Biomethanation I (Advances in Biochemical Engineering & Biotechnology, Volume 81)

Preface

Preface

In November 1776, Alessandro Volta performed his classic experiment disturbing the sediment of a shallow lake, collecting the gas and demonstrating that this gas was flammable. The science of Biomethanation was born and, ever since, scientists and engineers have worked at understanding this complex anaerobic biological process and harvesting the valuable methane gas produced during anaerobic decomposition. Two lines of exploitation have developed mainly during the last century: the use of anaerobic digestion for stabilization of sewage sludge, and biogas production from animal manure and/or household waste. Lately, the emphasis has been on the hygienic benefit of anaerobic treatment and its effect on pathogens or other infectious elements. The importance of producing a safe effluent suitable for recirculation to agricultural land has become a task just as important as producing the maximum yield of biogas from a given type of waste. Therefore, anaerobic digestion at elevated temperatures has become the main area of interest and has been growing during the last few years. Anaerobic digestion demands the concerted action of many groups of microbes each performing their special role in the overall degradation process. Both Bacteria and Archaea are involved in the anaerobic process while the importance, if any, of eukaryotic microorganisms outside the rumen environment is still unknown. The basic understanding of the dynamics of the complex microflora was elucidated during the latter part of the last century where the concept of inter-species hydrogen transfer was introduced and tested. The isolation of syntrophic bacteria specialized in oxidation of intermediates such as volatile fatty acids gave strength to the theories. Lately the use of molecular techniques has provided tools for studying the microflora during the biomethanation process in situ. However, until now the main focus has been on probing the dynamic changes of specific groups of microorganisms in anaerobic bioreactors and less emphasis has been devoted to evaluating the specific activities of the different groups of microbes during biomethanation. In the future we can expect that the molecular techniques will be developed to allow more dynamic studies of the action of specific microbes in the over-all process. From the present studies we know that many unknown microbes are found in anaerobic bioreactors. Especially within the domain of Archaea, there are whole phyla of microbes such as the Crenarchaeota, which make up significant fractions of microbes in a reactor but without cultured representatives. Improving the techniques for the isolation of presently unculturable microbes is a major task for the future.

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Anaerobic digestion of waste has been implemented throughout the world for treatment of wastewater, manure and solid waste and most countries have scientists, engineers and companies engaged in various aspects of this technology. Even though the implementation of anaerobic digestion has moved out of the experimental phase, there is still plenty of room for improvements. The basic understanding of the granulation process, the basis for the immobilization of anaerobic microbes to each other without support material in UASB reactors, is still lacking. Like any other bioprocess, anaerobic digestion needs further control and regulation for optimization. However, until now suitable sensors for direct evaluation of the biological process have been lacking and anaerobic bioreactors have generally been controlled by indirect measurements of biogas or methane production along with measurements of pH and temperature. The newly development of an on-line monitoring system for volatile fatty acids could be a major step in the right direction and the use of infra-red monitoring systems could bring the price down to a reasonable level. A better performance of large-scale anaerobic bioreactor systems for treatment of complex mixtures of waste can be expected to be based on on-line monitoring of the process in the future along with controlling software for qualified management of these plants. Besides treatment of waste, anaerobic digestion possesses a major potential for adding value to other biomass converting processes such as gasification, bioethanol or hydrogen from ligno-cellulosic materials. Conversion of ligno-cellulosic biomass will often leave a large fraction of the raw material untouched which will be a burden for the over-all economy of the process and will demand further treatment.Anaerobic digestion will on the other hand be capable of converting the residues from the primary conversion into valuable methane, which will decrease the cost and the environmental burden of the primary production. Biomethanation is an area in which both basic and applied research is involved. Major new developments will demand that both disciplines work together closely and take advantage of each other’s field of competence. The two volumes on Biomethanation within the series of Advances in Biochemical Engineering and Biotechnology have been constructed with this basic idea in mind and, therefore, both angles have been combined to give a full picture of the area. The first volume is devoted to giving an overview of the more fundamental aspects of anaerobic digestion while the second volume concentrates on some major applications and the potential of using anaerobic processes. The two volumes will therefore be of value for both scientists and practitioners within the field of environmental microbiology, anaerobic biotechnology, and environmental engineering. The general nature of most of the chapters along with the unique combination of new basic knowledge and practical experiences should, in addition, make the books valuable for teaching purposes. The volume editor is indebted to all the authors for their excellent contributions and their devotion and cooperation in preparing these two volumes on Biomethanation. Lyngby, January 2003

Birgitte K. Ahring

CHAPTER 6

Perspectives for Anaerobic Digestion Birgitte K. Ahring University of California, Los Angeles (UCLA), School of Engineering and Applied Science, Civil and Environmental Engineering Dept., 5732 Boelter Hall, Box 951593, Los Angeles, California 90095-1593, USA Present address: Biocentrum, The Technical University of Denmark, DTU, Block 227, 2800 Lyngby, Denmark. E-mail: [email protected]

The modern society generates large amounts of waste that represent a tremendous threat to the environment and human and animal health. To prevent and control this, a range of different waste treatment and disposal methods are used. The choice of method must always be based on maximum safety, minimum environmental impact and, as far as possible, on valorization of the waste and final recycling of the end products. One of the main trends of today’s waste management policies is to reduce the stream of waste going to landfills and to recycle the organic material and the plant nutrients back to the soil.Anaerobic digestion (AD) is one way of achieving this goal and it will, furthermore, reduce energy consumption or may even be net energy producing. This chapter aims at provide a basic understanding of the world in which anaerobic digestion is operating today. The newest process developments as well as future perspectives will be discussed. Keywords. Anaerobic digestion, Carbon-flow, Microbiology, Antimization, Gas yild, Effluent

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Introduction

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Anaerobic Digestion as a Way to Add Extra Value

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Optimization of Anaerobic Digestion . . . . . . . . . . . . . . . . 15

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Increasing the Digestibility of the Waste . . . . . Optimization of Reactor Configuration . . . . . . Optimizing Process Control and Stability . . . . . Improving the Microbial Process and its Efficiency

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Advances in Biochemical Engineering/ Biotechnology, Vol. 81 Series Editor: T. Scheper © Springer-Verlag Berlin Heidelberg 2003

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Optimization of Effluent Quality

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Inactivation of Pathogens and Other Biological Hazards . . . . . . 23 Control of Chemical Pollutants . . . . . . . . . . . . . . . . . . . 25

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1 Introduction The modern society generates large amounts of waste that represent a tremendous threat to the environment and human and animal health. To prevent and control this, a range of different waste treatment and disposal methods is used. The choice of method must always be based on maximum safety, minimum environmental impact and, as far as possible, on valorization of the waste and final recycling of the end products. One of the main trends of today’s waste management policies is to reduce the stream of waste going to landfills and to recycle the organic material and the plant nutrients back to soil. Waste is increasingly becoming a problem and secure recirculation is gaining more and more attention. Anaerobic digestion (AD) is one way of achieving this goal and it will, furthermore, reduce energy consumption, or may even be energy producing, which is of major importance to the global environment. Anaerobic digestion has been implemented for years as a means for the stabilization of sewage sludge; however, during the past years anaerobic digestion technologies have been expanded to emphasize treatment and energy recovery from many other types of wastes including animal wastes, source-sorted household wastes, organic industrial wastes and industrial wastewater. Compared to incineration, anaerobic digestion creates more energy during the treatment of wastes, which normally have high water content. During incineration the nutrients are lost. Following the increasing interest in implementation of anaerobic digestion, optimization of this process is becoming increasingly more important. Despite the increased efforts spent on waste reduction, the amounts of waste are increasing throughout the world. This has led to ideas for a total removal of waste through injection into the deep underground (below 2 km) into old oil wells far below any the groundwater level [1]. The recovery of methane will, however, be of importance for the feasibility and economy of this technique and methane development at these high temperatures, pressures and salinity is now under investigation. This chapter focuses on the perspectives for optimization of anaerobic digestion after a brief introduction to the microbiology of anaerobic digestion. Optimization is a double-sided task: it involves both an increase of the biogas yield, which again implies an increased removal of the organic material in the waste, as well as ensuring an effluent with a sufficiently high quality to allow for recycling of the material as a fertilizer. A number of areas for improving the biogas yield will be discussed such as, for example, increasing the digestibility of the

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waste, optimizing process control, and improving the microbial process. With respect to effluent quality, emphasis will be on inactivation of pathogens and control of chemical pollutants.

2 Microbiology of Anaerobic Digestion 2.1 General Scheme

A major value of anaerobic digestion is linked to the production of biogas (methane and carbon dioxide) formed as the end product during degradation of organic material without oxygen. This energy is renewable and CO2 neutral and can be used for production of electricity and heat. Many different consortia of microorganisms with different roles in the overall process scheme are needed for the AD process, which occurs naturally in anaerobic ecosystems such as sediments, paddy fields, water-logged soils and in the rumen [2]. Three major groups of microorganisms have been identified with different functions in the overall degradation process [3] (Fig. 1): 1. The hydrolyzing and fermenting microorganisms are responsible for the initial attack on polymers and monomers found in the waste material and produce mainly acetate and hydrogen, but also varying amounts of volatile fatty acids (VFA) such as propionate and butyrate as well as some alcohols.

Fig. 1. The anaerobic degradation process

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Fig. 2. Carbon flow in anaerobic environments with active methanogens

Fig. 3. Carbon flow in anaerobic environments without active methanogens

2. The obligate hydrogen-producing acetogenic bacteria convert propionate and butyrate into acetate and hydrogen. 3. Two groups of methanogenic Archaea produce methane from acetate or hydrogen, respectively. The major part of the carbon flow in a well-operating anaerobic reactor occurs between the fermentative microorganisms and the methanogens. Only between 20 and 30 % of the carbon is transformed into intermediary products before these are metabolized to methane and carbon dioxide (Fig. 2) [4]. A balanced anaerobic digestion process demands that the products from the first two groups of microbes responsible for hydrolyzing and fermenting the material to hydrogen and acetate, simultaneously are used by the third group of microbes for the production of methane and carbon dioxide. The first group of microorganisms can survive without the presence of methanogens but will, under these conditions, form an increased amount of reduced products such as VFA (Fig. 3). The second group does, however, rely on the activity of the methanogens for removing hydrogen to make their metabolism thermodynamically possible as their reactions are endergonic under standard conditions and only occur when hydrogen is kept below a certain concentration. The relationship between the VFA-degrading bacteria and the hydrogen-utilizing methanogens is defined as syntrophic due to the dependent nature of this relationship and the process is

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Fig. 4. Interspecies hydrogen transfer

called interspecies hydrogen transfer (Fig. 4) [3]. The lower the hydrogen concentration the better are the thermodynamics of the VFA degradation. The distance between the VFA degrader and the hydrogen utilizer does, therefore, affect the concentration of hydrogen in the liquid phase, which again affects the thermodynamics of the process. Therefore, the conversion is improved in granules and flocks compared to a situation where the microbes are distributed freely in a liquid solution [5]. The two partners have to share a very small amount of energy and the conditions for ensuring energy for both microbes is very strict and can only be met within a narrow range of hydrogen concentrations [6]. 2.2 Syntrophic Acetate Conversion

Syntrophic relationships have also been found to be of importance for conversion of acetate when the acetate-degrading methanogens are inhibited by high concentrations of ammonia [7, 8] or sulfite (unpublished). Under these conditions the acetate-utilizing methanogens are inhibited and other groups of microbes replace them to obtain energy from the oxidation of acetate to hydrogen and carbon dioxide (Fig. 5). Due to thermodynamic constrains this reaction proceeds much better at increased temperatures and is the way of acetate transformation when the temperature is higher than 60°C, close to the upper temperature limit of thermophilic acetate-utilizing methanogens [9, 10]. In accordance with this, the population of Methanosarcina species disappeared more or less instantaneously from a biogas reactor operated on manure, when the temperature was increased from 55 to 65°C [11]. Concurrently, the acetate concentration first increased and

