Bundesamt für Umwelt, BAFU, CH-3003 Bern Herzig R (2007) Monitoring flankierende Massnahmen Umwelt (MfMU): Luftqualitätsmonitoing mit Flechten entlang der Alpentransitautobahnen A2 und A13 und Zuweisung relevanter Luftschadstoffe zum Schwerverkehr. Schlussbericht Januar 2007. Schweiz. Bundesamt für Umwelt, BAFU, CH-3003 Bern Herzig R, Urech M (1991) Flechten als Bioindikatoren – Integriertes biologisches Messsystem der Luftverschmutzung im Schweizer Mittelland. Ergebnisse der NFP14-Methodenentwicklung. Bibiotheca Lichenologica Band 43. J. Cramer in Gebrüder Bornträger Verlagsbuch-hanlung. Berlin Stuttgart 283 Seiten. Krebsrisiko Von Diesel- und Benzinmotorabgasen (1993) Schriftenreihe Luft Nr. 222. Bundesamt für Umwelt, Wald und Lanschaft (BUWAL), Bern. Krishan V, Safe S (1993) Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs) as antiestrogens in MCF-7 human breast cancer cells: quantitative structure-activity relationship. Toxicol Appl Pharmacol 120: 55–61 Mason CF, Ratford JR (1994) PCB congeners in tissue of European otter. Bull Environ Toxicol Chem 53: 548–554, sowie in: Sinkt die Fertilität. Kind und Umwelt. SCHLUMPF & Lichtensteiger 1996, Zürich Motor Vehicles and Cleaner Air (1983) Health risks resulting from exposure to motor vehicle exhaust. Govermental Report (SOU 1983:27,28) Marklund S et al (1990) Emission of PCDDs and PCDFs in gasoline and diesel fueld cars. Chemosphere 20(5):553–561 NATO-CCMS (1988) International toxicity equivalency factor method of risk assesment for complex mixtures of dioxins and related compounds, Report No. 176 (1988) NABEL- Luftbelastung (1995) Schriftenreihe Umwelt Nr. 267. Bundesamt für Umwelt, Wald und Landschaft 1996 (BUWAL) Petry T, Schmid P, Schlatter CH (1996) The use of toxic equivalency factors in testing occupationale and environmental health risks assiciated with exposure to airborne mixtures of polycyclic aromatic hydrocarbons (PAGs). Cemosphere 32(4):639–648 Regulatory Toxicology Pharmacology 1985: 5, 329–383. Zit. in Römpp, Bd. pp. 881. Sambasiva R et al (1988) Carcinogenesis of 2378-TCDD in the Sirian Gold Hamster. Carcinogenesis 9(1988):1677 Schläpfer K et al (1995) PAK-Immissionsmessungen in den Kantonen Zürich, Schaffhausen und Luzern. Resultate der chemischen Messungen (GC/MS) und derjenigen mit dem photoelekrischen Aerosol-Sensor (PAS). Im Aufrag des Amtes für techn. Analgen und Lufthygiene des Kt. Zürich (ATAL), des Kantonalen Laboratoriums für Lebensmittelkontrolle und Umweltschutz Schaffhausen und des Amtes für Umweltschutz, Luzern Schweizerische Strassenverkehrszählung (1995) Bundesamt für Statistik Bern UN/ECE (1998) Unititd Nations Economic Commission for Europe. Protocol to the 1979 Convention on Long-range Transboundry Air Pollution on Persisant Organic Pollutants. Aarhus, 24. June 1998 VOC- und PAK-Immissionsmessungen in der Schweiz 1991/92: Umweltmaterialien Nr. 10. Bundesamt für Umwelt, Wald und Lanschaft (BUWAL), Bern WHO-Europe (2003) Health risks of persistent organic pollutants from long-range transboudary air pollution. Joint WHO/Convention Task Force on health aspects of Air Pollution, 252pp., Copenhagen (DK) WHO-Road Transport (2002) Emission Inventory Guidebook August 2002. WHO Geneva Weiss P (1998) Persistente organische Schadstoffe in Hintergrund-Waldgebieten Österreichs. Monografien Band 97. Umweltbundesamt Wien, 242 S Williams PT, Bartle KD, Andrews GE (1986) The relation between polycyclic compounds in diesel fuels and exhaust particulates. Fuel, Vol. 65, August, 1986
Part III
Pollutant Degradation and Ecosystem Remediation from Enzymes to Whole Plants
New Perspectives on the Metabolism and Detoxification of Synthetic Compounds in Plants Robert Edwards, David P. Dixon, Ian Cummins, Melissa Brazier-Hicks, and Mark Skipsey
Abstract In attempting to understand the mechanisms by which plants process synthetic compounds we have developed the concept of the ‘Xenome’, which we define as ‘the biosystem responsible for the detection, transport and detoxification of xenobiotics.’ In particular the last 10 years have given us unprecedented insights into the proteins responsible for the metabolism and transport of xenobiotics within plant cells and how these systems are regulated. In this review we identify recent advances in our understanding of the xenome and its role in the detoxification and processing of pollutants and pesticides. In particular, we focus on the role of the phase 1 (oxidoreductase/ hydrolytic), phase 2 (bioconjugation), phase 3 (transport) and phase 4 (metabolic recycling) stages of xenobiotic metabolism and the biosensing systems which control their expression. Ultimately, by understanding the capability of the plant xenome to detoxify xenobiotics, we may be able to predict the likely fate and environmental risk of new synthetic compounds entering the environment and food chain.
Introduction In attempting to understand the mechanisms by which plants process synthetic compounds we have developed the concept of the ‘Xenome’, which we define as ‘the biosystem responsible for the detection, transport and detoxification of xenobiotics.’ In particular the last 10 years have given us unprecedented insights into the proteins responsible for the metabolism and transport of xenobiotics within plant cells and how these systems are regulated. As discussed in the other chapters, plants in industrial or agricultural settings are constantly exposed to organic xenobiotics in R. Edwards () The Food and Environment Agency, Sand Hutton, York YO41 1LZ, UK e-mail:
[email protected] D.P. Dixon, I. Cummins, M. Brazier-Hicks, and M. Skipsey Centre for Bioactive Chemistry, Durham University, School of Biological and Biomedical Sciences, Durham DH1 3LE, UK P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_7, © Springer Science+Business Media B.V. 2011
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the form of crop protection agents and their associated formulations and pollutants. Depending on their route of application, hydrophobic compounds will pass directly into leaf cells by passive diffusion through the waxy cuticle, whereas more polar compounds have the potential for systemic uptake and movement throughout the plant via transport in the phloem or xylem depending on their physical chemical characteristics (Watanabe 2002). In the case of systemic transport, xenobiotics will pass from the symplast or apoplast across the plasma membrane either by passive diffusion, or more rarely by active transport being mistaken for essential solutes (Watanabe 2002). In any cells, internalised xenobiotics then immediately pose a potential toxic threat to the cell by disrupting membrane integrity or metabolic pathways and must therefore be rapidly safely sequestered, extruded or detoxified through biotransformation (Fig. 1). Interestingly where as bacteria and fungi typically deal with xenobiotics through extrusion from the cell using membrane transporter proteins linked to multiple drug resistance in both prokaryotes and
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Fig. 1 The plant xenome, showing the different phases of metabolism and their compartmentation as well as the transcriptional regulation of the associated pathways. Phase 1 reactions (ringed in brown) involve the action of esterases or cytochrome P450s which introduce or reveal functional groups which can then undergo phase 2 bioconjugation (red) through the action of glutathione transferases (GSTs) or UDP-sugar dependent glycosyltransferases (UGTs). In the case of glucosylated products, further phase 2 metabolism can involve esterification with malonic acid catalysed by malonyltransferases (MTs). The phase 2 conjugates and related catabolites are then actively removed from the cytosol through the action of ATP-dependent phase 3 transporters (green) and deposited into the vacuole for temporary storage and/or further catabolism. The vacuolar products resulting from phase 3 import are then re-exported back into the cytosol for mineralization or incorporation into bound residues, through ill defined phase 4 biotransformations (purple). The metabolic component of the xenome is regulated at the transcriptional level by exposure to xenobiotics, or their metabolites, through an undefined receptor system which oncc activated, induces the transcription of the detoxifying proteins
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eukaryotes, this is a relatively unusual mechanism in plants and has only been well described in the case of transgenic crops and cyanobacteria (Prosecka et al. 2009). Instead, plants like mammalian cells biotransform xenobiotics through a complex set of phased detoxification reactions which we have collectively termed the xenome (Fig. 1). We have defined the xenome as ‘the biosystem responsible for the detection, transport and metabolism of xenobiotics’ (Edwards et al. 2005a). The xenome therefore represents a molecular model of the ‘green liver’ concept described in earlier chapters. While the overall reactions of the xenome of plants and animals follow very similar biotransformation steps, there are some fundamental differences. Firstly, in animals, the xenome has evolved to fulfil a specialized role in the detoxification of foreign compounds. In contrast, due to their inherent ability to synthesize secondary metabolites, plants contain a much larger subset of potential xenobiotic biotransforming enzymes than animals, as reflected in the typical sizes of the xenome gene families in the model plant Arabidopsis thaliana and man (P450s: 273 vs. 57; Family 1 glyocosyltransferases: 107 vs. 27; soluble GSTs: 54 vs. 17; ABC transporters: 120 vs. 50). There is therefore the interesting possibility that while some xenobiotics are acted on by classic detoxifying xenome enzymes on entering plant cells, others are mistaken for secondary metabolites and biotransformed by enzymes normally actively involved in endogenous biosynthesis. Certainly the xenome enzymes in plants, unlike animals, must play a role in regulating the biological activity of their own natural products as well as those derived from the external environment. There is therefore an interesting evolutionary question to be asked as to whether the enormous repertoire of xenobiotic biotransformations recorded in both crops and weeds today (Van Eerd et al. 2009), is a consequence of evolution of xenome enzymes to deal with naturally occurring allelochemicals and bacterial toxins, or more a reflection of the diversification in plant secondary metabolic pathways. Secondly, a further key difference in drug metabolism in plants and animals lies in the fate of detoxified metabolites. Thus, whereas in mammals detoxified xenobiotics, such as pollutants, pesticides and drugs, are typically exported from cells for ultimate secretion in the bile or urine, such extrusion is unusual in plants, with the metabolites instead being directed for internal storage in the vacuole (Fig. 1). The upshot of this internalisation is that metabolised xenobiotics have a much greater turnover time within the cell than their counterparts in animals and this in turn means they are more likely to undergo further biotransformation into natural products, through being mistaken as endogenous intermediates.
The Plant Xenome and Its Organization Based on our existing understanding, a schematic showing the plant xenome is shown in Fig. 1. Essentially the xenome can be divided into two functional components; namely a xenobiotic sensing system and a series of phased metabolic
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enzymes comprising a detoxification pathway. The mechanism by which plants recognize xenobiotics is largely unknown, though we do know that the response invokes large scale transcriptional activation of genes encoding detoxifying proteins. In animals and yeasts, xenobiotic sensing involves the foreign compound alkylating a regulatory protein, such as the keap2 protein which then releases a transcriptional regulator which migrates to the nucleus and initiates mRNA transcription from genes containing specific DNA binding motifs in their promoters, termed electrophile response elements (EREs) or xenobiotic response elements (XREs). Interestingly, similarly functioning cis acting elements have been identified in the promoters of plant detoxification genes, though the upstream transcription factors and receptor proteins remain unknown. Certainly plants do not contain homologues of the electrophile-sensing keap2 protein (Baerson et al. 2005). By way of comparison, we know considerably more about the metabolic component of the xenome, particularly as a result of recent studies. The core activities making up the xenome are illustrated in Fig. 1. Xenobiotic detoxification in plants has been classically organized into three phases. These are the introduction or revealing of reactive functional groups, such as hydroxy or carboxy groups through the action of oxido-reductases or hydrolases (phase 1). These functionalized intermediates are then bioconjugated with sugars, amino/organic acids or peptides (phase 2) and then actively removed from the cytoplasm for deposition in the vacuole by trans-membrane ATP-dependent transporters (phase 3). In the event that the xenobiotic is sufficiently reactive, it may not require phase 1 metabolism and instead directly undergo phase 2 bioconjugation. As discussed above these phases of detoxification reactions in plants are essentially identical to those found in drug metabolism in animals with the exception of the fate of the final metabolites. As a consequence of the vacuolar deposition, metabolism in plants does not end with bioconjugation. Instead, metabolites can be further processed by hydrolytic reactions within the vacuole and/or be re-exported at a later date into the cytoplasm for further metabolism. In some cases, these recycled xenobiotic residues can re-enter the phases of detoxification (Brazier-Hicks et al. 2008), in others the residues can become incorporated into biomacromolecules, or enter primary catabolic metabolism and be ultimately mineralized to CO2 (Ertunç et al. 2004). This further biotransformation of conjugated residues is clearly distinct from that determined in animals and as such has been termed phase 4 metabolism. A further point worth raising is that a single compound may enter several routes of phased detoxification simultaneously. For example the herbicide atrazine can simultaneously undergo N-dealkylation (phase 1) and S-glutathionylation (phase 2) within single cells (Edwards and Owen 1986). As indicated in Fig. 1, the reactions within the xenome require both compartmentalisation and the organized transfer of intermediates along the organizational lines of plant secondary metabolism. Thus, hydrophobic metabolites are acted on by membrane bound oxido-reductases, with the intermediates then bioconjugated by cytoplasmic transferases. Intriguingly, the localisation of these membrane oxido-reductases is not always restricted to the endoplasmic reticulum and can also include plastids and mitochondia (Durst and O’Keefe 1995). Similarly, the transferases are described as
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soluble proteins, yet they are normally highly hydrophobic, suggesting that in planta they may aggregate around the membrane interface close to oxidoreductases. Once produced the mechanism by which the conjugated xenobiotics are transferred to the phase 3 transporters on the vacuolar membrane is unknown. Drawing parallels with secondary metabolism, such transfer would be unlikely to be through simple diffusion alone and may involve the conjugating enzymes delivering their reaction products to the transporter directly (Marrs 1996), or though the intervention of an as yet to be identified ligand binding protein fulfilling a role in facilitated diffusion. The transporters themselves may be comprised of several different classes of ATP-dependent proteins and are normally ascribed to the vacuole membrane. It is also apparent that members of these protein families are also to be found in the plasma membrane and in organelles (Rea 2007), suggesting that it is possible that conjugated xenobiotics can be delivered to different compartments within the plant cell. Certainly, while we know something of the mechanisms of vacuolar import, the means by which processed conjugates can pass back again into the cytoplasm of living cells for further metabolism are unknown. From this we can see that the current model of the xenome (Fig. 1) is both incomplete and over-simplifies the truth. However it does provide a useful context to explore its individual components.
Detoxifying Enzymes Phase 1 Enzymes – Oxido-Reductases With respect to the oxidases, there has been some interest in identifying roles for soluble laccases, peroxidases and dioxygenases in promoting phase 1 xenobiotic metabolism (Uchida et al. 2005). The most promising enzymes have been derived from microbes and transgenically expressed in plants. For example, over-expression of a fungal laccase in the apoplast of transgenic tobacco substantially enhanced the rate of removal of both bisphenol A and pentachlorophenol from hydroponic solution, with the lack of observed products suggesting polymerisation as the degradative route (Sonoki et al. 2005). Bacterial oxidases evolved to degrade aromatic pollutants for use as a carbon source can be engineered into plants to improve detoxification and tolerance (Uchida et al. 2005). For example, a bacterial dioxygenase from the bisphenol catabolic pathway provided enhanced tolerance to 2,3-dihydroxybiphenyl when overexpressed in tobacco (Novakova et al. 2009). In addition to these microbial oxidases, some plant enzymes have been shown to be useful in pollutant metabolism. Thus, the over-expression of a laccase from cotton in Arabidopsis was shown to enhance the degradation of 2,4,6-trichlorophenol, with metabolites being polymerised (Wang et al. 2004). With respect to other oxido-reductases, oxophytodienoate reductase, an enzyme usually associated with processing of oxylipins and other related lipid-derivied molecules, has been shown to have a role in TNT degradation (Beynon et al. 2009). However, the Cytochrome P450s (CYPs) appear to be far and
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away the most important enzymes catalyzing oxidation and reduction reactions with xenobiotics. These membrane-associated, haem containing oxido-reductases utilise molecular oxygen, with reduction of the catalytic iron atom maintained via an NADPH-dependent cytochrome reductase. In contrast to many other xenobioticmetabolizing enzymes, CYPs perform multiple types of biotransformations mediated through hydroxylation, epoxidation, isomerisation, desaturation, demethylation and dehalogenation steps (Guengerich 2001). In animals, where a small number of CYPs act on a large number of drugs and toxins, the respective gene family is relatively small. In contrast, as a consequence of the requirement of these enzymes to be selectively involved in the biosynthesis of diverse endogenous secondary metabolites, the CYP gene families in plants are much larger with 273 CYP genes (http://www.p450. kvl.dk/, http://drnelson.utmem.edu/Arablinks.html) reported to be present in the Arabidopsis thaliana genome and more than 450 in rice. With respect to xenobiotics, the study of CYPs has been largely focussed upon their roles in herbicide metabolism. More recently, there has also been an interest in harnessing these enzymes in plants for remediating chemically contaminated land (Morant et al. 2003). To date the major advances in CYP-engineered phytoremediation of xenobiotics have been achieved using drug metabolizing enzymes from animals and microbes expressed in transgenic plants. For example, three CYP genes from different families in man were coexpressed in potato, thereby conferring cross-tolerance to herbicides of differing classes (Inui et al. 2001). Similarly, transformation of poplar with rabbit CYP2E1 resulted in plants that demonstrated more rapid uptake and detoxification of trichloroethylene, vinyl chloride, carbon tetrachloride, chloroform and benzene from hydroponic media (Doty et al. 2007), while expression of a bacterial CYP in Arabidopsis allowed the transgenic plants to detoxify the explosive hexahydro-1,3, 5-trinitro-1,3,5-triazine (Rylott et al. 2006). Importantly, these studies have confirmed the importance of CYP-mediated biotransformations in the detoxification of herbicides, as well as enhancing the potential for using transgenic plants in phytoremediation. Whilst there is no suggestion that plant enzymes cannot perform the same functions, this is merely a representation of the maturity of the field of research in animals and microbes versus plants. In addition to the large size of the respective gene families another reason for the difficulty in studying plant CYPs relates to them being membrane bound, coupled enzyme dependent proteins, which rarely express functionally in bacteria. Instead, most of the work with CYP-recombinant microbes has been conducted in yeast (Schoch et al. 2001). However, CYP activities toward xenobiotics have been demonstrated for some time using microsomes prepared from plant tissues. For example, the metabolism of the herbicide diuron was first reported in microsomes prepared from cotton 40 years ago (Frear et al. 1969). Herbicide classes of diverse structure have since been shown to be metabolised by CYPs in vitro including bentazon, the sulfonylurea chlorimuron, substituted ureas (e.g. chlortoluron), imidazolinones (e.g. imazethapyr) and acetanilides (e.g. acetochlor) (Barrett 2000). However, while the activities have been shown in a range of crop plants, the enzymes responsible for these activities have generally yet to be identified and cloned. As an exception, in one approach xenobiotic-detoxifying plant CYPs have
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been identified by transgenic over-expression in tobacco. Thus a CYP from Jerusalem artichoke (Helianthus tuberosus) was shown to confer tolerance to the herbicide linuron, which is detoxified by a single dealkylation and to a lesser extent to chlortoluron, which requires further metabolism (Didierjean et al. 2002).
Phase 1 Enzymes – Hydrolases As with the CYPs, there is a large complement of proteins able to hydrolyze xenobiotic carboxylesters and amides in plants, although unlike the other classes of detoxification enzymes, these activities are dispersed across unrelated families of enzymes (Gershater et al. 2007). Thus esterase activities toward xenobiotic esters have been determined in alpha/beta- fold serine hydrolases, the microbial like GDS hydrolases and the hydroxynitrile lyase classes of proteins (Gershater et al. 2007; Cummins et al. 2007; Marshall et al. 2003). These enzymes have been termed family I, II and III carboxylesterases (Gershater and Edwards 2007) with many of these proteins identified in informatic screens and then their activities determined in recombinant bacteria (Cummins et al. 2007; Marshall et al. 2003). More rarely, their functional roles in xenobiotic biotransformation have been shown in planta. This is a key distinction, as many proteins show modest esterase activity as a consequence of conserved protein fold domains, but the physiological consequences of these catalytic abilities is questionable. In the small number of studies where xenobiotic hydrolases have been successfully purified and characterized from plants a surprising diversification has been determined in the enzymes responsible (Gershater and Edwards 2007). When an esterase activity toward 2,4-D methyl was purified from Arabidopsis thaliana, with the aid of a serine hydrolase-specific probe the enzyme was identified as a classic alpha/beta fold protein named AtCXE12 (Gershater et al. 2007). Gene knockout experiments were subsequently used to demonstrate the functional importance of the enzyme, with the deficient mutants being impaired in their ability hydrolyse, and so bioactivate, the pro- herbicidal 2-4-D methyl ester to the phytotoxic 2,4-D acid. The role of esterases in the bioactivation and selectivity of herbicides has also been studied in grass weeds and cereal crops. Many of the graminicidal aryloxyphenoxypropionate (AOPP) herbicides used in arable agriculture are applied in the field as ester formulations, a derivatisation which serves to increase the passage of the molecule through the waxy leaf cuticle. Once absorbed into the plant tissues the hydrolysis to the respective acids activates the herbicidal activity. This activity is shown in Fig. 1 occurring outside the cell (see below). To investigate the roles of such hydrolysis in controlling herbicide bioactivation and hence selectivity, the total esterase complement of wheat and competing weeds was determined with a range of xenobiotic esters including AOPP esters (Cummins et al. 2001). The results showed that the grass weeds wild oat (Avena fatua) and black-grass (Alopecurus myosuroides) had different esterase profiles to the crop and that when assayed for activity toward the AOPP esters, Alopecurus had tenfold
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greater activity than wheat (Cummins et al. 2001). This suggested that increased hydrolysis of the ester to the active acid form could be a contributing factor towards herbicide selectivity. Subsequent purification and cloning of the major diclofop methyl-hydrolysing activity from Alopecurus identified a GDS-hydrolase resembling enzymes previously identified in bacteria (Cummins and Edwards 2004). This GDS hydrolase was from a completely different protein family to the carboxylesterase activity in 2,4-D methyl metabolism in Arabidopsis (Gershater and Edwards 2007). Furthermore, whereas AtCXE12 was located in the cytosol of Arabidopsis (Gershater et al. 2007), the AOPP ester hydrolyzing enzymes are located in the apoplast of cereals and grasses (Haslam et al. 2001). Consistent with the observations that esterase activity leading to herbicide bioactivation is greater in weeds than in crops, the selectivity of cyhalofop butyl was attributed in part to a lack of esterase activity in rice, resulting in lower levels of cyhalofop acid relative to the rates of hydrolysis determined in susceptible weeds (Ruiz-Santaella et al. 2006). Similarly, a further analysis of the esterase complement of maize, rice, sorghum, soybean, flax and lucerne, compared with a number of weed species again demonstrated differences in activity, which could contribute to herbicide selectivity mechanisms (Gershater et al. 2006). In addition to carboxylesterases, amidases with activities toward xenobiotics have also been described in plants. An arylacyl amidase active toward the herbicide propanil has been described in rice and competing weeds (Leah et al. 1994). The enzyme responsible has yet to be characterized, though a mitochondrial protein appears to be the most likely candidate for catalyzing this unusual activity (Hirase and Hoagland 2006). In addition other hydrolytic processes can give rise to xenobiotic detoxification. For example, a bacterial enzyme atrazine chlorohydrolase, an atrazine-specific enzyme related to melamine deaminase and other amidohydrolases, was shown to confer tolerance to this traizine herbicide when over-expressed in a range of plants including Arabidopsis, alfalfa and tobacco in each case greatly enhanced atrazine resistance (Wang et al. 2005).
Phase 2 Enzymes – Glutathione Transferases (Gsts) Plant GSTs have long been known to have a crucial role in detoxifying many xenobiotics, and in particular herbicides, through the substitution of an electrophilic group with the tripeptide glutathione (Edwards and Dixon 2000; Dixon et al. 1998). As such there are several robust methodologies in place to monitor the detoxifying activities of these enzymes (Edwards and Dixon 2005). The soluble GSTs form a diverse superfamily of enzymes characterised by a shared overall structure and a well defined glutathione binding domain. Plants also contain an unrelated microsomal GST family, though their role in detoxifying xenobiotics has yet to be demonstrated and as such this class of enzymes will not be considered further in this review (Jakobsson et al. 1999). The soluble GSTs can be divided into classes based on sequence similarity and catalytic function, and in plants the two classes predominantly active in xenobiotic detoxification are the phi (F) and tau (U)
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classes. Both these classes are plant-specific and form large gene families in each plant species examined to date (Frova 2006). The much smaller lambda (L) class has members that are strongly induced by xenobiotic treatment (Dixon et al. 2002), but their likely redox-related role does not appear to extend to the detoxification of synthetic compounds. Other classes including the theta class and the dehydroascorbate reductases have no known ability to conjugate or detoxify synthetic compounds. However, whereas the zeta class of GSTs have been characterised as glutathione-dependent cis-trans isomerases involved in tyrosine detoxification, they can also catalyse the glutathione-dependent dechlorination of dichloroacetate. This is an unusual but interesting reaction as glutathione is not consumed but is instead used catalytically, with dichloroacetate converted to glyoxylic acid which can then enter primary metabolism, and be completely metabolised (Dixon et al. 2000). It remains to be seen however whether these zeta GSTs can act in the dechlorination of other xenobiotics. Intriguingly, plants have also recently been shown to contain a GST resembling the tetrachlorohydroquinone reductases from microorganisms. In bacteria these reductases are also known to dehalogenate aromatic xenobiotics and have potential roles in phytoremediation (Chen and Yang 2008), though the activities of this membrane associated GST in plants is yet to be determined (Dixon et al. 2009). The tau and phi class GSTs catalyse reactions that typically result in the formation of a glutathione-conjugated substrate. Reactions can either be addition reactions, where GSH is added to an unsaturated bond, or epoxide, or substitution reactions, where GSH displaces a group on the substrate. In both cases, the conjugation mechanism is essentially conserved, with the substrates containing an electrophilic centre, and the glutathione activated by the GST to form the highly nucleophilic thiolate anion through interaction with, and proton extraction by, an active site serine in the enzyme. The most commonly observed conjugation of xenobiotics involves substitution reactions, with the leaving group being a halide. This substitution is normally enough to greatly reduce the toxicity of the xenobiotic and along with the increased polarity brought about by glutathione conjugation, is a very effective means of metabolism. Due to their ability to act as product inhibitors of the GSTs it is then important to remove these glutathione conjugates from the cytosol, which is achieved by transporting these reaction products into the vacuole (see later). Rapid GST-mediated detoxification appears to account for the tolerance of several crops toward important herbicide classes, notably the chloroacetanilides, chlorotriazines, diphenylethers and AOPPs. GSTs also detoxify other agrochemicals, including the safeners benoxacor, dichlormid and fenclorim (Brazier-Hicks et al. 2008) and can act on drugs entering the environment as pollutants, such as the antibiotic chlortetracycline (Farkas et al. 2007) and the painkiller paracetamol (Huber et al. 2009). Occasionally, GST activity can increase xenobiotic toxicity. Two examples are the GST-mediated isomerisations involved in herbicide bioactivation with isourazoles converted to toxic urazoles, and thiadiazolidines to triazolidines (Edwards and Dixon 2000). The diversity of reactions catalysed by GSTs means that other unusual GST-mediated biotransformations no doubt await discovery.
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GSTs also appear able to indirectly enhance resistance to herbicides that are not GST substrates (Cummins et al. 1999). In these cases, it is likely that the toxic effects of the herbicide can be ameliorated by the GSTs acting to detoxify products formed due to chemical injury, for example by scavenging reactive oxygen species-damaged compounds such as lipid hydroperoxides (ROOH) being reduced to the corresponding alcohols (ROH), through the concomitant oxidation of glutathione (Cummins et al. 1999). Because of their clear role in detoxifying halogenated xenobiotics GSTs are excellent targets for engineering enhanced tolerance to pesticides and pollutants. Several studies have over-expressed GSTs in a range of plants and shown both enhanced tolerance to, and increased detoxification of, various xenobiotics. Some of the recent notable examples include the demonstration that over-expression of maize GSTs has conferred tolerance to chloroacetanilide and thiocarbamate herbicides in tobacco (Karavangeli et al. 2005) and wheat (Milligan et al. 2001). In an extension of this approach the diversity of detoxification reactions shown by GSTs from non-plant sources has also been employed. For example, tobacco over-expressing a fungal GST showed enhanced degradation of anthracene (Dixit et al. 2009). Additionally, native GST detoxifying activities can be enhanced by forced evolution approaches, with maize tau class GSTs engineered with an enhanced ability to detoxify diphenylethers conferring resistance to these herbicides in transgenic Arabidopsis plants (Dixon et al. 2003). An added consideration in GST-mediated detoxification is the availability of the co-substrate glutathione, with higher levels resulting in both increased enzymic and spontaneous conjugation. Glutathione depletion can become a problem when detoxifying large quantities of xenobiotics, particularly when such xenobiotics are highly reactive and/or efficiently conjugated (Brazier-Hicks et al. 2008). When considering using GSTs for phytoremediation applications, it would therefore be very desirable to ensure these enzymes were supplied with an adequate supply of glutathione. The approaches to engineer glutathione metabolism in plants are well established, though such strategies would require a good availability of inorganic sulphur nutrients (Foyer 2001). In addition to providing enough glutathione for these enzymes, it is also necessary to provide the optimal thiol substrate. Many major crops such as soybean, wheat and rice partially, or completely replace glutathione with closely related thiols, and this can give rise to surprising alterations in pesticide detoxification. For example, soybean uses homoglutathione (hGSH; g-glutamyl-cysteinyl-b-alanine) in place of glutathione, with the respective GSTs showing a marked prefernce for this alternative thiol in detoxifying the diphenylether herbicide fomesafen (Skipsey et al. 2005). The detoxification of this herbicide in transgenic tobacco could only be engineered when the plants were transformed with the soybean GSTs along with a homoglutathione synthetase to provide the preferred thiol substrate. With respect to future strategies for rationally using GSTs in xenobiotic detoxification applications, the basis of their substrate selectivity and catalysis is starting to be addressed through mutagenesis, structural determination and binding studies. In particular, recent structure determination of GSTs crystallised with xenobiotic-GSH conjugates at the active site has proved very informative. Soybean GSTU4 was
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c o-crystallised with S-(4-nitrobenzyl)-glutathione to reveal a typical highly specific GSH binding site adjacent to a large, hydrophobic co-substrate binding site (Axarli et al. 2009). Binding studies have shown that in related tau class enzymes this hydrophobic binding site can accommodate large, hydrophobic molecules such as glutathione conjugates of porphyrinogens (Dixon et al. 2008) and lipids (Dixon and Edwards 2009). Phi class GSTs have been co-crystallised with a range of herbicide and other xenobiotic conjugates (Prade et al. 1998), and again have been shown to have large, hydrophobic binding sites. Mutagenesis studies have used these structures to identify and then mutate important amino acid residues for binding, and this has helped identify residues important for binding certain xenobiotics (Axarli et al. 2009; Labrou et al. 2004). In future, it is possible that such structural information will allow GSTs to be designed to match the detoxifying activity required with a specific group of problem pollutants. Certainly this information is already proving useful in predicting the likely routes of GST-mediated detoxification of herbicides.
Phase 2 Enzymes – Glycosyltransferases The conjugation of xenobiotics with sugars is the most frequently observed phase 2 biotransformation seen in plants. The acceptor molecules may be conjugated directly following uptake into the plant if they already bear functional OH, SH or NH2 groups, or more commonly are derived from the action of phase 1 oxidoreductases or hydrolases. The sugar conjugation of small acceptor molecules is mediated by family 1 glycosyltransferases which use NDP-activated donors to catalyse glycosidic bond formation via an inverting mechanism. The most commonly observed conjugating activities toward xenobiotics in plants utilise UDP-glucose as donor with the respective glycosyltransferases termed UGTs. These UGTs are present in plants as large multi-gene families, with 107 coding sequences in Arabidopsis and 228 in rice (http://www.cazy.org/). The ability of UGTs to glycosylate xenobiotics is widespread in the plant kingdom, with both higher and lower plant species able to conjugate chlorinated phenols, anilines and thiophenols (Pflugmacher and Sandermann 1998). Much of the work on glycosylated xenobiotics has focused on the characterisation of the metabolites of herbicides in cereals (Cole and Edwards 2000). While the respective UGT activities toward herbicides and pollutants have been determined in crude enzyme preparations from many plant species (Frear 1968; Gallandt and Balke 1995; Sandermann et al. 1991), it is only relatively recently that progress has been made in identifying the respective enzymes. Many UGTs originally identified as conjugating natural products have also been shown to have activities with xenobiotic substrates. For example, arbutin synthase, a hydroquinone glucosylating UGT from Rauvolfia serpentia, was also shown to be have activity toward chlorinated phenols to produce the respective O-glucosides (Hefner et al. 2002). Two UGTs cloned from strawberry and three UGTs from tobacco which had similar broad substrate selectivities toward flavonoid and hydroxycoumarin natural products also
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conjugated the xenobiotics 1-naphthol and 2-naphthol (Griesser et al. 2008; Taguchi et al. 2003). In contrast only one UGT was able to N-glucosylate 3,4-dichloroaniline (DCA) when the respective activities were purified form Arabidopsis (Taguchi et al. 2005). The enzyme identified, UGT72B1, was then cloned and expressed and shown to be a bifunctional enzyme able to both N-glucosylate chloroanilines and O-conjugate chlorinated phenols (Loutre et al. 2003). Several other Arabidopsis UGTs were shown to O-glucosylate xenobiotics, and seven recombinant UGTs which had been shown to have activities toward natural phenolics were also able to glycosylate 2,4,5-trichlorophenol (TCP). A more extensive screen involving all 107 of the family 1 UGTs from Arabidopsis was subsequently carried out using representative O-GT, N-GT and S-GT chlorinated substrates to probe acceptor specificity. When expressed as GST-UGTs fusions in E. coli, 105 out of the 107 UGTs were found to be soluble. Of these, 44 were able conjugate TCP, six conjugated 4-chlorothiophenol but only UGT72B1 showed significant activity toward the N-GT substrate, DCA (Brazier-Hicks et al. 2007b). UGT activities are normally considered to be constitutively expressed, but recently it has also become apparent that their activities can be induced by treatment with xenobiotics, notably with herbicide safeners. In wheat, maize and Arabidopsis UGT activities toward TCP and DCA were enhanced up to 10-fold as compared to the control when treated with a range of herbicide safeners (Edwards et al. 2005b). In tobacco cultures, the transcript levels of three UGTs, NtGT1a, NtGT1b and NtGT3, which each showed conjugating activity toward naphthols were induced on treatment with naphthols, or salicylic acid (Taguchi et al. 2003). Four benzoxazolin-2(3H)-one inducible UGTs from Arabidopsis were also tested for their response to seven known inducers of xenobiotic metabolism including herbicide safeners (fenclorim and benoxacor) using real-time PCR. All four UGT transcript levels were found to be induced by the safeners and TCP, with UGT73B4 expression found to be unusually inducible (Baerson et al. 2005). Thus, UGT73B4 transcript levels were induced 100-fold following an exposure to fenclorim and TCP and 800-fold following a treatment with benoxacor. Interestingly, while all four Arabidopsis UGTs are able to glucosylate TCP (Brazier-Hicks et al. 2007b), glycosylation has not been observed in the metabolism of either fenclorim or benoxacor in Arabidopsis cell cultures (Brazier-Hicks et al. 2008; Edwards et al. 2005b). Although UGTs have the potential to conjugate a range of commonly encountered pollutants, these enzymes have received only limited attention with respect to their utility in engineering plants for applications in phytoremediation. The constitutive over-expression of UGT73C5 in Arabidopsis was demonstrated to be an effective method for enhancing detoxification of the Fusarium mycotoxin deoxynivalenol (Poppenberger et al. 2003). However, the attempt to use the Arabidopsis bifunctional N-GT and O-GT enzyme, UGT72B1 to detoxify synthetic xenobiotics produced mixed results. UGT72B1 was constitutively over- expressed in Arabidopsis leading to an increase in conjugating activity toward TCP and DCA in crude protein extracts (Brazier-Hicks et al. 2007b). Phytotoxicity studies were then performed using seeds germinated on agar containing TCP and
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DCA to ascertain whether the over-expression of UGT72B1 in Arabidopsis was an effective method of detoxifying pollutants. The over-expresser seedlings showed a similar level of susceptibility to controls when grown on TCP, probably as a consequence of the large number of other UGTs active in conjugating this xenobiotic (Brazier-Hicks et al. 2008). Unexpectedly, when grown on DCA, overexpression of UGT72B1 resulted in increased chemical injury to the seedlings as compared to control plants, with the corresponding T-DNA knockouts of UGT72B1 showed enhance tolerance. Further investigation found that in the absence of UGT72B1, DCA was incorporated into the lignin fraction of the cell wall presumably by the action of peroxidases, suggesting that bound residue formation was a more effective route of detoxification than glycosylation (Brazier-Hicks et al. 2007a). In a further example of UGTs causing the biological activation of xenobiotics, an unusual example of glycoactivation of a xenobiotic was discovered via a chemical genetic screen looking for growth inhibitors of etiolated hypocotyls in Arabidopsis. Multiple Arabidopsis accessions were screened for their natural variation in response to treatment with a library of bioactive compounds. This resulted in the identification of 12 accession selective molecules, with one compound, a cell expansion inhibitor called hydrostatin subjected to further analysis. UGT71B2 was identified as the cause of hydrostatin sensitivity, with hydrostatin resistant accessions found to contain mutations in the coding sequence of the respective enzyme (Zhao et al. 2007).
Phase 2 Enzymes – Malonyltransferases The conjugation of small molecules with malonic acid in the cytoplasm provides a signal for the export of the resulting acidic conjugates into the vacuole and has been shown to be an important biotransformation step in the detoxification of several xenobiotics (Cole 2000). The malonyltransferases (MTs) responsible use malonyl CoA as the donor species and can act on a range of natural product and xenobiotic acceptors. In the case of glucosides, MTs malonylate the acceptors in the 6″-Oposition of the glucose moiety. In addition, the amino groups of anilines and S-cysteinylated conjugates can also be malonylated. For example DCA is rapidly N-malonylated in soybean cell cultures with the resulting conjugates accumulating both in the vacuoles and in the cell medium (Lao et al. 2003). In tobacco, the enzyme responsible for the malonylation of glucosylated naphthols has recently been identified as a BADH acyltransferase. This MT was also able to malonylate glucosidic conjugates of flavonols and shares sequence similarity with other glucosylflavonoid BADH acyltransferases (Taguchi et al. 2005). Like the UGTs, MTs are also induced in response to xenobiotic exposure with the transcript levels of a closely related BADH acyltransferase from Arabidopsis enhanced on treatment with herbicide safeners (Baerson et al. 2005). The MTs acting on amino functions are yet to be characterized. However, the corresponding enzyme activities have been identified in Arabidopsis as contributing to the extensive biotransformation of
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the herbicide safener fenclorim, which was found to be metabolised in Arabidopsis cell cultures to the cysteinylated conjugate before undergoing N-malonylation of the cysteine moiety (Brazier-Hicks et al. 2008).
Phase 3 Transport Processes – ABC Transporter Proteins ATP-binding cassette (ABC) protein-mediated transporters are composed of 6 trans-membrane spanning peptide chains forming a barrel like structure, with two ATP binding motifs located on the ligand loading face to direct energised export through the channel. The ABC proteins use MgATP to drive the transport of ligands, with this process being characteristically insensitive to the transmembrane H+ electrochemical potential, but strongly inhibited by vanadate ions (Rea 2007). Like the other major classes of genes encoding detoxification proteins, the ABC transporter family is large consisting of 120 coding sequences in Arabidopsis and 121 in rice (Sánchez-Fernández et al. 2001). The ABC proteins are arranged in 13 subfamilies based on protein size, orientation, the presence or absence of idiotypic trans-membrane and/or linker domains and sequence similarity, with almost all of these groups represented in plants (Sánchez-Fernández et al. 2001; Rea 2007). Using the unified nomenclature adopted in 2008 (Verrier et al. 2008), the ABC subfamily C, is responsible for the transport of glutathionylated xenobiotics into vacuoles (Rea 2007). In common with the GSTs, these ABCC proteins show similar inducible regulation when plants are exposed to abiotic stresses (Klein et al. 2006). Despite the identification of up to 15 ABCC genes in Arabidopsis, only five proteins (AtABCC1-5) have been analysed with respect to their potential substrates (Klein et al. 2006). In the case of AtABCC1 and AtABCC2 (formerly called AtMRP1 and AtMRP2 respectively), the two transporters have been localised to the vacuolar membrane (Geisler et al. 2004; Liu et al. 2001). It is clear that these transporters have evolved to fulfil transport roles extending well beyond those identified in early studies where they were shown to be involved in the vacuolar sequestration of model GSH conjugated xenobiotics (Martinoia et al. 1993). These ABC transporters are now known to function with other substrates including flavonoid glucuronide conjugates, linear tetrapyrrole catabolites and folate derivatives (reviewed by (Rea 2007)). Interestingly, in the case of the Arabidopsis AtABCC2, differing substrates did not behave competitively with one another in inhibiting the transporter, but rather showed some reciprocal activation, suggesting spatially distinct binding sites for each class of ligand (Liu et al. 2001). Recent studies have shown that AtABCC1 which is known to be involved in the sequestration of xenobiotics translocates folates as its natural ligands (Raichaudhuri et al. 2009). It is therefore clear that as with other xenome enzymes, the ABC proteins involved in xenobiotic sequestration have been effectively hijacked from performing roles in vitamin and secondary metabolite processing. Relatively few studies have attempted to quantify the relative importance of ABC proteins in xenobiotic detoxification, which is in marked contrast to studies carried out in microbes where these
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transporters have long been associated with extrusion and resistance to antibiotics and other toxins (Rea 2007). In one rare study the importance of these transporters in xenobiotic detoxification has been clearly demonstrated by showing that transgenic Arabidopsis over-expressing an ABC protein showed an enhanced tolerance to multiple herbicides (Windsor et al. 2003).
Phase 4 – Further Processing of Xenobiotics The central dogma of xenobiotic metabolism in plants is that following the conjugation of xenobiotics they are ‘recognised’ by ABC transporters and subsequently compartmentalised in the vacuole (Van Eerd et al. 2009). It has been known for some time that the storage of these conjugates does not represent the end point of metabolism but that rather these polar derivatives can undergo a complex set of further processing reactions both in the vacuole and in the cytoplasm (Lamoureux and Rusness 1993). In several cases these intermediates of further processing are mistaken for natural products and subsequently become polymerized into macromolecules such as wall bound polysaccharides, lignin and proteins (Ertunç et al. 2004). The best studied routes of xenobiotic conjugate catabolism relate to glutathionylated pesticides (Brazier-Hicks et al. 2008). Initially, studies with the herbicide alachlor in barley showed that following import into the vacuole, the res pective glutathione conjugates were acted on by a carboxypeptidase to produce g-Glu-Cys alachlor conjugates (Wolf et al. 1996). More recent studies have suggested that the degradation of glutathione conjugates may actually occur by different routes and in several compartments (Blum et al. 2007). An analysis of several recent xenobiotic metabolism studies demonstrates that the order of processing events may be species or substrate specific, as highlighted in v. 2. In Arabidopsis, the glutathione conjugate of the fluorescent xenobiotic bimane is processed in the vacuole to the cysteine conjugate, with no accumulation of intermediates. This implies the first catabolic step is rate limiting, however the order of processing inferred from reverse genetic studies suggests processing of the g-glu moiety precedes the removal of the glycine moiety in the vacuole (Grzam et al. 2007; Ohkama-Ohtsu et al. 2007). Metabolism studies with the safener fenclorim in Arabidopsis and rice cell cultures suggested the glutathione conjugate was first metabolised to the g-glutamylcysteinyl conjugate prior to the cleavage of the g-glutamyl moiety to produce the fenclorim-cysteine conjugate (Brazier-Hicks et al. 2008). A similar route of catabolism of the glutathione conjugate derived from the metabolism of the AOPP herbicide fenoxaprop was also observed in wheat and the competing weed black grass, with the g-glutamylcysteinyl conjugate accumulating as a major metabolite (Cummins et al. 2009). Unlike the studies with the bimane derivatives, the sub-cellular localization of these reactions with the conjugates of the herbicide and safener were not determined. Studies in Arabidopsis have identified some of the individual gene products involved in xenobiotic conjugate metabolism and also revealed the potential
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GST
R-SG GS
(R - SG ABC Cytosol
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Fig. 2 Schematic of alternative glutathione conjugate degradation pathways observed in the cytosol and the vacuole in differing plant species. The vacuolar pathway can proceed via proteolytic processing from either the amino- or carboxy-terminus following import mediated by an ABC transporter. The cytosolic pathway proceeds via initial degradation from the carboxy-terminus
e xistence of a cytosolic as well as vacuolar conjugate processing pathway. Reverse genetics and reporter gene studies demonstrate that the g-glutamyl transpeptidase GGT4 (At4g29210) is located in the vacuole where it catalyses the metabolism of S-bimane-glutathione to S-bimane-cysteinylglycine, which does not accumulate as this is the rate limiting step of the two step process (Fig. 2). This intermediate is in turn converted to S-bimane-cysteine (Grzam et al. 2007; Martin et al. 2007; Ohkama-Ohtsu et al. 2007). The order of vacuolar processing starting at the N-terminus as determined in Arabidopsis is the reverse of that observed in barley, where hydrolysis proceeds from the C-terminus (Wolf et al. 1996). The vacuolar dipeptidase proposed to process the S-bimanecysteinylglycine product formed by GGT4 has not been identified in Arabidopsis.
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However similar bimane conjugate metabolism studies have revealed the potential existence of a cytosolic route of metabolism employing the carboxypeptidase activity of the enzyme phytochelatin synthase (Beck et al. 2003; Blum et al. 2007). Phytochelatin synthase (PCS) had previously been identified through its ability to help protect plants from toxic cadmium and arsenate ions, by synthesising the metal-chelating peptide phytochelatin from GSH (Grill et al. 1989). It had been known that this enzyme is constitutively expressed in an apparently ubiquitous manner in higher plants, despite the lack of these toxic metal ions in most environments, leading to suggestions for further roles (reviewed by Clemens and Peršoh 2009). Intriguingly, the conversion of the glutathione conjugate of bimane to the gamma-glutamyl-cysteinyl conjugate of bimane catalysed by the Arabidopsis AtPCS1 is strictly dependent on the presence of metal ions (Beck et al. 2003). In AtPCS1 knockout lines the production of the glutamylcysteinyl conjugate is significantly reduced, with the concomitant increase in the S-bimane-cysteinylglycine (Blum et al. 2007). These studies suggest the flux directed though each route of degradation of S-glutathionylated xenobiotics (Fig. 2), is dependent on both cell type and the relative availability of the conjugate. Very recent studies are now suggesting that dichotomous GSH conjugate processing pathways also exist in yeast (Wünschmann et al. 2010) giving further credibility to alternative routes of catabolism existing in plants. It is therefore of interest to speculate if the recently elucidated g-glutamyl transpeptidase independent pathway of glutathione catabolism to glutamate via 5-oxoproline mediated by g-glutamyl cyclotransferase and 5-oxoprolinase in Arabidopsis (Ohkama-Ohtsu et al. 2008) may also occur on glutathione conjugates. In addition to being processed to highly polar conjugates, phase 4 reactions can also result in the incorporation of xenobiotics into natural products. Relatively few xenobiotics can be completely metabolised (mineralized) by plants, and as secretion is not a viable option in most cases, at least from aerial tissues, long term storage of these synthetic residues occurs. While such storage can include the compartmentalisation of soluble polar residues in the vacuole, a common route for xenobiotic detoxification is the incorporation of degradation intermediates into bound residues. Such residues are typically polysaccharide or polyphenolic biomolecules located in the cell wall, or more occasionally proteins or lipids (Ertunç et al. 2004). While such residue formation is a potentially major sink of pollutants and pesticides, with the exception of the peroxidative incorporation of chloroanilines into lignin (Brazier-Hicks et al. 2007b), we know remarkably little about the associated biochemical processes or their toxicological significance.
Up-Regulation of the Xenome and Xenobiotic Resistance By piecing together the collective literature it is now clear that most of the metabolic component of the xenome is up-regulated in response to exposure to foreign compounds (Fig. 1). In the case of crop protection, a useful consequence of this
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induction has resulted in the commercialization of safeners, which are compounds which enhance the metabolism of herbicides in cereal crops and hence improve selectivity margins. Safener chemistry is very diverse, with specific compounds used in partnership with herbicides for selective applications in cereal crops (Davies and Caseley 1999). Despite many years of investigation, similar safener-mediated protection of broad-leaf crops against herbicides has yet to be convincingly demonstrated (Hatzios and Burgos 2004). By combining transcript induction and proteomics studies, the safening of xenome proteins in cereals such as wheat can be seen to extend to CYPs, GSTs, UGTs and ABC transporters (Theodoulou et al. 2003; Zhang et al. 2007; Cummins et al. 2009). Originally, it was inferred that safeners only induce detoxifying enzymes in large grained cereals (Davies and Caseley 1999). However, recent studies have shown that safeners also induce xenome enzymes, notably GSTs, in wild grasses (Del Buono et al. 2007), including problem weed species (Cummins et al. 2009). Various theories have been expounded as to the mode of action of safeners, with a respective binding protein apparently isolated from maize (Scott-Craig et al. 1998). However, it now seems that the receptor identified is probably an artefact and certainly the diversity of safener chemistries would suggest that a general recognition system is required, resembling the xenobiotic sensing system in mammalian cells (Edwards et al. 2005a). While safeners may not provide enhanced herbicide tolerance in dicotyledenous plants, that is not to say these compounds cannot induce their detoxifying enzymes. Thus, a large number of xenome genes have been reported to be induced in both poplar (Tanaka et al. 2007) and Arabidopsis (Baerson et al. 2005) following safener application. In the case of Arabidopsis, these studies have been extended to studying the effect of safeners on the expression of specific GSTs, notably AtGSTU19 (DeRidder and Goldsbrough 2006). Intriguingly, the safener induction pathways in cereals and dicots appear to be conserved, with a safener-responsive promoter from a maize lambda class GST being similarly responsive to these chemicals when driving the expression of a reported gene in transformed Arabidopsis plants (De Veylder et al. 1997). As detailed earlier, the induction of these GSTs in turn enhances the metabolism of herbicides (Edwards et al. 2005a) and safeners in Arabidopsis plants and cultures (Brazier-Hicks et al. 2008). In another example of the induced plant xenome, the phenomenon of multiple herbicide resistance (MHR), whereby plants, most commonly wild grasses are resistant to differing classes of herbicides, has proven to be a major problem in controlling weeds in cereal crops. MHR weeds are typically associated with up-regulated xenome enzymes, notably CYPs, GSTs and UGTs (Hall et al. 1997; Menendez and Prado 1996; Yuan et al. 2007). The effect of these up regulated activities is that the MHR plants can metabolize herbicides far more efficiently that wild type plants and become resistant to unrelated classes of graminicidal herbicides though their enhanced capacity to detoxify these compounds (Yuan et al. 2007). MHR has been best studied in Lolium spp. and in Alopecurus myosuroides (Yuan et al. 2007). In Lolium, MHR is best associated with an increased expression of CYPs, with resistance effectively reduced by using P450
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inhibitors applied in conjunction with herbicides such as chlortoluron (Hall et al. 1997). Similarly, the use of CYP inhibitors was also found to synergise graminicides in MHR lines of Lolium rigidum with differing inhibitors found to synergise different herbicides, suggesting, that multiple P450 enzymes were involved in MHR in these plants (Preston et al. 1995). In contrast, CYP inhibitors did not counteract resistance to herbicides, such as fenoxaprop ethyl, which are not detoxified by oxidation. Instead, resistance to fenoxaprop appears to be associated with an increased expression of GSTs, with this phenomenon best studies in A. myosuroides (Cummins et al. 2009). These studies also demonstrated that in addition to the GSTs the associated catabolic pathways of the glutathionylated herbicide conjugates were also subtly modified in the MHR weeds as compared with controls. These changes were shown to be very similar to those seen in A. myosuroides plants treated with herbicide safeners, suggesting a functional link between the changes in xenome enzymes determined in MHR and those observed due to safening.
Conclusion This review of the recent literature shows that we now have a much better understanding of the functional genomics of the major xenome enzymes than was the case 10 years ago. In all of these advances, the studies on Arabidopsis plants have proved particularly informative and brought the xenobiochemistry in plants onto a par with our knowledge of other branches of plant metabolism. Clearly, there is still a great deal of work to be done. The functional roles of the members of the CYP super-family in xenobiotic metabolism are only just beginning to be elucidated. Similarly the ABC proteins remain largely under studied with respect to their roles in herbicide detoxification and phytoremediation. One area which is deserving of much greater investment is the regulation of the xenome by chemicals and by acquired genetic factors such as MHR. In addition to the obvious value of such work in developing new strategies for manipulating herbicide selectivity and counteracting resistance, such perturbed systems offer a valuable model of how changes in the xenome can result in broad-ranging tolerance to xenobiotics. As such studying the changes in the xenome associated with enhanced herbicide metabolism and resistance will help identify genes which will be functionally useful in engineering plants for roles in phytoremediation in the future.
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Using Plants to Remove Foreign Compounds from Contaminated Water and Soil Jean-Paul Schwitzguébel, Valérie Page, Susete Martins-Dias, Luísa C. Davies, Galina Vasilyeva, and Elena Strijakova
Abstract Depending on the physico-chemical properties of the organic pollutant to be removed or detoxified, as well as on the specific plant physiology and biochemistry, different phytotreatments are available to decontaminate water and soils. For example, aquatic macrophytes or even terrestrial plants can be grown under hydroponic conditions or in constructed wetlands to remove many xenobiotic compounds, e.g. sulphonated anthraquinones and azo dyes present in wastewater from the dye and textile industries. Recent advances have also been made to remediate soils contaminated with hydrophobic compounds like PCBs, highlighting the roles of both plants and rhizospheric microorganisms, and the importance of their interactions. Sequestration of PCB by activated carbon or other adsorbents can be used to improve the phytoremediation of real highly contaminated soils. Activated carbon amendment in combination with mineral fertilizers has been shown to create favourable conditions for the development of soil microorganisms and plants. These examples aim to illustrate the potential of plants for the rhizofiltration, phytoaccumulation and phytodegradation of xenobiotics, as well as their ability to cooperate with bacteria (phytostimulation, rhizospheric interactions).
J.-P. Schwitzguébel (*) and V. Page Laboratory for Environmental Biotechnology (LBE), Swiss Federal Institute of Technology Lausanne (EPFL), Station 6, CH 1015 Lausanne, Switzerland e-mail:
[email protected] S. Martins-Dias and L.C. Davies IBB – Institute for Biotechnology and Bioengineering, Centre for Chemical and Biological Engineering, Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal G. Vasilyeva and E. Strijakova Institute of Physicochemical and Biological Problems in Soil Science, RAS, Building 2, Institutskaja St, RU 142290 Pushchino, Moscow Region, Russia
P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_8, © Springer Science+Business Media B.V. 2011
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Introduction Phytoremediation of Organics Over the last century, the high growth of industrialization and the increasing use of numerous aromatic synthetic chemicals in dyestuffs, pesticides and pharmaceuticals have resulted in serious environmental contamination. Hundreds of thousands of sites across Europe are contaminated with foreign compounds due to industrial processes, accidents, spills, and inappropriate use (Schröder 2007). Depending on their bioavailability, bioaccumulation in the food chain and toxicity, many contaminants threaten ecosystems and human health and are thus considered as pollutants (Chapman 2007). Soil and water pollution is widespread in Europe and is dramatic in large parts of the developing world, especially in India and China (Krämer 2005). The physico-chemical remediation of polluted soils and waters is very costly and for many pollutants a feasible and efficient clean up technology is not yet available. Emerging phytoremediation technologies have been successfully applied at various scales to treat moderately hydrophobic pollutants, such as benzene, toluene, ethylbenzene and xylene (BTEX), aniline, nitrobenzene, chlorinated solvents, trinitrotoluene or ammunition wastes, chlorinated solvents as well as trace elements (Haberl et al. 2003; Barac et al. 2004; Schwitzguébel et al. 2009; Weyens et al. 2009). Phytoremediation of organic xenobiotics often relies on the interactions between plants and their associated microorganisms in a process where the plants are in contact with the pollutants that exist in the rhizosphere or are intentionally placed there. It has been reported that clusters of rhizosphere bacteria can be two to four orders of magnitude greater than populations in the surrounding bulk soils (Alkorta and Garbisu 2001). Degradation occurs by the action of soil and plant microorganisms (endophytic bacteria) and also by the action of plant enzymes, excreted into the rhizosphere or inside their own tissues after plant uptake (Pieper and Reineke 2000; Araujo et al. 2002; Barac et al. 2004). Initially, phytoremediation of organics has focused on plant metabolism of pesticides and herbicides, with the goal of understanding selectivity, mode of action and resistance to herbicides. The awareness that plants could detoxify herbicides can be tracked back to more than 40 years ago (Alkorta and Garbisu 2001). Generally it is observed that the disappearance of organics is accelerated in vegetated soils compared with surrounding non-vegetated bulk soils (Macek et al. 2000). This is also the case for the treatment of dye rich wastewaters using a pilot scale constructed wetland (CW), since the overall efficiency obtained in units planted with cocoyam and cattail is more than twice that of the unplanted units (Mbuligwe 2005).
Selection of Plants for Phytoremediation The use of plants to remediate soils and wastewaters can be classified according to where the detoxification takes place (in situ or ex situ) or according to the plant status (in vivo or in vitro). In phytoremediation, plants modify the physical and chemical properties of
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contaminated soils, releasing root exudates and oxygen directly into the rhizosphere. Plants also play a role in retarding the movement of xenobiotics within the soil, enhancing their microbial and plant enzymatic transformation (Susarla et al. 2002). Phytostabilisation is a current practice using plant species growing on contaminated sites able to immobilise pollutants in the rhizosphere zone, thus decreasing leaching within the soil (Krämer 2005). In this way the migration of xenobiotics to groundwater is avoided. An example may be willow trees (Salix spp.) that can release more than 200 l of water per day by evapotranspiration (Susarla et al. 2002), thus assisting water and pollutant containment. In situ phytotransformation and phytodegradation processes can occur by the plant enzymes inside roots and leaves (Araujo et al. 2002) or by extracellular enzymes excreted by the roots (Siciliano and Germida 1998; Alkorta and Garbisu 2001; Gianfreda and Rao 2004). An example of a plant used intensively in phytodegradation is Phragmites australis in both in vivo and in vitro trials. An example is the use in vertical flow (VF), CW treating an azo-dye contaminated wastewater (Davies et al. 2007). Phragmites australis (Cav.) Trin. ex Steud. is a common reed widespread in wetlands and one of the most studied plants (Engloner 2009). Uptake and degradation capacity of hydrophobic molecules, such as TNT, can be enhanced by solubilising agents like urea, as demonstrated for Chrysopogon (Vetiveria) zizanioides in hydroponic conditions (Makris et al. 2007). Vetiver has a highly developed root system, which grows very fast and produces large quantities of biomass. Phyto-rhizodegradation also comprises the enhancement of plant capacity by the associated rhizosphere microorganisms (bacteria and fungi) and endophytic bacteria (Weyens et al. 2009). Three plant species, millet (Pannicum milliaceum), Indian goosegrass (Eleusine indica) and tall fescue (Festuca arundinacea) have a remarkable remediation effect on soils contaminated by petroleum hydrocarbons, improving degradation rates up to four times compared with unplanted soils, mostly due to an increase of microorganisms in the rhizosphere by three orders of magnitude (Jing et al. 2008). Another example is given by Vangronsveld and van der Lelie (2003) who report that 3 years after planting 250 poplar trees a BTEX containing plume is cut off in the plantation site. Phytovolatilisation is the ability of plants to remove low molecular mass compounds from soil or water and release them to the atmosphere through leaves via evapotranspiration (Gerhardt et al. 2009; Weyens et al. 2009). Phytoremediation involves the combination of all above mentioned mechanisms. Plants that have a large root system will be advantageous for the uptake of organic compounds from the soil. Deep rooting species will be able to access pollutant plumes that have already moved to deeper zones of the soil while surface pollution might be easily controlled with shallow rooting plants (Schröder 2007), and plants with extensive leaf cover might enhance volatilization.
The Applicability of Phytoremediation The uptake of xenobiotics by plants depends on roots/water/soil interactions that can be assessed by different physico-chemical descriptors: water solubility, vapour
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pressure, Henry’s law constant, acidity constant, octanol-water partition coefficient (Kow), soil sorption coefficient (Koc) and the bioconcentration factor (BCF), which characterizes the magnification of pollutant concentration by the plant. Soil sorption coefficient or adsorption coefficient is a measure of the distribution of an organic chemical between the soil/sediment solids and liquid phases, and thus of the mobility retardation of a compound through diffusive and advective transport. The bioavailability of organic molecules can be assessed by the estimation of Koc partition coefficient (ratio between the concentration of the contaminant adsorbed by the soil normalized to its organic carbon content and the respective equilibrium concentration in water) (Allen 2002). Koc is frequently estimated based on octanol-water partition coefficient and water solubility. Hydrophobic organic compounds have the tendency to accumulate in soils and sediments because they adsorb strongly as a consequence of their high Koc values, thus limiting their availability to biodegradation; example compounds include polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dioxins, and polybrominated flame retardants. Nevertheless, Koc correlates well with hydrophobicity, measured by Kow, widely used to estimate biodegradability (Razzaque and Grathwohl 2008). The permeation from plant roots to xylem (translocation) is optimal for those compounds that are only slightly hydrophobic, as more hydrophobic compounds tend to bind with lipid membranes and suberin in the roots (Chaudhry et al. 2002). Organic xenobiotics with an octanol/water partition coefficient (log Kow) < 1 are considered to be very water-soluble and will only sparsely penetrate the lipid-containing root epidermis, thus roots do not generally accumulate them. Contaminants with a log Kow > 3.5 show high sorption to the roots, but slow or no translocation to stems and leaves. Nevertheless, plants readily take up organic xenobiotics with a log Kow between 0.5 and 3.5, as well as weak electrolytes (weak acids and bases or amphoteres as herbicides) (Barac et al. 2004). The metabolism of these pollutants may occur in the leaf and stem tissue; or they may be released into the atmosphere through leaf tissue or incorporated as bound residue inside the plant (Barac et al. 2004; Schröder 2007). When the pollutants make contact with the root surface, the plant uptake is diffusion driven for compounds with lipophilicity close to that of the respective plant root (which depends on the root epidermis). The root uptake and transport of organic xenobiotics can be evaluated by a root concentration factor (RCF) that is dependent on the log Kow; for example, for barley the following relationship has been proposed (Briggs et al. 1982; Schröder 2007):
Log (RCF − 0.82 ) = 0.77logK ow − 1.52 Phytoremediation of organic compounds can occur inside the plant or within the rhizosphere, as already mentioned. Many organic compounds such as solvents (trichloroethylene and ethylene dibromide) in groundwater, petroleum and aromatic compounds (pesticides, explosive compounds) in soils and volatile compounds in the air are removed by either process (Newman and Reynolds 2004).
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The ability of plants to remove pollutants from wastewater has been thoroughly studied: a wide range of plants has this aptitude and is systematically selected for integration in CW systems. The vast majority of effluents treated by CW are domestic wastewaters, using hybrid systems in order to achieve both nitrification and denitrification processes; nevertheless, for secondary and tertiary treatment stages, horizontal (HF) or VF can be used. The more recent research aims to address the phytoremediation of trace organic compounds, such as pharmaceuticals and personal care products, also called micropollutants (Matamoros and Bayona 2006). CW are also increasingly applied in the treatment of industrial wastewaters such as dye-rich wastewaters (Mbuligwe 2005; Bulc and Ojstrsek 2008). For example, in Portugal there are two full scale systems in operation: one for aromatic and nitroaromatic effluents [4 VF (2625 m2 × 0.8 m)] (Haberl et al. 2003), and the other for effluents from a municipal solid waste transfer station and a closed landfill [1 VF (100 m2 × 0.7 m) + 2 HF (150 m2 × 0.7 m) + 1 lagoon (100 m3)] (Silva et al. 2003). Other applications include the treatment of acid mine drainage (full scale in USA), with the particularity of using mushroom compost and fermway as bed matrix (Mitsch and Wise 1998), BTEX and benzene (full scale, USA) (Wallace and Kadlec 2005) and phenol from papermill wastewaters (pilot scale in Kenya) (Abira et al. 2005). Other major applications are for runoff water and groundwater contamination (Scholes et al. 1999; Haberl et al. 2003). The most common bed matrix materials used in CW are sand, gravel, stones, and clay soils. Another approach that does not use soil for plant growth is hydroponic systems, which can be used to remove e.g. sulphonated anthraquinones and MTBE (Aubert and Schwitzguébel 2004; Ma et al. 2004). In the first case, plant metabolism is highlighted, whereas phytovolatilization is the main process for the removal of MTBE.
Sulphonated Aromatic Compounds in Wastewater The total world colourant production is estimated to be between 800,000 and 1 million tons per year (Heinfling et al. 1998; Mendez-Paz et al. 2005). More than 10% of this amount is released into the environment, mostly via industrial effluents. A huge variety of synthetic dye chemicals is used for textile dyeing and other industrial applications. Dye and textile industry effluents thus exhibit high colour, high suspended solids and dissolved organics. Except for colour, the other components can be removed using chemical and physical methods (Benkli et al. 2005). Synthetic sulphonated anthraquinones are very important starting material to produce a large palette of dyes (Fig. 1), and this family of compounds has a potential and actual impact on the environment. Not only sulphonated anthraquinones, but also many other sulphonated aromatic compounds are released into the environment, mainly via industrial wastewaters. For example, benzene sulphonate and p-toluene sulphonate are used as intermediates in the manufacture of optical brighteners, pickling agents, dyestuffs, tanning agents,
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Fig. 1 Chemical structure of anthraquinone and sulphonated derivatives; IUPAC names of the compounds: AQ-1-S: 9,10-dioxo-9,10-dihydro-1-anthracenesulphonic acid; AQ-2-S: 9,10-dioxo-9, 10-dihydro-2-anthracenesulphonic acid; AQ-1,8-SS: 10-dioxo-9,10-dihydro-1,8-anthracenedisulphonic acid; AQ-1,5-SS: 10-dioxo-9,10-dihydro-1,5-anthracenedisulphonic acid; AQ-2,6-SS: 10-dioxo-9, 10-dihydro-2,6-anthracenedisulphonic acid
insecticides, surfactants, antioxidants, wetting agents, and many other products. They are also used as acidic catalysts and standardizing agents in dyestuff manufacture. As with naphthalene sulphonic acids, they are important precursors for dye intermediates, wetting agents and dispersants.
Limits of Microbial Degradability Since they contain at least one sulphonate group and often also varying substitutions such as nitro groups, these foreign compounds are not uniformly susceptible to bacterial decolourisation and degradation in conventional wastewater treatment plants. The organosulphonate group plays an important role not only in altering the solubility and dispersion properties of the xenobiotic molecule, but also in increasing recalcitrance to microbial breakdown, because of the thermodynamically stable carbon-sulphur bond (Cook et al. 1999; Nigam et al. 2000). Effluents from detergent, dye and textile industry are thus often contaminated with sulphonated aromatic compounds. These loads are major sources of sulphur-organic pollutants to the environment, especially fresh water (Greim et al. 1994; Schwitzguébel et al. 2002). It has also been reported that benzene- and naphthalene-sulphonates are found in plumes and leachates from landfills (Riediker et al. 2000). Because dyes usually contain a wide variety of substituted sulphonated aromatic compounds, the substrate spectrum of many bacteria has thus been intensively investigated, for possible treatment of such industrial effluents. However, the microbial degradation of these pollutants often requires unusual catabolic activities rarely
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found in a single species (Cook et al. 1999; McMullan et al. 2001). An important step in the biodegradation appears to be catalyzed by dioxygenases, adding oxygen across the double bond bearing the sulphonate group, leading to its elimination. Special attention has thus been paid to the substrate specificity of dioxygenases, usually the rate-limiting step in the biodegradation of sulphonated aromatic compounds. Unfortunately, a rather limited substrate range has been observed for bacterial isolates and the accumulation of dead-end products often occurs. The decolourisation of several synthetic dyes including azo and anthraquinone derivatives has also been examined in white-rot fungal cultures, known to produce powerful ligninase and other peroxidases (POD) (Claus et al. 2002; Nyanhongo et al. 2002; Wesenberg et al. 2003; Vanhulle et al. 2007). However an inhibition occurs at rather low concentrations, depending on the individual dye structure. Because of the limited ability of microorganisms to degrade sulphonoaromatic compounds, conventional wastewater treatment plants and biofilters are usually ineffective in managing this significant class of pollutants.
Potential of Phytotreatment In this context, the development of alternative biological treatments to efficiently eliminate these pollutants from industrial effluents is needed (Vandevivere et al. 1998; Robinson et al. 2001; Schwitzguébel et al. 2002). More precisely, selected plant species could remove xenobiotics from wastewater, by the use of CW (Biddlestone et al. 1991; Davies and Cottingham 1994; Haberl et al. 2003; Ojstrsek et al. 2007), or hydroponic type treatment plants (Furukawa and Fujita 1993). Both options offer a potentially low cost, low maintenance biological method for wastewater treatment. Most of the systems currently in use have been designed to treat domestic wastewater, but have a great potential to treat industrial effluents containing recalcitrant organics such as priority pollutants and dyes (Davies and Cottingham 1994; Haberl et al. 2003; Ojstrsek et al. 2007). Anthraquinones naturally occur in several plant genera like Rheum, Rumex, Cinchona, Galium, Morinda and Rubia (Van der Plaas et al. 1998; Demirezer et al. 2001; Matsuda et al. 2001; Han et al. 2002; Morimoto et al. 2002). Furthermore, the biosynthetic pathways of natural anthraquinones, often glycosylated, have been recently unravelled and several enzymes involved in the process characterized (Khouri and Ibrahim 1987; Han et al. 2002). It has therefore been assumed that the hardy rhubarb (Rheum palmatum) might possess enzymes capable of transforming sulphonated anthraquinones and could be harnessed to treat wastewater from the dye, textile and detergent industries. As a first step, cells have been isolated from rhubarb and grown in bioreactors in the presence of anthraquinones with sulphonate groups in different positions (Fig. 1): AQ-1-S totally disappears from the medium and a phytotransformation occurs, but not desulphonation; AQ-2-S is partially but rapidly taken up by rhubarb cells and desulphonated; AQ-1,5-SS and AQ-1,8-SS rapidly disappear from the medium, and no intermediates are released (Schwitzguébel et al. 2002; Schwitzguébel and Vanek 2003).
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The ability of rhubarb cells to accumulate, transform and/or degrade other sulphonated aromatic compounds, all being by-products and pollutants present in industrial effluents has also been investigated: 2-hydroxy-4-sulpho1-naphthalenediazonium is completely removed and transformed; 2-hydroxy-4 -sulpho-6-nitro-1-naphthalenediazonium is accumulated and desulphonated; 7-nitro-1,3-naphthalene-disulphonic acid, 7-amino-1,3-naphthalenedisulphonic acid and 2-chloro-5-nitro benzene sulphonic acid are taken up and transformed by rhubarb cells (Duc et al 1999; Schwitzguébel et al. 2002; Schwitzguébel and Vanek 2003). Even if plant cell cultivation in shake flasks or in bioreactors is a useful and valuable tool for a first assessment of the ability of a plant species to deal with a pollutant, such systems are too expensive and too fragile for large scale wastewater treatment. The use of whole plants cultivated under hydroponic conditions or in CW is thus required. Plant species or cultivars used in any phytoremediation or rhizofiltration process must be tolerant to the pollutants to be treated. For optimal applications, the capability of whole plants to germinate, grow, and develop in the presence of amounts comparable to those found in industrial effluents must be investigated. Therefore, different species have been screened against several sulphonated aromatic pollutants of concern. Not only rhubarb (Fig. 2), but also other plants producing anthraquinones have been grown under hydroponic conditions, like Rumex hydrolapatum and R. acetosa.
Fig. 2 Rheum palmatum and Rumex hydrolapatum cultivated under hydroponic conditions in a greenhouse
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Table 1 Removal of various sulphonated anthraquinones by rhubarb (Valentine cultivar) and by maize cultivated under hydroponic conditions. The percentage of each sulphonated anthraquinone removed was measured and the transpiration stream concentration factor (TSCF) estimated 7 days after the simultaneous addition of 500 mmol l−1 of each sulphonated anthraquinone in the liquid medium Sulphonated Rhubarb Maize anthraquinone (% removal) Max. TSCF (% removal) Max. TSCF AQ-1-S 55 2.3 23 0.4 AQ-2-S 55 1.3 26 0.5 AQ-1,5-SS 45 0.9 19 0.4 AQ-1,8-SS 45 0.8 20 0.3 AQ-2,6-SS 48 2.4 21 0.4
As a comparison, plants not producing anthraquinones, like maize (Zea mays), rape (Brassica napus) and celery (Apium graveolens) have also been grown in the presence of different sulphonated anthraquinones. The most efficient plant to remove these xenobiotic compounds is the Valentine cultivar of rhubarb (Aubert and Schwitzguébel 2002, 2004; Schwitzguébel et al. 2008). Whereas rhubarb was able to remove 45–55% of all sulphonated anthraquinones added simultaneously to the cultivation medium within 7 days, maize could remove only 19–26% of these xenobiotics (Table 1). However, the disappearance of a pollutant from the medium does not mean automatically that it is accumulated by the plant. Therefore, the transpiration stream concentration factor (TSCF) was estimated for both plants and was found to be between 0.8 and 2.4 for rhubarb and 0.3–0.5 only for maize, suggesting a significant accumulation in the former plant species and cultivar (Table 1). The next step was to investigate any possible metabolism and degradation by the plant and its different organs. As measured by capillary electrophoresis, several sulphonated anthraquinones were found in leaves of rhubarb and R. hydrolapatum (Aubert and Schwitzguébel 2002). On the other hand, results obtained with different and complementary approaches suggest that apoplasmic storage plays a significant role in the phytoaccumulation of at least AQ-1,5-SS (Schwitzguébel et al. 2008). All these features indicate that these xenobiotics are taken up from the medium and translocated to the leaves. As compared to leaf extracts from plants cultivated in the absence of sulphonated anthraquinones, new metabolites appeared in leaf extracts from plants cultivated in the presence of these xenobiotics, suggesting that at least some of them were transformed by both plant species. Furthermore, the profile of metabolites produced depends on the plant used, highlighting the importance of a careful screening of plant species, ecotypes or cultivars before any application to phytoremediation. As shown in Fig. 3, the detoxification of xenobiotics in plant cells can follow three different pathways: vacuolar compartmentation without metabolism: conjugation to glutathione catalysed by glutathione-S-transferases, followed by accumulation of the conjugate in the vacuole (Coleman et al. 1997); transformation (hydroxylation) by microsomal cytochrome P450 monooxygenases (Gordeziani et al. 1999; Stiborova et al. 2000; Werck-Reichhart et al. 2000; Morant et al. 2003; Isin and Guengerich 2007),
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Fig. 3 Classical detoxification pathways of xenobiotics in plant cells
followed by glycosylation catalysed by glycosyltransferases (Pflugmacher and Sandermann 1998; Jones and Vogt 2001; Lim et al. 2002; Messner et al. 2003: Taguchi et al. 2003), then by accumulation either in the vacuole or in the cell wall as bound residues (Coleman et al. 1997). All pathways have been explored for sulphonated anthraquinones. It appears that direct accumulation in the vacuole or conjugation to glutathione play only a minor role, if any, in the detoxification of sulphonated anthraquinones (Schwitzguébel et al. 2008). In contrast, cytochromes P450 monooxygenases are able to accept different sulphonated anthraquinones as substrates (Page and Schwitzguébel 2009a and b). It has been reported that the transformation of hydroxy-9,10-anthraquinones is mediated by POD isolated from horseradish and from Senna angustifolia (Arrieta-Baez et al. 2002); the possible involvement of POD in the metabolism of sulphonated anthraquinones by anthraquinone-producing plants remains however unknown. On the other hand, natural anthraquinones are usually glycosylated (Khouri and Ibrahim 1987; Van der Plaas et al. 1998). Anthraquinone-producing plants could also glycosylate transformed and desulphonated synthetic anthraquinones. In such a case, there would be cross talks between secondary metabolism and detoxification mechanisms of foreign compounds (Singer et al. 2003; Wink 2003). Finally, the activity of enzymes like cytochrome P450 or glutathione transferase should have some effects on the maintenance of the plant cell redox homeostasis. The regulation of the redox status is of utmost importance for the plant and is closely related to the mitochondrial processes, also involved in maintaining the energy status. Redox and energy balance are important regulatory parameters in determining the relative flux of metabolites through the anabolic and catabolic pathways. Environmental factors, like xenobiotic compounds and the need for the plant to detoxify them if present in different organs, will undoubtedly disturb the whole redox and energy balance. For the efficiency of any phytoremediation
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process it is thus important to determine the critical threshold above which the efficiency of the process declines and the plant will suffer from stress conditions. In addition to cytochromes P450 in phase I are POD (Burken 2003), heme containing monomeric glycoproteins widely distributed in nature and easily extracted from most plant cells (Azevedo et al. 2003). Plant POD are mainly located in the cell walls and vacuoles and are associated with non-specific oxidative polymerisation of phenolic compounds in the cell wall to produce suberin and lignin, contributing to the improvement of the mechanical defence in plant tissues (Pflugmacher et al. 1999; Sandermann 2004). POD are involved in the general plant defence mechanism scavenging reactive oxygen species (ROS) formed during several stress conditions (wounding, exposure to sulphur compounds, xenobiotics or heavy metals) beyond those produced during normal plant metabolic activity (Passardi et al. 2005). They use H2O2 or O2 as the oxidant to catalyse a number of oxidative reactions, using a wide variety of organic and inorganic compounds (Veitch 2004). Plant contact with anthropogenic xenobiotics is directly related to plant endogenous capacity to produce ROS by the adjustments of photosystems I and II in chloroplast thylakoids, thus ROS such as superoxide ion (O2−), hydrogen peroxide (H2O2), hydroxyl radical (OH−) and singlet oxygen (1O2) are produced (Asada 2006). Under optimal growth conditions, the ROS production in cells is estimated at a constant rate of 240 mMs−1 O2−, and a steady state level of 0.5 mM H2O2. Under biotic and abiotic stress conditions cells enhance the production of ROS up to 720 mMs−1 O2− and 5–15 mM H2O2 (Mittler 2002). The imbalance between the plants capability of production and elimination of ROS leads to oxidative plant cell damage by inactivation of biomolecules (e.g. DNA mutation) or by initiation of chain reactions (lipid auto-oxidation), which can lead to tissue necrosis, senescence/ ageing processes and cell death (Kochhar and Kochhar 2005). ROS over-production signals to the plants the need to eliminate the xenobiotic from its tissues. Plant detoxification pathways comprise several metabolic functions, known as the “green-liver model” (Fig. 3) and the activation of antioxidant enzymes (Fig. 4). Superoxide dismutase (SOD) is a first line defence enzyme that converts ROS formed by the presence of the pollutant into H2O2 that will be converted into water and oxygen by the action of POD, catalase (CAT) and ascorbate peroxidase (APX). Thus, the balance between these enzymes is very important as H2O2 can easily cross biological membranes. If H2O2 enters the cell cytoplasm and reaches the nucleus it reacts with intracellular metal ions (e.g. Fenton reaction) to give OH− that is responsible for DNA site-specific attack (Mittler and Zilinskas 2003) and the initiation membrane self-perpetuating lipid peroxidation (Havaux 2003). The ascorbate-glutathione cycle (Fig. 4b) is important and has two main functions in plant cells: H2O2 detoxification and glutathione (GSH) regeneration (Asada 2006) allowing the conjugation with the pollutant and therefore its enzymatic degradation or transformation to less toxic compounds (Fig. 3), in order to sequestrate them in plant cell vacuoles or to bind them to insoluble cellular structures by covalent associations with macromolecules in the cell wall (lignin, hemicelluloses, protein, cellulose or pectin) or exudation of the conjugates to the rhizosphere (Coleman et al. 1997; Susarla et al. 2002; Harvey et al. 2002; Schröder et al. 2007).
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Fig. 4 Main cellular pathways for elimination of ROS in plants. (a) Water-water cycle in chloroplasts. (b) Ascorbate-glutathione cycle in the stroma, cytosol, mitochondria and apoplast. (c) Glutathione peroxidase (GPX) and its glutathione (GSH) regenerating cycle in the cytosol. (d) Catalase (CAT) in the peroxisomes and POD in the cell wall, cytosol and vacuoles. SOD – Superoxide dismutase; tAPX – thylakoid-bound ascorbate peroxidase; ASC – Ascorbate; GSH and GSSG – Reduced and oxidized glutathione; DHA – Dehydroascorbate; DHAR – Dehydroascorbate reductase; MDA – Monodehydroascorbate; MDAR – Monodehydroascorbate reductase; GR – glutathione reductase; PSI – photosystem I; Fd – Ferredoxin; e− – electron
Azo Dyes in Industrial Effluents Azo dyes production is estimated to be 60–70% of the annual colourant world production and about 10% of these are lost to domestic and industrial wastewaters (Bandara et al. 1996; Kim and Shoda 1999; Coughlin et al. 2002; Scheeren et al. 2002; Ahlström et al. 2005; Franciscon et al. 2009). The most important azo dye precursors are phenol, anhydride phthalic, aniline, nitrobenzene, salicylic acid, p-nitroaniline, p-chloronitrobenzene, b-naphthol and dimethylaniline (Gregory 1994). Usually they are being produced far from dye application sites and so their contribution to dyestuff environmental pollution is not considered.
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Azo dyes have been extensively used in textile (cotton dye), printing, leather and cosmetics industries. Textile effluents poses a threaten to environment wherever this industry is located due to its high salinity, dyes content, which have high water solubility and low degradability: high colour (3,000–4,500 ADMI units), high COD (800–1,600 mg l−1), alkaline pH (9–11) and high total solids content (6,000–7,000 mg l−1) (Manu and Chaudhari 2002). The presence of very small amounts of dyes in water (less than 1 ppm for some dyes) is highly visible and affects the aesthetic water transparency and gas solubility in lakes, rivers and other water bodies (Banat et al. 1996; Sanroman et al. 2004). Azo dyes pass through municipal wastewater treatment plants nearly unchanged due to their resistance to aerobic degradation (Méndez-Paz et al. 2005; Hailei et al. 2009). Azo dyes have one or more azo bonds (-N=N-) and the structural diversity of dyes derives from the use of different chromophoric groups (e.g. azo, anthraquinone, triarylmethane and phthalocyanine groups) and different application technologies (e.g. reactive, direct, disperse and vat dyeing). On the reactive dyes (azo dyes and anthraquinones), the reactive site reacts with the functional group on fibre to bind dye covalently under the influence of heat and alkaline pH to cotton, wool, silk and nylon; direct dyes (azo dyes) are applied using neutral or slightly alkaline baths containing additional electrolyte to cotton, rayon, paper, leather and nylon; disperse (azo dyes and anthraquinones) uses a fine aqueous dispersion that is often applied by high temperature-pressure or lower carrier methods to polyester, polyamide to acetate, acrylic and plastic; vat dyeing (anthraquinones) waterinsoluble dyes solubilised by reducing with sodium hydrosulphite applied to cotton, rayon and wool (O’Neill et al. 1999). Approximately 75% of the dyes discharged by textile processing industries belong to the classes of reactive (35%), acid (25%) and direct (15%) dyes (Franciscon et al. 2009). Textile factories use a high volume of water in each step of the process; cleaning, scouring, bleaching and dyeing. However dyeing, desizing and scouring processes are the major sources of water pollution (Manu and Chaudhari 2002). Around 1,000 mg l−1 of dye is present in a typical dye bath; however it is estimated that as much as 40% of the initial dyes remains unfixed and ends up in the textile wastewaters. In textile industry about 40–65 L of wastewater are produced per each kg of cloth produced (Manu and Chaudhari 2002). In India, an average textile mill produces 60 × 104 m of fabric and discharges approximately 1.5 × 106 l of effluent per day (Patil et al. 2009). A wide variety of dyes is used to fulfil the production orders request (Inthorn et al. 2004). Some examples are provided in Table 2. The log BCF, log Kow and Koc have been estimated for several azo dyes using EPI Suite programme developed by EPA office and are presented in Table 2. A log BCF of 0.5 indicates that in general they are biodegradable, and therefore, green technologies such as CW that exploit plant and microbial enzymatic interactions are suitable for their treatment. The major concern rely on biodegradation of Acid Dark Blue 5R and GR. Regarding Koc the higher values are also for the couple mentioned compounds, which indicates less mobile compounds, which adsorbs preferentially onto soil organic matter with estimated biotransformation half-lives of 6 up to 22 days (Reinhard and Drefahl 1999).
MW 534
452
551
327
350
Name CAS / C.I. Tartrazine / Acid Yellow 23 1934-21-0 / 19140
Sunset Yellow / Food Yellow 3 2783-94-0 / 15985
Fast Yellow/ Acid Yellow 17 6359-98-4 / 18965
Methyl Orange/ Acid Orange 52 547-58-0 / 13025
Orange I/ Acid Orange 20 523-44-4/14600
Structure
Log BCF 0.5
0.5
0.5
0.5
0.5
-1.18
-0.84
-0.66
-0.14
-6.74
Log Kow
1.45
0.93
0.87
0.87
-2.98
Koc
++
+++
++
+
h (%) +
Table 2 In-vitro colour removal efficiency by Phragmites sp. leaves crude extract, h ; + (0-25%), ++ (26-50%), ++++ (51-75%), ++++ (76-100%); C.I. - Colour Index; MW - Molecular Weight.
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MW
375
350
294
556
676
Name CAS / C.I.
Metanil Yellow/ Acid Yellow 36 587-98-4 / 13065
Orange II/ Acid Orange 7 633-96-5 / 15510
Chrysoin/ Acid Orange 6 547-57-9 / 14270
Ponceau BS/ Acid Red 66 4196-99-0 / 26905
Sirius Red 4B/ Direct Red 81 2610-11-9 / 28160
Structure
Log BCF
0.5
0.5
0.94
1.65
0.5
0.5
0.5
0.69
0.56
0.46
Log Kow
2.62
2.47
2.07
1.83
1.57
Koc
(continued)
+++
+++
++
+++
+++
h (%) Using Plants to Remove Foreign Compounds 163
MW
682
696
Name CAS / C.I.
Acid Dark Blue 5R/ Acid Blue 113 3351-05-1 / 26360
Acid Dark Blue GR/ Acid Blue 120 3529-01-9 / 26400
Structure 1.0
1.75
3.74
Log BCF
3.20
Log Kow
3.81
3.51
Koc
++
++++
h (%)
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Fig. 5 Acid Orange 7 reductive degradation (a) and oxidative degradation (b) into the by-product aromatic amines (sulphanilic acid, 1-amino-2-naphtol, 1,2 naphthoquinone, 4- phenolsulphonic acid) (Adapted from Wojnárovits and Takács 2008)
Acid Orange 7 (AO7, is being used as a dye model molecule for treatment efficiency assessment in CW (Davies et al. 2006). Acid dyes, like AO7, are used on nylon, wool, silk, paper inks and leather. The dyeing process usually occurs via the use of neutral to acid dye baths. During textile processing, concentrations of AO7 in wastewaters of 10 up to 80 mg l−1 have been reported (Coughlin et al. 2002; Scheeren et al. 2002). AO7 is resistant to light degradation and does not undergo biological degradation in wastewater treatment plants (Lucarelli et al. 2000). Bacterial cleavage of the AO7 azo-bond gives rise to two aromatic amines, sulphanilic acid and 1-amino-2-naphtol (Fig. 5). It has been shown that the first step in the bacterial degradation is the cleavage of the azo bond by studying the mechanism of Pseudomonas aeruginosa degradation of Navitan Fast Blue S5R (Nachiyar and Rajakumar 2005). Coughlin et al. (2002) have also reported that the cleavage of the azo bond by the action of azo reductases is invariably the first step in the biotransformation of azo dyes and the second step is the mineralization of the intermediates. The toxicity of AO7 has been assessed using a bioluminescent bacterium, Vibrio fischeri. It has been found that the toxicity and genotoxicity of decolourised AO7 is due to the production of 1-amino-2-naphtol, as the toxicity and genotoxicity of SA is similar to that of the AO7 before azo bond cleavage (Gottlieb et al. 2003).