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Fig. 5. Modified anaerobic degradation process with syntrophic acetate conversion

then stabilized at a level somewhat higher than that found at 60°C [12]. This coincided with a significant increase in the population of hydrogen-utilizing methanogens [11] indicating that this group had become dominant in the overall conversion. Both syntrophic acetate oxidation and methanogenesis from acetate can be simultaneously active in a reactor system as indicated by several isotope studies often showing that less than 95 % of the methane produced from acetate is derived from the methyl group. Isotope experiments with biomass from thermophilic reactors have further shown that the concentration of acetate affects the competition between the two processes. When the concentration of acetate is low, syntrophic acetate conversion is the major process for acetate transformation [13, 14]. However, when the concentration of acetate is above the threshold level [15] for the specific population of acetate-utilizing methanogens in the reactor, these will be the major group active in the system. These findings further explain why the numbers of hydrogen-utilizing methanogens are high in thermophilic granules, which have exclusively been fed with acetate for a long period [16]. Furthermore, the numbers of acetate-utilizing methanogens are highest close to the surface of the granules, where the concentration of acetate is highest, while the populations of hydrogen-utilizing methanogens increased towards the center of the granules [16]. The first microbe found to perform acetate oxidation was a thermophilic bacterium belonging to the group of homo-acetogenic bacteria capable of reversing the acetate-forming reaction from hydrogen and carbon dioxide [17]. This bacterium used a very limited range of substrates all related to its homo-acetogenic nature [17]. Over time more microbes have been identified as being capable of

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carrying out this reaction. Some of these microbes have been found to use a large variety of substrates [18] and, furthermore, to be normal members of the populations of fermentative microbes in thermophilic reactors. This indicates that, at least in thermophilic reactors, syntrophilic acetate oxidation could be performed by a variety of the fermentative bacteria in the reactor when no other substrates are available. This needs, however, further verification. 2.3 Microbiology of Thermophilic Digestion

Microbes thriving at high temperatures have been known for years [19]. The reaction rate of many chemical reactions will double by an increase of 10°C according to the Arrhenius equation. The same is, however, not always the case for microbial reactions where the temperature response is specific for the particular microbe. Different groups of microbes have been identified where the ones of interest for anaerobic digestion are mesophilic strains with an optimum between 30 and 40°C, and thermophilic strains with an optimum between 50 and 60 °C [20]. The mixed microflora found in an anaerobic bioreactor generally shows an increasing rate from a temperature of 20 to 60 °C and the theoretical temperature gap between mesophilic and thermophilic strains is not apparent when viewing the process as a whole [21]. Anaerobic digestion at a temperature below 20°C, or at a temperature above 60 °C, generally shows a lower methane yield than within these limits. However, anaerobic digestion has been shown to be possible even at extreme thermophilic conditions of 70 °C and more [28 – 30]. Experiments with high temperature digestion of manure showed that major changes occurred in the microbial populations of the anaerobic reactor when the temperature was increased from 55 to 65 °C [12]. Besides a significant increase in the population of Archaea compared to Bacteria, also the populations of methanogens underwent large changes over time. The population of hydrogen-utilizing methanogens did, for example, change from a major population belonging to the genus Methanobacterium to another belonging to Methanococcus over a 3-month period [11]. Such results clearly demonstrate that reactors operated at extreme conditions can take months before a stable microflora has established. With this in mind it is difficult to guess if the methane yield actually will be lower after an extended period of many months. Within the normal temperature range the general carbon flow of thermophilic reactors was found to be very similar to that of mesophilic reactors [31]. A slightly higher amount of the carbon was channeled directly into acetate and a slightly smaller amount of carbon was turned over via the pool of VFA [4]. Many extreme thermophilic Bacteria or Archaea have been found to produce mainly acetate and hydrogen as their end products [32]. Therefore, less butyrate and propionate can be expected at these high temperatures. Different maximum temperatures were found for the different microbial groups in a thermophilic anaerobic reactor treating manure [33]. For instance, among the methanogens, the acetate-utilizing methanogens have a much lower temperature maximum (ca. 62 °C) compared to the hydrogen-utilizing methanogens (ca. 75 °C) [33]. However, the actual temperature of the reactor affects the specific populations

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which are active in the reactor. Therefore, a higher temperature optimum and maximum is found for the main metabolic groups in extreme thermophilic reactors compared to thermophilic reactors [28]. Methane production was found in microbial mat samples taken from a slightly alkaline hot spring at 80 °C [34]. This demonstrates that methanogenesis is possible even at this very high temperature. 2.4 Establishing a Stable Microflora in Thermophilic Reactors

Waste such as sewage sludge, manure or household waste contains many different populations of anaerobic or facultative anaerobic microorganisms. Most of these microbes are mesophilic and only a very small number of true thermophiles is present. The number of microbes in raw sewage sludge utilizing substrates such as acetate or cellulose at 60 °C is extremely low (ca. 100 per g) [35]. The numbers are somewhat higher at 55 °C but still much lower than the numbers at 37 °C [35]. These facts clearly show the problems of establishing stable reactors at higher temperatures. Where the microflora of mesophilic reactors can be established directly based on the raw material fed to the reactor, the microflora of the thermophilic reactor has to be propagated from small minority populations found in the raw materials [36]. Many thermophilic full-scale reactors have failed through history, especially within the area of sewage sludge treatment. The reason is basically a lack of understanding of the principles for establishing a stable thermophilic microflora in the reactor. The same also applies to the literature, which is full of experiments with unstable thermophilic laboratory reactors often performing poorly compared to mesophilic reactors. When reviewing the literature describing these experiments, Wiegant [37] concluded that process stability is lower in thermophilic reactors and that thermophilic reactors generally have higher concentrations of volatile fatty acids in the effluent compared to mesophilic reactors. During recent years where more thermophilic reactors have been implemented, it has been shown that this conclusion it not correct and that stable thermophilic reactors with a balanced thermophilic microflora perform just as well as stable mesophilic reactors [33, 38, 39]. The key to obtain a balanced thermophilic microflora is to give optimal growth conditions to the small numbers of thermophilic populations found in the raw material during start-up of the bioreactor [36]. If sufficient thermophilic seed material is available, it is possible to carry out a rapid start-up of a thermophilic reactor [33]. The seed material should be evaluated before use with respect to the destruction of volatile solids in the reactor from which the seed is obtained as well as the concentration of VFA. If possible, it will be beneficial to perform a methanogenic activity testing of the seed material to establish the potential of this seed for transforming extra loads of methanogenic substrates (acetate and hydrogen) [40, 41]. After addition of the seed to an empty reactor it should be allowed to equilibrate for 1 day before feeding is initiated at the desired thermophilic temperature. A slow and graduate change of the temperature only prolongs the start-up phase and does not select for true thermophiles

Fig. 6. Start-up using thermophilic digested seed material

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24 h

t d = 24 h

24 h

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Fig. 7. Unbalanced growth of methanogens (dots) and fermentative bacteria (rods)

possessing an optimum growth rate at thermophilic temperatures. Probing of methanogens in bioreactors has clearly demonstrated that mesophilic methanogens are present in thermophilic reactors and vice versa [42]. However, the major populations are those with an optimum temperature close to the reactor temperature [43]. After equilibration, feeding should then be initiated corresponding to a hydraulic retention time approx. 25 % higher than the retention time of the reactor where the seed came from. Normally the reactor is only partly full at this stage and, therefore, the reactor is operated in a fed-batch mode during this period of time. During start-up, the VFA concentration should be monitored on a daily basis. If the VFA concentrations continue to decrease after approx. 3 days of feeding or remain at a stable low level, the hydraulic retention time can be lowered. By repeating this pattern and, at the same time, keeping a tight eye on the concentration of VFA – especially the isoacids [44, 45] – it is possible to reach the desired final retention time in approx. 1 month. A schematic drawing of the expectable feeding pattern and the expectable response in VFA is shown in Fig. 6. For sewage sludge it is possible to obtain a stable process with a high reduction of volatile solids at a hydraulic retention time as low as 6 days at thermophilic conditions. If no seed is available, it is even more important to plan the start-up in a controlled condition. It is important to avoid over-loading and build-up of VFA. Therefore, a start-up material containing only small amounts of organic material should be chosen. Mesophilic digested waste material has a much lower organic content than raw waste material and has at least as many thermophilic microorganisms as found in this material [46]. Immediately upon an increase of the temperature to the thermophilic region, these thermophilic microbes will start growing. As the acid-producing microbes grow much faster than the

Fig. 8. Start-up using mesophilic digested seed material. Arrows indicate feeding of the reactor

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methanogens, the first reaction is often an increase in the concentration of VFA [47] (Fig. 7). Due to the limited amount of undigested material present, this increase is only relatively small and does not affect the over-all digestion process. Immediately after a decreasing trend is seen in the volatile fatty acids corresponding to growth of the population of VFA-degrading and methanogenic microbes, it is appropriate to start feeding.A portion corresponding to approximately the double of the desired hydraulic retention time is appropriate. Depending on the response in VFA concentration, this trend can be continued every day unless the VFA starts to increase. After 3 to 5 days of continuous feeding it is time to lower the hydraulic retention time again and this pattern can be continued until the reactor has reached the desired hydraulic retention time. This is normally reached within a period of 2 months. A schematic drawing of the expectable feeding pattern and the expectable response in VFA is shown in Fig. 8. The time needed for performing the start-up with a small amount of thermophilic seed material can further be reduced by addition of mesophilic-digested material in addition to the daily feeding with raw waste material. This was used with success for start-up of the new thermophilic sewage sludge digester at Western Lake Superior Sanitary District in Duluth, Minnesota during the summer of 2001 [48].

3 Anaerobic Digestion Plants A large number of different AD-technologies and AD plants are found today throughout the world. The largest number of AD plants in the modern society treats primary and secondary sludge (biosolid) in municipal wastewater treatment plants. These plants basically stabilize the waste material and the biogas produced is often of minor importance. For some of the large wastewater plants, the biogas produced is used for electricity production and the idea of improving the biogas yield is attracting increased interest [49, 50]. A large number of single household biogas units have further been implemented in developing countries such as China, India and Africa [51, 52]. These units will normally provide gas for cooking and lighting in the households. Another major field for anaerobic digestion is the industrial wastewater from, especially, food processing industries where the wastewater is heavily polluted with easily degradable organic carbon [53]. Treatment of municipal wastewater has further been implemented in developing countries such as India especially where the average temperature is rather constant [54]. Anaerobic treatment is basically a way to reduce BOD while the nutrients such as nitrogen and phosphor are left untouched [50]. Recent studies have, however, shown that nitrogen can be denitrified in a chemoautotrophic anaerobic process using nitrite as an electron acceptor [55, 56]. A better way to implement anaerobic treatment of municipal wastewater would be to recover all nutrients and heavy metals from the wastewater after the anaerobic treatment using membrane technology. In this way, the benefits of anaerobic process with respect to space, speed, low sludge production and cost can be fully exploited and the valuable nutrients can be reused.

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A large-scale biogas facility for treating manure from several farms in combination with other organic wastes such as food wastes and source-sorted household wastes – the so-called co-digestion – was launched in Denmark at the end of the 1980s [57, 58]. Addition of even small amounts of organic industrial wastes increases the gas production significantly (Fig. 9). Especially fatty or oily wastes have a much higher gas potential than manure and a much higher concentration of organic material (higher dry matter content) – but also wastes rich in carbohydrates and proteins will improve the gas yield per unit of reactor volume. Digestion of sewage sludge or manure yields from about 1 – 2 cubic meter biogas per cubic meter reactor volume per day while the reactors will produce between 4 and 10 cubic meter biogas per cubic meter reactor volume with addition of ca. 20 % fatty waste. Today around 22 large-scale AD plants have been built in Denmark mainly in the regions with high manure production and all of these plants are co-digesting many types of raw materials [33, 59]. The idea of large scale centralized AD plants treating mixtures of waste have spread throughout Europe and to the rest of the world especially during the last ten years [60]. Besides common biogas plants, the numbers of farm biogas plants for large pig farms have steadily increased in many European countries [61]. Many of these plants further supplement the manure with raw materials with a higher gas potential. Recently, a large number of biogas projects are on their way in USA [62] and especially in California due to the energy crisis starting at the end of year 2000, which again

Fig. 9. Addition of 5 % fish oil will double the biogas production

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has resulted in higher prices on electricity. Biogas plants in USA are often much larger compared to the so-called “large-scale” biogas plants in Denmark and treat manure from diaries or feeding lots having up to hundred thousand cows or from major pig or chicken production. The AD technology is, therefore, suitable both for small and large-scale applications. The economics depend upon the scale, and larger plants will in general have a better economy than smaller plants. Raw sewage sludge has a very low dry matter content and, therefore, the potential for treating waste in these plants is only used to a low degree. Addition of food waste, restaurant waste or organic industrial waste could be a good way to make use of this potential. Several concepts are based on treatment of mixtures of sewage sludge in combination with household waste such as the Finish Waasa process [63]. Very good results have been obtained in Grindsted, Denmark with co-digestion of source-sorted food waste together with sewage sludge. The food waste was collected in paper bags and only a small amount was removed before the digestion process [65].