Conventional Dye Treatments Various methods have been suggested to handle the dye removal from water these include biodegradation, coagulation, adsorption, advanced oxidation process (AOP) and membrane process. Among these techniques the AOP appears to be a promising field of study (Donlagic and Levec 1998; Neamtu et al. 2004; Rauf and Ashraf 2009). In this process ROS that destroys the aromatic structures are produced. This evidence is very relevant to the application of green plants to the treatment of dyes, as plants
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produce ROS (OH• – hydroxyl radical) as a signalling pathway for the detoxification of xenobiotics and have POD isozymes, which are heme-containing proteins. Azo dyes microbial degradation in aerobic or anaerobic environment conditions are controversial (Tan et al. 1999; Padmavathy et al. 2003; Harmer and Bishop 1992; Coughlin et al. 2002). Anaerobic-aerobic treatment using sequencing reactors has been intensively studied (Melgoza et al. 2004). White-rot fungi are attractive organisms for bioremediation for several reasons; they are ubiquitous in natural environments and they have a powerful extracellular oxidative enzymatic system known as the lignin-degradative enzyme system (lignin POD and Mn-dependent POD) (Heinfling et al. 1998; Wesenberg et al. 2003). Despite the potential industrial use of fungal POD, the application in industrial processes is hampered by the limited protein availability and their rather low stability (Conesa et al. 2002).
Azo-Dyes Phytoremediation Log Kow has been estimated to vary from −10.17 (Acid Yellow 23) up to 3.74 (Acid Blue 120) for azo dyes that have aromatic amines such as sulphanilic acid and/or 1-amino-naphthol in its structure. For log Kow < 0.5 the azo dyes are so water soluble that they are readily lixiviated. Plants readily take up organic xenobiotics with a log Kow between 0.5 and 3.5, as well as weak electrolytes (weak acids and bases or amphoteres as herbicides) (Barac et al. 2004). Consequently, they can be sorbed into the soil or taken up by the plants. In Table 2, colour removal of several azo dyes using a crude saline extract of Phragmites australis leaves is presented. Despite the structural and hydrophobic differences, the azo-dyes colour removal is observed revealing that Kow is not restrictive for enzymatic biodegradation process if the compound can be translocated from rhizosphere to the leaves. When using CWs, a combination of aerobic, anaerobic and anoxic degradation occurs within the bed matrix, which is beneficial for dyes degradation. Plants use sunlight as an energy source and atmospheric CO2 as a source of carbon, however, as they also respire, they require catabolic enzymes to break down lignin, cellulose, coumarins, flavonoids and other complex molecules that are produced by the photoautotrophs. These molecules are similar to exogenous xenobiotics. So, plants tend to transform, conjugate and store endogenous and anthropogenic xenobiotics and get rid of them by delignification and degradation by enzymatic processes. Plant degradation process relies mostly on POD, enzymes typically activated as an oxidative stress response. Additionally, heterotrophic microorganisms use their enzymatic abilities to mineralize the xenobiotic compounds to obtain energy, carbon and nutrients. Any xenobiotic can find somewhere in the CW microbial and plant enzymatic tools that lead to its mineralization (McCutcheon and Schnoor 2003). Bioremediation by itself is not cost feasible unlike CWs, and also cannot compete with the plants ability to self-engineer and rapidly adapt to changes in pH, redox conditions, nutrient availability and to the presence of xenobiotics
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Rhizodegradation Ecological communities include bacteria, yeasts, fungi and protozoa. All are effective agents in the transformation of organic pollutants because of their extracellular enzymatic components, which are powerful catalysts able to extensively modify the structure and toxicological properties of contaminants, or to completely mineralize organic molecules into innocuous inorganic end-products. The extracellular enzymes include a large range of oxidoreductases and hydrolases, both having a degradation function transforming polymeric xenobiotics into partially degraded or oxidised products that can be easily taken up by the cells. Oxidoreductases have a protective function by oxidizing toxic soluble products to insoluble and thus protect the cell against it (Gianfreda and Rao 2004). An example of a possible bacterial enzymatic pathway for AO7 is given in Fig. 6. Phytodegradation The role of plants in the degradation of dyes has been addressed and among other research work, a study was performed using Origanum vulgare that is a high-phenolic containing plant on the ability of several oregano clonal lines to degrade polyanthraquinone dyes (Poly R-478 and Poly S-119). In this study POD activity increases and the total phenols decrease when compared with the controls (Zheng et al. 1998). A similar study has been conducted using thyme (Thymus vulgaris L.) and rosemary (Rosmarinus officinalis L.) clonal lines, where the increase in POD activity has been correlated with the activation of tolerance mechanisms in these plants to Poly S-119, enabling its survival in such environment (Zheng and Shetty 2000). These results show the possibility to develop new phytoremediation systems based on manipulated POD in plants tolerant to high polluted environments. Another study on degradation of textile dyes (Methyl Orange, Orange G, Azo Violet, Azocarmine, Methylene Blue, Bromophenol Blue) mediated by Ipomea palmate and Saccharum spontaneum POD has been carried out, using concentrations of dyes from 25 up to 200 mg l−1 (Shaffiqu et al. 2002). The Saccharum POD enzyme revealed to have high pH, temperature stability (30–80°C) and high specific activity degrading completely green textile dyes such as Procion Green and Supranol Green. The use of Saccharum POD immobilized on modified polyethylene matrix completely degraded Procion Navy Blue HER, Procion Green HE-4BD, Procion Blue H-7G and Supranol Green within 6–8 h at pH 3. The immobilized enzyme has been used in a reactor and found to be stable as the half-life was 60 h. Recently, plant polyphenol oxidases obtained by ammonium sulphate fractionation from potato (Solanum tuberosum) and brinjal (Solanum melongena) have been used to study the potential of decolourization and degradation of textile dyes such as Reactive Blue (4, 160, 171); Reactive Orange (4, 86); Reactive Red (11, 120); Reactive Yellow 84 and mixtures between them. Potato polyphenol oxidases are more effective in decolourization of individual dyes or mixture of dyes than the
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Fig. 6 A possible enzymatic pathway for the degradation of AO7 by bacterial strains (Blasco et al. 1995; Blumel et al. 1998). SA: sulphanilic acid (4-aminobenzenesulphonate); 4SC: 4-sulphocatechol; 3SM: 3-sulphomuconate; 4SL: 4-sulpholactone; MA: maleylacetate; 3OA: 3-oxoadipate; 2HMSA: 2-hydroxymuconic semialdehyde; CCM: cis,cis-muconate; ML: muconolactone; EL: enollactone ; I: 4-aminobenzenesulphonate 3,4-dioxygenase (deaminating); II: protocatechuate 3,4-dioxygenase type II; III: 3-carboxymuconate cycloisomerase type II; IV: sulpholactone hydrolase; V: maleylacetate reductase;VI: catechol 2,3-dioxygenase; VII: catechol 1,2-dioxygenase; VIII: muconate cycloisomerase; IX: muconolactone isomerase; X: enollactone hydrolase
brinjal polyphenol oxidases at pH 3, obtaining up to 99% decolourization for dyes concentrations between 50–100 mg l−1 (Khan and Husain 2007). Degradation of several dyes such as Golden yellow, Methyl orange, Orange M2RL, Navy blue HE2R, Reactive Red M5B and Reactive Red 198 has been verified
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using a cell culture of transgenic hairy roots, monitoring the enzyme activities of lignin POD, laccase, tyrosinase, Mn POD, DCIP reductase and azo reductase. It has been found that the hairy roots of Tagetes patula L. are able to decolourize the six structurally different textile dyes with an efficiency of more than 62%, and concentrations up to 110 mg l−1 are removed. Azo reductase, a key enzyme expressed in azo dye degrading bacteria that cleaves azo bonds reductively, is induced in Tagetes roots after decolourization, highlighting its role in the reduction of azo bonds. Reactive Red 198 decolourization has been monitored using analytical techniques such as UV-Vis spectroscopy, HPLC, GC-MS and FTIR spectroscopy, thus enabling the establishment of a possible degradation pathway. There are evidences that the metabolites produced by dye microbial biodegradation are different from the non toxic formed by hairy roots biodegrading activity (Patil et al. 2009). The technical advantage of using cell cultures instead of the whole plant rely on the fact that in vitro cultures are easily grown and maintained free from microbial contamination, therefore enabling the understanding of key enzyme pathways involved in the detoxification of pollutants allowing to distinguish among the metabolic capabilities of plant cells from those of microorganisms (Doran 2009). However, a question remains to be answered, how plant enzymes are activated when whole plants are used and integrated in a treatment system like a CW? Davies et al. (2006) have shown that using a pilot VFCW planted with Phragmites australis the decolourization and degradation of the azo dye model molecule, AO7 and the aromatic amines released is feasible (Fig. 7). AO7 has a log Kow of 0.56, so it is readily taken up by the plants, and as CW plants cannot move away from environmental stresses, they have to activate a signal transduction cascade in order to survive. Davies et al. (2005) have found using in vitro and in vivo studies that Phragmites australis POD, can play an active role in the degradation of AO7. For moderate stress conditions (130 mgAO7 l−1), Phragmites australis in contact with AO7 show POD activity increases in all tissues: 2.1-, 4.3- and 12.9-fold for leaves, stems and roots, respectively. The increase in POD activity is the result of Phragmites australis stress response, as the H2O2 is produced under oxidative stress conditions. For high oxidative conditions (700 mgAO7 l−1), POD activity inhibition occurs immediately after the toxic shock, which is related to the fact that POD is not able to degrade the high amounts of H2O2 produced immediately. However, a remarkable capacity of Phragmites australis to overcome the oxidative stress is observed, as after 2 days in the same feeding conditions, POD activity levels returns to previous levels, with no signs of phytotoxicity. In summary, plants do react to the presence of the model molecule (AO7) by increasing ROS concentration in order to signal plant defences. Carias et al. (2007) have shown that crude extracts of Phragmites leaves obtained by ammonium sulphate fractionation are successful in the decolourization of AO7. Activities of several enzymes involved in plant protection against stress have been assayed through the activity quantification of SOD, POD, CAT, APX, dehydroascorbate reductase (DHAR) and glutathione S-transferases (GST), obtained from Phragmites australis crude extracts of leaves, stems and roots. An activity increase has been detected for an AO7 concentration of 130 mg l−1 for most enzymes studied, especially in leaves, suggesting a response of the ROS scavenging enzymes to the chemical stress imposed. GST activity increases 6.6, 5.9 and 8.8-fold for leaves,
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Fig. 7 Vertical flow constructed wetland used on the treatment of Acid Orange 7 contaminated wastewater
stems and roots respectively, this situation can also be interpreted as an activation of the detoxification pathway and subsequent AO7 conjugation in order to minimize or eliminate the stressor agent effect. After the second shock (700 mgAO7 l−1), GST activity is inhibited and again the plants take only 2 days to restore GST activity in high oxidative stress conditions (Carias et al. 2008). Analysing Phragmites australis role at molecular level, the presence of AO7 in the plant root system leads to a subsequent cascade of biochemical reactions, which include mRNA accumulation for ROS production and ROS-scavenging enzymes as part of the integrated plant defence mechanism. This means that when a stress condition poses a threat to the plants, the DNA directs the transcription of several copies of mRNA, each of which leads to the production of hundreds of protein molecules (e.g. antioxidative enzymes and membrane repair enzymes). An example of a membrane repair enzyme is given by glutathione peroxidase (GPX), that repairs membranes by the reduction of lipid hydroperoxides to their corresponding alcohols and at the same time reducing free H2O2 to water (Edwards and Dixon 2003). Thus, the isolation of RNA from Phragmites australis coding for GPX revealed that gene expression is quickly reaching a maximum up to 1 day for the different
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Fig. 8 TAE 2% agarose gel electrophoresis of glutathione peroxidase gene expression in roots and leaves of plants (Phragmites sp.) submitted to high oxidative stress (700 mgAO7 l−1)
enzymes and fading after 8 days in contact with AO7 (Davies et al. 2009). This represents a very quick response to stress and a remarkable adaptation to different stress conditions, highlighting plants genetic plasticity (Fig. 8). This is an indirect methodology to measure the level of ROS produced after the azo dye is in contact with the plants in the pilot scale VFCW. Other studies also report short times after the exposure to a stress condition for instance, in RNA expression studies for sugarcane, gene overexpression reaches a maximum 2 days after exposure to low temperatures (Nogueira et al. 2003) and for rice the genes induced by cold, drought, high salinity and/or ABA are over expressed after 1 day exposure to those conditions (Rabbani et al. 2003). Plants survival in CWs that are in use for a long time in the treatment of wastewater containing xenobiotic compounds, is the proof that plants are able to biochemically selfengineer (Davies et al. 2009).
Hydrophobic Compounds: Phytoremediation of PCB-Contaminated Soils PCB are oily liquids consisting of a mixture of compounds, containing a total of 1–10 chlorine atoms on two C-C connected aromatic rings. There are 209 congeners in all. 3
2
2'
3'
4 Clm
4' 5
6
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5'
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Congeners of PCB homological groups with identical number of chlorine atoms (from 1 to 9) will be designated further as 1CB, 2CB, …, 9CB. From the 1940s until the 1980s, commercial PCBs have been widely used, mostly as hydraulic and dielectric fluids in electrical equipment. About 1.5 million tons of PCB are still in use today (mainly in closed systems) and about 0.5 million tons are contaminating
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the environment (Wiegel and Wu 2000). Contamination of soils and waters with PCB has often resulted from the manufacture, handling, use, and disposal of these chemicals. The PCB content in soils and sediments can reach hundreds or thousands milligrams per kg, while permissible concentrations range from 0.01 to 30 mg/kg, depending on country and land use (Bakker et al. 2000). One of the promising methods for cleaning up PCB-contaminated soils and sediments is bioremediation which differs from traditionally used methods (incineration or burial in secure landfills) by ecological compatibility, more economic, less energy consuming and saving soil fertility. PCB degradation in soil systems occurs basically under the influence of microorganisms. Microbial PCB degradation in soil depends on composition and concentration of the contaminants, presence and activity of PCB degraders and environmental conditions (Fig. 9). Highly chlorinated PCB congeners are more effectively transformed by anaerobic microorganisms through reductive dechlorination with formation of less chlorinated derivatives. However, full degradation of these pollutants in soil proceeds mainly by aerobic bacteria. Higher chlorinated PCB (with three and more chlorine atoms) are not capable to provide microorganisms with carbon or energy, therefore the majority of PCB are metabolized by microorganisms in co-oxidative conditions in the presence of other available substrates. The main role in PCB degradation belongs to bacteria capable to use biphenyl (BP) as a sole source of carbon and energy. BP-utilizing bacteria metabolize PCB primarily through bph-pathway to chlorobenzoic acids (CBA) which are usually mineralized by other bacteria. The rate and extent of aerobic degradation of PCB congeners negatively correlate with the number of chlorine atoms in the molecule. It strongly depends on their structure and on the specificity of enzymes in relation to various PCB congeners and varies for different bacterial strains. The bph-genes expression usually occurs during growth in the presence of BP or its utilized analogues. Microbial processes in the surface biofilms, which are formed at the interface between anaerobic and aerobic conditions, play a special role in PCB degradation (Furukawa 2006). High PCB persistence in soil is due to the absence and/or low activity of microorganisms, capable to degrade their numerous congeners and to transformation products which often possess high toxicity. Besides, high adsorptive ability of these extremely hydrophobic compounds limits their bioavailability in soil systems. The efficiency of bioremediation of PCB-contaminated matrices strongly depends on the character and degree of contamination. In the case of aerobic bioremediation, the best results are obtained with moderately contaminated soils and sediments (20–700 mg PCB/kg), in which the level of contamination decreased by 40–75%. These results could be achieved by repeated inoculation of a consortium of specific microorganisms (isolated or engineered) with concurrent addition of BP and biosurfactants. PCB concentration decreased mainly due to the degradation of congeners with one to three chlorine atoms. The content of higher-chlorinated PCB can be noticeably decreased only under sequential anaerobic/aerobic treatment; the best effect was achieved with anaerobic granules. However, only in individual cases, mainly in laboratory experiments with freshly spiked PCB at moderate concentrations, it is possible to reduce their content to a level permissible for technogenic soils (Vasilyeva and Strijakova 2007).
TCA cycle + COOH Cln O
Dioxigenase
HO
Cl-
(Cl-)
OH
OH
Cln (n-1)
COOH
Mineralization
OH
OHOH H
Cln
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OH
Biphenyl dehydrogenase
H
Mineralization Cl-
Biphenyl dioxigenase
HO Cln
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White rot fungies Plant cells
BPh-utilysing bacteria
COOH
Hydrolase
Cln
Aerobic conditions
Cln (n-1) (Cl-) HO
OH H2C
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Cln
Lignoperoxidase OH1-3 Mn-peroxidase Laccase Peroxidase
Methanotrophic bacteria
Biphenyl dioxigenase
Cl0-3
OH1,2
Conugation of intermediates with ce ll components and humic acids
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Methane Cl monooxygenase
Cl
Biofilm Cl-
Cl2 ClCl3
Cl-
Cl4 Cl
-
Dehalogenase Cl5
Cl8
Cl-
.. .
Cl-
Anaerobic conditions
Methanogenic, sulfidogenic and other anaerobic bacteria
Fig. 9 Schematic representation of the main pathways of PCB biotransformation in solid natural and artificial media under aerobic and anaerobic conditions, as well as in aerobic biofilms under microaerophilic conditions (Vasilyeva and Strijakova 2007)
Although phytoremediation is less effective for highly hydrophobic compounds (PCB, PAHs and dioxins) than for BTEX and other moderately hydrophobic pollutants, this inexpensive method presents some possibilities of decreasing the risk linked to those pollutants. The potential of plants for the remediation of PCB contaminated soils has been discussed in reviews (Campanella et al. 2002; Mackova et al. 2006;
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Vasilyeva and Strijakova 2007; Mackova et al. 2009) and in a number of recent publications mentioned below. There are three different ways for plants to reduce the PCB concentration in soil: phytoextraction, phytodegradation and phytostimulation (or rhizoremediation).
Phytoextraction Phytoextraction is based on the use of plants for the depletion of pollutants in soil through their uptake by plants growing in the contaminated soil and removal together with the plants harvest. The appropriate phytoremediators should be tolerant to the toxicant, should have significant root and shoot biomass as well as high potential to accumulate the toxicant and translocate it from roots to shoots. Chekol et al. (2004) have demonstrated possibilities of all studied plants to influence positively PCB depletion in soil. The legumes (alfalfa, flatpea and sericea lespedeza) are more sensitive to freshly spiked Aroclor 1248 (100 mg/kg) than grass species (deertongue, reed, canary grass, switchgrass, and tall fescue). The weight of shoots and roots of beans grown in the polluted soil is 1.5–5 times less than in pure soil, whereas the decline in the growth of grasses does not exceed 1–10%. Grasses also reduce PCB content in the soil more effectively than legumes. After removal of grass roots, PCB concentration in the soil decreases by 62% and in unplanted soil by only 18%. Deeper depletion of PCB in the planted soil relates to higher fresh weight of roots and shoots, as well as with a higher number of bacteria and enzyme activity of rhizosphere soil in comparison to unplanted control (Chekol et al. 2004). Phytoextraction of weathered Aroclor 1260 from historically contaminated Canadian soils (from 90 till 4,200 mg PCB/kg) was much less effective. In those experiments, the phytotoxicity of PCB contaminated soils is also the lowest for grass tall fescue compared to other studied plant species. However three studied cultivars of Cucurbita pepo ssp. pepo (pumpkin, squash and zucchini) have been chosen as the most promising plants for PCB phytoextraction, because they have revealed high potential to accumulate PCB and are stressed in highly contaminated soil only. The best PCB extractor C. pepo cv Senator hybrid (a zucchini) accumulates in its roots and shoots up to 6,700 and 470 mg PCB/kg, respectively (Zeeb et al. 2006). This is mainly due to root uptake of PCB and translocation to the shoots, rather than volatilization of PCB from soil and deposition on the leaves. Tetra- to hexa-chlorobiphenyls represent the largest proportions in shoot tissues, but also hepta- to nona-chorobiphenyls are present in measurable amounts. The varieties of C. pepo ssp. pepo reveal the highest bioaccumulation factors (BAFs), calculated ratios of PCB concentrations in plant tissues and soil ([PCBplant]/[PCBsoil]) – up to 0.5–1.1 compared to 0.02–0.3 for others. Besides, C. pepo ssp. pepo plants have high translocation factors (TLFs), calculated ratios of PCB concentrations in shoot tissues compared to root tissues ([PCBshoot]/[PCBroot]) – 0.21–1.1 in all soil types compared to 0.01–0.19 for other plants. A distinctive nature of cucurbits root exudates (high protein content and low total sugar content, with a high proportion
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of monosaccharides) may have solubilised the PCB in the rhizosphere, thus allowing the plants to take up and translocate the contaminant to the shoots. Carex normalis species (sedge) has also demonstrated high potential for PCB phytoextraction; however those plants can be mostly used in wetlands. Nevertheless the decrease of PCB concentration in soil in the described experiments remains low. Although plants are taking up substantial quantities of PCB, maximum plant uptake in all plants within a growing period (2 months) deviates between 0.2% and 1.3% of the total PCB mass in the soil (Zeeb et al. 2006). Similar results have been obtained with other soils containing from 0.6 to 200 mg/kg PCB (Aroclor 1254/1260) (Whitfield Aslund et al. 2007). Citric acid and surfactant amendments increase to some extent the availability of weathered PCB to plant and earthworm species; however the up taken levels of PCB remain low (White et al. 2006). This confirms that phytoextraction is a slow technology and requires more than one growth cycle to reduce concentrations of PCBs in soil by detectable amounts.
Phytodegradation Phytodegradation is based on the possibility of many organic contaminants to be transformed by endo- and exoenzymes that has been demonstrated for PCB in several experiments in vitro. Some plants can transform absorbed PCB to mono- and dihydroxy derivatives with their following conjugation to cellular metabolites. It has been shown that all 22 congeners of Delor 103 (including 2CB, 3CB, 4CB, and 5CB) are transformed by a hairy root culture of Solanum nigrum L. (clone SNC-9O) in vitro: from 9% to 72% within 14 days, depending on the congener and Delor 103 concentration (25 or 50 mg PCB/l). This ability to metabolize PCB is higher for differentiated cells and positively correlates with the accumulation of endo- or exocellular POD in the presence of PCB. High phytotoxicity of 1CB and 2CB has been detected: these compounds at a concentration of 3 mg/l inhibit, and at 30 mg/l completely stop the growth of plant cells (Kucerova et al. 2000). This phytotoxic effect can be explained through higher ability of low chlorinated biphenyls (2CB and 3CB) to be transformed by POD to toxic hydroxy-derivatives (Rezek et al. 2007). The presence of POD in roots of some plants, participating in the degradation of low chlorinated PCB, has also been confirmed by Chu et al. (2006). The degradation of low chlorinated PCB and DDT in crude fresh extracts from roots of the wetland plant Phragmites australis is partly mediated by peroxidase and the plant cytochrome P-450 system, whereas in rice plants (Oryza sativa) such activity is low. Transgenic Nicotiana tabacum plants transiently expressing the biphenyl dioxygenase genes from Burkholderia xenovorans LB400 have been constructed. Biphenyl dioxygenases isolated from these transgenic plants oxidize 4-CB to 2,3-dihydro- or 2,3-dihydroxy-4¢-chlorobiphenyl (Mohammadi et al. 2007). Plant-microbial interactions within rhizosphere can evolve beneficial effect on PCB degradation. Mackova et al. (2007) have shown the possibility of additional metabolic interactions between bacteria and plants in PCB contaminated environment
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on level of their intermediates and their transformation. Some plants and microorganisms can metabolize products of microbial degradation of PCB and vice versa. For example, enzymes of bacterial bph-operon are able to metabolize some hydroxy-intermediates of plant PCB transformation. Similarly plant cells are able to metabolize, to a limited extent, bacterial products – chlorobenzoates. Thus, in vitro cultures of horseradish and black nightshade showed significant transformation abilities and metabolized more than 90% of 2-chlorobenzoate and 20–40% of 2,3-; 2,4-; 2,5- and 2,6-dichlorobenzoates in 2 weeks.
Rhizoremediation Rhizoremediation (i.e., the breakdown of an organic contaminant in soil through microbial activity that is enhanced by the presence of the root zone) is one of the emerging technologies in bioremediation of soils contaminated with PCB and other persistent pollutants in situ. This technique uses a dual plant-microorganism system, in which the plants provide nutrients, support, and a greater availability of the substrate, and the microorganisms drive the enzymatic remediation of the soil. Plants can stimulate indigenous microorganisms able to degrade PCB or provide conditions for bioaugmentation of specially isolated or gene-modified microbial strains (Mackova et al. 2009). Biostimulation through rhizoremediation is based on the postulate that rhizosphere microflora is usually more abundant, and the relative number of PCB- and especially CBA-degrading microorganisms is considerably higher among rhizosphere microorganisms in comparison to bulk soil. This has been confirmed for rhizosphere soil contaminated with Delor 103 (Chekol et al. 2004). Rhizostimulation of aromatic pollutant-degrading bacteria is supposed to occur via several different mechanisms, including analogue enrichment and/or induction of degradative genes by secondary plant metabolites or by less specific stimulation due to increased availability of simple substrates (sugars, amino acids, and organic acids), improved soil aeration, or other processes. It has been proved that plant compounds such as flavonoids, coumarins, terpenes, and resin acids can function as growth substrates and/or inducers of PCB-degrading bacteria. Plant secondary compounds may be released into soil via exudation from living roots or via lysis of dead fine roots as a result of seasonal root turnover (Leigh et al. 2006). Rhizosphere strains can metabolize many aromatic compounds, including PCB. It has been shown with the help of PCR that genomic DNA isolated from rhizosphere strains possess a genetic potential similar to that of PCB-degrading strain Comamonas testosteroni B-356 for the ability to decompose BP (Mackova et al. 2006). Phenylpropanoids (the most active bph-inducers) constitute 84% of the secondary metabolites exuded from roots of Arabidopsis thaliana, and BP-utilizing microorganisms grow on Arabidopsis roots 100 times better than the mutants which do not have bph-genes (Narasimhan et al. 2003). Alfalfa, black nightshade and especially tobacco, grown up in soil historically contaminated with PCB, positively
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influence the rate of soil remediation: up to 34% PCB is degraded in the soil planted by tobacco compared to unplanted control soil (Ryslava et al. 2003). Mature trees also raise the potential of microbial degradation of PCB in soil. Cultivable PCB degraders are associated with all studied species of mature trees growing naturally in a PCB contaminated site (8–500 mg PCB/kg), examined in both the rhizosphere (tightly adhered to the root) and root zone (bulk soil beneath the trees). Significantly higher numbers of PCB degraders are detected in the root zones of Austrian pine (Pinus nigra) and goat willow (Salix caprea) than in the root zones of other trees (ash, black locust and birch) or non-root-containing soil in certain seasons and at certain soil depths. The most spreaded cultivable PCB degraders associated with those trees belong to the genus Rhodococcus. One isolated Rhodococcus strain exhibits degradation abilities similar to those of B. xenovorans LB400, a well known PCB degrader (Leigh et al. 2006). Positive role of plant residues has been shown during composting a soil low contaminated with PCB. These plant additives raise the number of PCB-degraders and the rate of PCB transformation (Mackova et al. 2006). However no clear relation between the quantity of plant terpenes and the rate of PCB transformation in soil has been established. Besides, soil amendment with several plant secondary metabolites (carvone, isoprene, limonene, naringin, and coumarin) has also not revealed any positive influence on the degradation of 17 PCB congeners with two to six chlorine substitutes (170 mg PCB/kg) in aerobically incubated soil. At the same time amendment with BP in combination with the surfactant hydroxypropyl-cyclodextrin enhances PCB removal from a high organic matter soil, but not from a low organic matter soil (Luo et al. 2007). Low transpiring wetland plants, such as lake sedge (Carex aquatalis) and prairie cord grass (Spartina pectivata), accelerate the depletion of Aroclor 1260 in dredged sediments (20 mg PCB/kg) in a greenhouse study. In 1.5 years, after three cycles (3.5 months in the saturated soil amended with readily available carbon and mineral N followed by 1-month aerobic period) removal of PCB is 17–18%. The dominant role apparently belongs to anaerobic dechlorinators, because the disappearance rate increases in the row 5CB < 6CB < 7CB; carbon additives and high moisture content are critical, and hightranspiring wetland plants do not therefore promote this depletion (Smith et al. 2007).
Bioaugmentation by Rhizosphere Colonising Strains It has been proposed that rhizosphere is an ideal place for the introduction of PCBdegrading microorganisms, and the greatest success should be with PCB-degrading strains which can easily colonize rhizosphere. A strain of the nodulating bacteria Sinorhizobium meliloti has been genetically modified by transferring the PCB-degrading plasmid pE43. Its inoculation together with seeds of the symbiotic plant alfalfa promotes an accelerated degradation of 2,3¢,4-trichlorobiphenyl in soil. In a growth chamber, the concentration of this PCB congener has decreased from 0.33 to 0.11 mg/kg within 1.5 month, while planting with wild strain S. meliloti, not having bph-plasmid – to 0.23 mg/kg only. Meantime the concentration of the same congener is decreased even further
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(to 0.09 mg/kg) in the soil sowed by alfalfa seeds treated by a mixture of not identified indigenous PCB-degrading microorganisms (Chen et al. 2005). Other investigations have been performed with engineered strains of Pseudomonas fluorescens which are capable to colonize rhizosphere of various plants and simultaneously express bph-genes. They survive well in rhizosphere and intensively colonize roots of different plants: sugar beet (Brazil et al. 1995), alfalfa (Villacieros et al. 2005), and willows (De Carcer et al. 2007; Rein et al. 2007). A new strain P. fluorescens F113L::1180 has been constructed that cometabolizes PCB congeners present in Delor103 better than strain LB400, the donor of the bph genes used. Delor 103 is degraded faster in soil planted by alfalfa seeds by inoculating strain P. fluorescens F113L:1180 in comparison with uninoculated soil or with soil inoculated by strain LB400 not possessing the ability to colonize the rhizosphere (Villacieros et al. 2005). Inoculation of transgenic strains of P. fluorescens to the rhizosphere of Salix sp. has no influence on the function and structure of microbial community of the basic soil, but raises the biodegradation of spiked PCB (168–186 mg/kg), whereas no degradation of the xenobiotics is noticed for 186 days in control soil with a wild strain (De Carcer et al. 2007). Thus, certain positive results of phytoremediation for PCB-contaminated soils have been obtained. However the majority of the experiments have been performed with low and fresh contaminated soils and degradation rates of PCB are not so high. The possibility of phytoremediation of moderately and highly contaminated soils has not been proven yet.
Use of Activated Carbon One of the main reasons of low microbial degradation of PCB in moderately and highly contaminated soils is their toxicity to degrading microorganisms and plants. Recently we have demonstrated that activated carbon (AC) can help to overcome the toxicity of various organic pollutants and facilitate soil bioremediation. A comparatively degradable compound 3,4-dichloroaniline is almost totally degraded in highly contaminated soil amended with AC and inoculated with chloroanilinedegrading bacterial strains. The explosive 2,4,6-trinitrotoluene is transformed to strongly bound products in soil amended with AC (Vasilyeva et al. 2006). We have used a similar approach for PCB-contaminated soils sampled near a capacitor plant in Serpukhov (Moscow Region, Russia). Initial PCB concentrations were 1,585 or 4,190 mg/kg with tri-, tetra- and pentachlorinated congeners prevailing there. The influence of granular activated carbon (GAC) and powdered activated carbon (PAC) on PCB availability and persistence in the soils has been determined under outdoor conditions (Vasilyeva et al. 2010). Results have confirmed the extreme persistence of PCB in both soils; extractable PCB in unamended soils decreases by 26% and 35% within 39 months, respectively. This reduction is mostly due to a 75–80% decrease in tri-chlorinated congeners and a 10–20% decrease in tetrachlorinated congeners. Significant PCB degradation has started after a long lag-period and did occur during the second season of the experiment, but subsequent
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degradation did proceed rather slowly. Because vapour loss and leaching are low, the loss of PCB in both control soils is likely due primarily to microbial degradation. The T50 of 3CB is about 1 year in both soils, while the T50 of 4CB and that of total PCB congeners is more than 4 years. Amending the soils with ACs does result in a sharp decrease in extractable PCB, mainly due to strong binding to the adsorbent. Amending those soils with the AC substantially decreases the concentrations of less chlorinated extractable PCB (2CB, 3CB and 4CB) and to a lesser extent of higher chlorinated congeners. The introduced ACs substantially reduces the concentration of readily available PCB and other toxic compounds (dissolved and reversibly adsorbed to soil particles). This effect has been demonstrated by the greatly reduced phytotoxicity of both PCB-contaminated soils shortly after mixing with ACs as well as by reduction of Tenax extractable PCB. Slow PCB degradation is evidently related to high toxicity of the contaminated soils. PCB and their transformation products (such as hydroxy derivatives) or associated pollutants may exceed critical levels of toxicity, impeding natural attenuation processes. The AC maintains a low concentration of readily available toxicants in soil solution, creating more favourable conditions for plant growth and microbial activity than in the untreated soils. PCB binding to the AC is evidently one of the main mechanisms regulating toxicity reduction, especially in GAC-amended soils. The dispersive interactions should be more important for the PCB sorption by PAC. Two mechanisms of PCB binding to soil and activated carbon have been proposed. The most probable mechanism of PCB binding to AC is p-p-bonds with its graphene surface. These bonds can be especially strong when planar PCB molecules penetrate nanopores of GAC and form p-p-bonds with both walls of the slit-like pores. Nonplanar PCB molecules with two and more ortho-chlorines should form weaker p-p-bonds with the AC and these bulkier molecules have less potential to penetrate into narrow pores. Our results are consistent with the mechanism of sorption of planar PCB congeners and other highly hydrophobic, persistent organic pollutants with planar structures by soot and soot-like carbon particles such as activated carbon (Zhu and Pignatello 2005; Burgess et al. 2006). In natural soils PCBs are likely adsorbed primarily to soil organic matter through hydrophobic interactions, and the more hydrophobic higher chlorinated congeners should be adsorbed with greater intensity. Some fraction of the contaminants can penetrate into a condensed part of soil humus consisting of polyaromatic structures. Less chlorinated congeners with planar structures (especially 2CB) may bind to soot-like particles usually present in minor amounts in soil. Both of those fractions are poorly extracted by the hexane-acetone mixture but should be released by an aromatic solvent like toluene. Overall degradation of PCB in AC-amended soils is comparable to control soils, and the final Ct similar. This means adsorption to the AC does not inhibit PCB degradation. However, an additional mechanism of PCB depletion has been revealed in the AC-amended soils which may accompany microbial degradation. The AC surface may promote slow reductive degradation of highly chlorinated congeners (5CB, 6CB, and especially 7CB). Thus, degradation of total PCB in soil is neither accelerated nor decelerated after adding AC. However, the AC sharply reduces extractable and readily available PCB, mostly due to strong binding to the adsorbent and dispersive adsorption.
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The added AC maintains a low toxicant concentration in soil solution, increasing clover germination and growth, while plants die or are greatly inhibited in control soils. These observations indicate that sequestration of PCB by activated carbon can be used for phytoremediation of real highly contaminated soils. AC amendment in combination with mineral fertilizers may create favourable conditions for development of soil microorganisms and plants. The growing plants together with adsorbents will prevent movements of the contaminants to atmosphere and thus permit condition for long-year phytoremediation of PCB-contaminated site in situ. This approach can be used e.g. in the case for emergency, if remediation of the site through alternative methods is difficult or postponed.
Conclusions: Phytoremediation Trends for the Near Future Phytoremediation requires a thorough understanding of the underlying processes at genetic, molecular, biochemical, physiological and agronomic levels (Krämer 2005). Phytoremediation studies in the future may concern the elucidation of genetic, molecular and cellular mechanisms in order to clarify how phytoremediation can be enhanced. Therefore high throughput techniques such as antibody or enzyme assays, PCR amplification, and DNA fingerprint or microarray gene chip may give more insights on the process and may lead to choose a specific plant variety or microbial strain to be applied on a specific pollutant within a CW or a particular soil. The knowledge of genome from plants to be applied in phytoremediation should provide a useful tool for CW design or site clean up planning. On the other hand, since pollutants and their by-products can be toxic to humans and other living organisms, including plants, an in-depth monitoring of phytoremediation should be carried out, to know if metabolites produced or released are still toxic. Nowadays, plant defence inducers such as naphthyl-acetic acid (a plant hormone analogue) and BION® (inductor of plant resistance against diseases) are used to magnify the glutathione S-transferase activity, an enzyme involved in the detoxification of herbicides (Schröder et al. 2008). The development of DNA micro-arrays with complete plants genome will constitute an innovative monitoring tool, enabling the detection of mRNA transcripts e.g. for a plant enzyme activity as a function of the pollutant loads applied in CWs. An interesting example of monitoring plants health has been developed by the European Space Agency, with a complete lightweight sensing system for monitoring the ambient conditions for plant growth in space missions (Baratto et al. 2005). Another example is given by a plant tissue bioelectrode, sensitive to a variety of mono and polyphenols by coupling potato (Solanum tuberosum) tissue, which contains high concentrations of the enzyme polyphenoloxidase to a traditional O2 selective Clark electrode. Plant tissue based chemiluminescent biosensors have also been developed, for instance the molecular recognition device being spinach, soybean and coconut tissue with glycolate oxidase, POD, urease, oxalate oxidase and phenol oxidase (Schwitzguébel and Porta 2003).
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Nowadays we are applying molecular technique that were unthinkable 50 years ago, therefore we must expect further unthinkable developments. Among them, improving the ability of plants to remove and degrade foreign compounds present in contaminated water and soil by genetic engineering. However, before transgenic plants can be applied in the field for phytoremediation, the following issues must be addressed: performance under real conditions, safety of the modified plants, potential risk of transferring genes across natural and cultivated species, possible transfer of contaminants to the food chain, and environmental risk assessment. However, exploring and exploiting the biological diversity of the natural gene pools can be a more sustainable approach and transgenic plants might not be necessary. Consideration of plant taxonomy and phytochemistry should be the first steps in the appropriate use of the huge biochemical potential of plant species, since plants often produce natural chemicals whose structure is close to foreign compounds (Singer et al. 2003; Wink 2003). Finally, the success of phytoremediation depends also largely on the ability of plants to tolerate the pollutant(s) to be removed. It is thus of utmost importance to determine the maximal possible amount of the xenobiotic compounds that can be accumulated and detoxified without injury, critical stress and disruption of plant metabolism or redox processes in the species under consideration. Such information will help to maintain the plant wellness, a key factor to correctly design and size the CW to remove the foreign compounds present in a particular wastewater or to estimate the time required to clean up a contaminated soil. All these fundamental aspects are essential issues to determine the impact of chemical stress on the nutritional qualities of vegetables and crops, and thus on food safety and quality, and to make the best use of plants for the remediation of contaminated environment. One of the most important challenges is now to use this basic scientific knowledge to improve the efficiency of phytotechnologies in the field. The dissemination of results, risk assessment, public awareness and acceptance of this green technology, as well as the promotion of networking between scientists, industrials, stakeholders, end-users, non-governmental organizations and governmental authorities are major issues that must be tackled to ensure that phytoremediation programmes are implemented successfully. It is clear that phytotechnologies are not hype, but offer promising and sustainable approaches towards environmental remediation and human health for the twenty-first century (Schwitzguébel et al. 2009).
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Biodegradation of Organic Xenobiotic Pollutants in the Rhizosphere Hassan Azaizeh, Paula M.L. Castro, and Petra Kidd
Abstract Soil contamination by xenobiotic organic compounds is a serious problem in most industrialized countries, causing acute and diffuse contamination of soil and waters on a global scale. Microbial transformation plays a major role in contaminant degradation of many persistent organic pollutants (POPs). However, microbial degradation can be limited by factors such as contaminant bioavailability, - adsorption and mass transfer, while combined plant-microbial systems can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses that support improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) cometabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability, and (v), selective increase in the number and activity of pollutant degraders. Root exudates or rhizodeposits not only provide a nutrientrich habitat for microorganisms but can potentially enhance biodegradation of xenobiotics in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability, and/or selectively increase the number and activity of pollutant degraders in the rhizosphere. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation. Bacteria, fungi and mycorrhizal fungi are a major component of
H. Azaizeh (*) Institute of Applied Research (affiliated with University of Haifa), The Galilee Society, Shefa Amr, 20200 Israel e-mail:
[email protected] P.M.L. Castro Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua Dr António Bernardino de Almeida, 4200-072 Porto, Portugal P. Kidd Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Investigaciones Agrobiológicas de Galicia (IIAG), Avda. de Vigo s/n, 15706 Santiago de Compostela, Spain P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_9, © Springer Science+Business Media B.V. 2011
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the rhizosphere and form mutualistic associations with most plant species and their involvement in the biotransformation and biodegradation of various xenobiotic organic compounds is discussed. The diversity of bacterial and fungal genes and degradation pathways expressed in the rhizosphere is potentially huge, and the ways in which plants and associated symbionts enhance biodegradation remains much unexplored. Although a wide range of microbes able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons have been discovered, the environmental pollution caused by these compounds remains an unsolved problem.
Introduction Soil contamination by xenobiotic organic compounds is a serious problem in most industrialized countries, causing acute and diffuse contamination of soil and waters on a global scale. Microbial transformation plays a major role in contaminant degradation of many persistent organic pollutants (POPs). However, microbial degradation can be limited by factors such as contaminant bioavailability, adsorption and mass transfer, while combined plant-microbial systems can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses that support improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) co-metabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability, and (v), selective increase in the number and activity of pollutant degraders. Root exudates or rhizodeposits not only provide a nutrient-rich habitat for microorganisms but can potentially enhance biodegradation of xenobiotics in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability, and/or selectively increase the number and activity of pollutant degraders in the rhizosphere. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation. Bacteria, fungi and mycorrhizal fungi are a major component of the rhizosphere and form mutualistic associations with most plant species and their involvement in the biotransformation and biodegradation of various xenobiotic organic compounds is discussed. The diversity of bacterial and fungal genes and degradation pathways expressed in the rhizosphere is potentially huge, and the ways in which plants and associated symbionts enhance biodegradation remains much unexplored. Although a wide range of microbes able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons have been discovered, the environmental pollution caused by these compounds remains an unsolved problem.
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A highly industrialised society has led to the widespread introduction of organic pollutants into our environment, causing acute and diffuse contamination of soil and waters on a global scale. Soil contamination by xenobiotic organic compounds (such as chlordane, dioxins, 1,1,1-trichloro-2,2-bis(4-chlorophenyl) ethane (DDT), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), nitroaromatics) is a serious problem in most industrialized countries. Anthropogenic activities contributing to their accumulation in soils include industrial activities, fuel combustion, military activity, and use of pesticides, fertilisers and soil amendments in high-production agriculture (Alkorta et al. 2001; Samanta et al. 2002). Pesticides (herbicides, insecticides, fungicides, algaecides, bactericides, etc.) are chemicals used for crop protection and pest control, and are probably the most widely distributed contaminants in the environment over the last century: millions of tons of pesticides are produced and spread annually all over the world (Schwitzguébel et al. 2006). The persistence of these pollutants in the environment depends upon their physicochemical properties (volatility, reactivity, absorption and adsorption, solubility in water, partition between polar and non-polar phases (log Kow) and between soil and water (Kd)), degradability by microorganisms (MO), climatic conditions (influencing pesticide degradation through soil genesis), soil physicochemical properties (especially amount and nature of organic matter) and uptake by terrestrial and aquatic species including plants. Many persistent organic pollutants (POPs) are recognised as a potential health risk due to their intrinsic chemical stability, recalcitrance, and potential acute toxicity, mutagenicity or carcinogenity. There is also increasing concern in their transformation products because these can be present at higher levels in soil than the parent pesticide itself. In some cases these products are more toxic and more mobile, representing a greater risk to the environment than parent molecules (Schwitzguébel et al. 2006). Microorganisms are often considered to be the best indicators of soil pollution due to their intimate contact with the soil environment (large surface area). In general, they are very sensitive to low concentrations of contaminants and rapidly respond to soil disturbance. An alteration in their activity and diversity may result, and this in turn can lead to a reduced soil quality (Schloter et al. 2003). Soil enzyme activities are the driving force behind all the biochemical transformations occurring in soil. Their evaluation may provide useful information on soil microbial activity and be helpful in establishing the effects of soil specific environmental conditions on their biochemical biotransformation processes (Dick et al. 1996). The contribution of rhizospheric MO (bacteria, fungi and mycorrhizae) to the biodegradation and metabolism of xenobiotic compounds are discussed. Plant growth promoting rhizobacteria (PGPR) communities also exist naturally in the rhizosphere and can be an important tool in the decontamination of contaminated soils. Rhizo remediation of PAHs (e.g. naphthalene, phenanthrene, benzo(a)pyrene) is emphasised in this review as they represent widely spread organic pollution in our terrestrial environment.
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Biodegradation and Rhizoremediation of Xenobiotics Using Rhizospheric Bacteria Biodegradation in the Rhizosphere and Plant Growth Promoting Bacteria Plants may contribute to the dissipation and/or mineralization of organic contaminants through an increase in microbial density (more than 1–3 orders of magnitude than in non-vegetated or bulk soil), diversity and/or metabolic activity; improvement of physical and chemical soil properties; increased contact between root-associated MO and soil contaminants; increased humification; and adsorption on root surfaces of pollutants at the root-soil interface or rhizosphere, but the impact of each process has not been clearly elucidated (Binet et al. 2000; Boyle and Shann 1998). Plants (and plant species) vary widely with respect to root parameters such as morphology, root exudation (Grayston et al. 1996), fine root turnover (Gill and Jackson 2000), root decomposition (van der Krift et al. 2002), and associated microbial communities (Smalla et al. 2001). Biogeochemical processes are known to differ between the bulk or non-vegetated soil and this unique soil-root interface, leading to sharp gradients in elemental concentrations, pH, pCO2, pO2, redox potential and organic ligand concentrations (Hinsinger and Courchesne 2008). Several studies have investigated the effects of plant–microbe interactions on the degradation of organic contaminants; based on the hypothesis that root exudates, mucigel and root lysates (ectoenzymes, amino acids, carbohydrates, low-molecular-mass carboxylic acids, flavonones and phenolics) influence the rhizosphere microbial community (Adam and Duncan 2002; Siciliano et al. 2003; Kaimi et al. 2006). Compared to bulk soil, the rhizosphere may be modified due to the activity of the root system (Adam and Duncan 2002). A plant may secrete 10–20% of its photosynthate in root exudates, which support the growth and metabolic activities of diverse fungal and bacterial communities in the rhizosphere (Siciliano et al. 2003; Kaimi et al. 2006). Walton et al. (1994) speculated that when chemical stress occurs in soil a plant may respond by increasing its rate of exudation or exudates composition, which in turn modifies the rhizosphere microbial community composition or activity. A dynamic environment between plants and microbial communities exists in the rhizosphere, with the root surfaces of plants being continually subjected to a two-way traffic of solutes from plants to the soil and vice versa (Bais et al. 2006), and rhizoremediation occurs within such constraints. For many persistent organic pollutants (POPs) their low water solubility and high hydrophobicity impedes their uptake and translocation within plants (Burken and Schnoor 1998). In situations like these, microbial transformations play a major role in contaminant degradation. Enzymes catalysing the oxidation of pesticides (such as peroxidases) are not only more widely distributed among MO but are often more efficient than the same functional proteins in plants (Chaudhry et al. 2005). However, microbial degradation can be limited by factors such as contaminant bioavailability, adsorption and mass transfer, while the combined plant-MO system
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can overcome these drawbacks, leading to more efficient contaminant degradation at the soil-root interface or rhizosphere. Hypotheses supporting improved degradation within the rhizosphere compared to nonvegetated soils include (i) increase in microbial density, diversity and/or metabolic activity, (ii) catabolic enzyme induction, (iii) co-metabolism of contaminants with similar structures to rhizodeposits, (iv) improved contaminant bioavailability (surfactant activity), and (v), selective increase in the number and activity of pollutant degraders (Anderson and Coats 1995; Schnoor et al. 1995; Nichols et al. 1997; Burken and Schnoor 1998; Miya and Firestone 2001; Shaw and Burns 2003). Plants can also improve the physical and chemical properties of contaminated soil, and increase contact between the root-associated MO and the soil contaminants. The combination of microbial bioremediation and phytoremediation in this complementary manner is known as rhizoremediation, phytostimulation or rhizosphere bioremediation (Anderson et al. 1993). A study on the degradation of trichloroethylene (TCE) in the rhizosphere of wheat by bacteria expressing a stable, chromosomally encoded toluene ortho-monooxygenase (TOM) coined the term rhizoremediation (Yee et al. 1998). In this dual plant-microorganism system the plant provides nutrients, support, and possibly a greater availability of the substrate, while the MO drive the enzymatic remediation (Villacieros et al. 2005). Although the mechanisms involved are unclear, rhizosphere-enhanced biodegradation has been demonstrated for a wide range of organic pollutants, including chlorinated ethenes, polycyclic aromatic hydrocarbons and polychlorinated biphenyls (PCBs) (Kuiper et al. 2004). For that to happen it is important that the bacteria proliferate well in the root system and that the degradative pathways occur in the rhizosphere (Ramos et al. 2008). Enhanced mineralisation of 2,4-dichlorophenoxyacetic acid (2,4-D) was shown in soil collected from the rhizosphere of Trifolium pratense (Shaw and Burns 2003). Chekol et al. (2002) showed enhanced transformation of the explosive, trinitrotoluene (TNT) by the forage grasses Phalaris arundinacea and Panicum virgatum. Concentrations of the organochlorine p,p´-DDE (2,2-bis(pchlorophenyl)1,1-dichloroethylene), a metabolite of DDT, were significantly reduced in the rhizosphere of field-grown zucchini, pumpkin and spinach compared to bulk soil (White et al. 2006). An increased degradation of petroleum hydrocarbons (such as phenanthrene, benzo[a]pyrene, benzo[a]anthracene, chrysene, hexadecane, benzene, toluene, etc.) as a result of modified microbial activity was found in the rhizosphere of grasses and legumes (Nichols et al. 1997; Miya and Firestone 2001). Plant growth promoting rhizobacteria (PGPR) communities also exist naturally in the rhizosphere and can be an important tool in the decontamination of contaminated soils, playing a major role in the establishment of plants in polluted soils (Vivas et al. 2006). Plant growth promoting bacteria can be divided into two groups according to their relationship with the plants: symbiotic bacteria and free living rhizobacteria (Khan 2005). The enhancement of crop plant growth and nutrition in the presence of PGPR is well documented (Reed and Glick 2004; Fließbach et al. 2009) but more recently these microorganisms have been used to reduce plant stress associated with soil contaminants (Reed and Glick 2005). PGPR are able to enhance plant growth
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through various mechanisms, such as the production of phytohormones (such as indoleacetic acid (IAA), auxins, cytokinins, and ethylene; Glick et al. 1995). Ethylene is important for plant growth, but excessive ethylene promoted by stresses can depress growth. Levels of ethylene stress can be reduced by consuming 1-aminocyclopropane1-carboxylic acid (ACC), the immediate precursor of ethylene, through the synthesis of ACC deaminase (ACCD; Glick et al. 1998). Other known mechanisms to stimulate plant growth include enhancing nutrient and water uptake (e.g. N2 fixers, PO4 solubilisers, siderophore-producers), altering root morphology, producing antibiotics or cell wall lytic enzymes such as gluconases or chitinases to inhibit pathogens, and the induction of plant defence mechanisms (Lin et al. 1983; Kapulnik et al. 1996; Khan 2005). Khan and co-authors (2009) reviewed recent developments in the utilization of PGPR for direct application in soils contaminated with heavy metals. As examples, Zaidi et al. (2006) have shown that Brassica juncea plants growing in Ni contaminated soils exhibited lowest toxicity signs when inoculated with Bacillus subtilis strain SJ-101, as this rhizobacteria shared the Ni load by accumulating the metal and also enhanced the biomass formation of the plant. Liu and co-workers (2007) developed a Comamonas-alfalfa system for rhizoremediation of 4-chloronitrobenzene (4CNB), through the association of a 4CNB degrading strain with alfalfa and application to the rhizoremediation of 4CNB-polluted soil. It has also been demonstrated that endophytic bacteria can play a major role in the rhizosphere remediation. Mastretta and co-workers (2009) reported that endophytic bacteria from seeds of Nicotiana tabacum can reduce cadmium phytotoxicity, in which inoculation with endophytes resulted in improved biomass production under conditions of Cd stress. The latter study points to the use of inoculated seeds as a vector for plant beneficial bacteria. Endophyte-assisted rhizoremediation is regarded as a potential new field to improve remediation through the use of microorganisms that live within plants to improve plant growth, increase stress tolerance, and degrade pollutants.
Rhizoremediation of Organic Pollutants Polycyclic aromatic hydrocarbons (PAHs) (e.g. naphthalene, phenanthrene, benzo(a) pyrene) are organic pollutants widely spread in the solid-phases of the terrestrial environment. They are produced throughout the world as industrial by-products of fossil fuel combustion, asphalt production, wood preservation, and coal-processing, and are rarely encountered alone in the environment (Samanta et al. 2002). Since the aqueous solubility of PAHs decreases in an almost logarithmic fashion with increasing molecular mass, high molecular weight PAHs with five to seven rings are of particular environmental concern (Johnsen et al. 2005). Metabolism of PAHs occurs via the cytochrome P450-mediated mixed function oxidase system with oxidation or hydroxylation as the first step (Samanta et al. 2002; Stegeman et al. 2001). Three agricultural soils with contrasting physico-chemical properties and hydrocarbon-pollution history were studied for their phenanthrene-degrading potential (Andreoni et al. 2004). The three soils showed different levels of polycyclic
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aromatic hydrocarbons (PAHs) due to different pollution histories. Enzyme activity and bacterial diversity (assessed using denaturing gradient gel electrophresis (DGGE)) were highest in soils with lowest PAH levels. The enrichment of four mixed cultures capable of degrading solid phenanthrene in batch liquid systems was also studied. Phenanthrene degradation rates in batch systems were culturedependent, and simple (one-slope) and complex (two-slope) kinetic behaviours were observed. DGGE-profiles showed common bands in enrichment cultures and native soil bacterial DNA, indicating potential phenanthrene-degrading strains. In accordance, a decrease in PAH and phenanthrene contents were observed in corresponding cultures of one of the soils and phenanthrene-degrading bacteria were isolated (Andreoni et al. 2004). From those cultures showing the fastest rates of phenanthrene degradation, representative strains were identified as Achromobacter xylosoxidans (100% similarity), Methylobacterium sp. (99%), Rhizobium galegae (99%), Rhodococcus aetherovorans (100%), Stenotrophomonas acidaminiphila (100%), Alcaligenes sp. (99%) and Aquamicrobium defluvium (100%). The isolation of Rhodococcus aetherovorans and Methylobacterium sp. supports the hypothesis that different phenanthrene-degrading strategies, cell surface properties, or the presence of xenobiotic-specific membrane carriers could play a role in the uptake/ degradation of solid phenanthrene (Andreoni et al. 2004). The PAH degradation ability of four native Korean plant species (Panicum bisulcatum, Echinogalus crus-galli, Astragalus membranaceus, and Aeschynomene indica) revealed that dissipation of PAHs was higher in planted soil (i.e., with a rhizosphere) than in unplanted soil. Plant-induced effects were more pronounced in the case of pyrene dissipation compared to phenanthrene dissipation (Lee et al. 2008). After 80 days, >99 and 77–94% of phenanthrene and pyrene, respectively, had been degraded in planted soil, whereas 99% and 69% had been degraded in unplanted soil. This enhanced dissipation of PAHs in planted soils might be the result of increased microbial activity and/or plant-released enzymes. During the experimental period, a relatively large amount of phenolic compounds, a high microbial activity, and high peroxidase activity were found in planted soils (Lee et al. 2008). Chen and Aitken (1999) induced the PAH-degrading enzyme system through salicylate additions so as to stimulate high molecular weight (HMW)-PAH degradation. Other studies evaluating the phytoremediation of pyrene-contaminated soil using alfalfa (Medicago sativa L.) showed that pyrene had an inhibitive effect on alfalfa growth, and an increasing pyrene concentration seriously affected alfalfa growth (Fan et al. 2008). At concentrations of 492 mg kg−1 in the soil, shoot and root biomass was only 34% and 22% of that of alfalfa growing in non-spiked soil, respectively. The rhizospheric bacterial and fungi counts were 5.0–7.5 and 1.8–2.3 times higher than that in non-rhizosphere soil, respectively. After 60 days, 69–85% and 59–80% of spiked pyrene disappeared from the rhizosphere and non-rhizosphere soils, respectively (Fan et al. 2008). The removal percentage decreased with increasing pyrene concentration. However, the average removal of pyrene in the rhizosphere soil was 6% higher than that in the non-rhizosphere soil. The presence of alfalfa roots was therefore effective in promoting the biodegradation of freshly added pyrene into the soil (Fan et al. 2008). Yoshitomi and Shann (2001) found that
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the addition of root exudates stimulated the mineralization of 14C-pyrene in an unplanted soil to the same degree as that observed in actual rhizosphere soil. In rice (Oryza sativa) paddy soils, bacterial microbial communities have also been shown to be stimulated in the rhizosphere, leading to higher degradation of PAHs (pyrene (PYR) and phenanthrene (PHN)) in the soil root zone as compared to non- rhizospheric soils (Sue and Yang 2009). Enhanced decrease in the levels of other pollutants, such as hexachlorobenzene, has also been shown in rhizosphere of rice, and the authors suggested that this system showed promise for in situ degradation of this type of persistent organic pollutant (Hua et al. 2008). In contrast, phenanthrene-degrading activity of Pseudomonas putida ATCC 17484 was repressed after incubation with plant root extracts (Rentz et al. 2004). However, these authors suggested that the enhanced microbial growth on rhizodeposits is likely to compensate for this partial repression since a larger microbial population leads to a faster degradation rate. PAHs can easily accumulate in rice straw used as cattle feed, which can eventually be transferred to human beings through the food chain. The same is true for various other persistent pollutants (Sue and Zu 2007). The anaerobic and reducing environment during the rice growth period in paddy soils is similar to that of a wetland system, thus allowing PAHs to be effectively distributed in soil-plant-water systems. Compared to plants grown in uplands, such as ryegrass, wheat, and maize, the rhizosphere effect of the paddy soil would suggest an important environment for PAH removal. Degradation of PAHs in soils occurs mainly in the root zone (Wild et al. 2005), and rhizosphere soils present a higher degree of degradation of those compounds. The uptake of selected PAHs by Oryza sativa seedlings grown in soil spiked with naphthalene, phenanthrene, and pyrene exhibited volatilization loss of 98%, 95%, and 30%, respectively, with the remaining fraction being fixed by soil organic matter and/or degraded by soil microbes (Yu-Hong and Yong-Guan 2008). The relative contributions of plant uptake and plant-promoted rhizosphere microbial biodegradation to the total mass balance were 0.24% and 14%, respectively, based on PYR concentrations in rhizosphere and non-rhizosphere soils, the biomass of rice roots, and the dry soil weight (Yu-Hong and Yong-Guan 2008). The promotion of PAH degradation was also demonstrated in the rhizosphere of Festuca arundinacea using the bacteria Pseudomonas fluorescens. P. fluorescens 5RL interacted more significantly with salicylate and dextrose in agar cultures containing tall fescue compared to those without plant roots (Ho et al. 2007). Although the presence of tall fescue did not promote catabolic enzyme induction in the absence of salicylate, an increase in dioxygenase activity relative to non-planted controls implies that this plant may enhance the degradation of PAHs or facilitate the genotypes that are capable of transforming PAHs in the rhizosphere. Sipila et al. (2008) studied the genetic degradation potential of pristine and PAH-polluted soils using molecular tools (restriction fragment length polymorphism (RFLP) and terminal restriction fragment length polymorphism (T-RFLP)). A greenhouse microcosm experiment was carried out to elucidate structural and functional bacterial diversity in PAH-polluted soil and to test the suitability of Betula pendula for remediation. Bacterial 16S rRNA T-RFLP fingerprinting
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revealed a high structural bacterial diversity in soil where PAH amendment altered the general community structure as well as the rhizosphere community. Birch augmented extradiol dioxygenase diversity in rhizosphere showing a rhizosphere effect, and pyrene was more efficiently degraded in planted pots. Degraders of aromatic compounds upon PAH amendment were shown by the changed extradiol ring-cleavage community structure in soil. The RFLP analysis grouped extradiol dioxygenase marker genes into 17 distinct operational taxonomic units displaying novel phylogenetic clusters of ring-cleavage dioxygenases representing putative catabolic pathways, and the peptide sequences contained conserved amino-acid signatures of extradiol dioxygenases. A branch of major environmental TS cluster was identified as being related to Parvibaculum lavantivorans ring-cleavage dioxygenase. The described structural and functional diversity demonstrated a complex interplay of bacteria in PAH pollution. A hydrocarbon-degrading thermophilic bacterium, Nocardia otitidiscaviarum TSH1, an actinomycete was isolated from the soil, is able to grow on phenol, PAHs (pyrene, phenanthrene, anthracene and naphthalene) and straight chain aliphatic hydrocarbons with intermediate chain length (dodecane and hexadecane) as the sole sources of carbon and energy (Zeinali et al. 2007). N. otitidiscaviarum TSH1 was the first reported Nocardia strain able to grow on such a broad range of aliphatic and aromatic hydrocarbons under thermophilic conditions. This strain is known to possess extremely lipophilic cell surfaces, which may make it better suited to the direct uptake of highly hydrophobic hydrocarbons. The capacity of the PAH-utilizing N. otitidiscaviarum TSH1 isolate to produce bio-surfactants was also investigated. Fatty acids (C14–C18) were detected by GC-MS analysis during bacterial growth in PAH supplemented mineral media. High cell surface hydrophobicity and the ability of N. otitidiscaviarum TSH1 to degrade different hydrocarbons at 50°C may make it an ideal candidate to treat oil-contaminated desert soils (Zeinali et al. 2007). In a recent study, the transformation of naphthalene by strain TSH1, revealed different metabolites suggests that strain TSH1 initiates its attack on naphthalene by dioxygenation at its C-1 and C-2 positions to give 1,2-dihydro-1,2-dihydroxynaphthalene(Zeinali et al. 2008). The intermediate 2-hydroxycinnamic acid, characteristic of the meta-cleavage of the resulting diol was identified in the acidic extract. Apart from typical metabolites of naphthalene degradation known from mesophiles, benzoic acid was identified as an intermediate for the naphthalene pathway of this Nocardia strain. Neither phthalic acid nor salicylic acid metabolites were detected in culture extracts. Enzymatic experiments with cell extract showed the catechol 1,2-dioxygenase activity while transformation of phthalic acid and protocatechuic acid was not observed. The results of enzyme activity assays and identification of benzoic acid in culture extract provide strong indications that further degradation goes through benzoate and beta-ketoadipate pathway (Zeinali et al. 2008). The residual ecotoxicity of soils spiked with a combination of three PAHs at four levels (15, 75, 150, 300 mg PAHs kg−1 soil) after long-term bioremediation was evaluated using physico-chemical analyses, bioassays and soil microbial analyses (Hamdi et al. 2007). The pot-scale bioremediation process consisted of weekly moderate watering in the presence or absence of sewage sludge compost
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(SSC). After 15 months, anthracene and pyrene were almost completely degraded whereas benzo[a]pyrene persisted, especially in SSC-amended soil treatments. However, no apparent toxic effects of the residual PAHs could be detected. Despite the smaller number of culturable bacterial populations in SSC-amended soils, soil enzymatic activities were not affected by the organic amendment and residual PAHs; and the bioremediation efficiency was likely to be more limited by the bioavailability of PAHs rather than by the total number of PAH-degraders (Hamdi et al. 2007). Polychlorinated biphenyls (PCBs) have been detected in most ecosystems, and especially in soils and sediments of industrial areas (McFarland and Clarke 1989). Such ubiquitous presence is explained by their high chemical stability and low water solubility, which contribute to their persistence in the environment, but also by the fact that more than 300 million kg have been released in the environment before their use was abandoned in most countries (Holoubek 2001). PCBs accumulate in higher trophic levels of the food chain and cause numerous toxic effects on different organisms (Safe 1994). A number of plants have been shown to accumulate PCBs, e.g., Curcubita pepo grown in PCB contaminated soil has been shown to bio-accumulate Aroclors 1254 and 1260 through root uptake and translocation (Whitfield et al. 2008). Monoterpenes (such as cymene, a-pyrene and a-terpinene) and phenolics (such as salicylate) have been shown to induce biphenyl dioxygenase in PCB-degrading bacteria, enhancing total PCB degradation (Chen and Aitken 1999). Terpenes are also considered likely analogues for co-metabolism of PCBs due to a similarity in their molecular structure (Gilbert and Crowley 1997; Hernandez et al. 1997). Degradation of 2,4-dichlorophenol by indigenous soil MO was greater in soils amended and “aged” with monoterpenes (a-pinene, limonene and p-cymene) than in freshly spiked or control soils (Rhodes et al. 2007). Phenolic compounds such as naringen, coumarin or catechin, released by roots of certain plants have been shown to support the growth of rhizospheric PCB-degrading bacteria (Chaudhry et al. 2005). Narasimhan et al. (2003) showed that phenylpropanoids (such as flavonoids) constitute 84% of the secondary metabolites exuded from Arabidopsis roots, and that phenylpropanoid-utilizing microbes are more competitive and grow better than their autotrophic mutants on the roots of plants that are able to synthesize or overproduce phenylpropanoids. PCB removal by Pseudomonas putida PML2, a phenylpropanoid-utilizing and PCB-degrading rhizobacteria, was significantly lower in the rhizosphere of an Arabidopsis mutant exuding less flavonoids than in the rhizosphere of the wild-type strain. Salix sp. produce salicylic acid and related compounds that induce the degradation of many xenobiotic molecules (such as PAHs) and sustain bacterial growth (Leigh et al. 2006). Hence these trees are good candidates for rhizoremediation of PCBcontaminated soils. A 168-day microcosm experiment was used to assess the possible functional and structural shifts occurring in the bacterial community of a site with a history of PCB contamination, after the introduction of plants inoculated with genetically modified (GM) MO designed for rhizoremediation (Aguirre de Cárcer et al. 2007a). Salix viminalis x schwerinii var. Björn were inoculated with two different GM Pseudomonas fluorescens strains or with their parental wild-type
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strain. The introduced transgenes had no effect on the function and structure of the bacterial community in non-rhizospheric soil, although chemical analyses showed an enhanced biodegradation of PCBs. However, the transgenes affected the development of functionally and genetically distinct bacterial communities in the rhizosphere (Aguirre de Cárcer et al. 2007b). An enrichment of Betaproteobacteria (especially Burkholderiales) was suggested to represent selection due to the presence of PCBs. Leigh et al. (2007) used stable isotope probing (SIP) integrated with comprehensive functional gene analyses to monitor changes in biphenyl-degrading bacterial populations in the rhizosphere of Pinus nigra. SIP revealed 75 different genera that derived carbon from C-13-biphenyl, with Pseudonocardia, Kribella, Nocardiodes and Sphingomonas predominating carbon acquisition. These authors showed that results obtained using molecular tools do not always correspond with those from traditional cultivation methods. The organochlorine 1,2,3,4,5,6-hexachlorocyclohexane (HCH) is a broad- spectrum insecticide that was used on a large-scale worldwide since the 1940s, and is available in two formulations: technical-grade HCH (a mixture of different isomers, mainly a- (60–70%), b- (5–12%), g- (10–15%), and d-HCH (6–10%)) and lindane (almost pure g-HCH). All HCH isomers (HCHs) are acutely toxic to mammals, and residues of lindane and other HCH isomers can be found worldwide in air, water, sediments and soils (Willett et al. 1998; Schwitzguébel et al. 2006). Due to its physicochemical characteristics, HCH isomers tend to sorb organic material in the environment and have a low bioavailability (Rodríguez Garrido 2003). The low water solubility and high hydrophobicity (logKOW 3.7–4.1; Willett et al. 1998) of HCH isomers make their uptake and translocation within the plant unlikely. Various authors coincide in that the most likely mechanism of HCH accumulation in plants is the sorption of soil HCH on roots and sorption of volatilized HCH on aerial plant tissues (Calvelo Pereira et al. 2006, 2008; Abhilash et al. 2008). The b-HCH isomer is generally the main isomer in all plant tissues. Rhizoremediation is considered the most viable approach for the remediation of lindane and other HCH isomers (Schwitzguébel et al. 2006; Kidd et al. 2008). In the field, a lower level of HCH isomers (a-, b-, g-, d-HCH) was observed in the rhizosphere of Cytisus striatus and Avena sativa compared to nonvegetated soil (Calvelo Pereira et al. 2006). The same authors observed an increase in the bioavailability of a- and b-HCH in the rhizosphere of Avena sativa compared to nonvegetated soil. Exudation of surfactant-type compounds through the roots could increase isomer bioavailability in the rhizosphere. Plant root exudates have been shown to stimulate growth of lindane degrading bacterium Pseudomonas sp. augmenting lindane (g-HCH) mineralisation in the rhizosphere (Schwitzguébel et al. 2006). Kidd et al. (2008) evaluated HCH dissipation, and microbial densities (total heterotrophs, ammonifiers, amylolytics) and C substrate utilization patterns among rhizosphere and bulk soil of two contrasting plants, Cytisus striatus (Hill) Rothm and Holcus lanatus L. HCH degradation was isomer-specific: an enhanced degradation of a-HCH, but not b- or d-HCH, was observed in the rhizosphere. Significant changes in the microbial densities were observed between bulk and rhizosphere soils of Cytisus, and an increase in C source utilization indicated changes in community level physiological
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profiles (CLPP) in the rhizosphere of this species when grown in contaminated soils. HCH dissipation was also greater in soils planted with this species. The authors related changes in microbial physiological groups and CLPP in the rhizosphere of this species to a possible selective enrichment of HCH-degrading populations. In fact, the establishment of mutualistic interactions in the rhizosphere between plants and degrading strains is of great relevance, leading to the need of selecting appropriate strains promoted the degradation of lindane and the related compounds ∂-hexachlorocyclohexane and b-hexachlorocyclohexane through the use of a double enrichment approach that led to the isolation of four lindane degrading Sphingomonas strains that proliferate in the corn rhizosphere whereas the parent strains could not colonize the plant (Ramos et al. 2008).
Biodegradation of Xenobiotics with the Help of Mycorrhizal Fungi Mycorrhizal fungi are a major component of the rhizosphere and form mutualistic associations with most plant species (Azcón-Aguilar and Barea 1992). Ninety to 95% of all land plants form some type of mycorrhizal associations and the symbiotic association – mycorrhiza – seems to be the chief organ of nutrient uptake in the majority of plants (Bago et al. 2000; Entry et al. 2002). Of the known mycorrhizal associations (ectomycorrhizas, arbuscular mycorrhizas, ericaceous mycorrhizas, and orchid mycorrhizas) the arbuscular mycorrhizas (AM) are those occuring most frequently (Entry et al. 2002), and associations between arbuscular mycorrhizal fungi (AMF) and the roots of terrestrial plant species are by far the most widespread (Smith and Read 1997). AMF can benefit plants through improved nutrition (Clark and Zeto 2000), through extensive extraradical hyphal networks, which explore the soil, absorb nutrients, and translocate them to the roots (Giovannetti et al. 2002), and through root system modifications, generally resulting in a more extensive length and increased branching, and therefore in a more efficient nutrient – and contaminant – absorption (Berta et al. 2002). In addition, the AM symbiotic status also changes the chemical composition of root exudates (Laheurte et al. 1990) and influences soil pH (Li et al. 1991), thus affecting the microbial populations in the rhizosphere (Azcón-Aguilar and Barea 1992; Barea 1997), and improving soil structure (Rillig and Mummey 2006). Recently, exudates produced by AM extraradical mycelia have been shown to influence the growth and development of bacterial communities, increasing bacterial growth and vitality and changing soil bacterial community composition (Toljander et al. 2007). Bacterial communities can be markedly altered in the mycorrhizosphere compared to the rhizosphere of non-mycorrhizal roots (diagram 1). Larsen et al. (2009) have reported interactions between the AMF Glomus intraradices and the plant growth promoting rhizobacteria Paenibacillus polymyxa in the mycorrhizosphere of Cucumis sativus. The role of AMF in soils contaminated with heavy metals has been investigated for many years (Leyval et al. 1997; Leyval et al. 2002), while studies with organic pollutants are relatively scarce. Pre-inoculation of four plant species (Acacia melanoxylum, Cytisus
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striatus, Allium cepa and Trifolium pratense) with an isolate of Glomus deserticola obtained from a HCH-contaminated soil resulted in increased growth and fungal colonization of roots compared with plants pre-inoculated with the introduced fungus G. macrocarpum or colonized by a consortium of indigenous AM fungal species, when those plants were transplanted to an HCH-contaminated soil (Sainz et al. 2006). This suggests that the fungus increases plant tolerance to the toxic soil environment. This effect has been observed in studies with anthropogenic alkaline industrial sediment, for which the combined used of bacteria and AMF Glomus species has ameliorated the soil conditions and reduced plant stress associated with that specific pollution (Oliveira et al. 2005a, b). Mycorrhizal fungi can influence plant uptake or translocation of soil metals (Khan et al. 2000). The role of AMF in the plant response to metal stress is variable (Joner et al. 2004). Some studies report reduced metal bioaccumulation by plants due to mycorrhizal colonisation (Heggo et al. 1990; Jentschke et al. 1998; Huang et al. (2002), which may be related to exclusion strategies, whereas other reports indicate enhanced metal uptake and accumulation in plants due to AMF colonisation (Marques et al. 2006, 2007). A bulk of evidence seems to suggest a speciesspecific effect of AM associations on plant metal uptake and accumulation. As examples, Marques and co-workers (2008) have shown that inoculation with the AMF G. intraradices or G. claroideum protected the host plant Solanum nigrum of excessive Zn, with a decrease in metal accumulation in AMF inoculated plants, whereas at lower Zn levels in the growing matrix there was an increase in metal accumulation. It is possible that such a type of defence mechanism can also be triggered in the presence of organic contaminants. In fact, a recent study on the effect of mycorrhizal inoculation on the phytoextraction of weathered p,p-DDE from soil by Cucurbita pepo showed increases of up to 60% on the accumulation of the organic contaminant by the plant, however the response was dependent on the type of AMF inocula used (White et al. 2006). Increased accumulation was attributed to enhanced pollutant availability. Successful in vitro fungal degradation of organic contaminants has been demonstrated for a range of compounds or compound mixtures, such as aliphatic hydrocarbons, fuel oil and other mixed petroleum hydrocarbons, PAHs, explosives, pesticides and chlorinated organic compounds (Chang et al. 1998; Nicolotti and Egli 1998; Pradhan et al. 1998). Degradation of metsulfuron-methyl in simulated wheat (Triticum asetivum L.) rhizospheric soil was promoted by inoculation with Penicillium sp (He et al. 2007). Only recently has the degradation of organic compounds by ectomycorrhizal fungi (ECMF) been addressed, with major classes of environmentally important POPs, including PAHs, being potentially degraded (Meharg and Cairney 2000), although the role of ECMF in metal-polluted soils has been under investigation for more than 2 decades (Leyval et al. 1997). In one of such recent studies, Huang et al. (2002) reported the biodegradation of 1,1,1-trichloro-2,2-bis (4-chlorophenyl) ethane (DDT) by 4 ectomycorrhizal fungi (ECMF) species, Boletus edulis, Gomphidius viscidus, Laccaria bicolor, and Leccinum scabrum, using a pathway similar to that found in white rot fungi. Green et al. (1999) had described the mycorrhizal degradation of halogenated biphenyls by the ectomycorrhizal fungus Tylospora fibrilosa and
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several other mycorrhizal fungi. Ectomycorrhizae associations are in fact known to enhance root protection against adverse conditions such as a lack of water, extreme pH and temperatures, and presence of contaminants (Anderson and Cairney 2007). More importantly, ECMF play a vital role in nutrient cycling by degrading complex minerals or organic substances present in soil and making them available to the host plant. As for the plant host, it releases photosynthetic compounds and other exudates via roots into the immediate environment, which are used by the fungal partner (Alexander 2007). Inoculation of fungal-bacterial co-cultures (Penicillium janthine - Stenotrophomonas maltophilia) into PAH-contaminated soil resulted in significantly improved degradation of HMW PAHs, benzo[a]pyrene mineralization (53% of added [14C]benzo[a] pyrene was recovered as 14CO2 in 100 days), and reduction in the mutagenicity of organic soil extracts, compared with the indigenous microbes and soil amended with only axenic inocula (Boonchan et al. 2000). The bacteria S. maltophilia could use pyrene as their sole carbon and energy source in a basal salts medium (BSM) and mineralized significant amounts of benzo[a]pyrene cometabolically when pyrene was also present in BSM. P. janthinellum VUO 10,201 could not utilize any HMW PAH as sole carbon and energy source but could partially degrade these if cultured in a nutrient broth (Boonchan et al. 2000). It was clear from the work performed by Boonchan and coworkers (2000) that the combination of bacterial and fungal activity increased the mineralization of high molecular weight PAHs in liquid culture and in soil. How plants and associated mycorrhizal symbionts are involved in biodegradation of pollutants is however yet much unexplored. An improved understanding of species dynamic interactions and synergisms with plants will provide new tools to promote bioremediation of polluted soils.
Practical Implementation of Plant-Microbial Systems in PAHs Biodegradation Microbial transformation of POPs is important in nature as well as for various technological applications of MO, such as wastewater treatment, biodegradation, bioremediation, and biocatalysis. Although a wide range of MO have been discovered that are able to degrade highly stable, toxic organic compounds such as polycyclic and aliphatic hydrocarbons (Habe and Omori 2003; Kanaly and Harayama 2000; van Hamme et al. 2003), the environmental pollution caused by these compounds remains an unresolved problem. Poor bioavailability due to the low aqueous solubility of PAHs may also account for their overall low biodegradation extents in nature. The activity of degrading MO depends upon many factors, including contaminant uptake and toxicity (Cerniglia 1992). Very little is known, however, about how PAHs travel across bacterial membranes to reach the cytoplasmic metabolic enzymes. Toxic effects of PAHs on MO are not well documented. Despite the high partition of hydrophobic compounds into membranes (Sikkema et al. 1994, 1995), PAHs can be considered nontoxic to bacteria because of their
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very low water solubility (Neumann et al. 2005). Microbial cells are adapted to respond rapidly to environmental stress by regulating the fluidity of their membrane through a modification in membrane lipid composition. The influence of membrane-active organic compounds on membrane lipids in Gram-negative bacteria has been extensively studied (Heipieper et al. 2003; Mrozik et al. 2005; Segura et al. 1999). On the other hand, the effect on the membrane lipids of Gram-positive bacteria, particularly those that are able to utilize such compounds as a sole carbon source, has not yet been studied in detail, in spite of their biotechnological interest for biodegradation or bioremediation. More recently, Zhang and Zhu (2009) demonstrated that sorption of PAHs to ryegrass root cell membranes was actually regulated by carbohydrates and lipids rather than lipids individually. Conceivably, plants could be exploited to aid in the removal of PAHs and other contaminants from soils via two main mechanisms: (1) the accumulation by, and the subsequent metabolisation in plant tissues following the contaminant uptake by plant roots - phytodegradation (Wild et al. 2005) and (2) the enhanced microbial activity induced by root exudation of enzymes to transform and/or mineralize contaminants – rhizodegradation (Rentz et al. 2004; Kamath et al. 2005). Thus, the bioavailability of soil associated PAHs and plant uptake capacity would greatly influence the efficiency of PAH phytoremediation. If the dominant mechanism of PAH dissipation in planted soil is associated with rhizosphere microbial activity, then the remediation potential will significantly vary between plant species and ecotypes. In fact, a growing number of studies indicate that plant species do not all have the same potential for enhanced remediation (Frick et al. 1999; Hutchinson et al. 2003; Gaskin et al. 2008). This may be a result of differences in plant morphology (e.g. roots), physiology (e.g. root exudates), and microbial interactions in the rhizosphere (Pichtel and Liskanen 2001; Walker et al. 2003, Fig. 1). Some plants such as Cucurbita pepo (zucchini) accumulate high levels of hydrophobic chemicals (Campanella et al. 2002; Bittzanski et al., this volume), others, such as alfalfa possess extensive root systems that exhibit a high affinity toward hydrophobic chemicals (Schwab et al. 1998). Grasses in particular have often been proposed as potential candidates for the rhizoremediation of hydrocarbons due to their highly branched, fibrous root systems, which can harbour large microbial numbers and exert a greater influence on the soil environment (Anderson et al. 1993). In one soil type, PAH dissipation was highest in soils planted with a grass species and lowest in those with clover (Banks et al. 2000), whereas in a different soil type grass (Panicum bisulcatum) inhibited PAH mineralization (Watkins et al. 1994). Gaskin and co-workers (2008) identified three Australian native grass species (Brachiaria decumbens, Cymbopogon ambiguous, Microlaena stipoides var. Griffin) as suitable candidates for rhizoremediation applications of aliphatic hydrocarbon-contaminated soils. Hydrocarbon-degrading microbial numbers were increased in the rhizosphere. The number of culturable PCB-degrading rhizobacteria was also found to differ significantly depending on the plant species from where they were isolated (Leigh et al. 2006). Significantly higher numbers of PCB degraders (2.7- to 56.7-fold-higher means) were detected in the rhizosphere of Pinus nigra and Salix caprea than that of other plant species or in non-vegetated soil in certain seasons
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and at certain soil depths. Olson and co-workers (2008) studied the effects of different types of vegetation on PAH removal, and on the interaction between the plants and their associated MO. Aged PAH-polluted soil (753 mg PAHs kg−1) was planted with 18 plant species representing eight families. Planting significantly enhanced the dissipation rates of 17 PAHs within the first 7 months, but this effect was not significant after 14 months. The extent of removal of LMW PAHs was similar for planted and unplanted control soils after 14 months, but removal of fiveand six-ring PAHs was significantly greater in planted soils. Poaceae (grasses) were the most effective of the families tested, and perennial ryegrass was the most effective species; after 14 months, soils planted with perennial ryegrass contained 30% of the initial total PAH concentration (compared with 51% of the initial concentrations in unplanted control soil). There was no correlation across plant species between PAH dissipation and the size of the PAH-degrading population. Further research is needed to understand differences among plant families and species for stimulating PAH dissipation. To achieve maximum contaminant dissipation and successfully establish stable vegetation cover, various criteria must be considered. Plants should be chosen carefully so that they provide a maximum root surface area. They should be native to the area in which they are being used and should be tolerant to local soil conditions. Since cost is an important factor, plants that require little attention (e.g., plants that do not need fertilization) are preferable. Due to the usually poor nutrient availability in contaminated soils (Harris et al. 1996), much research has been conducted on the use of legumes capable of fixing nitrogen (Liste and Alexander 2000). Phenanthrene uptake as well as the effect of phenanthrene on the membrane phospholipid and fatty acid composition in a newly isolated bacterial strain, Sphe3, identified as Arthrobacter sp, was studied (Kallimanis et al. 2007). Strain Sphe3 is able to utilize phenanthrene as a carbon source at high rates and appears to internalize phenanthrene with two mechanisms: passive diffusion when cells are grown on glucose and an inducible active transport system when cells are grown on phenanthrene as a sole carbon source. Active transport followed Michaelis-Menten kinetics, and it was amenable to inhibition by 2,4-dinitrophenol and sodium azide. Evidence provided here indicates that apart from inducing an active PAH uptake, the presence of phenanthrene elicits significant changes in membrane fluidity (Kallimanis et al. 2007). The efficiency of contaminant dissipation will be greatly influenced by contaminant bioavailability (influenced by contaminant hydrophobicity, aqueous solubility and polarity). Rhizoremediation can suffer from a drawback due to the formation of bound-residues or sequestration of organic compounds during processes of decomposition and humification of organic matter (Alexander 2000). Contaminated soils often contain a separate non-aqueous phase liquid (NAPL) that may be present as droplets or films on soil surfaces. Many pollutants, especially those that are hydrophobic, are virtually insoluble in water and remain adsorbed in the NAPL (Chauhan et al. 2008). Biosurfactants can potentially increase the bioavailability of PAHs via mechanisms such as emulsification of non-aqueous phase liquid (NAPLs) in the soil, enhancement of the apparent solubility of the PAHs, or mobilization of PAHs adsorbed to the soil (Volkering et al. 1995). Biosurfactant additions were
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shown to increase the apparent solubilities of PAHs five to 20-fold and significantly increase their rate of biodegradation (Barkay et al. 1999; Rosenberg et al. 1999). Garcia-Junco and co-workers (2001) isolated a biosurfactant-producing strain, Pseudomonas aeruginosa 19SJ, which also had the capability to degrade phenanthrene. Root exudates or rhizodeposits not only provide a nutrient-rich habitat for MO but can potentially enhance biodegradation in different ways: they may facilitate the co-metabolic transformation of pollutants with similar structures, induce genes encoding enzymes involved in the degradation process, increase contaminant bioavailability (surfactant activity), and/or selectively increase the number and activity of pollutant degraders in the rhizosphere (Anderson and Coats 1995; Schnoor et al. 1995; Nichols et al. 1997; Burken and Schnoor 1998; Miya and Firestone 2001; Shaw and Burns 2003). As a result, the microbial community might increase the transformation rates of toxicants. Since the release of compounds or enzymes from roots is presumed to be associated with rhizosphere biodegradation and the nature and quantity of compounds released varies according to plant type, it follows that the plant species used will be a significant factor influencing the efficacy of accumulation/degradation of organic pollutants and associated risk. Plants (and plant types) vary widely with respect to root parameters such as morphology, root exudation (Grayston et al. 1996), fine root turnover (Gill and Jackson 2000), root decomposition (van der Krift et al. 2002), and associated microbial communities (Smalla et al. 2001). Interactions in the rhizosphere are very complex and they can be further unravelled through the use of sophisticated metabolic techniques, such as whole-transcriptome profiling and proteomics (Wood 2008). In general, there is a need to better understand the mechanisms involved in root surface colonization, which encompass complex regulatory mechanisms that modulate gene expression in both the plant and the associated bacteria (Rudrappa et al. 2008). Bioremediation and biodegradation of POPs can be facilitated by growing certain plant species that enhance the biodegradation activities of bacteria and fungi in the rhizosphere (Fig. 1). Combining bacteria with suitable degradation capacities
Plants/ Exudates
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POPs Biodegradation/ Bioavailability to plants
Fig. 1 Effects of plants/rhizosphere interactions on the bioremediation of POPs
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with suitable plant hosts is of outmost importance. It is important to assess how microbial communities degrade POPs in order to understand their potential role for in situ bioremediation. The nature of plant microbial interactions in the rhizosphere, with a focus on those processes that are relevant to the breakdown and (or) removal of organic pollutants, has been overlooked so far. Key aspects are still unknown. Special attention goes to the degradation pathways expressed in the rhizosphere and how plants and associated symbionts are involved in biodegradation of pollutants. Our understanding of microbial dynamic interactions and synergisms with plants will provide new tools to promote bioremediation of polluted soils.
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Bioindicators and Biomonitors: Use of Organisms to Observe the Influence of Chemicals on the Environment* Bernd Markert and Simone Wünschmann
Abstract For a number of years “classical” programs for environmental monitoring are being supplemented by bioindication measures already. Here, investigations on living organisms or their remains (e.g. peat) are used to indicate the environmental situation in either qualitative (bioindication) or quantitative (biomonitoring) terms. This provides pieces of information on environmental burdens of a region at a given point of time or on its changes with time (trend analysis). Classical bioindication often deals with observation and measurements of chemical noxae (both inorganic and organic ones) in well-defined bioindicator plants or animals (including man). In terms of analytical procedures and results there are parallel developments between progresses in bioindication and innovation in analytical methods. After some 30 years of development in bioindication there are now following lines of further development: 1) more frequent inclusion of multi-element total analyses for a thorough investigation of mutual correlations in the sense of the Biological System of Elements, 2) more work on (analytical) speciation issues to proceed into real effect-oriented environmental sciences, and 3) there should and must be a focus on integrative bioindication methods because for a large number of environmental monitoring problems a single bioindicator will not provide any meaningful information: a single bioindicator is about as good as none at all. Integrative concepts
This article is in parts related to B. Markert, S. Wuenschmann, R. Herzig and Ph. Quevauviller, 2010: Bioindicateurs et biomoniteurs dans l´environnment: Définitions, stratégies et applications, Editions Techniques de l`Ingénieur, P 4 170, p. 1-16; Markert B.A., Breure A.M., and Zechmeister H.G., eds., 2003: Bioindicators and Biomonitors – Principles, Concepts and Applications, Elsevier, Amsterdam, New York, Tokyo; Markert B 1996: Instrumental Element and Multielement Analysis of Plant Samples, Wiley, Chichester, New York.
*
B. Markert (*) Former director of the Internationales Hochschulinstitut Zittau, Lehrstuhl für Umweltverfahrenstechnik, Markt 23, 02763 Zittau, Germany e-mail:
[email protected] B. Markert and S. Wünschmann Fliederweg 17, D-49733, Haren/Erika, Deutschland P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_10, © Springer Science+Business Media B.V. 2011
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such as the Multi-Markered Bioindication Concept (MMBC) provide basic means to get into precautionary environmental protection effects drawing upon such a second-generation bioindication methodology.