4 Anaerobic Digestion as a Way to Add Extra Value Production of biofuels from biomass such as bioethanol or gasification of biomass only makes use of a fraction of the biomass. The same is true for many other biomass-based productions of non-food products. The biomass fraction left is, however, often a good substrate for methane production (Fig. 10). In this way biogas production can add approximately 30 % more value to the production of bioethanol from biomass such as wet straw or corn stovers [66]. The AD process will further purify the process water allowing for recirculation within the system, which will further decrease the cost of ethanol production. In the future it is expected that more valuable products than methane will be sought from waste. However, these niche productions will as a rule only use a part of the waste and methane production from the final residues can add further value to the production and will decrease the pollution load of the end products before their final disposal.

Fig. 10. Simultaneous production of bioethanol and biogas

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5 Optimization of Anaerobic Digestion The economy of a biogas plant is directly linked to the amount of biogas produced per unit of raw material treated in the plant. Some costs are fixed such as the cost of transporting the material to the AD plant and back again to the end user or the end destination, while others are variable such as construction costs. Lowering the water content of the raw material and running the process with higher dry matter content can significantly decrease the cost of treatment. This is of major importance when the raw material has to be transported to a centralized biogas facility and in this case it is often beneficial to separate the manure into a solid fraction and a liquid fraction, which is left behind at the farm. The liquid fraction can be used as a nitrogen-rich fertilizer at the form. The potential for increasing the biogas yield of manure or sewage sludge is larger as only approximately half of the organic material is converted in this type of material. This is, however, not the case for most organic industrial wastes or source-sorted household waste, which have been found to be more easily degradable, and approximately 80 % of the organic material is converted to biogas [67, 68]. Manure is, however, the major raw material available for a largescale use of AD technology in most of the world and a large-scale implementation of AD will have to be based on this raw material. AD will further improve the quality of manure by making a more stable material with fewer pathogens and less odor. For wastewater sludge the interest in increasing the conversion of the organic material is further linked to the reduction in the final amount of biosolid, which has to be disposed after the treatment. Suitable end-use of digested sewage sludge or biosolids is becoming an increasing problem for many communities throughout the world. Some major ways to improve the gas yield in AD plants will be by (Fig. 11): 1. 2. 3. 4.

Increasing the digestibility of the waste, Optimizing the reactor configuration, Optimizing process control and stability, and Improving the microbial process and its efficiency

5.1 Increasing the Digestibility of the Waste

Several methods have been discovered to increase the digestibility of manure or sewage sludge ranging from mechanical, chemical to biological methods such as enzyme treatment. Chemical treatment with bases or acids or treatment with mixtures of enzymes have generally been found to increase the accessibility to microbial conversion into biogas – but these processes have all been found to be too expensive for practical implementation [69]. Decreasing the particle size was found to increase the gas production from manure and the increase in gas production exceeded by far the extra costs of implementing a macerating unit with several knives [70].

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More gas Sewage

Anaerobic digestion Animal wastes

Biogenic wastes

A. Optimization of biogas production

• Increasing the digestibility

• Optimizing reactor

Source-sorted household wastes

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Food-processing wastes

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• Inactivation of Crops

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• Control of chemical pollutants

Fig. 11. Major goals for anaerobic digestion of today

A handful of wastewater plants in the world have further implemented the use of thermal hydrolysis where concentrated sludge is treated by a combination of high temperature (133 – 180 °C) and pressure (3 –10 bar) with the aim of improving the digestibility of the sludge (The Cambi Process) [71]. However, the influence of this process on gas production per unit sludge treated is still not fully documented, and the amount of sludge ending as carbon dioxide due to the treatment is unknown. The wet oxidation process using alkaline conditions and oxygen in addition to high temperature and pressure has been found to be superior for breaking the lignin associated to hemicellulose and cellulose as lignocelluloses [72]. The products of the lignin-oxidation (carboxylic acids and phenolics) are further found to have highly convertible to methane and carbon dioxide (approx. 80 % COD removal) [73]. The pure cellulose and hemicellulose found in the hydrolysate is expected to give a methane yield corresponding to the methane potential of the mannouronic sugars. Due to the hydrolytic capability of microbes in the AD process, it is expected that enzyme addition is not needed for conversion of hydrolysates produced by wet oxidation. However, this still needs to be verified along with the optimal way of implementing wet oxidation as part of the AD process for materials such as manure containing a high fraction of lignocellulosic material. Furthermore, the economics of this extra step needs to be evaluated. Another way to enhance the digestibility of the raw material is by Pulse PowerTM technology developed by Scientific Utilization Inc. in Decatur, AL [74]. This equipment incorporates rapid-pulse high-power electric technology ori-

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ginally developed for antimissile laser and particle-beam devices, which produces disruptive shock waves in the raw material. The shocks are expected to break large molecules into shorter fractions and have been claimed to enhance destruction of volatile solids by 50 to 100 %. However, a study of the efficiency of this method in a full-scale system showed no improvements. 5.2 Optimization of Reactor Configuration

The AD process can be conducted in a single-step or multi-step process [47]. Continuous processes are generally most favorable when treating large amounts of waste and thermophilic temperatures have the largest potential due to higher reaction rates which corresponds to smaller reactor volumes. Separation of the solid phase from the liquid phase of manure or sewage sludge is a technical solution which has been well documented during the last years and which can be implemented both before and after the anaerobic reactor [75 – 77]. Separation will further allow for optimal design of the process so that the liquid fraction can be left at the farm, or treated locally in small compact plants, while the solid faction can be transported to a centralized plant for treatment. Especially pig manure contains very high concentrations of phosphorus and, therefore, large land areas are needed to use the manure as a fertilizer afterwards. If the solid fraction is removed from the manure, the farm is able to use the liquid fraction on a much smaller land area and pipes can be used for the spreading. After digestion the phosphorus-rich solid fraction is an excellent fertilizer [78]. If the separation is carried out on fresh manure approximately 70 % of the gas potential will remain in the solids [70]. Recently, we demonstrated that the conversion of organic material in manure could be increased along with an increase in the over-all biogas yield by using a two-phase system combining a short hydrolysis step performed at 70 °C followed by a methane-producing step at 55 °C, both done in continuous stirred tank reactors (CSTRs) (Fig. 12). The performance was compared to a singlephase process in a CSTR reactor with the same over-all retention time, and the first estimation showed that the extra gas produced was sufficient to justify the implementation of an additional reactor and the need for extra heating energy (unpublished). The possibility of using an immobilized reactor system after a short hydrolytic step during a two-phase conversion of waste such as manure, sewage sludge and household waste does possess a potential, which needs more attention (Fig. 11).A number of systems such as the up-flow anaerobic sludge blanket reactor are in use throughout the world for treatment of industrial wastewaters. However, only limited experience has been obtained from full-scale use of this reactor for the treatment of solids [79, 80]. Having retention times in the range of hours, the potential is apparent, as much smaller reactors are needed to treat the same amount of waste. Furthermore, the immobilized system has lower construction and running costs, no stirring system will be needed. The sludge produced in the system can be recirculated back to the hydrolysis step decreasing the final sludge production from the system, and again the phosphorus is concentrated in the sol-

Fig. 12. Different reactor configurations for anaerobic digestion of waste

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id fertilizer while the liquid fertilizer is rich in nitrogen. The over-all economy is, therefore, improved although a separation is needed between the hydrolysis reactor and the immobilized reactor to ensure that the amount of suspended solids in the influent to the immobilized system is acceptable. 5.3 Optimizing Process Control and Stability

Process stability is important for the operation and economy of any AD plant. Imbalance often affects the methanogens in the anaerobic process and leads to a VFA accumulation [44]. It is important to note that some inhibitory compounds equally affect all the major groups in the anaerobic digestion process. This is the case for long-chain fatty acids [81, 82] or for phthalate esters such as DEHP [83]. No VFA accumulation was observed when reactors were inhibited with DEHP. Inhibitory compounds in waste are, however, generally either ammonia or sulfide, which are found in high concentrations in some types of waste [84–87]. Furthermore, high concentrations of proteins in the incoming waste can lead to the development of inhibitory concentrations of ammonia and sulfide. For both of these compounds, the toxic effect is dependent on pH and temperature – the higher the temperature and pH, the higher the toxic effect [88]. Due to the high ammonia concentration, thermophilic digestion of swine manure has been found to be difficult [89]. Adaptation to an inhibitory compound is, however, possible over time and the anaerobic process can work with stable performance but with a lower gas yield as long as the concentration of the toxic compound is kept relatively constant. Process stability is, however, lost if the concentration of the inhibitor is fluctuating as seen in the large-scale biogas plants when treating many types of wastes in different ratios. In immobilized anaerobic systems, the biomass has generally been found capable of withstanding much higher concentrations of inhibitory compounds [90]. This is probably due to concentration gradients in the biofilm creating niches where the microbes are protected from toxic concentrations of the inhibitory compounds. The use of a two-phase digestion system is, therefore, expected to show superior performance by compared to a one-phase system for waste containing high concentrations of inhibitors. Increasing the biomass concentration in a biogas reactor by recirculation of the biomass was found to increase the gas yield during anaerobic digestion of swine waste [91, 92]. In accordance with this, the inhibitory effect of swine manure can be counter-acted by addition of other wastes such as lipid-containing wastes, which result in a higher biomass concentration in the reactor besides a dilution of the manure. Process problems in AD plants are generally difficult to detect before the process is severely affected and the gas production decreases. In general the plant operator has very little information about the condition of the process and no instruments inform him when the process is becoming unstable (Fig. 13). As a result, the plant is often operated with a very low organic loading to prevent problems from occurring. A good sensor would, however, make it possible to optimize the operation and to ensure maximum use of the reactor space without having any process failures (Fig. 14).

Fig. 13. Operation of today’s biogas plant

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Fig. 14. Future operation

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Recently, a sensor has been developed that can measure VFA on-line in biogas reactors [93]. This development allows a continuous monitoring of the anaerobic process and with the development of logic control systems it should be possible to improve the economy of AD plants through a more stable and optimized operation in the future. A number of kinetic models have been developed for anaerobic waste reactors but so far no control algorithm has been developed and tested based on VFA data in addition to the normal data available at the AD plant (flow rates, amount of raw materials, gas production, temperature etc.). A combined sensor and control system can be expected in the future. 5.4 Improving the Microbial Process and its Efficiency

Improving bioprocesses by implementation of microbial populations with improved degradation abilities (bioaugmentation) has been known for years. However, only few studies have been done on bioaugmentation and the results are inconsistent. To obtain a clear picture of the potential to use specific microbes for improvement of the process, it is necessary to follow the fate of the microbes added to the reactor system over time. Only microbes with the ability to thrive and proliferate in the reactor will be of importance in a longer-term prospective. Molecular techniques are available today for studies of populations in reactor systems and using such techniques we demonstrated that a specific cellulolytic bacterium, present in manure inhabited the reactor [94]. The same technique could be used to test for specific added microbes. Compared to controls without any pretreatment a more than 20 % increase in the methane yield was found by incubating separated fibers from cow manure with specific extreme thermophilic xylanolytic microbes for 2 days before the material was resuspended in the liquid and digested [69]. This finding seems promising in the context of the two-phase system described above and deserves further examination. Isolation and characterization of the acetate-utilizing methanogens from thermophilic manure plants in Denmark showed important differences between the different isolates of Methanosarcina species with respect to temperature optimum and growth rates [95]. The strain derived from the best performing thermophilic biogas plant was the acetate-utilizing methanogen with the highest growth rate and highest temperature optimum.When using this methanogen to seed reactors where the organic loading was increased by a sudden addition of lipids to the feed of manure, the seeded reactor was found to be superior to overcome the changes compared to the unseeded reactor, which was inhibited severely and accumulated VFA. No major accumulation of VFA was found in the seeded reactor compared to the unseeded reactor, and biomass from this reactor had a much higher specific methanogenic activity on acetate than for hydrogen and formate, which was almost the same in both reactors. The fact that seeding had an effect even after several retention times indicates that the added methanogen grew in the reactor as further demonstrated using a probe specific to this strain (unpublished). These findings have practical implications and

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show that better performance can be obtained when lipid-containing waste is introduced into a biogas reactor operated on manure if the reactor is seeded with a robust acetate-utiling methanogenic strain with a higher growth rate than the native strain in the system.

6 Optimization of Effluent Quality Besides production of biogas the AD plant produces a residue or effluent with a potential market value. Until now, no applicable standards for these products have been available and recycling of AD-residues has generally been poorly regulated in most countries. The main issues related to quality management when recycling AD-residues is 1. to break the chain of disease transmission by inactivation of pathogens and other biological hazards and 2. to control chemical pollutants (organic and inorganic). Inactivation of pathogens is increased with increasing temperatures and, therefore, thermophilic digestion has a much high sanitary effect than mesophilic digestion. 6.1 Inactivation of Pathogens and Other Biological Hazards

Sewage sludge and segregated household wastes are both high-risk wastes that can be heavily contaminated with pathogens [96]. Several reviews on pathogens in livestock waste, factors influencing microbial movement and methods for inactivating pathogens have been published [97 – 99] New regulations with respect to concentrations of pathogens and organic pollutants could potentially be threatening to land-disposal of digested material. The regulations made both by the US EPA and the European Union demand specific treatment processes before the use of sewage sludge on agricultural land [1, 100]. However, for unrestricted use of digested sewage sludge a further reduction of pathogens will be required. Several studies found anaerobic digestion to be superior to aerobic digestion in reducing the density level of pathogens [101]. Conventional mesophilic digestion was found to be insufficient for meeting the new requirements for unrestricted land-use [101, 102]. An increased elimination of pathogens can be achieved by using different treatment processes including composting of sewage sludge at high temperatures, exposing the material to radiation or specific temperatures for a defined period. Thermophilic digestion is still not included as a mean for producing a clean effluent. Biosolid A demands that the number of coliforms is less than 1000 per g dry solid, the number of Salmonella is less than 3 per 4 g dry solid, enteric virus and helmic ova should not be detectable. These restrictions are based on logarithmic reduction experiments performed in buffer with the respective microbes. However, experience obtained under anaerobic digestion showed that other parameters than time and temperature are of importance for reduction of

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pathogens. The anaerobic environment seems to have an additional effect, which is not accounted for in the biosolid classification [102]. It was demonstrated that the high level of VFA [103], ammonia and sulfide [104] and alkaline pH enhance inactivation of pathogens [105]. Several reports on thermophilic AD of sewage sludge showed that thermophilic digestion is more efficient in reducing the pathogens and pathogen indicators than mesophilic digestion [102, 106 –108]. Further improvement of the process by extension of one digester into a series of several reactors operating at 55 °C [110], application of a two-stage process with an acidogenic reactor operating at 55 °C and 60 °C coupled to a 37 °C methanogenic reactor [111], or combinations of thermophilic pretreatment or posttreatment at 62 °C with conventional mesophilic digestion [109] all showed that it is possible to make a stabilized sludge fulfilling the requirements for Biosolid A. Recent experiments done at Terminal Island Treatment Plant in Los Angeles clearly showed that Class A biosolid can be produced by switching the process from mesophilic digestion at around 35 °C to thermophilic digestion temperatures at 55 °C [112]. However, for unrestricted use of the effluent a required holding period of one day is needed at 55 °C, which is difficult to meet in a conventional sewage sludge treatment system. By testing the effluent quality and demonstrating that the solid meets all requirements it is possible to obtain a Class A biosolid classification. The testing program, however, has to be repeated on a regular basis to maintain the classification. Pathogens are further of major interest when manure from several farms is treated in centralized large-scale biogas plants. When the Danish Action Program for Large Scale Biogas Plants was implemented more than 10 years ago, hygienic aspects were central as a consequence of transperation of manure from several farms. Therefore, a veterinary program was initiated and this led to implementation of a number of control functions, which have gained major interest and respect throughout the world [96, 113]. In this program the fecal Streptococci (enterococci) were found to be excellent indicator organisms instead of coliforms (the FS method) during digestion at temperatures up to 60 °C. These microbes are present in manure or other materials of intestinal origin as the coliforms, but in contrast to coliforms, they are much more resistant to high temperatures and the anaerobic environments and, therefore, most pathogenic bacteria, viruses and parasitic eggs will be inactivated long before these microbes. An FS log10 reduction of around 4 and 5 was needed to give an acceptable effluent quality, which basically implies that the AD process is operated at thermophilic conditions or that a high-temperature step is added to a mesophilic reactor [96]. Pathogenic viruses have been identified in sewage sludge, segregated household waste and manure [96]. The absence of enteric viruses showed no correlation with porcine parvovirus in a previous study of thermophilic anaerobic digestion of manure [102]. This indicates that this virus could be a poor indicator for human pathogenic viruses. The effect of the AD process on viruses further depends on the way the virus is found the environment. For instance, it was found that a virus in tissue was less sensitive than a free-living virus [114]. Much more work is, however, needed to understand the fate of a virus during anaero-

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bic digestion including the possibility to remove a resistant virus by pretreatment such as thermal hydrolysis or wet oxidation. Besides the potential for pathogens, material of animal origin such as waste from slaughterhouses and milled bones from cows and sheep can contain infectious elements resulting in transmittable spongiform encephalopathy (TSE). To reduce the risk of spreading of these diseases the European Commission has very recently defined special methods for slaughtering of animals to ensure that all risk material is removed from the feed-chain [115]. This regulation defines how the different types of wastes (animal by-products) not used for human consumption have to be treated. All waste of animal origin is divided in three categories with different demands. The new regulation demands an approval of all plants treating animal waste including quality control by the plant and society. The banning of meat and bone meal as fodder for animals intended for human consumption following the increased number of European cases of bovine spongiform encephalopathy (BSE) has led to investigations of possible and safe disposal methods of the meal. During the discussion of disposal methods, anaerobic digestion followed by utilization of the fertilizer value by spreading the digested sludge on arable land was suggested. The idea was, however, abandoned, and instead, a large part of the Danish meat and bone meal is utilized in cement production and is thereby lost from cycling of nutrients. BSE and the variant of Creutzfeldt-Jakob disease (vCJD) are generally considered to be transmitted by the ingestion of proteinaceous agents (prions), which accumulate in the brain and spinal cord of infected animals and humans. The disease-causing protein (PrPSc) is an abnormal isomer of a host-encoded protein (PrP) that has the ability to change the conformation of normal PrP to PrPSc. The infectious PrPSc is, however, considered to be extremely resistant to enzymatic degradation, heat, and chemical treatment. Proteases are ineffective in inactivating PrPSc, and bioassays have shown that protein remained infectious after autoclaving at temperatures up to 138 °C for 60 min [116]. Among the different chemical inactivation methods tested, alkaline treatments have so far shown most promise, although they are not completely effective. Complete inactivation might, however, be achieved by combination of methods. Based upon one study, in which scrapie-infected hamster brain homogenate remained infectious after 3 years incubation in soil [117], we assume that only a minor reduction of prions will occur during the AD process and that sufficient pretreatment will be necessary to eliminate prions before the anaerobic reactor. 6.2 Control of Chemical Pollutants

Among the chemical pollutants, heavy metals are mainly problematic in wastes of industrial origin and are found in high concentrations in some organic waste and in sewage sludge from wastewater treatment plants with certain industrial influents. Through source reduction and elimination of specific types of wastes, it is generally possible to meet the standards regarding heavy metals for use of residues produced by AD.

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Agricultural waste can contain persistent organic contaminants such as pesticides, antibiotics, and other medicine residues. Industrial wastes, sewage sludge, and household wastes can contain aromatics, aliphatic and halogenated hydrocarbons, organochlorine pesticides, PCBs, PAHs, phthalates, linear alkyl benzenesulfonates (LAS), nonylphenol and nonylphenol ethoxylates. During AD most of the water-soluble organic contaminants are degraded to various degrees. However, hydrophobic compounds such as high molecular phthalates, PAH, and LAS are tightly bound to the particulate phase and are partly unavailable for biological conversion [118]. The potential to remove organic pollutants by pretreatment of sewage sludge by wet oxidation was studied very recently. Unfortunately, these results showed that the conditions suitable for keeping a biogas potential in the waste material resulted in production of high amounts of organic pollutants with a smaller molecular weight than the initial pollutants industries (unpublished). Effectively, a complete decontamination demands incubation at very high temperatures (more than 250 °C) and pressure, which implies that the final gas potential is marginal and that the costs are very high. When comparing full-scale mesophilic and thermophilic AD-reactors operated on the same sewage sludge, it was found that the thermophilic process delivered an effluent with significant lower concentrations of organic pollutants than the effluent from the mesophilic reactor [119]. A higher bioavailability due to a higher solubility of the hydrophobic elements could explain the differences observed. Recent experiments indicated that extreme thermophilic processes improve this reaction further but this needs to be further investigated before any conclusions can be drawn.

7 Conclusions Aaerobic digestion is an important way of handling waste in society. While the emphasis previously was focused on stabilization of sewage sludge, emphasis today is focusing on creation of an effluent, which safely can be used as a fertilizer on farmland. Production of biogas is furthermore gaining more attention, especially for treatment of manure from large-scale animal production. In this picture other types of organic waste such as wastes from food processing or from households will be interesting as a mean of boosting the gas production and, thereby, the economy of the AD plant. Anaerobic digestion can further add value during use of waste or other biomasses for the production of chemicals and energy and this synergy is expected to be further exploited in the future. Anaerobic digestion is a mature technology today. However, as demonstrated in this chapter, there is plenty of room for optimization and improvements. The standardized CSTR reactor has its limitations and implementation of more efficient reactor types such as the immobilized reactor systems has a major potential for treatment of solid waste. Process control on the current AD plant is still relying on in- and output data and no information is available on-line for checking the state of the process and its performance. The microbiology in AD plants is normally regarded as a big black box and very few attempts have been made to control the actual microflora in bioreactors treating waste. Recent research

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has demonstrated that AD plants within close distance of each other can possess different microfloras with different characteristics. Some microbial strains will add superior characteristics to the reactor system and this has major implications for the future of AD plants. Most waste is only partly degraded in the AD plant. Improving the digestibility of waste by using physical or chemical pretreatment methods, which will make the waste more accessible for anaerobic degradation, is another area with major perspectives. One of the most promising areas for the future is the use of extreme thermophilic digestion within the AD plant. The high temperature process will allow for better hydrolysis of the solids, for better sanitation and for better removal of xenobiotics during the treatment process. Acknowledgement. I would like to thank Zuzana Mladenovska, Hinnerk Hartmann, Thomas Ishøy and Peter Westermann for valuable input to this chapter.

8 References 1. Iranpour R, Oh S, Kim H, Shao YJ, Hagekhalil A, Schafer P, Stenstrom MK, Ahring BK (2002) Wat Environ Federation in press 2. Zinder SH (1993) Physiological ecology of methanogens. In: Ferry JG (ed), Methanogenesis. Ecology, physiology, biochemistry & genetics. Chapman & Hall, New York, p 128 3. Schink B (1992) Syntrophism among prokaryotes. In: Balows A, Trüper HG, Dworkin M, Harder W, Schleifer K-H (eds), The prokaryotes. Springer Verlag, Berlin Heidelberg New York p 276 4. Mackie RI, Bryant MP (1981) Appl Environ Microbiol 41:1363 5. Thiele JH, Zeikus JG (1988) Interactions between hydrogen- and formate-producing bacteria and methanogens during anaerobic digestion. In: Erickson LE, Fung DY (eds), Handbook on anaerobic fermentation. Marcel Dekker, New York, p 537 6. Westermann P (1996) World J Microbiol Biotechnol 12:497 7. Schnürer A, Zellner G, Svensson BH (1999) FEMS Microbiol Ecol 29: 249 8. Schnürer A, Houwen FP, Svensson BH (1994) Arch Microbiol 162:70 9. Zinder SH, Koch M (1984) Arch Microbiol 138:263 10. Lee MJ, Zinder SH (1988) Appl Environ Microbiol 54:1457 11. Ahring BK, Ibrahim A, Mladenovska Z (2002) FEMS Microbiology Ecology submitted 12. Ahring BK, Ibrahim AA, Mladenovska Z (2001) Wat Res 35:2446 13. Petersen SP, Ahring BK (1991) FEMS Microbiol Ecol 86:149 14. Ahring BK (1995) Antonie van Leeuwenhoek 67:91 15. Westermann P, Ahring BK, Mah RA (1989) Appl Environ Microbiol 55:514 16. Ahring BK, Schmidt JE, Winther-Nielsen M, Macario AJL, Conway de Macario E (1993) Appl Environ Microbiol 59:2538 17. Lee MJ, Zinder SH (1988) Appl Environ Microbiol 54:124 18. Schnürer A, Schink B, Svensson BH (1996) Int J Syst Bacteriol 46:1145 19. Wiegel J (1990) FEMS Microbiol Rev 75:155 20. Lettinga G (1995) Antonie van Leeuwenhoek 67:3 21. van Lier JB, ten Brummeler E, Lettinga G (1993) J Ferment Bioeng 76:140 22. Kettunen RH, Rintala JA (1997) Appl Microbiol Biotechnol 48:570 23. Kotsyurbenko OR, Nozhevnikova AN, Kalyuzhnyi SV, Zavarzin GA (1993) Microbiology 62:462 24. Zeikus JG, Winfrey MR (1976) Appl Environ Microbiol 31:99 25. Westermann P (1994) FEMS Microbiol Ecol 13:295 26. Varel VH, Hashimoto AG, Chen YR (1980) Appl Environ Microbiol 40:217