Introduction Investigations on living organisms are used to indicate the environmental situation in either qualitative (bioindication) or quantitative (biomonitoring) terms. Bioindication/biomonitoring often deals with observation and measurements of chemical noxae (both inorganic and organic ones) in well-defined bioindicator/ biomonitoring plants (including microbes) or animals (including man). According to the extracted conclusion, that the use of only one bioindicator/biomonitor is as good as none at all, an integrative approach (the Multi-Markered-Bioindication-Concept, MMBC) is presented and the final goal is focussed towards an absolutely essential need of international teaching campaigns. At all times, plants have served as particularly interesting and important objects of scientific investigation. This is seen even in Greek biology, in the works of Aristotle or Theophrastus, which were devoted to plant taxonomy. Other later examples are the studies of Linnaeus on taxonomy and evolution, and those on yield-based economy and agricultural chemistry for the nourishment of mankind, starting with “Gesetz des Minimums” by Justus von Liebig, as well as the general target of biological and chemical basic research at the molecular biological level as done by Calvin and Krebs, for example (Markert 1996). Interest in new scientific knowledge has increased exponentially in the last 50 years of plant research. There are essentially three reasons for this (Markert 1996). 1. Plants make up more than 99% of the total biomass of the earth. The protection and conservation of the species and of the diversity of species, particularly in tropical and subtropical areas, has been elevated to one of the most important ecological, economical and cultural demands in national and international politics. 2. Plants are responsible for the most important reaction on earth, photosynthesis, producing carbohydrates from water and carbon dioxide in a complicated, light driven reaction, and oxygen at the same time. Life on earth in its present form would not be possible without the production of carbohydrates and the oxygen which is necessary for the breakdown of carbohydrates in the respiratory chain. A system which diffuses into a state of imbalance, one that is fed constantly increasing CO2 emissions and the simultaneous indiscriminate destruction of tropical rain forests in particular, represents a great ecological and economical challenge for mankind today due to the resultant greenhouse effect. 3. With respect to their effect on the flow of matter and of energy in the food chain, plants represent an important link between the atmosphere and the soil on the one hand and between consumers from the first (microbes) to the highest order (animals and man) on the other. Frequently, pollutants are introduced into the food chain via plants which have taken them up, and these pollutants often cause
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irreversible damage of individual organisms to entire biological communities as a result of accumulation and exclusion processes. Point 3, in particular, places high demands on modern analytical chemistry. Through the development of increasingly sensitive analytical methods, today it is practically possible to quantify most of chemicals. In cooperation with associated disciplines such as geology, physics, and medicine, this has led to the situation where biological trace research has become very dynamic. Figure 1 gives the average concentration of 82 naturally occurring elements in plants and in the earth’s crust as a function of their atomic mass.
Fig. 1 Average concentration of 82 naturally occurring elements in earth´s crust and plants as a function of their atomic mass (Markert 1996)
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The largest portion of the fresh weight of living plants (showing active metabolism) consists of 85–90% water. The dried matter of the plant is made up mostly of the following elements: carbon (44.5%), oxygen (42.5%), hydrogen (6.5%), nitrogen (2.5%), phosphorus (0.2%), sulfur (0.3%), and the alkali or alkaline earth metals: potassium (1.9%), calcium (1.0%), and magnesium (0.2%). Thus, in contrast to the earth’s crust, the main mass of organic life consists largely of non-metals. There are also microelements, which are present in plants in reduced concentrations and which are vital for most plants. These microelements are chlorine (2,000 mg/kg dry material), silicon (1,000 mg/kg), manganese (200 mg/kg), sodium (150 mg/kg), iron (150 mg/kg), zinc (50 mg/kg), boron (40 mg/kg), copper (10 mg/kg), chromium (1.5 mg/kg), molybdenum (0.5 mg/kg), and cobalt (0.2 mg/kg). Macro- and microelements are plant nutrients necessary for the growth and normal development of the plant. Therefore they are essential (Fig. 2). With respect to inorganic environmental chemistry one can roughly envision that about every 2 years one of the chemical elements in the periodic table will change its former status. The developmental history of selenium is a classical example. In 1930, Se and its compounds were generally felt to be highly toxic. After 1943 they were also classed as carcinogenic. In 1957 it was recognized that selenium is required by some organisms, and in 1966 certain Se compounds were successfully used in
Fig. 2 The periodic table of the elements with indicators on elements that are essential and that have been quantitatively determined (Markert 1996). A so called Biological System of the Elements (BSE) is given in Markert (1994) and Fränzle & Markert (2002)
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c ancer therapy. In 1990 Prof. Braetter´s research group in Berlin characterized and isolated already a second Se protein and described its function.
General Information on the Environment Bioindication and biomonitoring must supply information on the extent of pollution or degradation of ecosystems. Two different forms of information are available from bioindication: firstly, general ones which oversimplify matters, e.g. plant damage, and secondly highly specific ones which latter is provided in a very detailed, objective, reproducible and precise manner. For example, a certain pollutant may be linked to one physiological reaction in a bioindicator organism in order to obtain some more general information on the environment. When data and information obtained by bioindication are extrapolated to provide some higher knowledge the subjectivity of interpretation increases with the complexity and dynamics of a system. This increase in subjectivity linked to an increase in knowledge is depicted by the “staircase of knowing” (Roots 1992). On the first step of this staircase (Fig. 3), observations and measurements become data when verified according to agreed standards.
Fig. 3 The staircase of “knowing” (modified after Roots 1992)
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When data are properly selected, tested and related to subject areas they can become (pieces of) information: In turn information, once being organized and interpreted or applied to areas of interest or concern, can become established knowledge. If assimilated and mentally assessed and backed by additional information, this knowledge may be comprehended and integrated into a basis of facts and notions assimilated before, eventually leading to understanding. And understanding combined with judgement according to certain values can become wisdom. In general, by moving up the staircase, the material and ideas become increasingly subjective, with increasing human value added (Roots 1996).
Specific Information on the Environment Specific and detailed information of systems are essential within bioindication to draw clear-cut conclusions e.g. in between a pollutant and an effect of an organism (bioindicator). Figure 4 gives a simplified representation of complex (eco-)system interrelations being influenced by some pollution, and of the consequences of changes as revealed by bioindication and biomonitoring (Markert 1996). As a rule, it is assumed that a pollutant affects an organism which latter is taken as bioindicator or biomonitor. Both the organism and the pollutant interact closely with other ecosystem compartments (Fig. 4).
Fig. 4 Simplified representation of complex (eco-)system interrelations with regard to a pollutant, and consequences for bioindication and biomonitoring (Markert 1996)
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The life activity of the organism is therefore influenced by a great number of abiotic and biotic factors and may often be subject to joint action of several pollutants, especially under “natural” field conditions (subjective selection of references of the large amount of literature by the authors of this article: Adriano 1992; Broadley et al. 2007, 2008; Cakmak 2008; Chaney et al. 2008; De Bruyn et al. 2009; Fargašová and Beinrohr 1998; Fraenzle 2009; Fraenzle and Markert 2002, 2007; Fraenzle et al. 2007, 2008; Franzering and van der Eerden 2000; Golan-Goldhirsh et al. 2004; Greger 2008; Herzig 1993, 2005; Hanikenne et al. 2008; Hartley and Lepp 2008; Herzig and Bieri 2002; Irtelli and Navari-Izzo 2008; Lepp and Madejon 2007; Li et al. 2008; Lux et al. 2004; Markert 1993, 1994, 1996, 2007; Markert and Weckert 1993; Markert et al. 2010; Marmiroli and Maestri 2008; Mench et al. 2006; Mohr 2007; Poschenrieder et al. 2008; Prasad 2008; Quartacci et al. 2007; Quevauviller and Maier 1999; Quevauviller et al. 2008; Rasemann and Markert 1998; Renella et al. 2004; Rezek et al. 2008; Schröder et al. 2007; Schröder et al. 2008a, b; Schwitzguébel et al. 2008; Smeets et al. 2008; Smodis 2003; Szárazová et al. 2008; Trapp et al. 2008; Verbruggen et al. 2008; Verkleij 2008; Wuenschmann et al. 2001, 2002, 2008). With regard to the interpretation of the “information” given by the bioindicator/ biomonitor, often the problem arises from where changes observed or measured by the bioindicator/biomonitor really originate. Even a combined multi-functional and multi-structural view of the various ecosystem compartments often left the specific operative mechanisms unaccounted for. What makes matters even more difficult is that the pollutant to be monitored is closely connected to all other environmental compartments. So it is by no means certain, although rather probable, that pollutant A does not interact synergistically or antagonistically with pollutant B (Fig. 4). Moreover, the absorption pathway, sites of actions and metabolisms of both A and B usually are not yet adequately described. Nevertheless pollutant A may also affect other biota which may react even more sensitively to A than the bioindicator itself. If this sensitivity alters the population density of a more sensitive organism the abundance of the very bioindicator may also be affected, at least if the former is in direct or indirect competition with the latter. It is an unsettled issue whether a statement about the current condition of an entire ecosystem can be obtained by examining a single bioindicator (Markert 1996). With respect to the age of “information technologies”, Lieth (1998) tries to render the “digitalized bit world” more efficient for ecosystem research. According to Lieth we have to ask: what is the crucial point of ecosystem research? What information does an ecosystem offer? Given the information content of all its parts an ecosystem readily compares to the level of an intelligent system. Toxicological implications often involve the flow of information as the cause of significant changes in material fluxes and energy fluxes in the system. Plants may produce chemicals to protect themselves against animal grazing. Animals may produce toxic chemicals as weapons; humans may produce toxic chemicals to kill each other. Each process is controlled by “bits of information” which flow from one point in the ecosystem to another, so-called biobits (Markert et al. 2002, 2003b). A detailed description of this straightforward concept for further study is given in Lieth (1998).
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Definitions It seemed clear from the start that bioindication and biomonitoring are promising (and possibly cheap) methods of observing the impact of external factors on ecosystems and their development over a long period, or of differentiating between one location (e.g. an unpolluted site) and another (polluted site) (Markert et al. 2002, 2003a, b). The overwhelming enthusiasm shown in developing these methods has resulted in a problem that is still unsolved: the definitions of bioindication and biomonitoring respectively, and therefore the expectations associated with these methods, have never led to a common approach by the international scientific community, so that different definitions (and expectations!) now exist simultaneously (Markert et al. 2002, 2003a, b). A fine overview of the various definitions is given by Wittig (1993). As a first starting point for the difficult use of bioindication methods following literature might be helpful (subjective selection of the large amount of literature by the authors: Altenburger and Schmitt 2003; Arndt 1992; Bacchi et al. 2000; Bargagli 1998; Bode et al. 2000; Breulmann et al. 1998; Carreras et al. 1998; Djingova and Kuleff 2000; Elias et al. 2006; Farago, 1994; Figueiredo et al. 2001; Fraenzle O 1993; Fraenzle and Markert 2002, 2007; França et al. 2005, 2007; Freitas et al. 2006, 1999; Garty 1998; Genßler et al. 2001; Herpin et al. 1997, 2001; Herzig 1993, 2005; Jeran et al., 1993; Klumpp et al. 2000; KostkaRick et al. 2001; Lieth 1998; Loppi and Bonini 2000; Markert 1993, 1994, 1996, 2007; Pacheco et al. 2003; Saiki et al. 1997; Schroeder et al. 2008a, b; Shtangeeva et al. 2005; Siewers and Herpin 1998; Siewers et al. 2000; Stoeppler et al. 1982; Suchara et al. 2007; Vtorova et al. 2001; Vutchkov 2001; Wolterbeek 2002; Wolterbeek et al. 1995; Zechmeister et al. 2007). In the following some definitions will be given that have been developed and used by us over the last 20 years (Markert et al. 1999, 2003b), since they differentiate clearly between bioindication and biomonitoring using the qualitative/quantitative approach to chemical substances in the environment. This makes bioindicators directly comparable to instrumental measuring systems (Markert et al. 2003a, b). From that angle it is possible to distinguish clearly between active and passive bioindication (biomonitoring). Especially where the bioindication of metals is concerned, the literature often makes a distinction between “accumulation indicators” and “effect indicators” in respect of the reaction of the indicator/monitor to changes in environmental conditions. Here we should bear in mind that this differentiation does not imply a pair of opposites; it merely reflects two aspects of analysis. As the accumulation of a substance by an organism already constitutes a reaction to exposure to this substance which – at least in the case of high accumulation factors – is measurably reflected in at least one of the parameters used in defining the term “effect indicator/monitor” (e.g. morphological changes at the cellular level; formation of metal-containing intracellular granules in many invertebrates after metal accumulation), we should discuss whether it is worthwhile distinguishing between accumulation and effect indicators or whether both terms fall under the more general expression “reaction indicator”. Often, too, it is not until a substance has been
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Fig. 5 Illustration of the terms reaction, accumulation and effect/impact indicator (Markert et al. 1997). Explanations are given in the text
accumulated in organisms that intercellular or intracellular concentrations are attained that produce effects which are then analyzed in the context of effect and impact monitoring (Fig. 5). From these preliminaries we come to the following definitions, firstly summarized in Markert et al. 1997 and 1999: A bioindicator is an organism (or part of an organism or a community of organisms) that contains information on the quality of the environment (or a part of the environment). A biomonitor, on the other hand, is an organism (or a part of an organism or a community of organisms) that contains information on the quantitative aspects of the quality of the environment. A biomonitor is always a bioindicator as well, but a bioindicator does not necessarily meet the requirements for a biomonitor. Both, bioindication and biomonitoring are referred to as “active”, when test organisms bred in laboratories are exposed in a standardized form in the field for a defined period of time. At the end of this exposure time the reactions provoked are recorded or the xenobiotics taken up by the organism are analyzed. In the case of passive biomonitoring, organisms already occurring naturally in the ecosystem are examined for their reactions. This classification of organisms (or communities of these) is according to their “origin”. A classification of organisms (or communities of these) according to their “mode of action” (Fig. 5) is as follows: Accumulation indicators/monitors are organisms that accumulate one or more elements and/or compounds from their environment. Effect or impact indicators/monitors are organisms that demonstrate specific or unspecific effects in response to exposure to a certain element or compound or a number of substances. Such effects may include changes in their morphological, histological or cellular structure, their metabolic-biochemical processes, their behavior or their population structure. In general the term “reaction indicator” also includes accumulation indicators/monitors and effect or impact indicators/monitors as described above.
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When studying accumulation processes it would seem useful to distinguish between the paths by which organisms take up elements/compounds. Various mechanisms contribute to overall accumulation (bioaccumulation), depending on the species-related interactions between the indicators / monitors and their biotic and abiotic environment. Biomagnification is the term used for absorption of the substances from nutrients via the epithelia of the intestines. It is therefore limited to heterotrophic organisms and is the most significant contamination pathway for many land animals except in the case of metals that form highly volatile compounds (e.g. Hg, As) and are taken up through the respiratory organs, (e.g. trachea, lungs). Bioconcentration means the direct uptake of the substances concerned from the surrounding media, i.e. the physical environment, through tissues or organs (including the respiratory organs). Besides plants, that can only take up substances in this way (mainly through roots or leaves), bioconcentration plays a major role in aquatic animals. The same may also apply to soil invertebrates with a low degree of solarization when they come into contact with the water in the soil. Besides the classic floristic, faunal and biocoenotic investigations that primarily record rather unspecific reactions to pollutant exposure at higher organizational levels of the biological system, various newer methods have been introduced as instruments of bioindication. Most of these are biomarkers and biosensors. Biomarkers are measurable biological parameters at the suborganismic (genetic, enzymatic, physiological, morphological) level in which structural or functional changes indicate environmental influences in general and the action of pollutants in particular in qualitative and sometimes also in quantitative terms. Examples: plant and animal enzyme or substrate induction of cytochrome P-450 and other Phase I enzymes by various halogenated hydrocarbons; the incidence of forms of industrial melanism as markers for air pollution; tanning of the human skin, but also protective colouring of plant leaves caused by UV radiation; changes in the morphological, histological or ultra-structure of organisms or monitor organs (flowers, chloroplasts, etc.) following exposure to pollutants. A biosensor is a measuring device that produces a signal in proportion to the concentration of a defined group of substances through a suitable combination of a selective biological system, e.g. enzyme, antibody, membrane, organelle, cell or tissue, and a physical transmission device (e.g. potentiometric or amperometric electrode, optical or optoelectronic receiver). Examples: toxiguard bacterial toximeter; EuCyano bacterial electrode. Biotest (bioassay): routine toxicological-pharmacological procedure for testing the effects of agents (environmental chemicals, pharmaceuticals) on organisms, usually in the laboratory but occasionally in the field, under standardized conditions (with respect to biotic or abiotic factors). In the broader sense this definition covers cell and tissue cultures when used for testing purposes, enzyme tests and tests using microorganisms, plants and animals in the form of single-species or multi-species procedures in model ecological systems (e.g. microcosms and mesocosms). In the narrower sense the term only covers single-species and model system tests, while the other procedures may be called suborganismic tests. Bioassays use certain biomarkers or – less often – specific biosensors and can be used in bioindication or biomonitoring.
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With regard to genetic and non-genetic adaptation of organisms and communities to environmental stress we have to differentiate between the terms tolerance, resistance and sensitivity. Tolerance (Oehlmann and Markert 1997): desired resistance of an organism or community to unfavorable abiotic (climate, radiation, pollutants) or biotic factors (parasites, pathogens), where adaptive physiological changes (e.g. enzyme induction, immune response) can be observed. Resistance, unlike tolerance, is a genetically derived ability to withstand stress (Oehlmann and Markert 1997).This means that all tolerant organisms are resistant, but not all resistant organisms are tolerant. However, in ecotoxicology the dividing line between tolerance and resistance is not always so clear. For example, the phenomenon of PICT (pollution induced community tolerance) is described as the phenomenon of community shifts towards more tolerant communities when contaminants are present. It can occur as a result of genetic or physiological adaptation within species or populations, or through the replacement of sensitive organisms by more resistant organisms (Blanck et al. 1988; Rutgers et al. 1998). Sensitivity of an organism or a community means its susceptibility to biotic or abiotic change. Sensitivity is low if the tolerance or resistance to an environmental stressor is high, and sensitivity is high if the tolerance or resistance is low. A fruitful example for a country-wide investigation by passive biomonitoring with lichen is the comparison of the POP burden in areas with different types of land use shows that the burden emanating from Switzerland, particularly in conurbations, is considerably greater than the amount transported over long-ranges and across national boundaries. However, the latter category certainly contributes to the background level of contamination. The first country-wide POP study established a representative and spatially differentiated biomonitoring network and a reference archive of samples in Switzerland. A monitoring instrument of this kind can also be used to document the success of efforts to reduce burden over time (Herzig and Bieri 2002; and chapter 3 in this book). Other stimulating examples of bioindication and biomonitoring studies for controlling organic pollutants are given amongst others in Schwarz and Jonas (1997).
Further Studies and Outlook: MMBC and Teaching Guidelines Bioindication and biomonitoring must supply information on the degree of pollution or degradation of ecosystems. For integrative approaches bioindication is not an “environmental monitoring machine” for a specific constellation of factors; ideally, it is an integrated consideration of various bioindicative test systems which attempts, in conjunction with other environmental parameters, to produce a definite picture of a pollution situation and its development in the interests of prophylactic care of health and the environment. Figure 6 is a diagram of a complete dynamic environmental monitoring system supported by bioindication which depicts how tightly bioindication is coupled to
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Singular use of TESTS
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Fig. 6 Multi-Markered-Bioindication-Concept (MMBC): Possible hierarchical structure of a bioindicative toolbox model for integrative approaches in human- and ecotoxicology. The toolboxes MED and ECO contain single sets of tests that can be combined functionally to allow an integrated approach to any frame of reference or a specific scientific problem. The toolboxes HSB (human specimen banking) and ESB (environmental specimen banking) represent years of results from international environmental sample banks specializing in environmental and human toxicology. In addition to MED and ECO they provide important information on ecotoxicology and human toxicology of environmental chemicals. In the integrated approach, all results obtained are substantiated by existing basic data available from (eco-) systems research, toxicology and environmental sample banks. The parameter constellations necessary for this are taken from the toolboxes TRE and DAT (Markert et al. 2003b)
human toxicology. Hence biomonitoring is an important contribution to public health. It can re-combine its measurement parameters according to the particular system to be monitored or the scientific frame of reference. The two main subjects of investigation – man and the environment – and the disciplines human toxicology and ecotoxicology derived from them are associated with various “toolboxes” and sets of tests (“tools”, e.g. bioassays) for integrated environmental monitoring. The system shown in Fig. 6 consists of six toolboxes. The first two are derived mainly from environmental research: DAT (for data) and TRE (for trend). DAT contains, as a set, all the data available from the (eco-)system under investigation, i.e. including data acquired by purely instrumental means, for example from the meteorological sphere. DAT also contains maximum permissible concentrations of
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substances in drinking water, food or air at the workplace and the data for the relevant ADI (“acceptable daily intake”) and NO(A)EL (“no observed (adverse) effect level”). The toolbox TRE contains data on trends; these have been compiled mainly from years of investigations by national environmental sample banks, or information available from long-term national and international studies (e.g. Duvigneaud and Denayer-De Smet 1973; Ellenberg et al. 1986; Likens et al. 1977). Specific conclusions and trend forecasts can then be prepared using the subsequent toolboxes HSB (human specimen banking) and ESB (environmental specimen banking) (see also Kettrup 2003). The toolbox MED (medicine) contains all the usual methods employed in haematological and chemical clinical investigations of subchronic and chronic toxicity, whereas ECO is largely made up of all the bioindicative testing systems and monitors relevant to ecosystems which may be combined to suit the particular situation to be monitored. The data from all the toolboxes must interact with each other in such a way that it is possible to assess the average health risk for specific groups of the population or determine a future upper limit of risk from pollutants by forming networks. This risk assessment ultimately makes use of all the toxicological limits that take the nature of the effect and dose–effect relationships into account according to the current status of scientific knowledge. Since toxicological experiments cannot be carried out on human beings, recourse has to be made to experience at the workplace and cases of poisoning in order to permit an evaluation and risk assessment. Besides examining reports on individual cases, greater efforts must be made to reveal the effects of substances as a cause of disease by means of epidemiological surveys with exposed groups as compared to a control group. The development and use of simulation models supported by information technology, taking all the data collected into account, will play an important role here, since a large number of parameters that do not interact directly have to be combined. They include various data from the field of epidemiology, from mutagenicity studies, toxicokinetics, metabolism research and structure–effect relationships. The conclusions of such networking in between different tool boxes can be used for a whole concept of bioindication in general, outlined in the so called Multi-MarkeredBioindication-Concept (MMBC), which is outlined in Markert et al. (2002, 2003b). In conclusion there is very much interest on integrated monitoring which will require an interdisciplinary design and formation of research groups in future surveys, too. This would permit rapid and flexible adjustment of the working groups to the particular frame of reference and enable a quick exchange of information between the individual disciplines. To come closer to a prophylactic health care system between ecotoxicologists and medical doctors we should follow a common integrative way, and we should not work along parallel paths and thus separately as was most often done in the past 20 years. To this end, it could be worthwhile to have a look at the former ideas of combination of geoscientific ecology and medical sciences, which have some tradition in the German landscape ecology (Jusatz 1958; Jusatz and Flohn 1937; Mueller 1980; Schweinfurth 1974). Additionally, we should be aware of the interrelationships between culture, environmental quality and human health (Dansereau 1971; Warren and Harrison 1984).
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Table 1 Possible tracks to follow from environmental monitoring to human health (Markert et al. 2008). In the past, a gap of scientific collaboration between analytical scientists, ecotoxicologists and people concerned with environmental medicine (human toxicology) was obvious. This can be in the present overcome by developing a more intensive collaboration by communication and defining common goals in research and education. To reach a common scientific interest in the future, different specific research methods should be used for similar problems, which mean a common learning by doing research on the same interdisciplinary problems (symbolized by the middle column between present and future status). MMBC: Multi-MarkeredBioindication-Concept, a newly developed multidisciplinary system including integrated and functional “windows” of prophylactic healthcare Past (1980) Present (2000) Future (2020) Classical Interdiscipling Integrated International Interregional Intercultural Developing a Developing the link Goal Qualitative and prophylactic health between environmental quantitative care approach biomonitoring to human measurement health of environmental parameters MMBC-concept Defining common research Methods Comparison of own and some others interests between results with available on the eco- and human“others” scientific market toxicology Education, communication Quality Mental driving Knowledge forces Language cooperation Common science and Tools Instrumental and common goals education, success bioindicative and acceptance measurements
Table 1 tries to symbolise the “dilemma” of what blocks at the moment a fast development from present eco- and humantoxicological bioindication methods towards a more integrated understanding. Obviously, there exists a lack of intensive discussion and collaboration between ecologists and human medical people. Simply one example, which is obviously present in our day by day work. In the fine and most recent “Lehrbuch der Toxikologie [Treatise on Toxicology]” by Marquard and Schaefer (2004), an excellent content is given by around 100 scientists including most relevant topics in our common scientific field, but scientific findings of the ecotoxicologists (for example, nationally and internationally organized in SETAC) have more or less not been taken into account. To overcome this discrepancy there are two important issues that must be considered: 1. Common education of “toxicologists” at universities by integrative textbooks (for example, Fomin et al.’s, textbook on practical use of biotests published in 2003) 2. Common scientific projects as for example given in Table. 1 (Markert et al. 2008)
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Especially for topic two, an interdisciplinary language, common goals and methods have to be developed and finally successful research should be initiated. Acknowledgements We would like to thank all colleagues, friends, clients and students of numerous field studies worldwide for their critical and intensive discussions on our common topic (bioindication and biomonitoring) since a lot of years. A lot of their thoughts have influenced our MS.
References Adriano DC (1992) Biogeochemistry of trace. Metals Lewis, Boca Raton, FL Altenburger R, Schmitt M (2003) Predicting toxic effects of contaminants in ecosystems using single species investigations. In: Markert B, Breure A, Zechmeister H (eds) Bioindicators and biomonitors: principles, concepts and applications. Elsevier, Amsterdam, pp 153–198 Arndt U (1992) Key reactions in forest disease used as effects criteria for biomonitoring. In: McKenzie DH, Hyatt DE, McDonald VJ (eds) Ecological indicators. Proceedings of the international symposium Fort Lauderdale USA, Oct 1990, pp 16–19. Elsevier, Applied Science Publications, London, pp 829–840 Bacchi MA, De Nadai Fernandes EA, Oliveira H (2000) Brazilian experience on K0-standardized neutron activation analysis. Radio Nucl Chem Budapest 245(1):217–222 Bargagli R (ed) (1998) Trace elements in terrestrial plants – an ecophysiological approach to biomonitoring and biorecovery. Springer, Heidelberg Blanck H, Wängberg SA, Molander S (1988) Pollution-induced community tolerance. A new ecotoxicological tool. In: Cairns JJ, Pratt JR (eds) Functional testing of aquatic biota for estimating hazards of chemicals, ASTM STP 988. American Society for Testing and Materials, Philadelphia, PA, pp 219–230 Bode P, De Nadai Fernandes EA, Greenberg RR (2000) Metrology for chemical measurements and the position of INAA. J Radioanal Nucl Chem Budapest 245(1):109–114 Breulmann G, Ogino K, Ninomiya I, Ashton PS, La Frankie JV, Leffler U, Weckert V, Lieth H, Konschak R, Markert B (1998) Chemical characterisation of Dipterocarpaceae by use of chemical fingerprinting – a multielement approach at Sarawak, Malaysia. Sci Total Environ 215:85–100 Broadley MR, White PJ, Hammond JP, Zelko I, Lux A (2007) Zinc in plants. New Phytol 173(4):677–702 Broadley MR, Hammond JP, King GJ, Astley D, Bowen HC, Meacham MC, Mead A, Pink DAC, Teakle GR, Hayden RM, Spracklen WP, White PJ (2008) Shoot calcium and magnesium concentrations differ between subtaxa, are highly heritable, and associate with potentially pleiotropic loci in Brassica oleracea. Plant Physiol 146(4):1707–1720 Cakmak I (2008) Enrichment of cereal grains with zinc: agronomic or genetic biofortification? Plant Soil 302(1–2):1–17 Carreras HA, Gudino GL, Pignata ML (1998) Comparative biomonitoring of atmospheric quality in five zones of Cordoba city (Argentina) employing the transplanted lichen Usnea sp. Environ Pollut 103:317 Chaney RL, Chen KY, Li YM, Angle JS, Baker AJM (2008) Effects of calcium and nickel tolerance and accumulation in Alyssum species and cabbage grown in nutrient solution. Plant Soil 311(1–2):131–140 Dansereau P (1971) Dimensions of environmental quality, Sarracenia no. 14. University of Montréal, Montréal De Bruyn U, Linders HW, Mohr K (2009) Epiphytische Flechten im Wandel von Immissionen und Klima – Ergebnisse einer Vergleichskartierung 1989/2007 in Nordwestdeutschland. Umweltwissenschaften Schadstoff-Forschung 21:63–75
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Siewers U, Herpin U, Strassburg S (2000) Schwermetalleinträge in Deutschland. Teil 2: MoosMonitoring 1995/1996Geol Jb, Sonderheft SD, Hannover, 3:1–121 Smeets K, Ruytinx J, Van Belleghem F, Semane B, Lin D, Vangronsveld J, Cuypers A (2008) Critical evaluation and statistical validation of a hydroponic culture system for Arabidopsis thaliana. Plant Physiol Biochem 46(2):212–218 Smodis B (2003) IAEA approaches to assessment of chemical elements in atmosphere. In: Markert BA, Breure AM, Zechmeister HG (eds) Bioindicators and biomonitors. Principles, concepts and applications. Elsevier, Amsterdam, The Netherlands, pp 875–902 Stoeppler M, Duerbeck HW, Nuernberg HW (1982) Environmental specimen banking. Talanta 29:963 Suchara I, Sucharova J, Hola M (2007) Bio-Monitoring of the atmospheric deposition of elements using moss analysis in the Czech Republic. Acta Pruhoniciana 87:186 Szárazová K, Fargašová A, Hiller E, Velická Z, Pastierová J (2008) Phytotoxic effects and translocation of Cr and Ni in washing wastewaters from cutlery production line to mustard (Sinapis alba L.) seedlings. Fresenius Environ Bull 17:58–65 Trapp S, Feificova D, Rasmussen NF, Bauer-Gottwein P (2008) Plant uptake of NaCl in relation to enzyme kinetics and toxic effects. Environ Exp Bot 64(1):1–7 Vtorova V, Kholopova L, Markert B, Leffler U (2001) Multi-elemental composition of tropical plants and bioindication of the environmental status. In: Biogeochemistry and geochemical ecology: selected presentations of the 2nd Russian School of Thought: Geochemical Ecology and the Biogeochemical Study of Taxons of the Biosphere, Moscow, 25–29 Jan 1999, pp 177–189 Vutchkov M (2001) Biomonitoring of air pollution in Jamaica through trace-element analysis of epiphytic plants using nuclear and related analytical techniques. In: Co-ordinated research project on validation and application of plants as biomonitors of trace element atmospheric pollution, analyzed by nuclear and related techniques, IAEA, NAHRES-63, Vienna Verbruggen N, Hermans Ch, Schat H (2008) Molecular mechanisms of metal hyperaccumulation and tolerance in plants. New Phytol. doi:10.1111/j.1469-8137.2998.02748x Verkleij JAC (2008) Mechanisms of metal hypertolerance and (hyper)accumulation in plants. Agrochimica 52(3):167–188 Warren A, Harrison CM (1984) People and the ecosystem: biogeography as a study of ecology and culture. Geoforum 15:365–381 Wittig R (1993) General aspects of biomonitoring heavy metals by plants. In: Markert B (ed) Plants as biomonitors – Indicators for heavy metals in the terrestrial environment. VCH, Weinheim, pp 3–27 Wolterbeek HT, Kuik P, Verburg TG, Herpin U, Markert B, Thöni L (1995) Moss interspecies comparisons in trace element concentrations. Enviro Moni Assess 35:263–286 Wolterbeek B (2002) Biomonitoring of trace element air pollution: principles, possibilities and perspectives. Environ Pollut London 120:11–21 Wuenschmann S, Oehlmann J, Delakowitz B, Markert B (2001) Untersuchungen zur Eignung wildlebender Wanderratten (Rattus norvegicus) als Indikatoren der Schwermetallbelastung, Teil 1. UWSF-Z Umweltchem Ökotox 13(5):259–265 Wuenschmann S, Oehlmann J, Delakowitz B, Markert B (2002) Untersuchungen zur Eignung wildlebender Wanderratten (Rattus norvegicus) als Indikatoren der Schwermetallbelastung, Teil 2. UWSF-Z Umweltchem Ökotox 14(2):96–103 Wuenschmann S, Fränzle S, Markert B, Zechmeister H (2008) Input and transfer of trace metals from food via mothermilk to the child: bioindicative aspects to human health, Chapter 22. In: Prasad MNV (ed) Trace elements – nutritional benefits, environmental contamination, and health implications. Wiley, New York, pp 555–592 Zechmeister HG, Dullinger S, Hohenwallner D, Riss A, Hanus-Illnar Scharf S (2007) Pilot study on road traffic emissions (PAHs, heavy metals) measured by using mosses in a tunnel experiment. Austria Embv Sci Poll Res 13:398–404
SAR Based Computational Models as Decision Making Tools in Bioremediation Nick Price and Qasim Chaudhry
Abstract Environmental pollution has been the focus of increasing concerns over potential harmful effects on human health and the environment. Amongst the available options for environmental cleanup, technologies based on biological remediation have emerged as low-cost, low-maintenance, environment-friendly, and renewable technologies for potential in situ remediation of organic and inorganic contaminants. However, both microbial and plant species used in these technologies have certain limitations, and it is desirable to know in the first instance whether a contaminant would need remedial action, and whether a biological process would be suitable to breakdown or remove it from the environment. This is where computational models based on structure-activity relationship can provide a quick assessment to support decision making. The (Q)SAR models and expert systems can help prioritise contaminants on the basis of potential toxicities, and inform on their likely behaviour and fate in the environment. This information is in turn helpful in the choice of appropriate remediation technologies, as well as in identifying the recalcitrant chemicals that can be monitored as markers for the success of remediation action. This chapter provides an overview of the rationale behind the development of structure-activity relationship models and provides an up-to-date list of the key relevant software tools that are currently available. However, the availability of a large number of software tools also requires a careful choice of appropriate models and/ or expert systems. The overview also shows that there is a need for development of more integrated systems that can cater specifically for biological remediation technologies.
N. Price () Technology for Growth, York, UK e-mail:
[email protected] Q. Chaudhry The Food and Environment Research Agency, Sand Hutton, York, UK P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_11, © Springer Science+Business Media B.V. 2011
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Introduction Increasing public concerns over the presence of certain chemical pollutants in the environment have led to search for suitable technologies for clean up of the contaminated environments. A large proportion of the pollutants released to the environment are degraded by natural factors, such as sunlight, oxygen, microbial degradation etc. However, some pollutants, such as polychlorinated aromatics and dioxins, are known to be recalcitrant to such degradation processes and can linger on in the environment for long periods. In recent decades, bio- and phyto-remediation have emerged as lowcost, low-maintenance, environment-friendly and renewable technologies for in situ degradation and/or removal of organic and inorganic contaminants from the environment. Both technologies are considered to be more cost-effective than the other available ex-situ decontamination techniques. However, there are certain limitations to the removal and/or degradation of certain pollutants by microorganisms or by plants alone. Some pollutants are structurally very stable, i.e. they have chemical groups which normal microbial processes can not break down, or they may be toxic to microbial species in the environment, and hence not amenable to bioremediation. Similarly, whilst phyto-remediation is particularly suited for removing heavy metal ions from contaminated soils, plants have a limited ability to remove certain organic pollutants that are either highly hydrophobic or are soil-bound (Chaudhry et al. 2002). Some phytotoxic compounds may further limit root establishment by plants in contaminated soils. This is where a synergy between soil microflora and plants is known to overcome many of the limitations. The ability of rhizospheric microflora to transform recalcitrant and soil-bound organic compounds, in return for a favourable environment provided by plant roots for their propagation, and in many cases provision of ready energy in the form of root exudates, seems to work hand in hand for the survival of both microorganisms and plants in contaminated soils (Chaudhry et al. 2005). The effectiveness of the remediation technologies, however, requires sound decision-making at the outset. For example, whether there are harmful compounds in a given contaminated environment that warrant a remedial action, and whether such an action would need the use of one or more remediation technologies. In situations where chemical profile of contaminants has been established in a soil or water environment, the use of computational chemistry based models can provide tools for such a decision-making. For example, the use of Quantitative Structure Activity Relationship (QSAR) based mathematical models can help in rapidly assessing whether an environmental contaminant of concern would be biodegradable or persistent, bioavailable or in a bound form, and whether it would be amenable to a remedial action by microorganisms or plants. Other models can provide further pieces of crucial information that can help select appropriate microbial or plant-based processes to suit a particular contamination situation, and highlight whether additional measures may be needed, such as application of surfactants to increase bioavailability of certain contaminants. This chapter provides an overview of the methodology behind QSAR modelling, and discusses examples of relevant models and expert systems that can facilitate decision-making in biological remediation of contaminated environments.
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Computational Models Based on QSAR QSARs are mathematical descriptions of the biological activity of a group of chemical compounds, in terms of one or more of their physicochemical properties. The foundations of QSAR stemmed from the observation that, in a closely related series of chemicals, such as a congeneric series, the biological activity of the compounds varied according to steric, electronic and hydrophobic properties of the series, and that this could be expressed mathematically. The development of the concept from these early beginnings to the present day have been documented in a number of “histories”, notably Selassie (2003). Although the original principles of QSAR were elaborated on whole organism functions (Hansch et al. 1962), the most successful era for QSAR was in the study of relationships at the single receptor or enzyme activity level. It was found that as single chemical substituents in a homologous series were varied, so biological activity at receptor or enzyme level, changed, in a way which could be mathematically related to the change in properties resulting from the altered substituents. Simple steric, (molecular weight, molar refractivity), hydrophobic, (Log KOW) or electronic, (Hammet constant) properties of common substituent chemical groups could be measured and were collated in look-up tables, for use in QSAR studies. It was found that the effect of varying properties of substituents produced a hyperbolic response demonstrating an optimal value of, for example, molecular size on the receptor or enzyme being studied. Thus using a second-order linear regression analysis it was possible to reduce the phenomenon to a simple linear relationship:
LogMolarActivity = c + a1P1 − a1P12 + b1P2 − b 2 P2 2 + n1Pz − n 2 Pz 2 2 In practice, the variation in properties, (descriptors) rarely extended beyond a linear portion of the response surface, and so a more simple first order linear relationship could be used in many cases.
LogAct = c + aP1 + bP2 + nPz This process leads to the logical conclusion that, if such a relationship is known, then it is possible to predict the biological response of any compound in the series simply by knowing the magnitude of those properties that influence it. This is the basis of QSAR and from the 1960s it has been used to aid the design of new pharmaceutical molecules, and assessment of biological properties of chemical compounds.
Target Level It is easy to see that one or two simple properties can account for the activity of a series of compounds at the molecular level, (size of compound in relation to active
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site, hydrophobicity in terms of binding, charges in terms of reactivity to key parts of the receptor.) However more recently the potential of QSAR has been turned towards whole organism disciplines such as toxicology, in which the target response under investigation is a function of the whole organism rather than a single enzyme or receptor. For whole organisms the factors influencing activity are much more complex and possibly even conflicting, such as penetration through skin (dermal), entry into bloodstream, crossing blood/tissue barrier, effect of metabolic enzymes and the effect of internal environment. The increased complexity in the biological response at the whole organism level has lead to the search for more descriptors, (molecular properties) that might encode such complexities and this in turn has necessitated consideration of more complex mathematical models than simple nth order equations. The main steps in constructing a QSAR model are as follows: • • • •
Molecular modelling of the compounds in the dataset Calculation of descriptors to represent physiochemical parameters Mathematical correlation of one or more descriptors with biological activity Testing and validating model reliability and prediction limits
Chemical Structures In the early days, the simple descriptors used in QSAR modelling (such as Log KOW, molar refractivity, size/bulk) could be measured in the laboratory so details of the 2D or 3D structures of the compounds was not needed, over and above knowing the basics such as structural formula and connectivity. As computing power increased, the use of molecular mechanics and quantum mechanics algorithms allowed building virtual chemicals inside the computer, and calculate many properties from such models. Many molecular packages are available to draw and optimise chemical structure in silico. They range from very comprehensive, highly functionalised commercial suites such QUANTA CHARMM1 to freeware. The academic community has given rise to many good open source programs some of which rival top end commercial computational chemistry suites. One example is VEGA-ZZ (Pedretti et al. 2002), which utilises molecular mechanics, quantum mechanics and molecular dynamics suites to carry out all the processes necessary to build and optimise chemical structures. It can handle a wide range of chemical structure files including the chemical structural database format .sdf. In addition to building and optimising molecules, some molecular modelling suites can also generate a range of properties that can be used as QSAR descriptors.
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Molecular Descriptors The chemical structure of a compound carries a lot of embedded information on physicochemical features that ultimately determines biological activity. For (Q)SAR modelling, this information is ‘extracted’ from structural features into numerical data on physicochemical properties, termed as molecular descriptors. Many hundreds or even thousands of descriptors can now be calculated for each chemical structure to build datasets that are then used in QSAR modelling. Many of the complex molecular descriptors cannot be easily measured directly in the laboratory, or even at all, but can instead be calculated or estimated by computational means. Before the advent and rapid development of desktop computing, this was a time consuming affair, which could only be carried out at those laboratories that had expensive computer hardware and software. Pioneers of computational chemistry were able to use such theoretical concepts as molecular mechanics and quantum mechanics to calculate steric and electronic properties of molecules but such calculations were expensive and time consuming. The huge advances in computing in the recent decades have led to cheap, fast and versatile combinations of hardware and software for the modelling of chemicals and calculation of a wide range of properties. Standard desktop PCs can now carry out the complex computational tasks required and both proprietary and Open Source software is available to carry out such work. Many molecular descriptors can be calculated from molecular modelling software but other standalone programs also exist for calculation of descriptors. Some examples are DRAGON, and implementations of CDK and JOELib. –– DRAGON2 is a commercial program that calculates over 3,000 descriptors, (3,224 in v5 at time of writing). The descriptors fall into 22 categories, and include topological, geometric, connectivity, charge and functional group descriptors as well as molecular properties such as Log Kow. Full details of all the DRAGON descriptors can be found in Todeschini and Consonni (2000). The Chemistry Development Kit (CDK) is an Open Source Java library for structural chemo- and bioinformatics. The CDK library has evolved into a full functionality chemoinformatics package with code ranging from QSAR descriptor calculations to 2D and 3D model building. Although CDK can be used as a standalone application, it is implemented in a number of more user friendly applications such as; CDK-Taverna,3 KNIME,4 PADEL5 and more recently T.E.S.T,6 which calculates over 700 CDK descriptors. –– JOELib is a platform independent open source computational chemistry package written in Java. As with CDK, JOELib can be used standalone or incorporated into other packages such as Bioclipse.7 JOELib calculates over 100 descriptors. http://www.talete.mi.it/products/dragon_description.htm http://sourceforge.net/apps/mediawiki/cdk/index.php?title=CDK_Taverna 4 http://www.Knime.org/ 5 http://padel.nus.edu.sg/software/padeldescriptor/ 6 www.epa.gov/nrmrl/std/cppb/qsar/index.html 7 www.qsarworld.com/Temp_Fileupload/Shorthistoryofqsar.pdf 2 3
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A number of other programs are available to computational chemists for the calculation of other types of descriptor but essentially they still reflect the original grouping of steric, hydrophobic and electronic properties.
Data Analysis and Model Construction As the number of descriptors calculated in a QSAR study have burgeoned, so it has become necessary to apply more sophisticated mathematical means to investigate the relationship,(of any of these descriptors) to biological activity. In many cases the number of descriptors may indeed exceed the number of observations in the dataset thus making it very likely that any correlations observed will be chance correlations and not causative. It has become necessary therefore to carry out a number of steps to ensure robust handling of the data. These steps include: • • • • •
Variable (descriptor) reduction Variable normalisation Algorithm selection Model selection Model validation
In order to reduce variables to a meaningful number, those which are irrelevant need to be removed. This may be done by removing those that do not vary (constant or zero), or vary too widely, and those which are cross-correlated to other variables A range of techniques is available to accomplish this step including pairwise correlation, principal component analysis and filtering algorithms. Variable normalisation is usually performed to prevent some variables having an undue influence on a model. Normalisation or scaling can reduce the variation of all descriptors to the same range (for example 0–1). Algorithm selection is a critical step and can make the difference between a good model and no model at all. If the biological data is numerical and continuous, (for example LD50), then some form of multiple regression would be a likely choice. This might be a forward or reverse stepwise regression technique to develop the model with the smallest number of most significant variables. If however the biological response is discrete, (for example, active/inactive, or inactive/weak/moderate/strong), then likely choices might include, decision trees, neural networks, support vector classification, or clustering. The choice of different statistical algorithms for QSAR building has recently been reviewed by Chaudhry et al. (2007). Any one of the chosen methods may give a number of apparently acceptable models and the decision is then which model to accept. In general terms, however the models must be developed taking into account the principle of parsimony, often called Ockham’s Razor: “entities should not be multiplied beyond necessity” or “avoid complexity if not necessary”.8 In other words “The simplest solution is the best.” http://www.qsarworld.com/Temp_Fileupload/Shorthistoryofqsar.pdf
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Above all of course the chosen QSAR model should have good predictivity, so tests for predictive robustness must be carried out. This may be by cross validation techniques or the use of “confusion tables”, but ultimately the true test of a model will be external validation. In external validation, a stratified random subsample of the dataset is removed prior to modelling. The model is constructed using the larger “training set” and the removed subsample or “test set” is used to test the predictive ability of the model. All the above steps can be carried out using data mining or machine learning packages, such as WEKA,9 TANAGRA,10 KNIME11 or commercially available packages such as Knowledgeminer.12
Expert Systems Expert systems utilise a number of different approaches to predicting bioactivity, including decision trees based on rules, “structural alerts” in which chemical substructural features may be associated with particular biological activities, and nested QSARs. Examples include TOPKAT, Derek for Windows and MULTICASE. TOPKAT13 uses a range of robust, cross-validated QSAR models of a range of toxicological endpoints whilst Derek for Windows14 works by matching structural entities in a query compound with predetermined “structural alerts” that are known to be associated with a similar range of toxicological endpoints. The use of TOPKAT and Derek for Windows in rapid assessment of heat-derived toxicants in food is more fully described by Chaudhry et al (2006). MCASE15 combines both these approaches. Other relevant programmes include Ambit Database Tools16 that contains over 450,000 chemical compounds from a range of high quality databases, and can be used to search for properties as well as biological endpoint experimental data. External compounds can be used to “read across” from data in the internal databases. Ambit XT17 is a high quality chemical database with five modules for searching, compound profiling and fingerprinting. In addition it comes with a module for predicting PBT, (persistence, bioaccumulation and toxicity) using read
http://www.cs.waikato.ac.nz/ml/weka/ http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html 11 http://www.knime.org/ 12 http://www.knowledgeminer.com/ 13 http://accelrys.com/products/discovery-studio/toxicology/ 14 http://lhasalimited.org/index.php?cat=2&sub_cat=64 15 http://multicase.com/ 16 http://ambit.acad.bg/ 17 http://ambit.sourceforge.net/intro.html 9
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across to compounds and PBT properties within the database. At present it does not support organometallics, polymers or mixtures. Toxtree18 estimates toxic hazard using a decision tree approach. Toxtree can be applied to datasets from a range of file types, or compounds can be entered by SMILES (Simplified Molecular Input Line Entry Specification) through the built-in 2D structure diagram editor. Another useful and comprehensive system is EPISuiteTM (US EPA 2009),19 which is a Windows®-based suite of physical/chemical property and environmental fate estimation programs developed by the US EPA’s Office of Pollution Prevention Toxics and Syracuse Research Corporation (SRC). EPI Suite™ uses a single input to run programs to estimate: • The log octanol–water partition coefficient, log KOW, of chemicals using an atom/fragment contribution method • The gas-phase reaction rate for the reaction between the most prevalent atmospheric oxidant, hydroxyl radicals, and a chemical • The Henry’s Law constant (air/water partition coefficient) using both the group contribution and the bond contribution methods. Melting point, boiling point, and vapor pressure of organic chemicals • Aerobic and anaerobic biodegradability of organic chemicals using seven different models • Biodegradation half-life for hydrocarbons.The organic carbon-normalized sorption coefficient for soil and sediment • Water solubility • Fish bioconcentration factor • Aqueous hydrolysis rate constants and half-lives for esters, carbamates, epoxides, halomethanes, selected alkyl halides, and phosphorus esters • The octanol/air partition coefficient. The fraction of airborne substance sorbed to airborne particulates • The rate of volatilization of a chemical from rivers and lakes • Predicts the removal of a chemical in a typical activated sludge-based sewage treatment plant • Partitioning of chemicals among air, soil, sediment, and water under steady state conditions for a default model environment • The toxicity of chemicals discharged to water. ECOSAR™ predicts toxicity to fish, aquatic invertebrates and algae using an extensive set of structure–activity relationships The Toxicity Estimation Software Tool (T.E.S.T.20 also developed by the US EPA) has been developed to allow users to easily estimate toxicity using a variety of QSAR methodologies. T.E.S.T. does not require molecular descriptors from
http://toxtree.sourceforge.net/ US EPA. (2009). Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.00. United States Environmental Protection Agency, Washington, DC, USA. 20 http://www.epa.gov/nrmrl/std/cppb/qsar/index.html#TEST 18
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e xternal software packages as the required descriptors are calculated within the programme, which estimates the value for several toxicity endpoints including 96h fathead minnow LC50, and 48h Tetrahymena pyriformis IGC50. The OECD QSAR ToolBox 21is another versatile suite of programs which can predict a range of endpoints for chemicals based on read-across, similarity or QSAR, using a substantial set of high quality databases. Additional databases with endpoint data can be imported into the toolbox to expand its usefulness. The Toolbox contains: –– Databases with results from experimental studies –– A library of QSAR models –– Tools to estimate missing experimental values by read-across, i.e. extrapolating results from tested chemicals to untested chemicals within a category –– Tools to estimate missing experimental values by trend analysis, i.e. interpolating or extrapolating from a trend (increasing, decreasing, or constant) in results for tested chemicals to untested chemicals within a category
QSAR and Expert Systems in Bioremediation Chemical Persistence, Bioaccumulation and Toxicity Chemical compounds which contaminate the environment are usually those characterised as “Persistent Bioaccumulative and Toxic”, (PBT). PBT compounds are of considerable interest to regulatory authorities as their use and possible release to the environment are key factors in the evolving chemical legislation in many countries. PBT compounds are also most likely to be those for which bio/ phyto-remediation solutions are sought and so it is relevant in this chapter to consider some methods available for the estimation or prediction of the PBT properties of chemicals. Under the EU REACH regulations, PBT compounds are defined in terms of half-life in water, sediments and soil, (persistence); bioconcentration factor (BCF), (bioaccumulation); and the No Observed Effect Concentration (NOEC), together with mammalian carcinogenicity, mutagenicity and reproductive toxicity (CMR). A number of SAR/QSAR/Expert system software tools are available to estimate each of these parameters and thus to make an overall estimate of PBT property of any given chemical. Persistence may be predicted from models using real half life data, or by an inverse property, biodegradability. The more rapid the inherent biodegradability of a compound, the shorter its persistence will be in soil or water. Bioaccumulation may be predicted from physical properties such as the octanol/water partition coefficient, (often called Log KOW), or from other physicochemical indicators such as molecular size (Koch 2008). Toxicity can be predicted
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from existing animal toxicity data, by read across from related compounds or by structural alerts or group contribution as previously described.
Prediction of Persistence/Biodegradation Persistence is the length of time that a compound remains in a particular environment, and is dependant on a number of factors including the chemical nature of the compound, the conditions in the environment, and the availability of biological organisms to perform biodegradation (Cronin and Livingstone 2004). Clearly an insight into the persistence of chemicals in any particular environmental situation is useful in determining any bioremediation strategy. For most compounds, direct data on persistence in the environment is not available and predictions of persistence must rely on estimates of biodegradability. Most of the methods for predicting persistence rely on biodegradation as an inverse predictor of persistence. A recent EU report compared 78 different SARs for biodegradation (Pavan and Worth 2006). The main conclusion was that only a few models provided an acceptable level of agreement between estimated and experimental data. Some of the main ones will be considered further here.
BIOWIN BIOWIN is part of the US EPA EPISUITETM package,22 and is freely downloadable. EPISUITETM is a set of routines for estimating a range of physicochemical properties and environmental fates of chemical compounds. BIOWIN estimates the probability of rapid biodegradation of an organic chemical in the presence of mixed populations of environmental microorganisms. The basic model is based on the use of 40 structural fragments from 295 organic compounds in the BIODEG database. The latest version of BIOWIN estimates aerobic and anaerobic biodegradability of organic chemicals using seven different models, one of which is an anaerobic model. A separate module of EPISUITETM, BioHCwin, is parameterised specifically for hydrocarbons. Also within EPISUITETM, HYDROWIN estimates aqueous hydrolysis rate constants and half-lives for esters, carbamates, epoxides, halomethanes, selected alkyl halides, and phosphorus esters whilst WVOLWIN estimates the rate of volatilization of a chemical from rivers and lakes and calculates the half-life for these two processes from their rates. Careful use of the BIOWIN program especially the use of the non-linear MITI model can give accurate prediction of biodegradability for up to 85% of compounds tested biodegradation (Pavan and Worth 2006).
http://www.epa.gov/oppt/exposure/pubs/episuite.htm
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TOPKAT TOPKAT23 is a commercial suite of predictive toxicology programmes. TOPKAT is a QSAR-based system which generates and validates accurate, rapid assessments of chemical toxicity solely from a chemical’s molecular structure. TOPKAT uses patented Optimum Prediction Space technology to assure that the compounds under investigation are well represented in the models. TOPKAT includes an aerobic biodegradability module consisting of four submodels based on QSARs derived from MITI test results for 894 compounds. Within its domains, (acyclics, alicyclics, benzenes and heteroaromatics), it has been shown to be up to 96% accurate in prediction of biodegradability. MULTICASE MULTICASE24 is another commercial program which uses the MITI dataset of 894 compounds to derive its biodegradability model. MULTICASE uses all available molecular fragments to search for “biophores” responsible for biodegradability, and is accurate for up to 92% of compounds within the domain. Used in conjunction with the expert system META (Pavan and Worth 2006), MULTICASE can predict biodegradation products of a given organic chemical under realistic environmental conditions. META is an expert system linked to a dictionary consisting of metabolic rules, which can predict the metabolic transformations likely to occur when the chemical is disposed into the environment. CATABOL CATABOL25 is a commercial expert system for prediction of the biotransformation pathways linked to a probabilistic model that calculates probabilities of the individual transformations. The catabolic steps are derived from the set of most plausible metabolic pathways predicted by experts for each chemical from the training set. The MITI-I database is used, to provide a large structural diversity and consistent biodegradability assessments. Under the conditions tested32, CATABOL demonstrated about 85% accuracy of prediction. TOXTREE TOXTREE26 is a freely available decision tree tool for the prediction of toxic hazards of chemicals. In addition TOXTREE uses a system of structural alerts to predict biodegradability. http://accelrys.com/products/discovery-studio/toxicology/ http://www.multicase.com/ 25 http://oasis-lmc.org/?section=software&swid=1 26 http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=TOXTREE 23
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The biodegradation and persistence module is based on a compilation of structural alerts for environmental persistence and biodegradability. These structural alerts are molecular functional groups or substructures that are known to be linked to the environmental persistence or biodegradability of chemicals. The rule base utilises the structural alerts in logical decision trees. If one or more of the structural alerts found in the molecular structure of the chemical are recognized, the system flags the potential persistence or biodegradability of the chemical. There are 32 structural alerts built into TOXTREE; 23 relate to the mechanisms of action of environmentally persistent chemicals, while nine relate to easily biodegradable chemicals. The structural alerts were derived from the Guidance Manual for the Categorization of Organic and Inorganic Substances on Canada’s Domestic Substances List (Guidance Manual 2003). According to the alerts detected chemicals are classified into one of the following three categories: –– Class 1 (easily biodegradable chemical) –– Class 2 (persistent chemical) –– Class 3 (unknown biodegradability) The performance of TOXTREE has not been extensively evaluated as yet. PBT Profiler The US EPA’s PBT Profiler27 is a free online tool for the estimation of PBT properties against the EPA criteria. The PBT Profiler first determines the amount of the chemical expected to be found in water, soil, and using a mass balance model. It then determines which of these three compartments the chemical is most likely to partition to. Using this predominant compartment, the half-life in that compartment is then compared to the EPA criteria to determine the persistence summary. If the half-life in the predominant compartment exceeds the EPA criteria, the chemical is designated as persistent or very persistent in the summary output. The PBT Profiler methodology was developed with the aid of a database of experimental biodegradation rates for 136 chemicals. AMBIT XT AmbitXT28 is a set of flexible user-friendly open source applications, which are able to store information about chemical compounds and efficiently search large databases of chemicals by various criteria. The application supports exact structure/ substructure searching, as well as fingerprint based similar structure searching, search by set of descriptors and search by experimental data. http://www.pbtprofiler.net/ http://ambit.acad.bg/
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AmbitXT includes a comprehensive set of modules for PBT assessment. The included database encompasses substances from the European INventory of Existing Commercial chemical Substances (EINECS),29 the BCF Gold standard Database,30 the ECETOC Aquatic Toxicity (EAT) Database Supplement (ECETOC 2003), historical skin sensitisation data (Gerberick et al. 2005), and the skin irritation and corrosion reference chemicals database (Skin irritation report 1995). Unlike the PBT Profiler which requires only a chemical identification to perform a PBT assessment based on QSARs within EPISUITETM, AMBIT XT uses measured, estimated or read across data for the PBT assessment and addresses the EU authorities’ position that some QSARs, e.g. the BCF and Biodegradation QSARs may be too optimistic in their estimates. This however does require that a considerable amount of data needs to be entered, (or available in the AMBIT2 database), to allow PBT assessment to be performed. It does on the other hand make a more accurate prediction of PBT more likely.
OECD QSAR Toolbox The OECD QSAR Toolbox31 is a multi-function application allowing the user to perform a number of operations, aimed at predicting a range of endpoints, (including PBT) for any given chemical or set of chemicals. The Toolbox can: –– Identify analogues for a chemical, retrieve experimental results available for those analogues and fill data gaps by read-across or trend analysis –– Categorise large inventories of chemicals according to mechanisms or modes of action –– Fill data gaps for any chemical by using the library of QSAR models –– Evaluate the robustness of a potential analogue for read-across –– Evaluate the appropriateness of a (Q)SAR model for filling a data gap for a particular target chemical –– Build QSAR models A comprehensive set of 18 chemical inventories comprising the OASIS Centralised Database of Existing Chemicals of over 200,000 compounds32 is built into the toolbox including a number of biodegradation databases. The principle behind the toolbox is that of categorising an unknown compound according to its physicochemical and toxicological properties, and then predicting the required properties based on such categorisation. Biodegradation and bioaccumulation are predicted from data for compounds in one or more of the datasets included in the full suite.
http://ecb.jrc.ec.europa.eu/esis/index.php?PGM=ein http://www.euras.be/eng/project.asp?ProjectId=92 31 http://www.oecd.org/document/23/0,3343,en_2649_34379_33957015_1_1_1_1,00.html 32 http://oasis-lmc.org/?section=software&swid=8 29 30
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Prediction of Bioaccumulation According to international guidelines “Bioaccumulation” is defined as the process where the chemical concentration in an aquatic organism achieves a level that exceeds that in the water as a result of chemical uptake through all routes of chemical exposure. The usual measure of bioaccumulation in the Biological Concentration Factor, (BCF) which is defined as, “the concentration of test substance in/on the fish or specified tissues thereof divided by the concentration of the chemical in the surrounding medium at steady state”. Pavan et al. (2006) have reviewed the QSAR methodologies used in the prediction of bioconcentration. The majority of QSARs for BCF are simple linear regression models using Log KOW, though the relationship breaks down with compounds of Log KOW > 6, (i.e. very hydrophobic compounds). A number of parabolic or bilinear models have been proposed to account for this. Although there have been many attempts to predict BCF using QSARs, most of these were before the OECD principles were formulated and are not necessarily acceptable as robust models under modern criteria. In addition they are dispersed amongst the scientific literature and not so widely available in software applications as the biodegradation models. Other QSAR models have been published which do not rely on measured or calculated values of Log KOW, but instead have been based on a range of theoretical molecular descriptors, such as, quantum chemical values, connectivity indices and descriptors derived from programs such as DRAGON, (see Section 1.3). Some of the applications discussed in Section 2.2 also allow the user to predict or get experimental values for, bioaccumulation, but in addition there are many published QSARs and other predictive applications which will give values for BCF31. Most models that perform well with a wide range of structural types are based on the relationship of BCF with Log KOW, (OW = octanol/water partition coefficient).
BCF Prediction Tools The BCFBAF model, (previously called BCFWIN), in the EPISUITETM application is according to Cronin and Livingstone (2004), one of the better predictors31 with 83% of Log.BCF predictions within one order of magnitude. The CATABOL commercial program computes BCF predictions based on a deterministic and probabilistic hybrid expert system. A hybrid model is also used in the online CAESAR model. CAESAR33 was an EU funded research project which developed QSAR models for five toxicological endpoints of regulatory importance, primarily for use in the REACH legislation process. All five models are freely available for use online (www.caesar-project.eu/software/index.htm), one of which is a BCF model. 33
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Both AMBIT XT and the USEPA PBT profiler have modules for BCF prediction, and as discussed earlier the AMBIT model uses, where possible experimental values input by the user to generate more accurate estimates, whilst the PBT profiler and the BCFBAF models are based on QSAR data. The OECD QSAR Toolbox also incorporates BCF and more general bioaccumulation estimations using a wide range of databases. Archived experimental BCF values will be extracted from the databases if the query chemical is found, otherwise the Toolbox allows read across and molecular similarity projections to be made prior to a prediction of BCF.
Accumulation/Concentration in Soil From a bioremediation point of view, although BCF is a useful indicator of the bioaccumulative properties of given pollutants, predictions of chemical accumulation for specific sites and conditions could be more useful in designing a bioremediation plan. QSAR can help in establishing the accumulative/ degradative profile of individual pollutants in order to establish the scope and magnitude of any specific problem and its likely response to bioremedial strategies. A compound which readily accumulates in soil, but is rapidly degraded, is unlikely to require bioremedial action but rapid accumulation with slow degradation may require remediation. Accumulation/concentration of chemicals in soils is usually referred to as sorption, and many QSARs have been derived for prediction of the soil sorption coefficient KOC. These have been reviewed by Dearden (2004). As with BCF, most QSARS for KOC rely heavily on the octanol–water partition coefficient Log KOW, though molecular connectivity descriptors have also been used successfully to predict soil sorption. Indeed a model based on such indices forms the basis of the only freely available application for prediction of soil sorption, KOCWIN, which is part of the EPISUITETM application previously discussed.27
Prediction of Toxicity The prediction of toxicity is a much wider field than persistence or bioaccumulation. This is mainly because of the large number of potential endpoints covered by the broad term “toxicity”. Toxicity can mean acute toxicity, for example an LD50 value for a particular species, or it can also mean chronic toxicity relating to a specific lesion or pathology, such as mutagenicity, carcinogenicity, or developmental toxicity. The field of predictive toxicology is fairly new and a good overview of most aspects has been provided by Cronin and Livingstone (2004). From an environmental, regulatory and bioremedial standpoints, the important aspects of toxicity which may need to be predicted are those which define the “T” in PBT. Whilst the actual details vary from country to country the definition of a
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Table 1 Toxicity prediction tools Application Status EPISUITE Free
Toxicological endpoints – Daphnid – Fish – Earthworm and green algae
Type of predication QSAR
DEREK
Commercial
– Carcinogenicity – Mutagenicity – Genotoxicity – Skin sensitization – Teratogenicity – Irritancy – Respiratory sensitization – Hepatotoxicity – Oculartoxicity
Structural alerts and QSARS
TOPKAT
Commercial
– Carcinogenicity – Mutagenicity – Daphnid – Fish – Skin sensitization – Skin irritancy – Inhalation toxicity
QSAR based
MULTICASE TOXTREE
Commercial Free
Over 180 endpoints
Biophor/QSAR Rules-based
OECD Toolbox
Free
QSAR, read-across, categorisation
T.E.S.T
Free
Many acute and chronic toxicity endpoints Fish, protozoa, rat oral
CAESAR
Free
– BCF – Mutagenicity – Carcinogenicity – Developmental toxicity – Skin sensitization
Hybrid QSARs
TOXMATCH1
Free
– Aquatic toxicity – BCF – Skin sensitivity
Read across
– Mutagenicity – Carcinogenicity – Skin sensitivity – Oral toxicity
QSARs
http://ecb.jrc.europa.eu/qsar/qsar-tools/index.php?c=TOXMATCH
1
PBT chemical relate to a chronic NOEC (No Effect Concentration), of one or more aquatic organisms and/or a specified level of carcinogenicity or mutagenicity. These so called “regulatory endpoints” have, in recent years been extensively modelled, and most of the applications referred to in this chapter have the capability to predict such toxicological endpoints. A list of the applications and their toxicological endpoint predictions is given in Table 1.
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Biotransformation Whilst the prediction of PBT properties can assist in assessing the challenge of a particular bioremediation situation, prediction of the biotransformation pathways of the offending compounds can aid the selection of a bioremediation strategy. Whilst phytoremediation may rely on the uptake and sequestration of pollutants, remediation by micro-organisms relies on the degradative metabolism or biotransformation of the pollutants.
Microbial Metabolism SAR or QSAR-based models on the microbial metabolism of compounds which are of interest in bioremediation are of three types; QSARs on specific chemical groups, (PCBs or hydrocarbons), rule-based predictive models based on metabolic pathways or molecular fragment approaches, and large (usually commercial) applications which predict metabolites for a range of medical and regulatory purposes. Polychlorinated biphenyls represent a group of compounds with significant environmental hazards on industrial sites. Microbial remediation is an attractive option for PCBs but susceptibility to microbial degradation varies widely amongst the PCBs. Cartwright (2002) used a self-organising map to analyse data on the degradation of a range of PCBs by Aspergillus niger. A QSAR based on the selforganising map was shown to predict biodegradability to within 25% of experimental values for 33 of a set of 44 PCBs, though it appeared that dichloro-PCBs appeared more difficult to predict. Other QSAR approaches to the microbial degradation of PCBs include Comparative molecular Field Analysis (Comfa) for biphenyl utilization by Pseudomonas stutzeri in aqueous media,34 and regression analysis for fungal laccase activity, (showing heat of formation and the quantum indices HOMO and LUMO as significant descriptors) (Jiang et al. 2008). Microbial degradation, in addition to being a useful predictor of bioremediation, is conventionally used as an indicator of environmental persistence, as previously discussed, and the QSAR-based approaches are reviewed in detail by Pavan and Worth (2006). Ideally a predictive tool should indicate biotransformation via a specific pathway. One way to achieve this is to use microbial biosensors linked to some kind of reporter gene such as fluorescence. Since hydrocarbon metabolism has long been a subject of study and hydrocarbons are frequently environmental problems, it’s not surprising that a number of biosensor systems have been developed for monitoring their biotransformation. Paton et al. (2004) have applied QSAR models to the data from a number of microbial biosensor systems, reporting on pathways in the catabolism of benzenes, PAHs, alkanes, naphthalenes and other 34 http://cfpub1.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/347/ report/F
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hydrocarbons occurring in petroleum and diesel oils. They concluded that QSARs can be applied to predict the specificity of regulatory proteins for hydrocarbon biotransformation pathways. Hydrophobicity appeared to be the most important descriptor, possibly because it determines uptake of the hydrocarbons into the cell systems used in the biosensor assay. However, ELUMO, the energy of the lowest unoccupied molecular orbital, (a quantum chemical descriptor), was also significant in a number of their models.
Software Tools Biotechnology has, in recent years generated vast amounts of data on genomics and metabolomics, which can be harnessed with modelling techniques to make predictions about biotransformations of interest in bioremediation projects. Pazos et al (2005) developed the expert system MetaRouter to mine biotransformation data from the number of sources, primarily the University of Minnesota Biocatalysis/Biodegradation Database.35 The system allowed the exploration and design of biodegradative strategies for chemical compounds depending on conditions such as environment and bacterial ecosystem. In addition to searching information in the contributing databases it incorporated the development of a system for predicting the biodegradative fate of chemical compounds based on their chemical structure using compounds in the database. http://pdg.cnb.uam.es/ MetaRouter as a training set. The application of these predictive tools to chemicals released into the environment can provide early indications that compounds are biodegradable or recalcitrant, and some indications of which microbial species may be most likely to degrade them. Gomez et al. (2007) further developed this approach using machine learning to establish a correlation between the frequency of 149 atomic triads (chemotopes) common in organic chemicals, and the capacity of microorganisms to metabolise them. Depending on the type of environmental fate defined, the system can correctly predict the biodegradative outcome for 73–87% of compounds. This system is available online.36 Also available from the home of the original database used in this work is the University of Minnesota Biocatalysis/Biodegradation Database Pathway Prediction System (PPS).37 The PPS predicts plausible pathways for microbial degradation of chemical compounds. Predictions use biotransformation rules, based on reactions found in the database or in the scientific literature. PPS predictions are most accurate for compounds that are: similar to compounds whose biodegradation pathways are reported in the scientific literature; in environments exposed to air, in moist soil http://umbbd.msi.umn.edu/index.html http://www.pdg.cnb.uam.es/BDPSERVER/ 37 http://umbbd.msi.umn.edu/predict/ 35 36
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or water, at moderate temperatures and pH, with no competing chemicals or toxins; and the sole source of energy, carbon, nitrogen, or other essential element for the microbes in these environments, rather than present in trace amounts. The OECD QSAR Toolbox has already been discussed, and in addition to its utility in predicting PBT parameters, it also has a module for the prediction of metabolism. The engine used is the same as that used in the CATABOL application, (see below). Single pathway catabolism is simulated using the abiotic and enzymemediated reactions extracted from documented hierarchical metabolic pathway databases. The simulation is based on a hierarchical searching, looking at the most likely transformations first and then working down the hierarchy. The procedure is repeated for the newly-formed products. The level of predictability based on documented experimental results of microbial catabolism of 200 chemicals is estimated at 83%. Recently under EU funding, CRAFT Explorer and CRAFT Editor have been released publicly under LGPL licensing terms.38 CRAFT Explorer generates all possible metabolites of the target chemicals and then uses the University of Minnesota Biocatalysis/Biodegradation Database likelihood model to derive the most probable metabolic route. CRAFT Editor allows the user to edit the knowledge base. CRAFT Explorer is a workflow-based program which has five steps: 1 . Import of target chemical by graphical editor from a file. 2. Preprocessing can be specified in terms of allowed reactions handling of isomers, structural validation checks. 3. User specifies the set of reaction rules that should be applied to the candidate chemicals. 4. All possible metabolites are generated. 5. Evaluation of most probable pathways. CRAFT appears to be a very comprehensive program for the generation of metabolic routes, though as yet there is little information on its performance.
Commercial Systems for Biotranformation CATABOL39 has been discussed in Section 2.2.4 with respect to estimation of persistence. The core of CATABOL is the biodegradability simulator including a library of hierarchically ordered individual transformations (catabolic steps) and matching substructure engine. The catabolic steps are derived from a set of most plausible metabolic pathways predicted by experts for each chemical from a training set extracted from the MITI-I. The model allows for identifying potentially persistent catabolic intermediates, their molar amounts, solubility and toxic properties.
http://www.molecular-networks.com/products/craft http://oasis-lmc.org/?section=software&swid=1
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METEOR40 is a knowledge-based expert system for predicting the metabolism of xenobiotics based on fragments in the parent compound. From a query structure input by the user Meteor generates results using reasoning rules within a knowledge base containing biotransformations, intermediates and reasoning rules. Predictions take into account lipophilicity, the prevalence of the biotransformation in the literature, species, and the relative likelihood of competing biotransformations. Meteor can also predict the likely chemical structures of, and metabolic pathways to, metabolites identified by mass spectrometry studies. META41 uses provided dictionaries to create metabolic paths of molecules submitted to it. All rules are based on reliable literature sources. Microbial metabolism is predicted on the basis of two separate models. Aerobic microbial biodegradation is based on observed rates of biodegradation of chemicals in sludge and a compendium of mechanistic studies of experimentally observed biodegradation products. This module contains information related to 505 metabolic transformation reactions. The Anaerobic Microbial Biodegradation (ANAR) Dictionary is based on observed rates of biodegradation of chemicals in anaerobic conditions. This module contains information related to 344 metabolic transformation reactions.
Phytoremediation Perhaps because of the inherent complexity of the modelling process for plant uptake and metabolism, there are only a few chemical structure-based models in the literature. Those models of uptake that have been reported have been reviewed by McKone and Maddalena (2007) who found that uncertainties in design, lack of data and lack of consistency in definition of terms, led to a wide discrepancy between models with no clear way of distinguishing the good from the poor. Gramatica et al. (2005) modelled the bioconcentration tendency of a range of plants towards 44 compounds of environmental concern, (pesticides, PAHs, PCBs). The approach was classical QSAR using theoretical molecular descriptors derived from the commercial programme DRAGON (Todeschini and Consonni 2000). Analysis of the data by principal component analysis and best model selection using a genetic algorithm led to a regression model based on the topological descriptor “Whete” (a Wiener-type index from electronegativity weighted distance matrix). The model enabled an estimation of those plants which were good root accumulators and those which were good leaf accumulators. The model showed a good predictivity. Collins et al. (2006) reviewed the basis for the standard model for predicting uptake of chemicals from the soil by plants, used in the UK’s Contaminated Land Exposure Assessment Model. The model is based on Log KOW. But had only been validated for compounds for Log KOW values between −1 and 5. The authors http://www.lhasalimited.org/index.php?cat=2&sub_cat=68 http://www.multicase.com/products/prod05.htm
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c oncluded that, model performance was highly variable, with predicted concentrations up to five orders of magnitude different from observed concentrations. None of the models tested were considered suitable as a general screening tool for all chemicals and vegetable types of relevance to contaminated land. An additional complication in a bioremediation context is the inability of many computational chemistry applications to handle metal or other inorganic elements, thus restricting modelling to organic compounds. The field of QSAR applied to metal ions is currently under review (Walker et al. 2010).
Conclusions The availability of reliable chemical property/effect databases, powerful data mining algorithms, and enormous computational power in the past decade have all led to the development of QSAR-based computational tools for decision making in situations where potential harmful effects of an environmental chemical are unknown, and chances of a successful remediation action are uncertain. Such chemical structure based modelling has already demonstrated clear benefits in predicting the properties of contaminants and pollutants that make them particularly troublesome environmentally. Whilst defining the problem is one half of the battle, identifying possible solutions is a more exciting prospect for QSAR and related approaches. In this respect, computational modelling approaches described in this chapter can provide answers to many of the uncertainties, and enable rapid and sound decisions on whether a remedial action would be needed and how best could it be accomplished in a prevailing contaminant situation in the field. For example, (Q)SAR based models and expert systems can rapidly assess potential toxicities of the main contaminants to indicate whether there are any compounds of concern in terms of adverse human health or environmental effects. Such systems can thus help in identifying priority compounds among the contaminants that are not likely to breakdown naturally in the environment, and for which additional remedial actions may be required. The models can also help assess whether a compound of interest will be persistent, (bio)accumulative, or toxic, how will it breakdown in the environment, and whether the breakdown products will still be harmful. The priority compounds thus identified can be used as indicators to monitor the success of a remediation action. The use of some of the expert systems described in this chapter can further help identify appropriate microbial species/strains that would suit best for the given contaminant profile of a soil/ water environment. The convergence of computational chemistry approaches with genomic and metabolomic databases, is also expected to lead to the discovery of novel natural biotransformation pathways, especially in micro-organisms, which could be exploited for bioremediation technologies. As discussed in this chapter, numerous computational tools are available to support decision making in bioremediation of contaminated environments. However,
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the shear large number of the available models/ systems may make it difficult for a novice to choose appropriate system(s), and to decide whether to rely on one or more of the computational tools. The reliability of prediction obtained from different computational models and expert systems is also likely to vary, as they will reflect the variability in the data that were used to build them, and the way models were developed, tested and validated. Although a few comprehensive software suites are available – such as EPA’s EPISUITETM – one may still need a number of different programmes to assess physicochemical properties, environmental fate, behaviour, and toxicity of the environmental chemicals. There is therefore a need for development of more focused and comprehensively integrated systems that specifically cater for bio- and phyto-remediation technologies.
References Cartwright HM (2002) Investigation of structure – biodegradability relationships in polychlorinated biphenyls using self-organising maps. Neural Comput Appl 11:30–36 Chaudhry Q, Blom-Zandstra M, Gupta S, Joner EJ (2005) Utilising the synergy between plants and rhizosphere microorganisms to enhance breakdown of organic pollutants in the environment. Environ Sci Pollut Res 12(1):34–48 Chaudhry Q, Chrétien J, Craciun, M, Guo, G, Lemke F, Müller JA, Neagu, D Piclin N, Pintore M, Trundle P (2007), Chapter 4. In: Benfenati E (ed) Algorithms for (Q)SAR model building; in quantitative structure-activity relationship (QSAR) for pesticide regulatory purposes. Elsevier, pp 111, ISBN 13: 978-0444-52710-3 Chaudhry Q, Cotterill J, Watkins R, Price NR (2006) A molecular modelling approach to predict the toxicity of compounds generated during heat treatment of foods. In: Skoog K, Alexander J (eds) Acrylamide and other hazardous compounds in heat treated foods. Woodhead Publishing Ltd, Cambridge, UK, pp 132–160 Chaudhry Q, Schroeder P, Werck-Reichhart D, Grajek W, Marecik R (2002) Prospects and limitations of phytoremediation for the removal of persistent pesticides in the environment. Environ Sci Pollut Res 9(1):4–17 Collins C, Martin I, Fryer M (2006) Evaluation of models for predicting plant uptake of chemicals from soil. UK Environment Agency. Science Report – SC050021/SR Cronin MTD, Livingstone DJ (2004) Predicting chemical toxicity and fate. CRC Press, Boca Raton, FL Dearden JC (2004) QSAR modelling of soil sorption. In: Cronin MTD, Livingstone DJ (eds) Predicting chemical toxicity and fate. CRC Press, Boca Raton, FL, pp 357–371 ECETOC (2003) Aquatic Hazard Assessment II. Technical Report No. 91. European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels, Belgium Gerberick GF, Ryan CA, Kern PS, Schlatter H, Dearman RJ, Kimber I, Patlewicz G, Basketter DA (2005) Compilation of historical local lymph node assay data for the evaluation of skin sensitization alternatives. Dermatitis 16(4):157–202 Gomez MJ, Pazos F, Guijarro FJ, de Lorenzo V, Valencia A (2007) The environmental fate of organic pollutants through the global microbial metabolism. Mol Syst Biol 3(114):1–11 Gramatica P, Papa, E, Giani E, Cenci R, Preatoni D (2005). Organic pollutant uptake by vegetable probes and biomonitoring of moss-linked metals in regions of Northern Italy. In: 15th annual meeting SETAC-Europe, Lille, 22–26 May 2005 Guidance Manual for the Categorization of Organic and Inorganic Substances on Canada’s Domestic Substances List, Existing Substances Branch, Environment Canada, 2003, pp 89–90 Hansch C, Maloney PP, Fujita T, Muir RM (1962) Nature 194:178
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Jiang GX, Niu JF, Zhang SP, Zhang ZY, Xie B (2008) Prediction of biodegradation rate constants of hydroxylated polychlorinated biphenyls by fungal laccases from Trametes versicolor and Pleurotus ostreatus. Bull Environ Contam Toxicol 81(1):1–6 Koch V (2008) PBT assessment and category approach. In: 1st SETAC Europe special science symposium, Integrated Testing Strategies for REACH, Brussels McKone TE, Maddalena RL (2007) Plant uptake of organic pollutants from soil: a critical review of bioconcentration estimates based on models and experiments. Environ Toxicol Chem 26(12):494–504 Paton GI, Bundy JG, Campbell CD, Maciel H (2004) Application of catabolic-based biosensors to develop QSARs for degradation. In: Cronin MTD, Livingstone DJ (eds) Predicting chemical toxicity and fate. CRC Press, Boca Raton, FL Pavan M, Worth AP (2006) Review of QSAR models for ready biodegradation. EUR 22355 Pavan M, Worth AP, Netzeva TI (2006) Review of QSAR models for bioconcentration. EUR 22327 Pazos F, Guijas D, Valencia A, De Lorenzo V (2005) MetaRouter: bioinformatics for bioremediation. Nucl Acids Res 33:D588–D592 Pedretti A, Villa L, Vistoli G (2002) VEGA: a versatile program to convert, handle and visualise molecular structure on Windows-Based PCs. J Mol Graph 21:47–49 Selassie CD (2003) History of quantitative structure activity relationships. In: Abraham DJ (ed) Burger’s medicinal chemistry and drug discovery, vol 1, 6th edn. Wiley, New York Skin Irritation and Corrosion: Reference Chemicals Data Base (1995) ECETOC Technical Report No. 66 Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim, Germany Walker J, Enache M, Newman MC, Lepadatu C (2010). Fundamentals QSARs for Metal Ions. Routledge, Taylor & Francis, Oxford, UK, Kentucky, USA
State-of-the-Art Chemical Analyses: Xenobiotics, Plant Proteomics, and Residues in Plant Based Products Touradj Solouki, Mohammad Ali Khalvati, Mahsan Miladi, and Behrooz Zekavat
Abstract Utilizing modern analytical tools, “x-omics” approaches (e.g., genomics, metabolomics, proteomics, etc.), and data mining techniques for comprehensive characterization of plant metabolism of xenobiotics can enhance our ability to assess environmental impacts. However, a solid understanding of metabolic pathways at the molecular level is required for targeted exploitation of species-specific detoxifying abilities of various plants. Characterization of phytotoxic pathways and dynamic molecular interactions in biological systems requires a systematic approach that can merge data from multiple analytical techniques. In this chapter, a brief review on recent advances in analytical instruments, particularly high performance mass spectrometers (MS) and allied techniques, and their impact on integrative biological studies in plant proteomics and botany are provided. Moreover, the importance of sample preparation, analyte separation, and standardization techniques are discussed. The significance of data correlation from high throughput and high resolution MS, multistage MS (MSn), “bottom-up” and “top-down” proteomics, determination of various stress responses, and identification of post-translational modifications in plants are also discussed. The conclusions provide a summary of the current instrumental limitations and anticipated future directions and challenges in plant system biology studies.
Complex Sample Analyses and System Biology Analyses of xenobiotics and complex biological/environmental sample mixtures require state-of-the-art instruments that can resolve different components of a mixture, provide individual molecular identities, yield relative abundance or concentration T. Solouki (), M. Miladi, and B. Zekavat Department of Chemistry, University of Maine, Orono ME 04469-5706 USA e-mail:
[email protected] M.A. Khalvati Department of Microbe–Plant Interactions, German Research Centre for Environmental Health (Helmholtz-Zentrum München) Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany P. Schröder and C.D. Collins (eds.), Organic Xenobiotics and Plants: From Mode of Action to Ecophysiology, Plant Ecophysiology 8, DOI 10.1007/978-90-481-9852-8_12, © Springer Science+Business Media B.V. 2011
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information, and unravel potential interactions among the various constituents of the sample at a high level of confidence. Performance characteristics of modern instruments are continuously improved to address analytical requirements in emerging areas of science, including “x-omics”. Here, we use “x-omics” to refer to comprehensive study of various complex systems such as the components of a living organism (e.g., genomics, metabolomics, proteomics, etc.) and/or a non- living system (e.g., petroleumics). Analogous to the use of the term “system biology” (Von Bertalanffy 1950; Mesarovic 1968; Founds 2009), “x-omics” refers to studies in genetics, protein biology, enzyme kinetics, lipids characterizations, cybernetics, neurophysiology, immunology, and others. System biology has recently emerged as a distinct discipline and several journals dedicated to this topic are now available (e.g., EURASIP Journal on Bioinformatics and Systems Biology published by the European Association for Signal Processing (EURASIP) and Molecular Systems Biology published by Nature Publishing Group (npg) and the European Molecular Biology Organization (EMBO)). Ideally, for comprehensive characterization of a complex ensemble and in system biology studies, three general questions regarding: (a) types, (b) concentrations, and (c) nature of the interactions of all individual components of the mixture under the investigation must be addressed (as a function of time). For example, as depicted pictorially in Fig. 1, a comprehensive “x-omics” study should provide information about the types (“what is there?”, Fig. 1a) and quantities (“how much is there?”, Fig. 1b) of all analytes. Moreover, beyond the detection and identification of all components of a biological system, it is desired to characterize all potential post-translational modifications as well as modulations of non-covalent interactions (“what are the relationships?”, Fig. 1c). For the specific case of mass spectrometry, the widths of lines in the m/z spectrum (in Fig. 1a) and y-axis (in Fig. 1b) represent the mass resolution and confidence in determining the relative abundance (RA) of each species, respectively. In part c of Fig. 1, more challenging questions about analyte interactions within the
Fig. 1 Demonstrates a general analytical approach to address questions on (a) what is the analyte?, (b) how much of the analyte is present in the sample, and (c) what are the interactions between different species present in the complex sample
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complex system are addressed which are represented by additional or multiple analysis dimensions (X). The X in Fig. 1c represents additional analysis dimensions and could include a number of complementary data analysis approaches such as the use of: ion-molecule reactions (e.g., Hydrogen/Deuterium (H/D) exchange reactions (Solouki et al. 1999; Kazazic et al. 2010), proton affinity (PA) measurements (Szulejko et al. 2006), etc.), multistage mass spectrometry (tandem MSn and ion fragmentation patterns), measurement of thermochemical properties (e.g., DG, DS), or others. The font size decrease in Fig. 1 is meant to suggest a progression in the degree of experimental difficulty by going from assigning the unknown’s identity (in Fig. 1a) to examination of its detailed interactions (in Fig. 1c). Characterization of the abovementioned parameters requires careful experimental designs and quantitative measurements (Oeljeklaus et al. 2009) of all species present in a sample. Currently and among the various analytical techniques available for proteomics studies, mass spectrometry offers a nearly “universal detection” capability that can provide meaningful quantification for complex mixtures; however, even mass spectrometry often provides a limited dynamic range, sensitivity, and precision for an ideal quantification approach. In other words, in high performance mass spectrometers (viz., ultrahigh resolution mass spectrometers such as FT-ICR MS and Orbitrap MS) precision and accuracy are far more superior in the x-axis (mass measurement accuracy (MMA) in sub part-per-million (ppm) (He et al. 2004; Heffner et al. 2007; Williams and Muddiman 2007) range is achievable for confident identification of the type, Fig. 1a) than in the y-scale (relative abundance or quantity of each species, Figure 1b). A common approach to improve quantification is to use internal or external standards (often using isotopically labeled species (Stewart et al. 2001; Previs et al. 2008; Winter et al. 2009) or immunoaffinity methods (Nicol et al. 2008)). Although there are numerous examples of successful quantification methods, confident quantitative analysis remains as a challenge for detecting minor variations of a few species in the presence of multitude of analytes that are present at much higher concentrations (a typical challenge in quantitative and/or comparative “x-omics” studies). For example, differential protein expressions or post-translational modifications may yield variations in protein concentrations in excess of orders of magnitude and such differences must be characterized for successful identification of biomarkers or panel of markers in proteomics studies. Successful characterization of the interactions between various components of a biological sample mixture (including protein–protein and non-covalent interactions, metal–protein interactions, etc.) can be more demanding than establishing their identities or concentrations. For example, metals may interact with biological molecules and change their conformations and functions. It is worth noting that interactions between proteins/macromolecules and potential xenobiotics may also yield conformational changes with significant consequences for altering biochemical interactions. Hence, a comprehensive characterization of the amounts of xenobiotics in the plant, its metabolites, and the interacting biomolecule partners at the molecular level is necessary for advancing retrospective data mining approaches to develop predictive models. Recent instrumental development efforts and research attempt to further our knowledge in these areas.
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Metabolic Profiling of Xenobiotics Heterotrophic plant cell suspension and in vitro assays are commonly used (Swisher 1987; Schmidt 2001, 2002) to acquire qualitative data on the metabolism of xenobiotics (viz., pesticides in plants). Detoxification of the hazardous foreign compounds (xenobiotics) is essential for survival of organisms. Numerous studies have investigated the uptake and metabolic fate of xenobiotics in plants and revealed a reaction sequence consisting of a first phase of chemical modification, a second phase of conjugation, and a third phase of storage (Swisher 1987; Lamoureux and Rusness 1989; Sandermann 1994). Advantage of in vitro assays is the elimination/reduction of interfering photochemicals and microbial transformations. Moreover, the absence of chlorophyll and other pigments facilitate extraction and identification of xenobiotics and their metabolites. Importance of the “earning” identification points through the use of high resolution mass spectrometry and multidimensional analyses for improving identification of xenobiotics has been highlighted (Szulejko et al. 2006; Thurman et al. 2006; Luo et al. 2009). Judicious utilization of the high MMA and additional analysis dimensions can improve selectivity and reduce the probability of error for identification of xenobiotics and microbial metabolites (Jackson et al. 2009). Recent instrumental improvements are allowing the analysis of macromolecules at a higher level of confidence and increase the likelihood for comprehensive studies of complex biological systems. Plant cell suspensions can be grown in scaled-up assays (up to 50 g fresh weight) or air-lift fermenters (~500 g fresh weight) (Knops et al. 1995; Schmidt 2002); cell cultures are thus convenient laboratory systems for monitoring the metabolic pathways of xenobiotics through plants and characterization of corresponding metabolites. Although xenobiotic turnover in plant cell cultures is usually higher than in plants, large-scale characterization of the pesticide or insecticide metabolites by plant cells (and systematic identifications, including conventional 1H-NMR analysis) is not possible with many compounds. This limitation is mainly due to the fact that often chemical modifications and reactions catalyzed by P450s exhibit slow kinetics. To date, details of many of these reactions and substrate specificities for xenobiotics metabolism are lacking (Durst et al. 1997; Siminszky et al. 1999). However, with recent advances in analytical instrumentation, including mass spectrometry, it is anticipated that significant parameters influencing reaction kinetics in metabolic pathways will be identified to resolve discrepancies between different plant studies.