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B. K. Ahring Uemura S, Tseng I-C, Harada H (1995) Environ Technol 16:987 Rintala J, Lepistö S, Ahring B (1993) Appl Environ Microbiol 59:1742 Lepistö R, Rintala J (1996) Biores Technol 56:221 van Lier JB (1996) Antonie van Leeuwenhoek 69:1 Zinder SH (1990) FEMS Microbiol Rev 75:125 Wiegel J (1992) The obligatory anaerobic thermophilic bacteria. In: Kristjansson JK (ed), Thermophilic bacteria. Boca Raton, CRC Press, p 105 Ahring BK (1994) Wat Sci Tech 30:241 Ahring BK (1992) Proceedings from International conference Thermophiles. Science and Technology. Reykjavik, Iceland, p 130 Chen M (1983) Appl Environ Microbiol 45:1271 Ahring BK, Mladenovska Z, Iranpour R, Westermann P (2002) Wat Sci Tech 45:293 Wiegant WM (1986) PhD Thesis, Landbouwhogeschool Wageningen, Netherlands Dinsdale RM, Hawkes FR, Hawkes DL (1997) Wat Res 31:163 Mackie RI, Bryant MP (1995) Appl Microbiol Biotechnol 43:346 Sørensen AH, Ahring BK (1993) Appl Microbiol Biotechnol 40:427 Nopharatana A, Clarke WP, Pullammanappallil PC, Silvey P, Chynoweth DP (1998) Biores Technol 64:169 Zheng D, Raskin L (2000) Microb Ecol 39:246 van Lier JB, Grolle KCF, Stams AJM, Conway de Macario E, Lettinga G (1992) Appl Microbiol Biotechnol 37:130 Ahring BK, Sandberg M, Angelidaki I (1995) Appl Microbiol Biotechnol 43:559 Angelidaki I, Ahring BK (1995) Antonie van Leeuwenhoek 68:285 Griffin ME, McMahon KD, Mackie RI, Raskin L (1998) Biotechnol Bioeng 57:342 Pohland FG, Ghosh S (1971) Environ Lett 1:255 Krugel S, Hamel K, Ahring BK (2002) WEF’s 16th Annual Residuals and Biosolids Management Conference. Austin, Texas, USA, March 3–6, 2002 Verstraete W, de Beer D, Pena M, Lettinga G, Lens P (1996) World J Microbiol Biotechnol 12:221 Verstraete W, Vandevivere P (1999) Critical Reviews Environ Sci Technol 28:151 Qureshi MA, Kharbanda VP (1983) J Scient Ind Res 42:597 Day DL, Chen TH, Anderson JC, Steinberg MP (1990) Biomass 21:83 Driessen W, Yspeert P (1999) Wat Sci Tech 40:221 Seghezzo L, Zeeman G, van Lier JB, Hamelers HVM, Lettinga G (1998) Biores Technol 65:175 Jetten MSM, Wagner M, Fuerst J, van Loosdrecht M, Kuenen G, Strous M (2001) Current Opinion in Biotechnology 12:283 Van Loosdrecht MCM, Jetten MSM (1998) Wat Sci Tech 38:1 Ahring BK, Angelidaki I, Johansen K (1992) Wat Sci Tech 25:311 Tafdrup, S (1992) Proceedings from Seventh International Symposium on Anaerobic digestion, 23–27 January, 1994, Cape Town, South Africa, p 460 Mæng H, Lund H, Hvelplund F (1999) Appl Energy 64:195 Dagnall S (1995) Biores Technol 52:275 Hammond G (1993) Biorecovery 2:141 Lusk P (1999) BioCycle 40:52 Rintala JA, Järvinen KT (1996) Waste Management Research 14:163 Rosenwinkel K-H, Meyer H (1999) Wat Sci Tech 40:101 Hartmann H, Møller HB, Ahring BK (2002) Proceedings from VII Latin American Workshop and Symposium on Anaerobic Digestion. Yucatán, México, October 2002 Clausen A (2001) PhD Thesis, Technical University of Denmark, Lyngby, Denmark Hartmann H, Angelidaki I, Ahring BK (2001) Proceedings from 9th World Congress of Anaerobic Digestion, Antwerpen – Belgium, Sept 2–6, 2001, p 301 Scherer PA, Vollmer G-R, Fakhouri T, Martensen S (2000) Wat Sci Tech 41:83 Angelidaki I, Ahring BK (2000) Wat Sci Tech 41:189 Hartmann H, Angelidaki I, Ahring BK (2000) Wat Sci Tech 41:145

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71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114.

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Kepp U, Machenbach I, Weisz N, Solheim OE (2000) Wat Sci Tech 42:89 Ahring BK, Jensen K, Nielsen P, Bjerre AB, Schmidt AS (1996) Biores Technol 58:107 Torry-Smith M, Sommer P, Ahring BK (2003) Biotechnol – Bio Wat Res submitted Greene H (1995) Anaerobic digester process enhancement by pulse power treatment. Scientific Utilization, Inc., Decatur, AL Møller HB, Sommer SG, Ahring BK (2002) Biores Technol submitted Kalyuzhnyi S, Fedorovich V, Nozhevnikova A (1998) Biores Technol 65:221 Kalyuzhnyi S, Sklyar V, Fedorovich V, Kovalev A, Nozhevnikova A, Klapwijk A (1999) Wat Sci Tech 40:223 Worley JW, Das KC (2000) Appl Eng Agri 16:555 Aoki N, Kawase M (1991) Wat Sci Tech 23:1147 Kübler H, Schertler C (1994) Wat Sci Tech 30:367 Angelidaki I, Ahring BK (1992) Appl Microbiol Biotechnol 37:808 Koster IW, Cramer A (1987) Appl Environ Microbiol 53:403 Alatriste-Mondragon F, Iranpour R, Ahring BK (2002) Wat Res in press Sandberg M, Ahring BK (1992) Appl Microbiol Biotechnol 36:800 Omil F, Mendez R, Lema JM (1996) Water SA 22:173 Athanassopoulos N, Kouinis J, Papadimitriou A, Koutinas AA (1989) Biological Wastes 30:53 Daoming S, Forster CF (1994) Environ Technol 15:287 Koster IW, Koomen E (1988) Appl Microbiol Biotechnol 28:500 Hansen KH, Angelidaki I, Ahring BK (1998) Wat Res 32:5 Speece RE. (1996) Anaerobic biotechnology for industrial wastewaters. Archae Press, Nashville, Tennessee, USA Hansen KH, Angelidaki I, Ahring BK (1999) Wat Res 33:1805 Boopathy R (1998) Biores Technol 64:1 Pind PF, Angelidaki I, Ahring BK (2002) Biotechnol Bioengineering submitted Mladenovska Z, Ishøy T, Mandiralioglu A, Westermann P, Ahring BK (2001) Proceedings from International conference Anaerobic Digestion 2001, 2–6 September 2001, Antwerpen, Belgium, p 183–188 Mladenovska Z, Ahring BK (2000) FEMS Microbiol Ecol 31:225 Bendixen HJ (1999) IEA Bioenergy Workshop. Hygienic and environmental aspects of anaerobic digestion: Legislation and experience in Europe, Stuttgart 29–31 March 1999, p 27 Mawdsley JL, Bardgett RD, Merry RJ, Pain BF, Theodorou MK (1995) Appl Soil Ecol 2:1 Pell AN (1997) J Dairy Sci 80:2673 Turner C, Burton CH (1997) Biores Technol 61:9 Aitken MD, Mullennix RW (1992) Wat Environ Res 64:915 Ponugoti PR, Dahab MF, Surampalli R (1997) Wat Environ Res 69:1195 Lund B, Jensen VF, Have P, Ahring B (1996) Antonie van Leeuwenhoek 69:25 Kunte DP, Yeole TY, Chiplonkar SA, Ranade DR (1998) J Appl Microbiol 84:138 Arridge H, Oragui JI, Pearson HW, Mara DD, Silva SA (1995) Wat Sci Tech 31:249 Carrington EG, Pike EB, Auty D, Morris R (1991) Wat Sci Tech 24:377 Watanabe H, Kitamura T, Ochi S, Ozaki M (1997) Wat Sci Tech 36:25 Nielsen B, Petersen G (2000) Wat Sci Tech 42:65 Duarte EA, Mendes B, Oliveira JS (1992) Wat Sci Tech 26:2169 Cheunbarn TP, Krishna R (2000) J Environ Engineering 126:796 Krugel S, Nemeth L, Peddie C (1998) Wat Sci Tech 38:409 Huyard A, Ferran B, Audic J-M (2000) Wat Sci Tech 42:41 Iranpour R, Shao YJ, Stenstrom M, Ahring BK (2002) Wat Environ Res in press Bendixen HJ (1995) Wat Sci Tech 30:171 Ahring BK, Lund B, Jungersen G, Have P, Frøkjær Jensen V (1995) Modelstudier vedrørende overlevelse af virus i gyllebaseret biomasse under udrådning i laboratorieskala biogasanlæg. Smitstofreduktion i biomasse. Danish Veterinary Service, Frederiksberg. Vol II: Rep. no. 10

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115. Regulation of the European Parliament and of the Council laying down health rules concerning animal by-products not intended for human consumption, 2001 116. Taylor DM (1998) Journal of Food Safety 18:265 117. Brown P, Gajdusek DC (1991) Lancet 337:269 118. Ejlertsson J, Alnervik M, Jonsson S, Svensson BH (1997) Environ Sci Tech 31:2761 119. Alatriste-Mondragon F, Ahring BK (2002) Wat Res submitted

Received: March 2002

CHAPTER 6

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria A.J.M. Stams 1 · S.J.W.H. Oude Elferink 2 · P. Westermann 3 1

2 3

Wageningen University and Research Centre, Laboratory of Microbiology, Hesselink van Suchtelenweg 4, 6703 CT Wageningen, The Netherlands. E-mail: [email protected] ID TNO Animal Nutrition, Edelhertweg 15, P.O. Box 65, 8200 AB, The Netherlands. E-mail: [email protected] Department of Environmental Microbiology and Biotechnology, The Technical University of Denmark, Building 227, 2800 Lyngby, Denmark. E-mail: [email protected]

Most types of anaerobic respiration are able to outcompete methanogenic consortia for common substrates if the respective electron acceptors are present in sufficient amounts. Furthermore, several products or intermediate compounds formed by anaerobic respiring bacteria are toxic to methanogenic consortia. Despite the potentially adverse effects, only few inorganic electron acceptors potentially utilizable for anaerobic respiration have been investigated with respect to negative interactions in anaerobic digesters. In this chapter we review competitive and inhibitory interactions between anaerobic respiring populations and methanogenic consortia in bioreactors. Due to the few studies in anaerobic digesters, many of our discussions are based upon studies of defined cultures or natural ecosystems. Keywords. Competition, Sulfate reduction, Denitrification, Acetogenesis, Inhibition

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

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Metabolic Interactions in Methanogenic Bioreactors

2.1 2.1.1 2.1.2 2.2

Competitive Interactions . . . Kinetic Competition . . . . . Thermodynamic Competition Inhibitory Interactions . . . .

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Competition in the Presence of Oxygen . . . . . . . . . . . . . . Competition Between Nitrogen Reducers and Methanogenic Consortia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Competition Between Manganese and Iron Reducers and Methanogenic Consortia . . . . . . . . . . . . . . . . . . . . . . 3.4 Competition Between Sulfate-Reducing and Acetogenic Bacteria and Methanogenic Consortia . . . . . . . . . . . . . . . . . . . 3.4.1 Competition for Hydrogen . . . . . . . . . . . . . . . . . . . . . 3.4.2 Competition for Acetate . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Competition for Methanol . . . . . . . . . . . . . . . . . . . . .

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3.4.4 Competition for Organic Acids and Ethanol . . . . . . . . . . . . . 46 3.4.5 Competition for Sulfate . . . . . . . . . . . . . . . . . . . . . . . . 48 3.5 Competition Between Sulfate-Reducers and Acetogens in the Absence of Sulfate . . . . . . . . . . . . . . . . . . . . . . . . 49 4

Inhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

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Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

6

References

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1 Introduction Very few environments exist in which only one population of microorganisms thrives or where populations of microorganisms do not affect each other either positively or negatively. As discussed in Chap. 1, anaerobic ecosystems such as methanogenic bioreactors are characteristic by their complex food-chains and the close symbiotic relationship between the different links in the chain, and are often exemplified as classical symbiotic ecosystems in which organisms consume the products of the preceding link in the chain, rather than consuming each other. The symbiosis between hydrogen-producing and hydrogenconsuming microorganisms is confined to a narrow range of hydrogen partial pressures outside which the reactions become thermodynamically unfavorable for one or the other part of the relationship. This can be caused by overloading with easily degradable compounds or by unintentional influence of inhibitory compounds. Compounds inhibiting methane production in a digester might exert their action either by direct inhibition of microbes in the anaerobic degradation chain or by stimulating microorganisms present in the digester to compete with methanogens or preceding links leading to reduced methane production and other unfavorable effects such as corrosion [1]. In this chapter we will discuss various types of direct and indirect competitive interactions between methanogenic consortia and anaerobic respiring bacteria in anaerobic bioreactors.

2 Metabolic Interactions in Methanogenic Bioreactors 2.1 Competitive Interactions

Competition between two or more populations of microorganisms is a negative relationship in which the different populations often are adversely affected with respect to their survival and growth. Also competition is considered the most important interaction among organisms, and is one of the major responsible causes of the selection pressure leading to the evolution of species.

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Fig. 1. Model of kinetic and thermodynamic competition among sulfate-reducing bacteria and methanogenic Archaea

The competitive interactions among anaerobic microorganisms can be roughly divided into kinetic competition and thermodynamic competition (Fig. 1). Kinetic competition refers to the determination of competitive capabilities by kinetic measurements of microbial growth, although the underlying mechanism for the observed effects might be thermodynamic. Thermodynamic competition means that one organism is capable of growing at and maintaining a substrate concentration below the minimum concentration for uptake (threshold concentration) of other organisms due to a higher energy yield in the conversion of the compound. In anaerobic fermentation of organic compounds, numerous pathways and combinations of pathways are used leading to different energy yields. However, since anaerobic fermentation is internally optimized in the cells to gain a maximum energy yield and an optimal redox balance [2, 3] the energetic outcome is often the same. This has the consequence that fermentative competitive interactions are mainly of kinetic character. Most of the studies which have examined competition between anaerobic fermenting bacteria have focused on gastrointestinal systems [4] and very little is known on this type of competitive interaction in anaerobic digestion processes. Apart from interactions between fermenting sulfate-reducing bacteria and acetogenic bacteria, we will not discuss this topic in this chapter.

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Table 1. The respiration hierarchy Acceptor

Product

E0¢ (V)

Oxygen O2 Manganic ion Mn4+ Ferric ion Fe3+ Nitrate NO3– Selenate SeO42– Arsenate AsO43– Sulfate SO42– Carbon dioxide CO2 Carbon dioxide CO2

Water H2O Manganous ion Mn2+ Ferrous ion Fe2+ Nitrogen N2 Selenite SeO32– Arsenite AsO33– Sulfide HS – Methane CH4 Acetate CH3COO –

+0.82 +0,80 +0.77 +0.76 +0.48 +0.14 –0.22 –0.24 –0.29

In contrast to aerobic conditions where most heterotrophic microorganisms utilize oxygen as a terminal electron acceptor and in most cases follow the same metabolic pathway ending in complete mineralization of the organic compounds into CO2 and H2O, the biochemical diversity of anaerobic microbial communities is huge. A large number of electron acceptors can be used by different anaerobic organisms in anaerobic respiration processes (Table 1). The most important inorganic electron acceptors are Mn4+, Fe3+, NO3– , SO42– and CO2 . The respiration processes where these acceptors are used are normally separated either in space or time. This is due to a different energy outcome of the processes according to the Gibbs equation: DG0¢ = –n · F · DE0¢ in which DG0¢ is the Gibbs free energy at pH = 7; n is the number of electrons transferred in the oxidation-reduction reaction; F is Faraday’s constant (96.490 kJ/V) and DE0¢ is the redox potential (E0¢) of the electron-accepting reaction minus the redox potential of the electron-donating reaction. From this equation it is obvious that the larger the difference is between the redox potentials of the half-reactions, the larger is the amount of energy available to the organism performing the reaction. The consequence is a hierarchy, which often resembles the order seen in Table 1. In most environments, some of the respiration processes do not occur, or only occur to a minor extent, due to the lack or exhaustion of available electron acceptors. The energy available to a respiring organism is not only dependent upon the difference in redox potential between electron donor and acceptor. Also concentrations of the reactants and temperatures deviating from standard conditions affect the energy outcome according to the Nernst equation DG = DG0 + RT · ln [B]/[A] in which DG0 is the change in Gibbs free energy under standard conditions, R is the gas constant, T is temperature and [B] and [A] are the concentrations of the two components of the reaction A ¤ B. According to the respiration hierarchy, sulfate reduction excludes methanogenic utilization of common substrates, which is verified in high-sulfate environments such as marine sediments [5]. However in, e.g., freshwater sediments, the two processes can coexist or even be dominated by methanogenesis due to equilibrium displacements caused by low sulfate concentrations making sulfate reduction thermodynamically less favorable than methane production [6].

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2.1.1 Kinetic Competition

This is the classical competitive interaction, the theory of which has been established in studies of defined cultures in chemostats [7, 8]. According to kinetically-based competition models, the outcome of interactions between two microorganisms competing for the same growth-limiting substrate can be predicted from the relationship between substrate concentration and the specific growth rate (µ) according to the Monod equation: µ = µmax ¥ S/Ks + S. Two typical relationships can be observed in studies of competitive interactions (Fig. 2 a, b). In Fig. 2a, organism I will grow faster than organism II at any substrate concentration, while the outcome in Fig. 2b is dependent upon the substrate concentration. The pattern seen in Fig. 2a is typical of organisms utilizing different electron acceptors with different energy yields for the oxidation of a common substrate, since the energy yield is higher for the electron acceptor with the highest redox potential at all electron donor concentrations. The pattern seen in Fig. 2b is typical for organisms utilizing the same metabolism but having different ecological strategies. In natural ecosystems, such as sediments, the concentration of nutrients needed to support growth is often very low. Among the organisms using the same type of metabolism under these conditions, type II in Fig. 2b having a high substrate affinity (low Ks) and a relatively low maximal growth rate (µmax) will normally dominate. This group is assigned to “K selec-

Growth rate

a

b

Substrate concentration Fig. 2. Growth rate as a function of substrate concentration in two different scenarios (a and

b). a represents two organisms with different energy metabolism, I having the highest energy yield. b represents two organisms with the same energy metabolism, but with different ecological strategies. I is assigned to “r” selection while II is assigned to “K” selection

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tion” which refers to organisms that can most effectively utilize the resources available [9]. In gastrointestinal environments and anaerobic bioreactors, opportunistic types of organisms (type I) will normally dominate, since type II has a longer doubling time than the retention time of the system. This group is assigned to “r selection” referring to a high potential r value (rate of population growth/individual) [9]. 2.1.2 Thermodynamic Competition

In natural environments, the substrate concentration for most organisms is normally well below Ks . For all organisms, there is a specific minimum concentration of substrate necessary to gain conservable energy. This minimal “quantum” of energy, which can be conserved, corresponds to the energy needed for translocation of 1 proton. The phosphorylation of ATP to ADP has a DG¢ of +49 kJ/mol corresponding to 60–70 kJ/mol when compensating for energy conservation efficiency [10]. Since 3 protons are needed in the phosphorylation of ADP to ATP, we can assume that the smallest amount of energy which can be conserved is 1/3 of the phosphorylation energy, corresponding to a minimum DG¢ of –20 kJ/mol. Inserting this value and DG¢0 for different respiration processes in the Nernst equation, the substrate concentration yielding the minimum amount of energy (the threshold concentration) can be calculated for each process under the prevailing conditions of the specific ecosystem. Several authors have shown that organisms utilizing electron acceptors with higher redox potentials can maintain electron donor concentrations below the threshold for uptake of organisms utilizing electron acceptors with lower redox potentials [11–13]. Other studies have shown that significant differences in threshold values for common substrates also can be found among species utilizing the same type of metabolism [14]. 2.2 Inhibitory Interactions

Several compounds, which serve as electron donors to respiring bacteria, might inhibit members of the methanogenic consortia. Also some products from anaerobic respiration might affect the activity of these consortia. The modes of action can be indirect by increasing the redox potential to levels that interfere with the biochemistry of the anaerobic microorganisms, or direct by chemical reaction with proteins or other cell constituents. It has been assumed that many anaerobic microorganisms have specific demands for low redox potentials in their environment to make their energy metabolism thermodynamically possible [15]. This conception has since been moderated and several reports have shown that the parameters controlling growth of most anaerobes is the oxygen concentration and only to a lesser degree the redox potential of the environment. This has been demonstrated in studies of fermentative rumen bacteria, but also in studies of microorganisms considered extremely sensitive to aerobic conditions [16]. Fetzer and Conrad

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37

[17] have, for instance, demonstrated that methane production in axenic cultures of Methanosarcina barkeri proceeded at normal rates in oxygen-free media where the redox potential was elevated to +420 mV. The direct inhibition of methanogenic consortia by electron acceptors is mediated by several mechanisms. Oxygen is toxic to all obligatory anaerobic microorganisms. Many anaerobes are rich in flavine enzymes, and may also contain quinones and iron-sulfur proteins, which can react spontaneously with oxygen to yield hydrogen peroxide, superoxide and hydroxyl radicals. Since most anaerobes lack peroxidase, catalase and superoxide dismutases, which destroy the reactive oxygen species, damage of essential cell components can occur upon oxygen exposure. Superoxide dismutase has, however, been demonstrated in some anaerobic microorganisms. Kirby et al. [18] have, for instance, characterized a superoxide dismutase from the obligatory anaerobe Methanobacterium bryantii. Other electron acceptors, such as oxidized nitrogen and sulfur species, have also been shown inhibitory to anaerobic microorganisms. Although the metabolism of these electron acceptors is competitive to anaerobes utilizing electron acceptors with a more negative redox potential, the reduction of the inhibitory compounds might lead to the production of less inhibitory compounds and, hence, relieve the inhibition. In some cases, however, the products of anaerobic respiration are more toxic than the parent compounds. This will be discussed in details in the next chapters.

3 Competition 3.1 Competition in the Presence of Oxygen

Although oxygen is the naturally occurring electron acceptor yielding the highest amount of energy leading to effective outcompetition of anaerobic microorganisms, oxygen respiration and anaerobic metabolism are mutually exclusive processes mainly due to the toxicity of oxygen, which can be observed in all aerobic environments. Most facultatively aerobic microorganisms capable of anaerobic respiration suppress these processes in favor of oxygen respiration when oxygen is present. Only environments in which rapid changes between oxic and anoxic conditions occur, such as alternating sludge treatment basins, favor constitutively anaerobic respiring bacteria [19]. In true oxic environments, anaerobic processes are normally only occurring in organic-rich micro- and macro-niches, where oxygen is depleted at a higher rate than it diffuses into the niche. Oxygen is normally excluded in anaerobic digestion processes, and only small amounts might enter the reactors together with, e.g., strongly aerated substrates [20]. Due to the low solubility of oxygen, this does normally not pose a problem to the anaerobic microorganisms in the digester and is rapidly scavenged by facultative bacteria. Kato et al. [21] demonstrated a high oxygen tolerance of methanogens in granular sludge due to mainly oxygen consumption by facultatively anaerobic bacteria metabolizing easily degradable substrates.