Proteomics: An Overview Proteome is defined as the time- and cell-specific protein complement of the genome and consists of all proteins that are expressed in a cell at one time, including isoforms and post-translationally modified species (Rappsilber and
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Mann 2002a, b). The term proteome was initially used in 1994 at the first two dimensional electrophoresis (2-DE) meeting in Siena, Italy (Wilkins et al. 1996). The rapid growth in biological MS and addition of sequence databases have fueled the field of proteomics (Patterson and Aebersold 2003) and large scale protein studies (Anderson et al. 2000). Proteomics is the study of integrated function of all expressed proteins (Tyers and Mann 2003) and has been used for the mapping of complete proteomes, comparison of proteomes, and identification of differentially expressed proteins (Jacobs et al. 2000). Advancements in high resolution protein separation, introduction of more powerful MS software and hardware, and progress in bioinformatics technology have increased the applications of proteomics in all biological fields (Wang et al. 2008), including plant science. For instance, resistance-related proteins in plants can be identified by comparing resistant and susceptible plant tissues; just as the comparison between the proteomes of red and white flowers allows identification of the proteins that are involved in flower pigmentation (Jacobs et al. 2000). Recent improvements in biological mass spectrometry (Carpentier et al. 2008; Baginsky 2009), and in particular, ionization techniques such as matrix-assisted laser desorption ionization (MALDI) (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988), electrospray ionization (ESI) (Whitehouse et al. 1985; Fenn et al. 1989), desorption ESI (DESI), and data analysis methods are making it possible for scientists to explore and study real-world complex biological systems (Harris et al. 2008; Chen et al. 2009).
Proteomics in Plants In this chapter, a brief overview of various analytical techniques that are used in plant and soil research, with a particular emphasis on applications of plant proteomics and mass spectrometry, is provided. In general, analytical techniques can be classified into major categories such as: (a) extraction and separation techniques (e.g., supercritical fluid chromatography (SFC), liquid chromatography (LC), gas chromatography (GC), capillary zone electrophoresis (CZE), etc.), (b) spectroscopy and photon/electromagnetic wave based detection systems (e.g., fluorescent, nuclear magnetic resonance (NMR), infrared (IR), ultraviolet-visible (UV-Vis), X-ray, etc.), (c) surface analysis techniques (e.g., low energy electron diffraction (LEED), ultraviolet photoelectron spectroscopy (UPS), x-ray photoelectron spectroscopy (XPS), Raman, and Auger Spectroscopy, etc.), (d) electrochemical methods (e.g., potentiometry, coulometry, voltammetry, etc.), (e) spectrometry, and (f) others. Although unique contributions from each of the (a) to (f) techniques are quite valuable to the field, the major focus of this chapter is on the use of spectrometry techniques. The use of ion mobility spectrometry (IMS) (Kanu et al. 2008; Kanu and Hill 2008; Scarff et al. 2008; Trimpin and Clemmer 2008) is relatively new for classification of complex biological systems and mass spectrometry continues to play its historical role as a major contributor for characterization of small and macromolecules in complex mixtures. Depending on the ionization source types (e.g., electron impact
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(EI) (Mirsaleh-Kohan et al. 2008) and chemical ionization (CI) (Munson and Field 1966; Solouki and Szulejko 2007), MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988), ESI (Whitehouse et al. 1985; Fenn et al. 1989) , etc.), analyzer choices (e.g., electric sector, magnetic sector, time-of-flight (TOF)), and ion detection approaches (particle detectors, image detectors, etc.), mass spectrometers can be divided into different categories (e.g., MALDI-TOF, ESI FT-ICR, etc.). Another common classification approach for MS is based on the ion detection theory and the relationship between the ionization and detection events (e.g., ion image detectors (Comisarow and Marshall 1974)). For example, based on the partition of the ionization and detection processes, quadrupole, TOF, and magnetic and/or electric field sector instruments are classified as spatial (separated in space) whereas ion trapping instruments such as FT-ICR MS (Comisarow and Marshall 1974; Solouki and Russell 1992; Marshall et al. 1998; Kelleher 2004; Qian et al. 2004; Cooper et al. 2005; McLafferty et al. 2007) and Orbitraps (Makarov 1999b; Makarov et al. 2006; Perry et al. 2008) can be classified as temporal (separated in time) mass spectrometers. These spatial and temporal mass spectrometers offer application-specific advantages and each has its limitations. In addition to the enhanced mass resolving power (MRP), the performance characteristics of the ion-trapping mass spectrometers such as FT-ICR are often further exploited for acquiring multistage experiments and thermochemcial data via ionmolecule reactions (Szulejko et al. 2006). In this section, examples from different multidimensional approaches are presented. Experimental Design of Plant Developmental Proteome Analyses Plant proteome studies can be divided into two general categories (Rose et al. 2004) that (a) focus on establishing proteome reference maps of a defined organ at a certain developmental stage through identification of as many proteins of a particular proteome as possible and (b) address comparative proteome analyses. Both (a) and (b) types of proteomics studies utilize experimental designs (Schnable et al. 2004) centered around genotypic comparative approach (e.g., proteomes of two different genotypes such as mutant vs, wild type), temporal proteomic analysis (proteomes as a function of different developmental stages of a common genotype), or comparisons from a common genotype before and after application of an exogenous abiotic or biotic stimulus (Schnable et al. 2004). These experimental designs generally address questions related to “what” and “how much” (Figs. 1a and b). Other dimensions of analyses beyond addressing the “what” and “how much” are increasingly becoming more important in proteomics studies (Fig. 1c) and it is anticipated that future plant proteomics studies will provide crucial information about interactions between different ions and molecules. For example, we have utilized gas-phase hydrogen/deuterium (H/D) exchange reactions and ab initio calculations to show drastic peptide conformational changes upon alkali metal ion complexation (Solouki et al. 2001). Such conformational changes play significant role in biochemical reactions and are not limited to peptides or alkali metals. In the context of phytoremediation, transition
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metal ions contain dense and small atomic radii that can interact via both electromagnetic and electrostatic attraction to make such coordinating interactions one of the strongest of the metal–protein interactions (Freidberg 1974). The structural rearrangements induced by metal complexation may yield protein stabilities by restricting the mobility of domains via non-covalent cross-linking of charged amino acid side groups (Reed and Poyner 1997). These interactions can drastically change internal and/or external hydrogen bindings and stabilize specific conformation(s) of a protein and alter its enzymatic activities (Holm et al. 1996). Deciphering the details of chemical interactions between proteins and metals and understanding the biological implications of these interactions are of a broad interest. A wide range of biophysical techniques (e.g., absorbance spectroscopy, radiolabeled metal ion overlays on electrophoresis gels or blotted membranes (Maruyama et al. 1984), methods using metal affinity columns (Lopez et al. 2000), mass spectrometry (Lopez et al. 2000), and ion mobility (Ruotolo et al. 2002)) are being developed to probe molecular conformations. However, these approaches generally require a purified or semi-purified target of interest and do not facilitate identification of unknown targets from complex protein mixtures, or require complex multi-step processes and very specialized equipment. The metal affinity shift assay, developed by Kameshita and Fujisawa (1997), is similar to IMS in that binding of metal ions to proteins are presumed to change both the charge characteristics and the conformation(s) of proteins yielding mobility variations during the electrophoresis. Recent studies suggest that even under the harsh denaturing conditions of sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE), changes in mobility of metal-binding proteins could be detected. For example, divalent cation binding proteins were purified from complex mixtures of rat cerebral cortex (Kameshita and Fujisawa 1997) by using combinations of either divalent cation (Ca2+/Mg2+) treatment followed by or preceded by chelator (ethylenediamine tetra-acetic acid (EDTA)/ethylene glycolbis(2-aminoethyl-ether)-N,N,N’,N’-tetra-acetic acid (EGTA)) treatment in diagonal PAGE gels. An identical technique was used to characterize changes in expression of divalent cation-binding proteins during sperm maturation in mice (Gye et al. 2001). Gas-phase ion mobility and mass spectrometry techniques provide valuable complementary information; these techniques are quite sensitive and appropriate for investigating reactions occurring at physiological concentrations. A potential drawback with many of these gas-phase techniques is the possible convolution of the structural/ conformational changes caused by the analytical probe itself (e.g., ionization processes in MS and mobility experiments). Efforts on comparing gas- and solutionphase structures are ongoing and are expected to expand (Chung et al. 1997; Bogdanov and Smith 2005; Scarff et al. 2008). Other non-covalent binding interactions between proteins are also among the central physicochemical phenomena underlying biological signaling and functional control at the molecular level. For example, protein-protein interactions are intrinsic to virtually all cellular processes such as DNA replication, transcription, translation, signal transduction, and intermediary metabolism. In recent years, advances in analytical methods, and in particular molecular modeling and mass spectrometry, have aided the study of non-covalent interactions with unprecedented details (Barrera et al. 2008).
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Plant Proteome Analysis Availability of the nucleotide sequence information and sensitive analytical instruments (viz., high performance mass spectrometers) is opening up new perspectives for proteomic approaches and investigation of the complex functions of model plants and crop species. Although a true proteome analysis is still a distant objective and current studies focus on select portions of the total protein profiles, progress in proteomics has been rapid (Wilkins et al. 1996; Oeljeklaus et al. 2009; Pan et al. 2009; Sheoran et al. 2009; Sun et al. 2009). In other words, a true functional proteomics study which requires qualitative and quantitative characterization (e.g., temporal and spatial variations) of proteome as well as characterization of all chemical interactions is currently not possible. Nevertheless, interest in proteomics and plant proteomics (e.g., protein variations in different plant organs (Thiellement et al. 1999), variations in response to physiological events (Gallardo et al. 2003), identification of unknown plant viruses due to their proteome (Cooper et al. 2003), and the identification of microtubule binding proteins in plants (Chan et al. 2003) is increasing rapidly. Often, proteins that are involved in important dynamic cellular processes such as signal-transduction are also expressed at low physiological concentrations and exhibit rapid turnover. It is possible to employ metabolic 14N/15N labeling and MS-based proteomics techniques for identification of protein dynamics and turnover in plants. In most of the plant proteomics studies, model organisms such as Arabidopsis thaliana (that have a fully sequenced genome and a multitude of genetic mutants available for comparative experiments) are used (Kruger et al. 2007). Characterization of the minor components of a biological system at low-expression levels is a challenging analytical task and requires instruments with high sensitivity and wide dynamic range. Modern analytical approaches are being developed to address such complex biochemical questions. For example, modern “soft” ionization techniques such as MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989) are allowing scientists to analyze larger proteins and more complex systems. However, these rapidly expanding fields are still in their infancy and further developments in sample preparation, analytical characterization, and advanced bioinformatics will play significant roles in “x-omics” studies of plants.
Protein Profiling in Plants Conventionally, proteomic analyses include four steps: (a) sample preparation, (b) protein separation and/or purification, (c) functional analysis, and (d) protein identification. Plants can grow in different culture types and each of the tissues or culture types may be associated with a specific set of proteins. There are some limitations in plant protein analysis compared to animal (or mammalian) protein characterization. The dynamic range challenges related to isolation of minor
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components of complex mixtures in the presence of other major components and “contaminants” are not limited to plant proteomics. However, comparisons between animal and plant cells indicate that plants’ protein contents and concentrations are lower. Moreover, plant cells are rich in other interfering compounds that can potentially hinder the sample preparation, separation, and characterization processes. In addition, plant cells generally have numerous vacuoles that contain additional interfering compounds such as salts, organic acids, phenolics, proteases, pigments, terpenes, and inhibitory ions. The presence of these seemingly superfluous compounds in plant cells has a negative impact on protein extraction and separation. Proteases and phenolic compounds can modify proteins and change their molecular weights or isoelectric points (PIs) (Jacobs et al. 2000). For example, phenolic compounds in banana plant combine with proteins reversibly by hydrogen binding and irreversibly by oxidation followed by covalent condensations. These bindings cause some issues in 2D electrophoresis and yield charge heterogeneity and streaks in the gel. Moreover, gel pores can be blocked by carbohydrates and cause precipitation and extend the focusing time. Other compounds like terpenoids, pigments, lipids, and wax-like polymers can also produce streaking and charge heterogeneity (Carpentier et al. 2008). Therefore, sample preparation in plant proteomics is a very important step and is associated with technical challenges (Wang et al. 2008). It is assumed that an ideal sample preparation for mass spectrometry characterization should result in disruption of all non-covalent bound protein complexes and removal of various interfering compounds such as salts, polysaccharides, and phenolic compounds to yield a solution of individual polypeptides. However, for more sophisticated analyses, where the preservation of the non-covalent bindings and other innate chemical interactions are desired, sample preparations must be less intrusive; this is an important current limitation. (a) Sample Preparation: Sample preparation in plant proteomics mass spectrometry may typically include three steps: (a-1) tissue disruption, (a-2) protein extraction from the source materials, and (a-3) solubilization of the available proteins prior to the final analysis (Jacobs et al. 2000; Wang et al. 2008). It is likely that future research on microfluidic devices (Ohno et al. 2008; Blow 2009; Kovarik and Jacobson 2009; Mukhopadhyay 2009) will focus on both reducing sample sizes and, more importantly, sample preparation steps to develop alternative direct analysis approaches in plant proteomics and allied fields of “x-omics”. Direct analysis of biological samples is an important consideration to preserve innate interactions between various components of the sample (i.e., chemical interactions can be lost during various stages of the conventional sample preparation approach in steps a-1 to a-3). Convenient sources of plant proteins for proteomic studies can be found from young seedlings which have higher protein contents and less interfering compounds (Jacobs et al. 2000). The amount and types of various proteins in seed depend on the species, genotype, and plant’s environment. Although, the concentration of proteins in the seed is higher, a major problem in seed’s protein extraction is the disturbance of lipids and carbohydrates (Jacobs et al. 2000).
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(b) Protein Separation and/or Purification: Among the various approaches for sample preparations in proteomics studies, the three commonly used/reported techniques for extraction of proteins from plant tissues include: (a) trichloroacetic acid (TCA)/acetone extraction (Catherine et al. 1986), (b) phenol extraction (Hurkman and Tanaka 1986), and (c) TCA/acetone/phenol extraction (Wang et al. 2003, 2006). Examples of the protein extraction methods have been reviewed recently (Wang et al. 2008; Jorrin-Novo et al. 2009). Technological advances in separation methodologies are expected to play a crucial role in advancing plant proteomics in several dimensions. Future improvements in separation resolving power will allow more comprehensive characterization of the proteome through the use of tandem separation techniques. Regardless of the projected progressions in performance characteristics of the analytical detectors in proteomics (e.g., mass spectrometers, ion mobility spectrometers/ devices, spectroscopic techniques), sample preparation will likely remain one of the most important and challenging steps in plant proteomics. Scientific appetite to continue combining mass spectrometers to other separation devices is fueled by the past remarkable successes in this area and tremendous future needs. Advances in small volume sampling (Song et al. 2006; Ohno et al. 2008; Blow 2009; Kovarik and Jacobson 2009; Mukhopadhyay 2009), CZE (Tagliaro et al. 2010; Viglio et al. 2010), IMS (Kanu et al. 2008; Trimpin and Clemmer 2008; Fernandez-Lima et al. 2009), and other relevant areas that can enhance sample handling for quantitative measurements (Jorrin-Novo et al. 2009) hold great promises and suggest a greater expansion of the plant proteomics. (c) Functional Analysis: Functional analysis of proteins (after identification) or “functional proteomics” is focused on characterizing protein-protein and other intermolecular interactions in biological systems. “Functional proteomics” facilitates the relationship between specific protein(s) to a particular biological pathway/process. Quantitative information regarding the identified proteins can also be obtained which is important in studying the variation of proteome in response to stimuli. “Functional proteomics” techniques can be classified as (i) affinitybased purification, (ii) pair-wise testing of the two partners, (iii) genetic-based techniques, and (iv) computational methods. The data obtained from these approaches may be combined to gain additional information about the function and evolution of biological systems. Recently, Orchard et al. have reported on a technique called “the minimum information required for reporting a molecular interaction experiment (MIMIx)” for improving the public access to the interacting protein data (Orchard et al. 2007). Mass spectrometry has played a major role in this area and interested readers are referred to a recent review by Köcher et al. (Köcher and Superti-Furga 2007). (d) Protein Identification: Several analytical approaches are available for identification of macromolecules. The following section provides a brief summary of some of the frequently used analytical techniques in plant studies. These techniques, such as chromatography, nuclear magnetic resonance (NMR), spectroscopy, and spectrometry, can be used as standalone or tandem systems for x-omic characterization.
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Analytical Techniques Chromatography Chromatographic techniques including high performance liquid chromatography (HPLC), 2D-HPLC, electrophoresis (Lei et al. 2005) (e.g., CZE, gel electrophoresis, micellar electrokinetic capillary chromatography (MEKC), isoelectric focusing), and 2D-electrophoresis are among the powerful separation techniques used in analytical chemistry and proteomics. These separation techniques offer excellent supplementary advantages when coupled to mass spectrometry (i.e., MS as detectors) in so called “hyphenated” instruments; these hyphenated instruments such as LC/ MS (Mitulovic and Mechtler 2006) and LC/FT-ICR MS (Smith et al. 2004) can be used for analyzing complex mixtures in proteomics and metabolomics. Additional information on hyphenated chromatographic techniques can be found in recent review articles (Mitulovic and Mechtler 2006; Deborah 2009).
Nuclear Magnetic Resonance (NMR) NMR is one of the most valuable analytical tools for structural determination of proteins/peptides (Chung et al. 1997; Shin and Lee 2008). In contrast to other types of spectroscopy, nuclei of atoms instead of their electrons (e.g., outer electrons for UV, IR, Vis, UV and inner electrons for x-ray techniques) are involved in the absorption of the electromagnetic energy. Moreover, NMR utilizes the magnetic component of the electromagnetic wave in the radiofrequency range (RF) of roughly 4 MHz–1 GHz (~23.5 T magnetic field) and requires a strong magnet for its operation. The use of Fourier transform and a strong superconducting magnet in most modern NMR instruments are common features between this and FT-ICR MS. As with the MS methods, and perhaps even more effectively, FT-NMR can provide a wealth of information for structural elucidation and about the chemical bonding/environment of a compound. However, NMR suffers from low sensitivity and slow data acquisition/collection rate; this is also true for multidimensional NMR which limits the use of separation system with NMR. For example, a simple 2D NMR protein characterization experiments may require orders of magnitude increased data acquisition time than the most sophisticated MS approaches (e.g., a single scan TOF experiment may only require tens of microseconds and even more sophisticated ultrahigh resolution FT-ICR MS experiments conducted under the ultrahigh vacuum (UHV) conditions can be completed in seconds). Moreover, experimental design and data interpretation in NMR can be very challenging for complex biological samples and large proteins. A significant advantage of the NMR approach is the non destructive nature of signal detection (as opposed to the required ionization step for all MS experiments); the non destructive nature of NMR approach makes it possible to study reaction kinetics, protein folding, and other chemical reactions in “real-time” and without significantly perturbing the chemical environment/system. However, serious instrumental
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limitations exist for using NMR to study fast reactions at physiologically relevant concentrations. Interested readers are referred to a recent review by Shin et al. (Shin and Lee 2008) on the topic. Spectroscopy Spectroscopy techniques cover a wide range of instruments that employ different wavelengths and/or photon energies to interrogate chemical bonds and structures. All spectroscopy methods, excluding NMR, utilize the electric component of the electromagnetic (light) wave. The light or energy spectrum may cover the range from high energy (e.g., x-ray) down to low energy ultraviolet (UV), infrared (IR), and rotational mode photons. For example, in the context of the plant studies, x-ray photoelectron spectroscopy (XPS) is an x-ray-based technique for elemental analyses and provides valuable information about distribution of various elements (e.g., metals) within a biological system, cell, and/or organ (Pothan et al. 2006; Fang and Wan 2008; Guo et al. 2008). Atomic absorption (AA) has also been historically a popular approach for elemental analysis in plants (Ogura 1970; Caldas et al. 2009; Wen et al. 2009 ) utilizing various types of sample extraction methods (Meeravali and Kumar 2000; Li et al. 2002b; Narin et al. 2004; Wei et al. 2007; Jiang et al. 2008; Malekpour et al. 2009). Fluorescence and IR spectroscopy (Berthomieu and Hienerwadel 2009; Cozzolino 2009) can provide structural and molecular fingerprints but most of these spectroscopic techniques have the disadvantage of low sensitivity (excluding fluorescence) and specificity for identification of individual components of a complex sample. Readers are referred to a review by Fournier et al. (2009) on the subject. Mass Spectrometry (MS) and Ion Mobility (IMS) Both MS and IMS techniques have been used for characterization of complex sample mixtures and are suitable for analysis of small and large molecules. These instruments can be used as standalone detectors but combination of the two instruments is a powerful arsenal for “x-omics” studies. Numerous types and configurations of mass spectrometers are used in biology, chemistry, environmental studies, forensics, physics, and other areas of science; it is therefore difficult to provide a comprehensive coverage of these diverse fields where MS has played a significant role to advance. In the following section, brief overviews of (a) MS and (b) IMS are provided. However, MS techniques are further explored in Section 10 of this chapter. Mass Spectrometry Ionization of large labile molecules (polar non-volatile compounds) such as proteins was initially reported by using plasma desorption (PD) (Macfarlane and Torgerson 1976). This invention was followed by introduction of various other ionization techniques (e.g., fast atom bombardment (FAB) (Barberr et al. 1981), secondary
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ionization MS (SIMS) (Benninghoven et al. 1976), including two of the very important ionization techniques in biological mass spectrometry, namely, MALDI (Karas et al. 1987; Karas and Hillenkamp 1988; Tanaka et al. 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989)) that changed the field of modern mass spectrometry (each of the latter two ionization techniques resulted in the latest Nobel Prizes devoted to MS-related topics). Other important contributions and technological advances were related to the hardware and software improvements; for example, data mining has become a major field of its own in mass spectrometry (Matallana-Surget et al. 2010). Initially, the use of sector instruments, quadruple or TOF mass spectrometers were quite popular but their MRP (specially, in the case of TOF) was limited (e.g., M/DM50% << 60,000 at m/z ~ 1,000). The use of high performance mass spectrometers such as FT-ICR (originally developed in mid 1970s (Comisarow and Marshall 1974) and based on the application of FT techniques using a conventional ICR (Sommer et al. 1951) with MALDI (Solouki and Russell 1992; Castoro and Wilkins 1993) and ESI (Loo et al. 1992) allowed identification of larger biomolecules at higher MRPs. The MMA and ion trapping capabilities of FT-ICR make this device a superb instrument for identification of volatile (Szulejko and Solouki 2002) and nonvolatile (Marshall and Rodgers 2004, 2008) unknowns in very complex mixtures; a recent review article on FT-ICR provides performance characteristics of these instruments (Qian et al. 2004). Subsequently, several technological advances improved the achievable MMA and mass resolving power (MRP) for other instruments, such as TOF, and allowed commercial production of multitude of MS systems and their coupling to separation systems such as LC for proteomics analyses; these advances have expanded the field of MS remarkably. Currently, there are several commercially available instruments for proteomics use. Depending on the type of the data required and financial affordability, quadrupole, TOF, electric or magnetic sector instruments, and high performance mass spectrometers such as FT-ICR and Orbitrap MS may be employed with different ionization sources (most commonly, MALDI or ESI for proteomics studies). Although the combined performance characteristics (e.g., MMA, MRP, ion–molecule reaction capabilities, ion–electron interactions such as electron capture dissociation (ECD) (Zubarev et al. 1998), ability to perform multistage MS (MSn) capabilities using sustained off resonance irradiation collision induced dissociation (SORI-CID) (Gauthier et al. 1991), and stored waveform inverse FT (SWIFT) (Wang et al. 1986), etc.) of FT-ICR MS are unmatched by other MS instruments (Qian et al. 2004), their costs and the need for large superconducting magnets (and required maintenance) for normal FT-ICR MS operations have limited their use. Another recently introduced ion trap device that is rapidly becoming a popular tool for “x-omics” analyses is Orbitrap. The Orbitrap mass spectrometer (OMS) was originally developed by Makarov (1999a, b) in late 1990s and the so called “hyperlogarithmic field” or “quadro-logarithmic field” type of orbital ion trapping/detection is similar to dynamic ion trapping in an electrostatic field which was demonstrated originally by Kingdon (1923) and later utilized in mass spectrometry (Solouki et al. 1994; Gillig et al. 1996; Perry et al. 2008). The new Orbitrap device is a high performance ion trap (similar to FT-ICR) mass analyzer in which the m/z ratio of the trapped ions can be obtained from the frequency of ion oscillation along the
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axial direction of the trapping device (Makarov 2000). Mass resolving power (MRP) of ~ 150,000 at m/z 23 and 80,000 at m/z 1,000 with MMA of 2–3 ppm has been reported for OMS (Perry et al. 2008). Since its introduction, OMS has been further configured for high throughput analyses (Makarov et al. 2006). For a comprehensive review on instrumentation and application of Orbitrap, interested readers are referred to a recent review paper by Perry et al. (2008). Similar to the renewed interest in using gas chromatography (GC)/FT-ICR MS (Harris 2001; Szulejko and Solouki 2002; Heffner et al. 2007; Luo et al. 2009), Orbitrap has been coupled to GC (Peterson et al. 2009) for small molecule analysis. The combination of GC/Orbitrap has enabled the acquisition of high mass resolving power GC mass spectra in a high throughput fashion. Orbitrap has also been implemented in metabolomics for precise mapping of metabolome networks, utilizing high MMA and MRP of the technique (Breitling et al. 2006). In the area of proteomics, OMS has also permitted confident top-down protein analysis (owing to its high MMA and MRP) which has previously been limited to the state-of-the-art FT-ICR MS instruments (Scigelova and Makarov 2006). For example, Mann et al. have recently applied a hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer for MS3 top-down protein sequencing (Macek et al. 2006) and in vivo quantitation and identification of protein phosphorylation sites as a function of time, stimulus, and subcellular location (Olsen et al. 2006). Ion Mobility Spectrometry Ion mobility spectrometry (IMS) is an analytical tool that was originally developed for detection of explosives, drugs, and chemical warfare agents. This technique was first introduced by McDaniel (McDaniel et al. 1962) for ion–molecule reaction studies in 1960s. A similar technique (i.e., plasma chromatography) was later developed by Karasek and Cohen (Cohen and Karasek 1970; Karasek 1970, 1974) for analyzing/detecting organic chemicals in 1970s. Since the initial coupling of a drift tube to a magnetic sector mass spectrometer (MS) by McDaniel (McDaniel et al. 1962), this technique has been coupled to other mass analyzers for more sophisticated chemical analyses (Gillig et al. 2000; Kemper et al. 2009); for example, combined use of the ultrahigh resolution MS and IMS provides unique fingerprinting ability in petroleumics (Fernandez-Lima et al. 2009). Additional details on the tandem use of the IMS/MS technique are available in a recent review paper by Kanu et al. (2008). IMS/ MS has been utilized in the area of protein conformational analysis by comparison of experimentally obtained cross sections with those from theoretical calculations (Kanu et al. 2008). For example, a modified version of this technique (i.e., travelling wave ion mobility mass spectrometry (TWIMS)) has been used to estimate the threedimensional structures of proteins in the gas-phase (Scarff et al. 2008). TWIMS has the potential to be used as a screening technique to check whether a recombinant protein is biologically active or not. As a technique to distinguish between phos phorylated and non-phosphorylated peptides/proteins, IMS/MS has been utilized by Russell’s group (Ruotolo et al. 2002). IMS/MS is another analytical tool with significant potential for analysis of complex mixtures. Clemmer’s group has
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implemented this technique to differentiate between complex polymer mixtures at structural isomeric level (Trimpin and Clemmer 2008). IMS coupled to a MS and/or GC has also been utilized for analysis of volatile organic compounds in complex mixtures. Instrumental details and application of the GC/IMS and GC/IMS/MS are provided in a recent review paper by Kanu et al. (Kanu and Hill 2008).
Quantitative Plant Proteomics Many biomolecular interactions are transient and their complex and interrelated chemical kinetics need to be addressed. Although analytical approaches have progressed from genomics to address a host of other questions (e.g., proteomics, metabolomics, characterization of post-translational modifications, protein-protein interactions, and conformational changes that occur upon protein complexations), to date, quantitative and detailed understanding of chemical interactions at the molecular level remain as imperative challenges. Future studies are likely to focus on these challenging areas and sample pre-treatment for quantitative plant proteomic studies is expected to receive considerable attention. Another significant area will be on data analysis and statistical treatment of the acquired data. This area is one of the most important areas as different data analysis methods using the same raw data set may yield vastly different conclusions. Hence, it will be helpful to generate online and international scientific data repositories (Domon and Aebersold 2006a) for plant proteomics where raw data can be easily stored, disseminated, and shared widely for evaluation of inter-laboratory reproducibility. With the anticipated further economical and environmental impacts of plant proteomics and improvements in sample preparation, wider access to high performance instrumentation, modern data analysis techniques, and more effective (raw) data sharing, the plant proteomics field promises to be a very exciting and expanding field of fundamental and applied sciences. Although mass spectrometry provides excellent opportunities to study system biology in plants, instrumental limitations hamper our progress towards rapid characterization of x-ome. Recent improvements in performance characteristics of mass spectrometers (e.g., new ionization methods, MRP, MMA, MSn capabilities) have significantly contributed to our knowledge but difficulties associated with the limited dynamic range remain as a serious challenge. Issues with the limited dynamic range are quite challenging for identification of post-translational modifications where only a very small fraction of the endogenous protein population may be modified. New advances in proteomic studies are making it possible to accomplish such arduous tasks using modern instrumentations.
Analysis of Post Translational Modifications in Plant Proteins The synthesized proteins may not be able to control complex biological functions of the cell in their native forms and often need to be modified to perform their
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f unctions. Protein modifications are known as post-translational modifications (PTMs). PTMs are defined as the “series of chemical reactions whereby a newly synthesized polypeptide chain is converted into a functional protein” (Appel and Bairoch 2004). Based on the last update of RESID online database, more than 506 types of PTMs have been identified (Garavelli 2003, 2004; Farriol-Mathis et al. 2004). Some commonly observed and reported PTMs include phosphorylation, acetylation, glycosylation, ubiquitylation, and proteolytic processing. These posttranslational modifications often cause changes in the protein folding and function, e.g., enzymatic activities, substrate specificities, protein–protein interaction (PPI), complex formation, localization, and degradation (Kersten et al. 2006). A few specific examples of phosphorylation, glycosylphosphatidylinositol (GPI) modification, and ubiquitination in plants are provided in the following sections. Generally, databases are used for identification of PTMs (Garavelli 2003, 2004; Farriol-Mathis et al. 2004) and it is important that these databases contain the relevant structural information about all proteins of interest (e.g., informative fragment ions instead of “unusual” fragmentation patterns). For example, Fig. 2 shows how isobaric fragment ions may have different structures and/or conformations and yield different subsequent fragmentation results; for
Fig. 2 ESI/FT-ICR mass spectrum of (a) substance P (RPKPQQFFLM) after in-source CID, (b) first isotopic peak of b10+2 (i.e., b10 (12Call) 2+) after SWIFT isolation , and (c) b10 (12Call) 2+ after 300 s reaction with ND3 at reagent pressure of P(ND3) = 2.1 × 10-6 torr. Inset in b illustrates two possible structures of a b10 fragment ions.
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successful “x-omics” analyses and top-down peptide/protein sequencing (refer to Section 10 of this chapter) these important processes must be understood and accounted for. Here, substance P was subjected to in-source CID process (Fig. 2a). The resulting doubly-charged b10 fragment ions (i.e., b10) were isolated using SWIFT technique (Fig. 2b) and subsequently subjected to H/D exchange reaction (Fig. 2c). In this manner, the H/D exchange reaction was used to kinetically differentiate the structurally dissimilar isobaric b10 fragment ions (structures #1 and #2 in the inset b and c of Fig. 2). The difference in H/D exchange rate constants and exchange patterns of the different isobaric ion population can be used to differentiate between the two structures of bn fragment ions (Fattahi et al. 2010). The b10 fragment ion can be further fragmented to increase the sequence coverage. Due to the possible existence of a cyclic structure for b10 fragment ion, further fragmentation may yield uninformative fragment ions and loss of initial sequence information (i.e., sequence scrambling) in top-down proteomics.
Phosphorylation Protein phosphorylation is a post-translational modification (PTM) of proteins and it regulates cellular signaling processes. Identification of signaling processes and phosphoproteins at the plasma membrane is important as plasma membrane proteins are often involved in the regulation of cell–cell interactions in developmental processes, response to the environment, and modulation of signaling pathways. In this context, phosphoproteins in chloroplast thylakoid (involved in photosynthesis activities) and plasma membranes have been the major targets for studying plant PTMs (Allen and Forsberg 2001; Zer and Ohad 2003; Zer et al. 2003; Kwon et al. 2006). Other important roles of protein phosphorylation in plants include: 1. Regulation of the photosynthetic activities in plants which is effected by lightand redox-controlled protein phosphorylation (Allen and Forsberg 2001; Zer and Ohad 2003; Zer et al. 2003; Kwon et al. 2006). 2. Pathogen response such as in plant–pathogen interactions and defense signaling (Xing et al. 2002). For example, AtPhos43, one of the proteins in suspensioncultured cells of Arabidopsis is phosphorylated within minutes after treatment with flagellin or chitin fragments (Peck et al. 2001). Phosphorylation of AtPhos43 was identified by two dimensional gel electrophoresis and nanospray ESI MS/ MS (Peck et al. 2001). The results showed that AtPhos43 is a novel protein containing ankyrin repeats and its phosphorylation is catalyzed by FLS2, a receptor-like kinase involved in flagellin (not chitin) perception. Again, it should be noted that in most cases detection and analysis of phosphoproteins are difficult processes as phosphorylated form of a protein may be a small fraction of the total population of the protein (Peck 2006). Interested readers on plant phosphorproteins
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are referred to recent comprehensive reviews on this topic (Laugesen et al. 2006; Rossignol 2006). In Section 10 of this chapter, additional information on localization of post-translational modifications using MS-based fragmentation techniques is provided.
Glycosylphosphatidylinositol (GPI) Modification GPI anchoring is a kind of PTMs in cell surface proteins which are tethered to extracellular membrane. Identification of GPI-anchored proteins (GAPs) have been performed by genomic and proteomic analyses; these proteins are involved in diverse cellular functions such as cell signaling, adhesion, matrix remodeling, and pathogen response in plants (Borner et al. 2002). Arabinogalactan proteins (AGPs) are a family of glycosylated hydroxyprolinerich glycoproteins and their function in plant is growth and development (Schultz et al. 2000). These proteins are attached to outer surface of plasma membrane through GPI anchor. The deglycosylated AG peptides from AGPs were analyzed by MALDI-TOF MS and tandem MS and the cleavage sites of the N-terminal endoplasmic reticulum secretion signal and the C-terminal GPI anchor signal were determined. In addition, it was predicted that classical AG proteins, AG peptides, fasciclin-like proteins, and many other proteins of unknown function to be GPI anchored. GPI-anchored proteins of Arabidopsis callus cells were fractioned and then isolated proteins (without GPI anchor) were analyzed by LC MS/MS. Identified proteins included b-1,3 glucanases, phytocyanins, fasciclin-like arabinogalactan proteins, receptor-like proteins, Hedgehog-interacting-like proteins, putative glycerophosphodiesterases, lipid transfer-like protein, COBRA-like protein, SKU5, and SKS1 (Schultz et al. 2000).
Ubiquitination Ubiquitination refers to the post-translational modification of a protein by covalent conjugation of ubiquitin (Ub) to Lys residues and it has regulatory effect in cellular processes (Kwon et al. 2006). Its functional role is to target the cell proteins for degradation. Degradation (of targeted proteins) of regulated proteins controls the cellular activities such as pathogenesis defense (Devoto et al. 2003), self-incompatibility (SI) in flowering plants (Zhang et al. 2009), growth, development, and hormone response (Moon et al. 2004). For example, proteolytic pathway in Arabidopsis involves Ub and the 26S proteasome, a 2-MDa protease complex; this pathway has been studied for proteomics analysis (Smalle and Vierstra 2004). Ubiquitination is fairly simple and is emerging as a common regulatory mechanism controlling a wide range of cellular processes in plants. Recent discoveries are suggesting that ubiquitination may also play an important role in plant disease resistance (Han and Martinage 1992; Yew 2001; Devoto et al. 2003; Kwon et al. 2006).
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Proteomics in Plant Stress Response Actual and transitory states of the cells or organisms are expressed by their proteomes; a properly functioning cell does not have a static state as it is affected by multiple modifications such as cell cycle, changes of external conditions or environmental stresses, and physiological states (Timperio et al. 2008). Environmental stresses can be classified into abiotic and biotic stresses.
Abiotic Stresses There are several kinds of abiotic stresses that can damage plant cells. Abiotic stresses are produced by inappropriate levels of physical components of the environment and include (a) primary stresses such as cold (Cui et al. 2005), drought (Salekdeh et al. 2002; Hajheidari et al. 2005, 2007; He et al. 2007), salinity, heavy metals (e.g., copper stress (Bona et al. 2007), heat (He et al. 2007), and chemical pollution (xenobiotics), and (b) secondary stresses such as oxidative stress (Vitamvas et al. 2007). The cold stress responses of the Arabidopsis leaves, poplar leaves, rice anthers, and mitochondria of Pisum sativum were studied by 2-DE analysis (Bae et al. 2003). Using MALDI-TOF MS, it was found that out of the 184 identified proteins, 54 were up- or down-regulated with a greater than twofold change in response to cold treatment (Bae et al. 2003). Pesticides, herbicides, and metals are among the most prevalent abiotic chemical stresses that can compromise healthy and normal development of plants. The uptake and regulation of trace metals in a cell is the basis of many life functions of an organism. For example, the activity of intracellular metal ions is controlled by several families of proteins that are involved in various processes such as detoxification or protection of the cell, cell cycle, proliferation, and/or apoptosis (Finney and óHalloran 2003). Moreover, the function of many proteins (referred to as metalloproteins) may depend on their interaction with a metal (e.g., transition metals such as Cu, Fe, Zn, or Mo). Other proteins such as metallothioneins are expressed as a defense mechanism of an organism against heavy metal stress, and many others serve within an organism as transporters of essential nutrient ions, contaminants and metal probes. Various analytical techniques such as atomic absorption, x-ray spectroscopy, or ICP MS can be used to detect metals (Szpunar 2000; Outten and óHalloran 2001; Szpunar et al. 2003). In addition to qualitative and quantitative information about the types and concentrations of metals present in biological systems, the knowledge about the molecular basis of mechanisms by which metals are detected, deposited/stored, or incorporated as a cofactor in a cell is important. In part, due to the complexity of the chemical processes that are involved in metal– protein interactions such information is scarce (Williams 2001). For instance, even a short and simple peptide may interact with different metals to yield structures
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with significantly different conformations (Solouki et al. 2001); such conformational variations may play important roles for biological activities of larger proteins. Modern MS techniques such as H/D exchange offer powerful approaches for detailed analysis of protein conformations (Fattahi et al. 2010). Therefore, a comprehensive understanding of these chemical processes may require multiple instruments for identification of the metals and proteins involved as well as characterization of interactions that yield metal-complexes (Outten and óHalloran 2001; Solouki et al. 2001). Although bioinorganic compounds play a vital role in biological systems, the difficulties with their characterization have hampered the growth in this area in the past. However, recent advances in inorganic and analytical spectroscopy, genetics, molecular biology, and structural biology are contributing to rapid expansion of bioinorganic chemistry discipline. Some of the compounds of interest in plant proteomics include polypeptides, proteins and their metal complexes, and metal-binding enzymatic metabolites as well as products of the metabolism of arsenic, phosphorus, or selenium that lead to the formation of the covalent C–As, C–P, or C–Se bonds (Szpunar 2000; Szpunar et al. 2003; Lobinski et al. 2006). Identification of the protein ligands and determination of the metal(s) stoichiometry are among active areas of the current research. An emerging trend is the development of high throughput screens for protein-binding transition-metal ions to assay all of the proteins in a given proteome (e.g., yeast, serum) to identify the corresponding metallome. The analysis of a metallome may inform us about (i) the distribution of an element (metal or metalloid) among the cellular compartments of a given cell type, (ii) metal coordination environment(s), and (iii) the concentrations of various metal species that are present in a biological system. The use of x-ray, atomic absorption (AA), inductively coupled plasma mass spectrometry (ICP-MS) (Szpunar 2000; Lobinski et al. 2006; Kruger et al. 2007) or similar techniques can provide valuable information about the types and concentrations of various metals present in a sample; however, understanding the exact roles of such elements requires analytical approaches that can bestow detailed information about the intact molecular system in which metals interact. Advances in high performance and ultrahigh resolution MS instrumentation and multidimensional techniques are allowing for integration of complex samples. For example, ESI makes it possible to generate multiply charged ions and extend the high mass range for protein detection. On the other hand, multidimensional analysis schemes provide means for studying complex systems. For instance, ESI FT-ICR was used to show a highly congested fulvic acid sample in the m/z range of 200–1,800 (Fievre et al. 1997); these complex samples cannot be resolved using conventional separation systems but using ion ejection techniques (viz., using SWIFT (Wang et al. 1986)), ions ions can be selectively isolated in an ICR cell for carrying out subsequent ion-molecule reactions (e.g., for characterization of their functional groups (Solouki et al. 1999) and interactions with metals (Alomary et al. 2000).
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Biotic Stresses Biotic stresses are caused by pathogens, parasites, predators, and other competing organisms (Peck et al. 2001; Peck 2003; Padliya and Cooper 2006; Vitamvas et al. 2007). Under these “stressed” conditions plants’ physiological, biochemical, and metabolic functions are modified (Vitamvas et al. 2007). Plants responds to environmental stresses such as pathogens, cold, frost, drought and heat by producing protective compounds and proteins such as pathogenesis related proteins (Vitamvas et al. 2007), heat-shock proteins (HSPs) (Guy et al. 1985; Vitamvas et al. 2007; Timperio et al. 2008), antifreeze proteins (AFBs), and dehydrins (Vitamvas et al. 2007). In other words, biotic or abiotic stressors may alter the patterns of gene expression leading to qualitative and quantitative changes in protein compositions that ultimately can cause modulation of various metabolic and defensive pathways. Thus, proteomics can be a powerful tool for analyzing various biochemical pathways and complex responses of plants to environmental stimuli. Proteomic comparisons of plants between before and after specific stress conditions may provide additional information about the defense mechanism of a plant (Timperio et al. 2008). Beyond the fundamental understanding of the environmental stress-related proteome variations, it is anticipated that such proteome alterations may be exploited as intelligent natural sensing mechanisms in sensor technology. Interested readers are referred to the recent reviews on the applications of proteomic analysis in abiotic and biotic stress studies (Vitamvas et al. 2007; Timperio et al. 2008).