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3.2 Competition Between Nitrogen Reducers and Methanogenic Consortia

From an immediate evaluation of redox potentials of methanogenesis and nitrate reduction, it is obvious that nitrate reducers should outcompete methanogens due to the much higher energy yield of nitrate respiration. This has been verified in a few natural environments [22]. Under most circumstances, however, the effects of nitrogen oxides to anaerobic digestion are ambiguous and very complex, and to our knowledge no certain verification of competition in anaerobic digesters in which inhibition has been excluded has been published so far. Denitrification and methanogenesis are performed by microbial populations each requiring their distinct environmental conditions. Most true denitrifiers are facultatively anaerobic bacteria utilizing either oxygen respiration or denitrification as sole energy source. If none of these metabolisms are possible due to the lack of appropriate electron acceptors, the bacteria will probably not thrive in the digester. Instead, fermentative bacteria reduce oxidized nitrogen species for dissimilatory electron dissipation. The product is either nitrite or ammonia, and only the reduction of nitrate to nitrite is energy yielding. The further reduction to ammonia is considered non-energy yielding and hence without competitive value. Several authors have shown that high carbon to nitrogen ratios which are normally found in anaerobic digesters favor dissimilatory nitrate reduction to ammonia [23], while others [24] found that a high COD/NO3– did not favor dissimilatory reduction of nitrate to ammonia. The nature of the carbon source has also been shown to influence whether nitrate is reduced to ammonia or dinitrogen [25]. When glucose or glycerol was added as carbon source, 50% of the nitrate was reduced to ammonium, while 100% was denitrified completely in the presence of acetate or lactate. Several authors have demonstrated that denitrification and methanogenesis can proceed in the same reactor as long as the two processes are spatially separated. Hendriksen and Ahring [26] found that denitrification took place in the bottom of an upflow anaerobic sludge blanket reactor utilizing all available nitrate. Methanogenensis occurred in the uppermost part of the reactor, which was depleted from nitrogen oxides. In a mixed culture system of denitrifying and methanogenic sludge in a digester enriched with methanol, Chen and Lin [27] observed no competitive interactions between the two communities. Methanogenesis was, however, inhibited as long as nitrate or nitrite was present in the reactor. Percheron et al. [24] studied methanogenesis and nitrate reduction in an anaerobic digester fed with sulfate-rich wastewater. Sulfate reduction was inhibited by the presence of nitrate while methanogenesis proceeded until the onset of denitrification and production of nitrite after which it also was inhibited. Sulfide served as electron donor for some of the denitrifying bacteria. When sulfide was precipitated by ferrous iron, only dissimilatory nitrate reduction occurred with no nitrite production. This led to a stimulation of methanogenesis compared to a control digester, probably due to extensive acetate production by the dissimilatory nitrate reducers. No specific competitive interactions between methanogens and denitrifiers were verified in this study either. Clarens et al. [28] studied the effects of nitrogen oxides and denitrification on a

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39

pure culture of an aceticlastic methanogen (Methanosarcina mazei) and mixed cultures of M. mazei and a denitrifying bacterium (Pseudomonas stutzeri). It was demonstrated that the observed cessation of methanogenesis upon nitrate addition was a consequence of inhibition by denitrification products (NO2– , N2O) rather than competition by the denitrifying bacterium. The authors found that 50 mM NO3– inhibited methanogenesis from acetate by 65% while 0.18 mM NO2– and 0.32 mM N2O were almost completely inhibitory. In a study of the effects of nitrogen oxides on methanogenesis and other metabolic activities in an anoxic rice-field soil, Klüber and Conrad [22] tried to resolve inhibition and competition among nitrogen-respiring bacteria and methanogens. The addition of nitrate, nitrite, nitrous oxide and nitric oxide all resulted in an immediate arrest of methanogenesis until the nitrogen oxides were consumed. Methanogenesis then resumed at a similar or lower rate. None of the nitrogen oxides affected acetate concentrations negatively while nitrate, nitrite and nitrous oxide additions temporarily reduced hydrogen partial pressures to low exergonic or even endergonic values for methanogenesis. Since more than 70% of the methane produced was derived from acetate, Klüber and Conrad’s results indicate that toxicity rather than competition is responsible for the inhibition observed. When nitrate, nitrite or nitrous oxide were added, sulfate or/and ferric iron concentrations increased, probably as respiration products of sulfide and ferrous iron oxidation coupled to denitrification. The maintenance of low hydrogen partial pressures might, therefore, be due to the activity of iron or sulfate reducing bacteria rather than denitrifying bacteria. The mechanisms of nitrogen oxide inhibition of methanogenesis in anaerobic digesters can be considered far from solved, and is probably a complex mechanism composed of toxicity, competition, and indirect stimulation of other respiring bacteria by oxidation of reduced electron acceptors such as ferrous iron and sulfide. 3.3 Competition Between Manganese and Iron Reducers and Methanogenic Consortia

Both manganic ions [Mn(IV)] and ferric ions [Fe(III)] can act as potent electron acceptors in anaerobic respiratory processes carried out by a variety of microorganisms coupled to the oxidation of organic and inorganic compounds. Mn(IV) and Fe(III) can also be reduced in non-enzymatic chemical reactions under anaerobic conditions, and much of the effort in earlier studies of respiration of the two compounds was devoted to the separation of non-biological from biological reductions [29]. The isolation of numerous bacteria capable of Fe(III) and Mn(IV) reduction and properly designed experiments with environmental samples have, however, unambiguously verified this type of bacterial respiration. Several authors have shown that methanogenesis and other terminal anaerobic processes can be outcompeted by ferric- and manganic-reducing bacteria due to their maintenance of acetate concentrations and H2 partial pressures below the threshold of methanogenic Archaea and sulfate-reducing bacteria [12]. Although respiration with Mn(IV) or Fe(III) is thermodynamically more favorable than sulfate reduction or methanogenesis, several authors have shown

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that Mn(IV) and Fe(III) respiration is less efficient with crystalline than with amorphous forms of the two electron acceptors [30, 31]. Lovley and Phillips [12] showed that sulfate reduction and methanogenesis are only inhibited by Fe(III)reducing bacteria when Fe(III) is in an amorphous form. In a study of anaerobic respiration processes in flooded soils, Peters and Conrad [32] found that Mn(IV), Fe(III), and sulfate reduction proceeded simultaneously, possibly due to the crystalline structures of the Mn and Fe minerals in the soils. Since oxidation of short-chain fatty acids and H2 are the main electrondonating processes of both Fe(III) and Mn(IV) reduction, one could expect that these two electron acceptors could play a significant role in anaerobic digestion when present in high concentrations. Besides direct competitive interactions, Mn(IV) has been shown to act as an electron acceptor in the oxidation of elemental sulfur (S0) to sulfate catalyzed by sulfate-reducing bacteria [33]. This could lead to the stimulation of sulfate reduction upon exhaustion of Mn(IV). Very few investigations have, however, been carried out regarding the effects of Fe(III) and Mn(IV) on anaerobic digestion. One major reason for this could be the very low contents of iron and manganese normally found in wastewater. In average wastewater with a BOD of 290 g O2/m3, the typical iron and manganese concentrations have been estimated to 3.5 mg/g BOD and 0.35 mg/g BOD, respectively [34]. In a study of the effect of ferric chloride addition to anaerobic sludge digesters to precipitate struvite (MgNH4PO4 · 6 H2O), Mamais et al. [35] added FeCl3 at doses ranging from 0 to 20.5 mM Fe/L. A slight increase in gas production was observed upon FeCl3 addition, but no other effects were found. Further investigations are needed with respect to these two electron acceptors to clarify their actual and potential effects on different anaerobic digestion processes. 3.4 Competition Between Sulfate-Reducing and Acetogenic Bacteria and Methanogenic Consortia

In environments where sulfate is present, sulfate-reducing bacteria will compete with methanogenic consortia for common substrates. Direct competition will occur for substrates like hydrogen, acetate and methanol. Compared with methanogens, sulfate-reducing bacteria are much more versatile than methanogens. Compounds like propionate and butyrate, which require syntrophic consortia in methanogenic environments, are degraded directly by single species of sulfatereducing bacteria. The physiology of sulfate-reducing bacteria has been reviewed before by Widdel [36],Widdel and Hansen [37] and Colleran et al. [38], while the physiology of methanogenic consortia was reviewed by Stams [3], Schink [39] and Verstraete et al. [40]. Some key reactions in anaerobic environments are listed in Table 2. Kinetic properties of sulfate-reducers, methanogens, and acetogens can be used to predict the outcome of the competition for these common substrates [6, 41–44]. For bacteria growing in suspension, Monod kinetic parameters such as the half-saturation constant (Ks) and the specific growth rate (µmax) can be used. When bacterial growth is negligible, as is often the case in reactors with a dense

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Table 2. Acetogenic and methanogenic reactions, and sulfate-reducing reactions involved in the degradation of organic matter in methanogenic bioreactors, and sulfate-reducing bioreactors, respectively DG0¢ a [kJ/reaction]

Reaction Syntrophic Acetogenic reactions Æ Propionate– + 3 H2O Æ Butyrate– + 2 H2O Æ Lactate– + 2 H2O Æ Ethanol + H2O Æ Methanol + 2 H2O

Acetate– + HCO3– + H+ + 3 H2 2 Acetate– + H+ + 2 H2 Acetate– + HCO3– + H+ + 2 H2 Acetate– + H+ + 2 H2 HCO3– + H+ + 3 H2

+76.1 +48.3 –4.2 +9.6 +23.5

Methanogenic reactions 4 H2 + HCO3– + H+ Acetate– + H2O Methanol

Æ CH4 + 3 H2O Æ CH4 + HCO3– Æ 3/4 CH4 + 1/4 HCO3– + 1/4 H+ + 1/4 H2O

Sulfate-reducing reactions 4 H2 + SO42– + H+ Acetate– + SO42– Propionate– + 3/4 SO42– Butyrate– + 1/2 SO42– Lactate– + 1/2 SO42– Ethanol + 1/2 SO42– Methanol + 3/4 SO42– + 1/4 H+

Æ Æ Æ Æ Æ Æ Æ

HS– + 4 H2O 2 HCO3– + HS– Acetate– + HCO3– + 3/4 HS– + 1/4 H+ 2 Acetate– + 1/2 HS– + 1/2 H+ Acetate– + HCO3– + 1/2 HS– + 1/2 H+ Acetate– + 1/2 HS– + 1/2 H+ + H2O HCO3– + 3/4 HS–

–151.9 –47.6 –37.7 –27.8 –80.0 –66.4 –90.4

Homoacetogenic reactions Lactate– Ethanol + HCO3– Methanol + 1/2 HCO3– 4 H2 + 2 HCO3– + H+

Æ Æ Æ Æ

11/2 Acetate– + 1/2 H+ 11/2 Acetate– + H2O + 1/2 H+ 3/ Acetate– + H O 4 2 Acetate– + 4 H2O

–56.6 –42.6 –55.0 –104.6

a

–135.6 –31.0 – 78.2

DG0¢-values are taken from Thauer et al. (1977) [2].

biomass concentration, Michaelis-Menten kinetics may be used to predict which type of organism has the most appropriate enzyme systems to degrade substrates. Therefore, both the Vmax/Km and the µmax/Ks ratio gives an indication of the outcome of competition at low substrate concentrations [42]. 3.4.1 Competition for Hydrogen

In anaerobic environments methanogens, homoacetogens and sulfate-reducers will compete for hydrogen. Thermodynamically, homoacetogenesis is less favorable than methanogenesis and sulfate reduction. Homoacetogens are very poor hydrogen-utilizing organisms [13]. When grown on organic substrates like ethanol and lactate in the presence of hydrogenotrophic methanogens, they even produce hydrogen. In the absence of methanogens 1.5 acetate is produced per lactate or ethanol that is degraded. However, in the presence of methanogens only 1 acetate per lactate or ethanol is produced, while reducing equivalents are disposed of as hydrogen.

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Table 3. Selected growth kinetic data of hydrogenotrophic sulfate-reducing bacteria and methanogens. For references see Oude Elferink [81] and Oude Elferink et al. [149] Microorganism Sulfate reducers Desulfovibrio desulfuricans b vulgaris b Desulfovibrio G11 Desulfobacter hydrogenophilus Desulfobacterium autotrophicum Desulfobulbus propionicus b Desufomicrobium escambium Methanogens Methanobacterium bryantii formicicum b ivanovii Methanobrevibacter arboriphilus b smithii Methanococcus vannielii Methanospirillum hungatei strain BD Methanosarcina barkeri b mazei a b

Ks (µM)

µmax (1/day)

1.6–4.3 0.7–5.5 2.4–4.2 1.2–1.6 1.0 0.7–1.1 0.2–1.7 1.4

0.3–1.9 1.2–3.1 0.8–1.7 0.7–3.4 4.1 4.1 5.8–7.3 1.2–1.8 2.4–2.8 1.4–1.8 1.4–1.7

Yield a Km (g/mol H2) (µM)

1.9 0.6–3.1 1.4–2.0

0.6 0.9 1.1

1.8–4.0 1.3–4.0 1.1

88 30 65

2 14

0.6–1.3

6.6

0.3–0.5

5.0

1.6–2.2

Vmax (µmol/min · g)

13

70 110

The yield is given in gram cell dry weight per mol. Several strains.