Oxidative Stresses Oxidative stresses can be of biotic or abiotic sources; Møller et al. provide a comprehensive review on oxidative modification of cellular components in plants (Møller et al. 2007). Some of these cellular changes may occur in time scales comparable to a chemical bond vibration (i.e., femtosecond range) and others may proceed at much longer time scales. For example, the division of an individual cell into two cells does not take a long time (e.g., <1 h in eukaryotes). Whereas the “interphase” step in cell cycle takes much longer time (e.g., ~ 10–30 h) during which cell becomes ready for cell division. In mammalian and plant cells, cell division duration varies extensively depending on the tissue type and species. Average time for mitosis in plant and mammalian cells might be 1–5 h (Singh 2002). For instance, in skin cells and liver cells the total cell division cycle may take about 24 h, with mitosis time of ~90 min (Fisher 1968). In bacteria, cell division takes (depending on the nutrition requirement and environmental factors) about 20 min (Morgan 2007). Also a temporal profile of apoptotic cascades or evolution of apoptotic steps (e.g., loss of the mitochondrial transmembrane potential (MTP), transfer of biochemicals such as phosphatidyl serine (PS), and DNA fragmentation) leading to cell death are not clearly understood (Rodriguez and Schaper 2005); such variations can complicate
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the design of comparative studies. Plants need particular conditions for their optimal growth but under environmental stresses (Vitamvas et al. 2007), the modified conditions may be unfavorable for the optimal plant growth (Kaplan et al. 2004). Therefore, plants provide an opportunity to design studies that can provide fundamental understanding of these dynamic processes by judicious selection of different plants, model systems, and cell cycles for “course” and “fine” profiling of the “x-ome kinetics”. Regardless of the kinetics of these processes, maintaining functional conformations of proteins to prevent the aggregation of non native proteins is important for cell survival under stress conditions (Timperio et al. 2008). The use of mass spectral data and advanced mathematical modeling of the experimental data for cell maturation/ proliferation processes should provide better understanding of the cell kinetics (Bernard et al. 2003).
Protection of Tissues from Oxidative Damage The damage initiated by hydroxyl radicals can be induced either externally or from endogenous products of oxidation (Møller et al. 2007; Gotloib 2009). These damages may include membrane lipid peroxides (Cejas et al. 2004) such as the 4-hydroxyalkenals or products of oxidative DNA (Cooke et al. 2003) degradation. Plant and animal glutathione s-transferases (GSTs) are the enzymes that can catalyze the glutathione (GSH), a tripeptide, conjugation to endogenously produced electrophiles and promote detoxification (Bahrman and Petit 1995; Gallardo et al. 2001; Chan et al. 2003; Cooper et al. 2003; Mo et al. 2003). Some GSTs also function as glutathione peroxidases to detoxify such products directly (Gallardo et al. 2001; Chan et al. 2003; Mo et al. 2003). A review of functions and regulations of plant GSTs by Marrs (1996) provides helpful details about the normal cellular functions and detoxification of various xenobiotics including herbicides. Clearly, a comprehensive elucidation of metabolic pathways of xenobiotics (pesticides, pharmaceuticals, and industrial pollutants) in human, animals, and plants and chemical identification of the corresponding metabolites are required for inclusive (eco-) toxicological evaluation of the compounds prior to their usage. Thus simultaneous and quantitative analysis of xenobiotics (both intact and degraded), the ensuing metabolites, and macromolecules that participate in the detoxification processes (responding to the stressors) are necessary; the simultaneous use of multiple analytical tools in plant studies require advanced data analysis and data reduction approaches (Sanchez et al. 2008). Modern MS techniques provide opportunities for ionization of both small (e.g., using electron impact (EI), chemical ionization (CI), photo-ionization (PI), etc.) and large molecules (e.g., utilizing so called “soft” ionization techniques such as MALDI and ESI). The ionized species can be detected using a host of mass spectrometers including FT-ICR, Orbitrap, quadrupole, sector, TOF, and other application-specific mass spectrometers to yield an all-inclusive picture. Data mining and treatment is another exciting area of contemporary research that enables researchers to handle very complex and large data sets and search for small variations in intricately dynamic biological systems. For instance, the glutathione
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s-transferases serve as non-enzymatic carrier proteins (ligandins) and are often involved in the intracellular transport of steroids, bilirubin, heme, and bile salts in animal cells (Litwack et al. 1971; Ketley et al. 1975; Listowski et al. 1988). However, compounds that bind GSTs as non-substrate ligands may interact at different sites than the presumed catalytic site of an enzyme and understanding the exact mechanisms for molecular transportations in plants’ intracellular regions requires characterization of these interaction sites (Jones 1994; Bilang and Sturm 1995). Molecular understanding of microbial transformation of pollutants is currently lacking and there is a need for quantitative identification of potential xenobiotics metabolites. The use of model systems can be quite helpful in such studies. For example, heterotrophically grown plant cells are not injured by moderate concentrations of most herbicides. Additionally, plant cell suspensions can be grown in scaled-up assays (up to 50 g fresh weight) or air-lift fermenters. Plant cell cultures are thus convenient in vitro systems for screening of metabolic profiles of xenobiotics in plants. Microbial bioremediation, using mixed culture systems such as plant and soil microbe symbiosis, is an attractive new approach to address environmental pollutions (Khalvati et al. 2010); these experiments can be initially preformed in vitro and ultimately applied to in vivo or large scale field applications. However, detailed understanding of the chemical interactions in the plant, between all endogenous small and large molecules as well as the xenobiotics, requires analysis and characterization of all chemical participants and biochemical pathways. For instance, a number of factors, such as temperature, salinity, pH, redox potential, microbial biomass, and prior exposure can affect the degradation rate and thus the fate of a toxicant. Therefore, for successful implementations of the in vitro assays and their efficient application to in vivo studies, it is important to identify the rate-determining factors to estimate ultimate biodegradation rates in the field.
Proteomics Analysis in Symbionts Plants with Soil Microbes Previous studies have demonstrated that adaptation of soil microorganisms can potentially play a major role in determining biodegradation rates (Torstensson et al. 1975; Simon-Sylvestre and Fournier 1979; Fournier et al. 1981). As reported by Spain and Van Veld (1983), microbial adaptation is defined as an alteration in the microbial community in such a manner that increases the rate of a particular xenobiotics’ transformation due to community’s prior exposure to the same (or similar) compound(s). As with any analytical sampling technique, it is critical to minimize (or avoid if possible) changes in biodegradation rates that are result of the sampling itself or subsequent laboratory manipulations; the effects of adaptation can be dramatic and proper controls and calibration procedures are necessary for accurate and precise measurements (Spain and Van Veld 1983). For example, even when plants are exposed to xenobiotic compounds at concentrations below 100 ppb (100 ng/ml), the degradation rates may be 1,000-fold higher in populations that are pre-exposed to the compound.
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Proteomics is expected to contribute significantly to understanding of the interactions in symbionts plants with soil microbes. Proteome analysis is expanding to reveal proteins involved in arbuscular mycorrhizal (AM) symbiosis (DumasGaudot et al. 2001; Bestel-Corre et al. 2002). In a time-course analysis, results from proteomic analysis of two M. truncatula symbioses where roots were inoculated with either the AM fungus Glomus mosseae or the nitrogen-fixing bacterium S. meliloti were compared. Although it was expected from the molecular data (Gianinazzi-Pearson and Denarie 1997; Catoira et al. 2000) not to observe common plant proteins, studies confirmed this prediction; moreover, numerous down- and up-regulated proteins, as well as newly synthesized proteins were identified through the use of tandem MS/MS. Although protein patterns of dormant and germinated spores of several AM fungi were established earlier (Samra et al. 1996), to date, the lack of databases on fungi (and particularly on AM fungi) has slowed down the use of proteomics but research in this area is expanding (Bestel-Corre et al. 2002; Zhou et al. 2008; Alguacil et al. 2009; Long et al. 2009).
Emerging Technologies for Sensitive Metabolic Flux Analysis The field of plant lipid biochemistry is deeply rooted by radiolabeling tracer studies that provide powerful methods for understanding the inter-relationships of lipid metabolites and metabolic chemical pathways in plant cells. There is an urgent need to utilize these types of studies and characterize the compounds and nonlinear pathways of storage oil formation and fatty acid channeling; for example, the development of advanced metabolic flux analyses using various precursors labeled with radioactivity or heavy stable isotopes, and measurement of label, including isotopomers, in various metabolite pools has provided new insights into central carbon metabolism in plants (Allen et al. 2007; Alonso et al. 2007). These studies can be carried out in unperturbed whole plants, providing an unprecedented view of metabolic flux under in vivo conditions (Romisch-Margl et al. 2007). The value of these types of experiments was demonstrated by a recent study of fatty acid flux from plastids to phosphatidylcholine; in this study, it was shown that fatty acids are transferred directly to the phosphatidylcholine fraction rather than first being incorporated into glycerol-3-phosphate to yield phosphatidic acid (Bates et al. 2007). Another recent technological advancement with regard to plant lipid metabolism and proteomics is in IMS and MS approaches. For instance, “lipidomic” profiling has now been developed for each of the major classes of lipids involved in storage oil production, including phospholipids (Devaiah et al. 2006), fatty acyl CoA esters (Larson and Graham 2001), and triacylglycerols (TAGs) (Leskinen et al. 2007). The MS and IMS are also among the major driving forces in other areas of “x-omics” such as proteomics and metabolomics. Rapid characterization of protein biomarkers in bacterial identification can help to reduce morbidity and mortality across the globe (Demirev 2004) and rapid bacterial characterization
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through protein identification can aid civil and military defense organizations to combat bioterrorism (Drake et al. 2005). Mass spectrometry-based metabolomics can also be used for bacterial differentiation. For example, we recently showed that a GC/FT-ICR MS can be used to differentiate bacteria, based on volatile metabolic waste products (Jackson et al. 2009); analogous approaches, utilizing high MMA of GC/FT-ICR MS coupled to a three-stage pre concentrator (PC), was employed for characterization of volatile organic compounds (VOCs) emitted from plants (Solouki et al. 2004a); FT-ICR MS is also quite suitable for analysis of biological macromolecules (Qian et al. 2004). The main limiting steps for rapid identification of plant proteins include protein diversity and sample treatment. The conventional sample preparation protocols are tedious and include several steps. For instance, the time required for an enzymatic digestion for protein identification may be from 4 h to overnight whereas mass spectrometry or spectroscopic detection of the products and isolated material may only take a few minutes or less. Most improvements reported in the literature and in the past 5 years have been devoted to enhancing the throughput for sample preparation. Another emerging area of current interest is microfluidics (Ohno et al. 2008; Blow 2009; Kovarik and Jacobson 2009; Mukhopadhyay 2009) for reducing sample sizes and high throughput protein purification (Li et al. 2002a; Wilson and Konermann 2005).
Plant Proteomics as a Tool to Identify Xenobiotics Plant development is regulated by the activity and interaction of complex molecular networks. Several large scale quantitative biological approaches are now available to study the complements of the various biologically relevant molecules. Among these techniques, transcriptomics, which allows for the parallel expression analysis of thousands of mRNA molecules via microarray studies (Schnabel et al. 2004) and proteomics, (or quantification and subsequent identification of hundreds of proteins of an organism) are among the widely adopted approaches in plant developmental biology. The proteome is defined as the protein complement of a particular plant organ, tissue, cell type, or organelle at a defined developmental stage. Plant proteomics is entering a new phase and beyond the use of conventional gel-free or 2-DE based platforms and multidimensional separation methods, modern high performance quantitative proteomic techniques such as IMS and ultrahigh resolution MS are expected to make major contributions to the field. Each analytical method provides a piece of the puzzle and the availability of various modern tools provides the opportunity to combine the acquired data and cover the ‘total” proteome or even approach characterization of the x-ome (in this context, x-ome may be defined as the quantitative characterization of all small and large molecules and their transitory interactions for an organism). Clearly, this ultimate characterization of x-ome is a very ambitious and seemingly unattainable goal but recent advances are propelling the science in this exciting direction. For instance, comparisons of proteomic data
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with other complementary “x-omics” information and classical biochemical or cell biology techniques are providing the required “minimal information about a proteomic experiment “(MIAPE)”” (Jorrin-Novo et al. 2009). Although these MIAPE approaches are in their infancy, they are aimed at increasing the coverage of the plant cell proteome and general plant biology knowledge. For example, McLafferty and coworkers utilized top-down and bottom-up FT-ICR MS for characterization of the chloroplast proteins of the model plant Arabidopsis thaliana (a plant proteome of 3,000 proteins predicted by the genome sequence); 97 proteins were identified in two separate “bottom-up” mass spectrometry studies in which the proteins were purified and digested to yield MS measured masses of the resulting peptides that matched against those expected from the DNA predicted proteins. The advantage of direct tandem mass spectrometry and high potential of “top-down” approach was highlighted through identification of an unusual N-methylation on the N-terminal amino group of one of the two otherwise identical proteins which was missed in the “bottom-up” approach (Zabrouskov et al. 2003).
Mass Spectrometry in “X-omics” Studies One of the most valuable analytical arsenal in “x-omics” is MS which can be used to establish identifies of both small and large molecules. MS offers unparallel advantages for unknown identification and there are many different types of mass spectrometers commercially available; although this diversity is a powerful advantage, the selection of an appropriate MS system for a particular application can be a source of frustration for a novice in the field. Unfortunately, parameters related to performance characteristics are defined based on several criteria (e.g., limit of detection (LOD), MMA, MRP, etc.) and there is no single universal number that can indicate an instrument’s capabilities. Therefore, prudent selection of an MS type requires a thorough knowledge of its performance characteristics and desired experimental qualities. Various important aspects of MS instrumenttation and methodologies are presented in the following sections.
Top-down and Bottom-up Mass Spectrometry The first step in MS plant proteomics studies is the protein isolation and/or purification for use in “top-down” or “bottom-up” studies. For the “bottom-up” studies (Bogdanov and Smith 2005), specific cleavage of the peptide bonds in a protein is achieved by using digestion enzymes such as pepsin or trypsin (Solouki et al. 1996) to obtain a pool of small (neutral fragment) peptides. Various MS based techniques such as MALDI-TOF MS or liquid chromatography- MS (LC-MS) can be used for identification of ionized fragments. Conversely, in “top-down” proteomics (McLafferty et al. 2007), high performance mass spectrometers such as FT-ICR are utilized to fragment the ionized intact proteins in the gas-phase (Ouvry-Patat et al. 2009). Generally, high MMA and a set of fragment ions are sufficient for unambiguous
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identification of a protein using the so called “peptide-mass fingerprint” (PMF) (Henzel et al. 2003). Special search programs (Pappin et al. 1993) or engines are often required for rapid unknown identification. As shown in Fig. 3, “top-down” and “bottom-up” sequencing can be used for protein identification. In Fig. 3, selected proteins (zoomed area “A”) from the silver stained SDS-PAGE (e.g., from complex biological samples, labeled 1–5) can be (I) extracted and (II) purified prior to mass spectral analysis; nano-spray ionization and ion accumulation techniques can be utilized to improve analytical detection efficiencies. In the “top-down” approach (III-A), intact molecules (in this case, phosphoric acid cluster of ubiquitin) can be ionized and subsequently fragmented inside the mass spectrometer (MSn). Conversely, purified proteins (in this case, cytochrome c (Solouki et al. 1996)) can be digested enzymatically for the “bottom-up” (III-B) mass spectral analysis (MS1). Moreover, in the “bottom up” approach, the observed enzymatic digest products can be further fragmented to acquire supplementary information (e.g., sequence conformation (MSn), etc.). Acquired data from bottom-up proteolysis and top-down experiments in conjuncttion with various mass spectral and whole-genome sequence databases can be used to identify unknown proteins, deduce primary structures, and ascertain the degree and location of potential post-translational modifications.
Ion Fragmentation Techniques for Biomolecular Sequencing in MSn Development of different ion fragmentation techniques has increased the protein sequence coverage/information in proteomics. These ion fragmentation techniques can be classified into two categories: (a) activational (e.g., sustained off-resonance irradiation collision induced dissociation (SORI CID) (Gauthier et al. 1991), multiphoton dissociation (MPD) (Little et al. 1994), surface induced dissociation (SID) (Dongre et al. 1996; Laskin and Futrell 2003; Wysocki et al. 2008), and blackbody infrared radiative dissociation (BIRD) (Price et al. 1996)) and (b) non-activational (or non-ergotic) (e.g., electron capture dissociation (ECD) (Zubarev et al. 1998), electron transfer dissociation (ETD), and electron detachment dissociation ((EDD) (Syka et al. 2004)) techniques. The complementary utilization of activational and non-activational fragmentation techniques can improve the percentage of sequence coverage of proteins/ peptides. However, in the activational approaches, activated ions may go through structural rearrangements prior to fragmentation and thus limit their utility; this severely restricts the use of activational fragmentation techniques for characterization of labile post-translational modifications in proteins. In activational techniques such as CID and MPD, ion’s vibrational energy is increased stepwise via multiple collisions or absorption of IR photons until the accumulated vibrational energy becomes sufficient to cleave the weakest bond (e.g., peptide amide bond). During the vibrational activation, the acquired energy by protein/peptide can be randomized throughout different chemical bonds (Marcus 1988; Laskin and Futrell 2003).
Fig. 3 A pictorial representation for proteomics approaches including “bottom-up” and “top-down” mass spectral characterization of biomolecules
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Thus, there is a high probability for the loss of post-translational modification on a protein/peptide backbone which can make it difficult or impossible to localize the site of modifications. An extensive review on the activational fragmentation techniques is provided by Laskin et al. (Laskin and Futrell 2005). Conversely, in non-activational/-ergotic techniques there is a low chance for the acquired energy by protein/peptide backbone to be randomized through the different chemical bonds. Hence post-translational modifications are stable toward nonergotic fragmentations and can be localized more efficiently. Marshall and co-workers have provided a comprehensive review of the ECD and its role in biological mass spectrometry (Cooper et al. 2005).
Performance Characteristics of Various Mass Spectrometers Different mass spectrometers offer specific advantages and disadvantages and the selection of a MS system depends on the intended purpose for the particular experiment. Therefore, several parameters must be considered when selecting a mass spectrometer. For example, for biological tissue imaging (e.g., using LD, MALDI, and ion beams) and interfacing MS to fast separation systems such CZEs (which yield chromatographs with narrow peak widths) fast scanning is required; TOF instruments (with average scan rates > KHz range) are most suitable for this purpose. Conversely, high-performance mass spectrometers such as FT-ICR MS and Orbitrap can be much slower (e.g., scan rates >1 Hz) but offer other valuable advantages such as ultrahigh MMA, ultrahigh MRP, capabilities to perform ion–molecule reactions, and MSn under high mass resolving power conditions; these capabilities are necessary for “x-omics” analyses. Another consideration for instrument selection is the required routine maintenance and the ease of operation; all mass spectrometers operate under high vacuum or ultrahigh vacuum (UHV) conditions and require various degrees of routine maintenance (e.g., oil change for vacuum pumps, system cleanliness, etc.). Fortunately, nowadays, most commercial instruments are comparably friendly and require minimal training for routine analyses. However, for more complex experiments that necessitate high performance instruments, highly experienced workforce/scientists with professional training in mass spectrometry are often used so that instruments can be utilized near their full capacity. Ultimately, as with other analytical instruments, cost is an important determining factor.
Components of Mass Spectrometers There are several important factors that influence the performance characteristics in mass spectrometry. All mass spectrometers share the common feature of having (a) vacuum systems, (b) ionization sources, (c) analyzers, (d) detectors or signal transducers, and (e) software and computer system for data acquisitions, storage,
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processing, and interpretation. Following text provides a brief overview about different components of mass spectrometers and their influence on performance characteristics. (a) Vacuum Requirements: Ionization of the neutrals is required for all types of mass spectrometers. The generated negative and/or positive ions and potential charged radicals are generally unstable towards ion–molecule collisions and hence all MS experiments require high vacuum to avoid unwanted chemical reactions. In addition, because various ion optics and equations of motion are involved for unknown identification, ion–molecule collisions during the experiment should be minimized. In TOF experiments, ions travelling the distance between the ionization source and detector (~1 m for most typical TOF instruments and between ~1 and ~5 m for reflectron and high resolution TOF MS) may spend several hundreds of microseconds before hitting the detector; conversely, in FT-ICR MS or Orbitrap experiments ions may travel several kilometers during the course of an experiment. Hence, the UHV requirements for FT-ICR MS and Orbitrap instruments are more stringent (typically in the 10−10 torr range, where the room temperature mean free path for air molecules is about 500 km) than TOF and other mass spectrometers (e.g., ~ 10−7 torr for TOF and sector instruments). In order to take advantage of the state-of-the-art ultrahigh resolution mass spectrometers such as the FT-ICR MS or Orbitrap, UHV in the 10−10 torr range must be maintained which adds to the cost of these systems. (b) MS Ionization Sources: Selection of the “hard” (EI, LD, etc.) or “soft” (CI, MALDI, ESI, etc.) ionization types normally depends on the states (gas-, liquid-, or solid-phase) and classes of compounds under the study. In addition, ionization sources influence the selection of separation systems. For instance, both EI and CI ionization sources are more appropriately interfaced to GC systems whereas HPLC and CZE separation systems are easier to couple to ESI. Efficiency of the ionization and ion transport out of the source play a significant role on instrument sensitivity (detection limit). In case of TOF and pulsed experiments such as MALDI, the laser beam width can influence the resolving power; for example, commonly used nitrogen lasers with 3 ns beam width in the pulsed detection mode can limit the time resolution of such experiments to this value. (c) MS Analyzer Types: Different mass analyzer types include linear quadrupoles, various configurations of ion traps (e.g., ICR cell, Orbitrap, quadrupole/quistor, etc.), electrostatic (E) and magnetic (B) analyzers/sector instruments, TOF, and others. Generally, the analyzer type plays a key role in establishing achievable mass resolving power and the nature of the tandem MS (e.g., low and high energy CID, ECD, MPD, etc.). For example, the highest mass resolving power is achieved with FT-ICR systems, which incidentally are also suitable to perform multiple stages of mass spectrometry. TOF analyzers are simple to operate and provide reasonable mass resolving power once the unwanted ionization source related limitations (kinetic energy spread, laser pulse width in case of MALDI, post-source metastable decay, etc.) are addressed. Recent advances in
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data acquisition sampling rate (e.g., >GHz) and instrumental setup have improved the performance characteristics of high-end TOF instrument where MMA in the ppm range is achievable. The highest achievable MRP in biological MS is still routinely obtained using FT-ICR MS but the performance characteristics of other instruments are also continuously improving. (d) MS Detectors/Ion Transducers: Various types of image (e.g., in FT-ICR, Orbitrap) and particle detectors (e.g., discrete dynode electron multiplier, Faraday cup, charged-coupled devices) are used in mass spectrometry; detectors influence the dynamic range and sensitivity. (e) Data Acquisition: Most mass spectrometers use advanced electronics for signal detection and manipulation. For example, in an FT-ICR MS experiment (and in the absence of ion losses due to pressure dampening, Coulombic repulsions, ion-molecule reactions, etc.), the longer time-domain signals and higher sampling rates will yield better quality mass spectra (e.g., higher MRP, better MMA, higher S/N, and more accurate relative ion abundance). In TOF experiments, data acquisition rate influences mass resolution (e.g., faster oscilloscopes can record closely spaced ion arrival events); currently, fast acquisition oscilloscopes with sampling rates as high as several tens of GHz are commercially available.
Data Related Parameters (MRP, MMA, LOD) (a) Mass Resolving Power (MRP): MRP is generally defined as M/DM50%, and is the ratio of the experimentally measured mass (M) to the width of the peak at 50% or half height (DM50%). Other definitions of MRP include M/DM, for example at 10% valley, where DM refers to the distance between centroids of the two peaks when the overlap at their base is at 10% of the peak height. The reported MRPs from most high performance mass spectrometers (e.g., FT-ICR MS) are based on the M/DM50% definition; better mass separation leads to higher MRP. Mass resolution is the smallest mass difference between two adjacent peaks that can be resolved and it is the inverse of MRP. Higher MRPs allow separation of closely related peaks and can be utilized for direct detection of sulfur-containing amino acids and disulfide bridges in biomolecules (Solouki et al. 1997) and is valuable in “x-omics” studies (Shi et al. 1998). The need for a superconducting magnet and better UHV conditions contribute to the high cost of FT-ICR MS instruments. However, FT based mass spectrometers continue to provide the highest MRP currently achievable (e.g., for small molecule analysis using GC/FT-ICR MS (Solouki et al. 2004b) and GC/Orbitrap (Peterson et al. 2009) as well as macromolecules in biological mass spectrometry (Shi et al. 1998)) at high sensitivity. Other mass spectrometers also provide distinct advantages such as high scan rate (e.g., TOF MS), ease of operation (e.g., quadrupole MS), high throughput (e.g., TOF and linear ion trap), portability (e.g., quadrupole ion traps, TOF), (Domon and Aebersold 2006a, b) higher dynamic range (e.g., triple quadrupole and double quadrupole/linear ion traps), unique applications for
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fundamental studies of ion high and low energy collisions/fragmentation processes (e.g., sector instruments), etc. Comparisons of various commonly used biological mass spectrometers (Domon and Aebersold 2006b) reveal that the progress in the instrument development is closely related to the desired performance characteristics. Interested readers are referred to a recent review on this topic (Domon and Aebersold 2006b). (b) Mass Measurement Accuracy (MMA): MMA is defined as the relative difference between a measured mass and its theoretically calculated value in parts-permillion (ppm) {i.e., MMA (in ppm) = ((Mexperimental − Mtheoretical)/(Mtheoretical) × 106}. Smaller numbers for MMA indicate higher accuracy; FT based mass spectrometer can provide sub ppm (or ppb) range MMA values (Heffner et al. 2007; Williams and Muddiman 2007; Luo et al. 2009) and recent improvements in instrumentation are allowing MMA in the ppm range for the state-of-the-art TOF mass spectrometers (Kuzyk et al. 2009; Peters et al. 2009). (c) Limit of Detection (LOD) in Biological Mass Spectrometry: Introduction of the so called “soft” ionization techniques in mass spectrometry, especially MALDI (Karas et al. 1987; Karas and Hillenkamp 1988) and ESI (Whitehouse et al. 1985; Fenn et al. 1989) has enabled characterizations of a host of biological macromolecules and MS is becoming one of the major tools for understanding complex biological systems. One of the major goals in biological MS is characterization of single cell x-ome and biomarker discovery for disease diagnosis and prognosis. Therefore, improving MS sensitivity plays one the most important roles in biological mass spectrometry. Following the introduction of MALDI and ESI, efforts were focused on improving the sensitivity. Two of the initial attempts on femtomole peptide detection were reported using a MALDI/magnetic sector/ TOF instrument (Strobel et al. 1991) and a new MALDI matrix (i.e., 2, 5-dihydroxybenzoic acid (DHB)) (Strupat et al. 1991). Subsequent reports on improved detection limits using MALDI-type ionization (emphasizing the ionization or post-ionization steps) include: attomole peptide detection using picoliter vials (Jespersen et al. 1994) (in a TOF MS), small indentation (Solouki et al. 1995) (in FT-ICR MS), and multishot accumulation (in a high pressure MALDI/FT-ICR MS) (Moyer et al. 2003). Efforts to improve sensitivity were not limited to MALDI interface and improvements to ESI/MS and ESI/LC (or CZE)/MS played a major role in advancing the field. For example, using a dynamic ion funnel interfaced to ESI source, Belov et al. reported zeptomole sensitivity (~18,000 molecules) for analysis of proteins with molecular weights ranging from 8 to 20 kDa in an FT-ICR instrument (Belov et al. 2000). Ultrahigh sensitive protein identification (~75 zmol) of whole proteome extracts (~ 0.5 pg total sample size) was reported by online coupling of a capillary LC to solid-phase micro extraction (SPME) and nanoESI using an FT-ICR MS instrument (Shen et al. 2004). If ions are generated with sufficient number of charges, FT-ICR MS can be used for detecting multiply charged individual ions. Detection of multiply charged (~30 charges) single ions has been reported with FT-ICR MS (Bruce et al. 1994) to produce detectable image current (Bruce et al. 1994; Smith et al. 1994). Application of these
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single ion detection approaches for routine proteomic experiments will constitute a significant advance. Reduced sample consumption and the reported subattomole sensitivity in biological MS using accelerator MS (AMS) (Salehpour et al. 2008) hold promise for identification of isotopically labeled molecular markers in plant “x-omics”.
Future Directions and Challenges Although mass spectrometry continues to contribute significantly to the field of science, many future challenges remain. For example, current mass spectrometers are limited to analyzing molecules with masses that do not extend beyond several hundred thousand dalton and this limited range must be expanded. Moreover, instrumental limitations in terms of the required dynamic range for complex sample and “x-omics” studies must be addressed. Rapid characterization of biological samples (e.g., plants, microbes, etc.) outside the laboratory and in the field, using portable devices, is desirable and should contribute to statistical validity of data mining. Another significant current limitation is the lack of fast responding instruments that can be used to resolve temporal x-ome variations in biological systems (e.g., cell); although the use of temperature cooling and “reaction freezing” might be helpful (Ospelkaus et al. 2010), critical improvements are needed to be explored in the future. To understand the system biology, various multidimensional techniques and more precise/accurate quantification must be used and, in parallel, data from different techniques must be pooled and statistically analyzed; this requires extending interdisciplinary collaborations to beyond their current level where a team of analytical chemists, environmental scientists, instrumentalists, plant biologists, computer experts, physicists, and other professionals can work closely on a focused project. Such efforts should enhance our understanding of the plant biology and enable us to extent in vitro experiments to in vivo studies and ultimate field use. Ultimately, instrumental advances should increase the efficient use of plants for cleaning the environment and using plants as reliable sensors and indicators of environment and human health. Moreover, identifications of useful extractable biomarkers and natural products should enhance human health and pharmaceutical usage of these products. Because various statistical methods are used to handle complex and large data sizes for specific interpretations, the use of data repositories for sharing raw experimental data is strongly encouraged. A Philosophical Viewpoint: Discovering the intricate and dynamic interactions influencing signaling pathways in plants and other biosystems is the goal of many existing studies including mass spectrometry based “x-omics”, gene expression profiling and analyses, protein–protein interaction methods, protein microarray studies, and gene-disruption and engineering approaches (Zhu and Snyder 2002). Combing reliable experimental data from various analytical methods (with improved accuracy and precision in all measurement dimensions) and using progressive mathematical tools may someday allow scientists to define a
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multidimensional “phase space” and represent all possible states of the x-ome as a function of time. Achieving such ambitious goals will require the use of simpler biological models (Grigorov 2006) and characterization of their developmental x-ome, “pluralistic” research approach (Bruggeman et al. 2002), modern statistical tools and mathematical approaches including molecular dynamics-type simulations (Berne and Straub 1997), variety of analytical arsenal (e.g., including those methods presented in this chapter), massive computing power, and unprecedented interdisciplinary collaborations. Analytical instrumentation in general and mass spectrometry in particular is expected to continue its decisive role in decoding the concert of life by connecting seemingly chaotic relationships between complex molecular networks in biological systems and plant “x-omics”. Acknowledgements Partial Financial support from the Institute for Therapeutic Discovery and United States Civilian Research Development Foundation (US CRDF) is gratefully acknowledged. Authors would like to thank Sabina Solouki for her assistance with the editing.
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Index
A ABC proteins, 138, 143 ABC transporters, 127, 138, 139, 142 Abiotic stress, 138, 159, 279–281 Achromobacter xylosoxidans, 197 Acid orange, 162, 163, 165, 170 Agrochemicals, 133 Alachlor, 139 Alcaligenes, 197 Allium cepa, 203 Alopecurus myosuroides, 131, 142, 143 1-Aminocyclopropane-1-carboxylic acid (ACC), 196 Anthraquinones, 153, 155–158, 161 Arabidopsis thaliana, 127, 130, 131, 176, 268, 286 Aspergillus niger, 253 Astragalus membranaceus, 197 ATP-binding cassette (ABC), 138 ATP-dependent, 126, 128, 129 Avena sativa, 201 B Bacillus subtilis, 196 BADH acyltransferases, 137 Benzene, 12, 31, 48, 49, 130, 150, 153, 154, 195, 247, 253 Benzene sulphonate, 153, 154 Bioaccumulation, 58, 150, 174, 203, 226, 243, 245–246, 249, 250 Bioavailability, 30, 35, 64, 69, 70, 77, 80, 150, 152, 172, 192, 194, 195, 200, 201, 204–207, 238 Bioconcentration factor, 11, 80, 82, 152, 161, 244, 245 Biofilm, 172, 173 Bioindication, 218, 221, 222, 224–230 Bioinformatics, 241, 262, 265, 268
Biological concentration factor (BCF), 82, 152, 161–164, 245, 249–252 Biomonitoring, 68, 106, 107, 109, 112, 118, 218, 221, 222, 224–228, 230 Bioreactors, 155, 156 Biosensor, 180, 226, 253, 254 Biosurfactant, 64, 172, 206, 207 Biotic stress, 279, 281 Biotransformation, 126–128, 130, 131, 133, 135, 137, 161, 165, 173, 192, 193, 247, 253–257 Bisphenol A, 129 Boletus edulis, 203 Brachiaria decumbens, 205 Brassica juncea, 196 Brassica napus, 157 BTEX, 150, 151, 153, 173 Burkholderiales, 201 C Capillary chromatography, 271 Carboxypeptidase, 139, 141 Carrots, 11 Chlordane, 35, 81, 83, 193 Chloroacetanilide, 133, 134 Chloroform, 18, 28, 29, 37, 87, 88, 90, 95, 130 4-Chlorothiophenol, 136 Chlortetracycline, 133 Chlortoluron, 130, 131, 143 Cinchona, 155 Contamination, 10, 25, 28, 106, 109, 114, 118, 119, 150, 153, 169, 172, 192, 193, 200, 226, 227, 238 Cucurbita pepo, 80, 82, 83, 174, 203, 205 Cyanobacteria, 127 Cymbopogon ambiguous, 205 CYP, 129–131, 142, 143
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307
308 Cytisus striatus, 201, 202 Cytochromes P450, 158, 159 D 2,4-D, 36, 131, 132, 195 DDT, 18, 34, 80, 106, 108, 113, 175, 193, 195, 203 Degradation, 10, 17–38, 57, 63, 64, 67, 68, 88–90, 95–98, 108, 129, 134, 139–141, 150, 151, 154, 157, 159, 161, 165–169, 172, 175–179, 191–208, 221, 227, 238, 251, 253, 254, 276, 278, 282, 283 Denaturing gradient gel electrophresis (DGGE), 197 Detoxification pathway, 128, 158, 159, 170 Dibenzofurans, 5, 28, 34, 80, 82 Diesel oil, 254 2,4-Dinitrophenol, 206 Dioxins, 5, 34, 78, 80, 82, 106, 114, 115, 117, 152, 173, 193, 238 Dioxygenase, 32, 64, 129, 155, 168, 175, 198–200 Domestic wastewaters, 153, 155 Dyestuffs, 150, 153, 154, 160 E Earthworm, 70, 82, 175, 252 Echinogalus crus-galli, 197 Ecotoxicologist, 229, 230 Ectomycorrhiza, 202 Ectomycorrhizal fungi (ECMF), 203, 204 Effect indicator, 224 Electrophile response element (ERE), 128 Electrospray ionization (ESI), 265, 266, 268, 273, 276, 277, 280, 282, 290, 292 Eleusine indica, 151 Emulsification, 206 Endophyte, 196 Endophytic bacteria, 12, 83, 88, 150, 151, 196 Environmental monitoring, 227, 228, 230 ESI. See Electrospray ionization ERE. See Electrophile response element Ericaceous mycorrhiza, 202 Ethylene dibromide, 152 Extradiol dioxygenases, 199 Exudation, 159, 176, 194, 201, 205, 207 F Fenoxaprop, 139, 143 Festuca arundinacea, 82, 151, 198
Index Fingerprint, 180, 198, 243, 248, 272, 274, 287 FT-ICR, 266, 273, 276, 280, 282, 286, 290–292 FT-ICR MS, 263, 266, 271, 273, 274, 285, 286, 289–292 G Galium, 155 Genomics, 143, 254, 262, 275 Glomus mosseae, 284 Glucosylflavonoid, 157 g-Glu-Cys, 139 g-Glutamyl transpeptidase, 140, 141 Glutathione-S-transferases, 157 Glutathione transferase (GST), 68, 92, 126, 127, 132–136, 138, 142, 143, 158, 169, 170, 282, 283 Glycosyltransferase, 126, 135–137, 158 H Helianthus tuberosus, 131 Henry’s law constant, 152 Herbicides, 4, 8, 36, 69, 87, 89, 90, 92, 128, 130–139, 142, 143, 150, 152, 166, 180, 193, 279, 282, 283 Herbicide safeners, 136, 137, 142, 143 Hexachlorocyclohexane, 26, 108, 201, 202 Hexadecane, 199 Homoglutathione, 134 Human health, 6, 78, 150, 181, 229, 230, 257, 293 Hydrophobic, 27, 58, 64, 77, 82, 126, 128, 129, 135, 150–152, 166, 171–174, 179, 194, 199, 204–206, 238, 239, 242, 250 Hydrophobicity, 27, 31, 64, 152, 194, 199, 201, 206, 240, 254 Hydroxycinnamic acid, 199 I Imidazolinones, 130 Impact indicators, 225 Indoleacetic acid (IAA), 196 Ion mobility spectrometry (IMS), 265, 274–275 K KOA, 9, 10, 49, 56, 58, 59 Koc, 152, 161–164, 251 Kow, 5, 8, 9, 30, 64, 152, 166
Index L Laccaria bicolor, 203 Lipid hydroperoxides, 134, 170 Lipids, 4–6, 8, 9, 11, 58, 135, 141, 205, 262, 269, 284 Log Kow, 8, 166, 193 Lolium, 142 M Malonyltransferase, 126, 137–138 Mammals, 127 Matrix-assisted laser desorption ionization (MALDI), 265, 266, 268, 273, 278, 279, 282, 286, 289, 290, 292 Medicago sativa, 82, 197 Metabolomics, 254, 262, 271, 274, 284, 285 Metallothioneins, 279 Methylchloroform, 87, 90 Microlaena stipoides, 205 Microorganisms, 21–23, 31, 32, 35–37, 83, 90, 95–97, 133, 150, 151, 155, 166, 169, 172, 176–178, 180, 192, 193, 195, 196, 226, 238, 246, 254, 283 Mineralization, 32, 34, 35, 126, 165, 166, 194, 198, 204, 205 Morinda, 155 Multi-Markered-Bioindication-Concept (MMBC), 218, 228 Multistage MS, 273 Mycotoxin, 136 N N-malonylation, 138 Nocardia otitidiscaviarum, 199 Non-aqueous phase liquid (NAPL), 206 No observed effect concentration (NOEC), 245, 252 Nuclear magnetic resonance (NMR), 264, 265, 270–272 O Octanol-water partition coefficient, 5, 6, 9, 152 O-glucosides, 135 Orchid mycorrhiza, 202 Organosulphonate, 154 Oryza sativa, 175, 198 Oxidative polymerisation, 159 Oxidative stress, 68, 166, 169–171, 279, 281–282 5-Oxoprolinase1, 141
309 P Paenibacillus polymyxa, 202 PAGE, 267, 287 PAHs. See Polycyclic aromatic hydrocarbons Panicum bisulcatum, 205 Panicum virgatum, 195 Pannicum milliaceum, 151 Particulate deposition, 3, 9–10 PCDD. See Polychlorinated dibenzo-p-dioxins Periodic table, 220 Permissible concentrations, 172, 228 Peroxidases, 24, 32, 129, 137, 158, 159, 175, 194, 282 Persistent bioaccumulative and toxic (PBT), 243–245, 248, 249, 251–253, 255 Persistent organic pollutants (POP), 26, 37, 48, 52–54, 58, 64, 65, 67, 77–80, 82, 83, 105–120, 192–194, 203, 204, 207, 208, 227 Pesticides, 7, 8, 12, 25–27, 34, 35, 79, 106, 108–114, 127, 134, 139, 141, 150, 152, 193, 194, 203, 256, 264, 279, 282 Petroleum hydrocarbons, 151, 203 Phalaris arundinacea, 195 Phenanthrene, 48–51, 53, 59, 63, 67–69, 193, 195–199, 206, 207 Phenylpropanoid, 176, 200 Phospholipids, 284 Phragmites australis, 151, 162, 166, 169, 170, 175 Phytochelatin, 141 Phytoextraction, 81–83, 174–175, 203 Phytoremediation, 77, 79–81, 83, 92, 130, 133, 134, 136, 143, 150–153, 156–158, 166, 167, 171–174, 178, 180–181, 192, 195, 197, 205, 253, 256–257, 266 Phytostabilisation, 151 Phytostimulation, 192, 195 Phytotoxicity, 136, 169, 174, 175, 179, 196 Phytotransformation, 151, 155 Phytovolatilization, 153 Pinus nigra, 35, 177, 201, 205 Pisum sativum, 279 pKa, 7, 8 Plant growth promoting rhizobacteria (PGPR), 193, 195, 196 Poaceae, 206 Polar residues, 141 Polychlorinated biphenyls (PCBs), 5, 9, 26, 29, 30, 32, 34, 35, 64, 65, 81, 82, 106, 107, 109–114, 152, 171, 174, 175, 179, 193, 195, 200, 201, 253, 256 congeners, 107, 172, 177–179 transformation, 176, 177
310 Polychlorinated dibenzo-p-dioxins (PCDD), 5, 9–11, 18, 20, 21, 26, 30, 38, 69, 80, 106, 114–117 Polycyclic aromatic hydrocarbons (PAHs), 5, 9–11, 20, 29, 30, 38, 47–70, 106, 109–111, 117–119, 152, 173, 193, 196–200, 203–207, 253, 256 Polyphenoloxidase, 180 POP. See Persistent organic pollutants Post-translational modifications (PTMs), 262, 275, 276, 287, 289 Precipitation, 19, 26, 28, 59, 91, 95, 97, 269 Protein phosphorylation, 274, 277–278 Proteomics, 142, 207, 261–294 Pseudomonas fluorescens, 178, 198, 200 Pseudomonas putida, 198, 200 Q Quantitative structure-activity relationships (QSAR), 5, 69, 238–247, 249–257 R Radiolabel, 267, 284 RCF. See Root concentration factor Reaction indicator, 224, 225 Reactive oxygen species (ROS), 159, 160, 165, 166, 169–171 Residues, 27, 51, 52, 81, 88, 126, 128, 135, 137, 141, 152, 158, 177, 201, 206, 261–294 Resistance, 9, 31, 79, 126, 132, 134, 139, 141–143, 150, 161, 180, 227, 265, 278 Rheum, 155, 156 Rhizhoremediation, 174, 176–177, 192, 194–202, 205, 206 Rhizofiltration, 156 Rhizosphere, 35, 91, 95–97, 150–152, 159, 166, 175–178, 191–208 Rhodococcus aetherovorans, 197 Rhubarb, 155–157 Ring cleavage, 23, 32–34, 95, 199 Root concentration factor (RCF), 4, 152 Root exudates, 11, 82, 83, 151, 174, 192, 194, 198, 201, 202, 205, 207, 238 ROS scavenging, 169, 170 Rubia, 155 Rumex, 155 Rumex hydrolapatum, 156 S Salix caprea, 35, 177, 205 Salix viminalis, 200
Index Sample preparation, 268–270, 275, 285 S-bimane-cysteinylglycine, 140, 141 S-bimane-glutathione, 140 Senna angustifolia, 158 Sensitivity, 137, 223, 227, 252, 263, 268, 271, 272, 290–293 S-glutathionylation, 128 Soil horizon, 26, 28, 29, 38, 96 Solanum nigrum, 175, 203 Stenotrophomonas acidaminiphila, 197 Surfactants, 154, 175, 177, 195, 201, 207, 238 System biology, 261–263, 275, 293 T Tanning agents, 153 Tertiary treatment, 153 Tetrahymena pyriformis, 245 Textile industry, 153, 154, 161 Tobacco, 51, 129, 131, 132, 134–137, 176, 177 Tolerance, 129–134, 137, 139, 142, 143, 167, 196, 203, 227 Toxicity equivalent (TEQ), 114, 117 Translocation, 11, 27, 64, 81–83, 92, 96, 98, 152, 174, 194, 200, 201, 203 Transpiration, 5, 6, 9, 10, 12, 82, 91, 92, 157 Transpiration stream concentration factor (TSCF), 6, 7, 157 Trichloroethylene, 12, 32, 35, 130, 152, 195 Trifolium pratense, 195, 203 Trinitrotoluene (TNT), 129, 151, 195 Triticum asetivum, 203 Tylospora fibrilosa, 203 U UDP-glucose, 135 UGT, 126, 135–137, 142 UNEP Convention, 106, 118 V Vacuole, 126–129, 133, 137–141, 157–160, 269 Vetiveria, 151 Vibrio fischeri, 165 Volatilisation, 12, 28–30 Volatilization, 26, 27, 30, 37, 57, 151, 174, 198, 244, 246 W Weak acids, 7, 8, 152, 166 Weak bases, 7, 152, 166
Index Wetting agents, 154 Willow, 35, 151, 177, 178 Wood-degrading, 28 X Xenobiochemistry, 143 Xenobiotic response elements (XREs), 128
311 Xenobiotics, 3–13, 36, 68, 88, 91, 125–130, 132–141, 143, 150–152, 155, 157–159, 166, 167, 178, 192, 194–196, 202–204, 225, 256, 261–294 Xenome, 125–129, 138, 141–143 Z Zea mays, 81, 157