Studies with sediments and sludge from bioreactors have indicated that at an excess of sulfate hydrogen is mainly consumed by sulfate reducers [6, 45–49]. In reactors with immobilized biomass the activity of hydrogenotrophic methanogens is completely suppressed within a few weeks when sulfate is added [50]. As hydrogenotrophic methanogens are still present in high numbers in such reactors, this effect cannot simply be explained by Michaelis-Menten or Monod kinetic data (Table 3). In methanogenic environments the hydrogen partial pressure is low. However, by addition of sulfate the hydrogen partial pressure may even become lower. The hydrogen partial pressure becomes so low that thermodynamically hydrogenotrophic methanogenesis is not possible any more (Fig. 1). In freshwater sediments a threshold hydrogen concentration of 1.1 Pa has been measured; this value was lowered to 0.2 Pa by the addition of sulfate [6]. An additional effect of the addition of sulfate is that hydrogen formation becomes less important. In the absence of sulfate, hydrogen has to be formed by acetogenic bacteria in the oxidation of compounds like lactate, alcohols, propionate and butyrate. However, in the presence of sulfate, all these compounds can be oxidized directly by sulfate-reducers without the intermediate formation

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria

43

of hydrogen. However, this explanation cannot be the only one because fermentative glucose- and amino acid-degrading bacteria will always form some hydrogen. Methanogens, which grow on H2/CO2 , are autotrophic [51].Among the hydrogen-utilizing sulfate-reducing bacteria both autotrophic and heterotrophic species have been isolated [37]. The classical Desulfovibrio species require acetate and carbon dioxide or another organic carbon source for growth whereas, e.g., Desulfobacterium sp. can use CO2 as the sole source of carbon [37, 52, 53]. An interesting observation has been made by Brysch et al. [54]. Enrichments in media with H2 and sulfate as energy substrates and carbon dioxide as the sole carbon substrate resulted in stable cultures of Desulfovibrio and Acetobacterium, in a cell ratio of about 20 to 1. The Desulfovibrio species required acetate for growth, which was provided by the homoacetogenic Acetobacterium species. Sulfate-reducing bacteria have a higher affinity for hydrogen than homoacetogens, but apparently the sulfate-reducers are dependent on the homoacetogens for synthesis of their carbon source acetate. It can be speculated that under these conditions the kinetic properties of homoacetogens determine the kinetic properties of the sulfate-reducers. In that case, methanogens would win the competition for hydrogen from the sulfate-reducers even at an excess of sulfate. Unfortunately, an experiment which could demonstrate this has never been performed. Van Houten et al. [55, 56] started up bioreactors at high hydrogen partial pressures with solely bicarbonate as carbon source. This led to the coexistence of sulfate-reducers and homoacetogens. 3.4.2 Competition for Acetate

It has been shown that in marine and freshwater sediments acetate is mainly consumed by sulfate-reducers when sufficient sulfate is present [45, 46, 49, 57]. However, for anaerobic digesters it is less clear how acetate is degraded. A complete conversion of acetate by methanogens, even at an excess of sulfate, has been reported [46–48, 50, 58–61]. However, in some studies a predominance of acetate-degrading sulfate-reducers was found [62–64]. Some factors which may affect the competition between sulfate-reducers and methanogens are discussed below. The work of Schönheit et al. [43] has indicated that the predominance of Desulfobacter postgatei in marine sediments could be explained by its higher affinity for acetate than Methanosarcina barkeri. The Km values were 0.2 and 3.0 mM, respectively (Table 4). However, in bioreactors Methanosarcina sp. are only present in high numbers when the reactors are operated at a high acetate concentration or operated at a low pH [65]. Generally, Methanosaeta (former Methanothrix, [66]) sp. are the most important aceticlastic methanogens in anaerobic bioreactors [65, 67–69]. Also in freshwater sediments Methanosaeta seems to be the most numerous acetoclastic methanogen [70]. Methanosaeta sp. have a higher affinity for acetate than Methanosarcina sp.; their Ks is about 0.4 mM [71]. In addition, D. postgatei and other Desulfobacter species are typical marine bacteria, which have not yet been isolated in freshwater media [72].

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Table 4. Selected growth kinetic data of acetotrophic sulfate-reducing bacteria and methanogenic bacteria. For references see Oude Elferink [81] and Oude Elferink et al. [149] Microorganism

Ks µmax (µM) (1/day)

Sulfate reducers Desulfobacter curvatus hydrogenophilus latus postgatei b Desulfotomaculum acetoxidans Desulforhabdus amnigenus Desulfobacca acetoxidans Methanogens Methanosarcina barkeri b mazei b Methanosaeta soehngenii b concilii a b

Yield a Km (g/mol ac.) (mM)

0.79 0.92 0.79 0.72–1.11 4.3–4.8 0.65–1.39 5.6 0.14–0.20 0.31–0.41

Vmax (µmol/min · g)

0.07–0.23

53

0.6 0.6

28 43

5.0

0.46–0.69 1.6–3.4 0.49–0.53 1.9

3.0

0.5

0.08–0.29 1.1–1.4 0.21–0.69 1.1–1.2

0.39–0.7 0.84–1.2

38 16

The yield is given in gram cell dry weight per mol. Several strains.

The aceticlastic sulfate-reducers that prefer freshwater conditions, such as Desulfoarculus baarsii [73], Desulfobacterium catecholicum [74], and Desulfococcus biacutus [75], show very poor growth with acetate. Only Desulfobacterium strain AcKo and Desulfotomaculum acetoxidans show good growth with acetate under mesophilic conditions (see Table 4). Unfortunately no Ks or Km values are available for these bacteria. Two abundant acetate-degrading sulfate-reducers, Desulforhabdus amnigenus and Desulfobacca acetoxidans, were isolated from sulfate-reducing bioreactors [76, 77]. The Michaelis-Menten parameters for D. amnigenus (KM = 0.2–1 mM, Vmax = 21–35 µmol · min–1 · g protein–1) and D. acetoxidans (KM = 0.1–1 mM, Vmax = 29–57 µmol · min–1 · g protein–1) were in the same range as or slightly better than those of most Methanosaeta species (KM = 0.4–1.2 mM, Vmax = 32–170 µmol · min–1 · g protein–1). This was also the case for the specific growth rate and the threshold value for acetate, which were 0.14–0.20 day –1 and <15 µM for D. amnigenus and 0.31–041 day–1 and <15 µM for D. acetoxidans. Reported values for Methanosaeta species are 0.08–0.69 day–1 and 7–69 µM, respectively. Putting all kinetic information together, it seems that the growth kinetic properties of acetate-degrading sulfate-reducers are only slightly better than those of Methanosaeta. When the growth kinetic properties of the sulfate-reducers are only slightly better than those of the methanogens it can be expected that the initial relative cell numbers affect the outcome of competition experiments. This is in particular the case for methanogenic sludge from bioreactors where a major part of the microbial biomass may consist of Methanosaeta. When methanogenic bioreac-

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45

tors are fed with sulfate, the few initial acetate-degrading sulfate-reducers have to compete with huge numbers of aceticlastic Methanosaeta species. In UASB reactors the sludge age can be as high as 0.5–1 year [78]. Visser et al. [79] have simulated the competition between sulfate-reducing bacteria and methanogens using a biomass retention time in the reactor of 0.02 day–1, a maximum specific growth rate of 0.055 and 0.07 day–1 for the methanogen and sulfate-reducing bacterium, respectively, a Ks value for acetate of 0.08 and 0.4 mM acetate, respectively, and different initial ratios of bacteria. Starting with a ratio of methanogens/sulfate reducers of 104, it will take already one year before the numbers of acetate-degrading sulfate-reducing bacteria and acetate-degrading methanogens are equal. Nevertheless, long-term UASB reactor experiments of Visser [65] showed that sulfate-reducers are able to outcompete methanogens for acetate, even if the seed sludge initially only contains low numbers of aceticlastic sulfate-reducers. In his acetate- and sulfate-fed UASB reactor it took 50 days before acetate degradation via sulfate reduction was observed, and another 50 days to increase it to 10%. The shift from 50 to 90% of acetate degradation via sulfate reduction took approximately 400 days. Methanosaeta can only grow on acetate, whereas Methanosarcina can use a few other substrates besides acetate, like hydrogen, methanol and methylated amines [71, 79]. Aceticlastic Desulfobacter sp. also use a limited range of substrates; solely hydrogen, acetate and ethanol provide good growth [72]. Desulfobacca acetoxidans is also a true specialist. It only showed growth on acetate [76]. However, Desulfotomaculum acetoxidans and Desulforhabdus amnigenus use a wide range of the common substrates for sulfate-reducers for growth [77, 80]. It is not clear to which extent these bacteria can grow mixotrophically. During growth on, e.g., butyrate or ethanol acetate is even excreted [80, 81]. However, if low concentrations of acetate and other substrates are used at the same time the outcome of the competition between Methanosaeta and these sulfate-reducers will be affected. Gottschal and Thingstad [82] described a model in which it is shown that during competition on mixtures of substrates in continuous cultures not only the specific growth rate determines the outcome of a competition, but also the yield on the different substrates. 3.4.3 Competition for Methanol

Methanol is an excellent substrate for mesophilic methanogens and homoacetogens. Methanosarcina species, Acetobacterium woodii, Eubacterium limosum and Butyribacterium methylotrophicum show very fast growth on methanol [83–88] (Table 5). The homoacetogens require externally supplied bicarbonate for growth, while the methanogens do not. Remarkably, only a very few mesophilic species of sulfate-reducing bacteria can grow on methanol [89–91]. The maximum specific growth rates of these sulfate-reducers are much lower than those of the methanogens and homoacetogens. This suggests that sulfate-reducers are poor competitors for methanol. The competition between methanogens and homoacetogens in bioreactors has been studied by Florencio [92] it appears that the Ks value of methanogens

46

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Table 5. Specific growth rates and growth yields (g dry weight · mol–1 ) of methanol-utilizing anaerobic bacteria. For references see Florencio [92], and Nanninga and Gottschal [90] Microorganism

µmax (1/day)

Yield (g/mol methanol)

Methanogens Methanosarcina barkeri strain MS strain 227 Methanosarcina mazei Methanosarcina acetivorans

2.35 1.85 3.24 3.20

3.5 3.8

Homoacetogens Acetobacterium woodii Eubacterium limosum Butyribacterium methylotrophicum

2.38 1.85

Sulfate reducers Desulfovibrio carbinolicum

0.22

5.3–8.2 7.1 8.2

for methanol is 0.25 mM, while that of the homoacetogens is much higher (16 mM). This indicates that at a low methanol concentration methanol is mainly used by methanogens. Only at a high methanol concentration, and additionally a high bicarbonate concentration, was a substantial part of the methanol consumed by homoacetogens. During growth on methanol methanogens and homoacetogens produce some hydrogen. The amount of hydrogen which is produced is affected by the presence of sulfate-reducers. This results in the coexistence of methanol-utilizing and hydrogen-utilizing anaerobes [84, 93–95]. Thus, it seems that in mixed communities growing on methanol there is an indirect competition between methanogens and sulfate-reducers as well. We have studied methanol conversion in mesophilic and thermophilic sulfatereducing bioreactors at high sulfate concentrations.At low temperature methanogenesis became the dominant process, indicating that methanol is mainly consumed by methanogens (Weijma, unpublished results). However, at a high temperature (65 °C) sulfate reduction became the dominant process [96]. Some thermophilic Desulfotomaculum species show excellent growth with methanol. 3.4.4 Competition for Organic Acids and Ethanol

In anaerobic environments with high sulfate concentrations, sulfate-reducing bacteria compete with acetogenic bacteria for substrates like lactate, ethanol, propionate and butyrate. Little is known about this competition. The fate of ethanol and lactate in anaerobic environments is not completely clear. A few methanogens are able to oxidize ethanol and other alcohols [97, 98]. In the presence of sulfate they can be oxidized by, e.g., Desulfovibrio species. However, lactate and ethanol (+CO2) can also be fermented by bacteria in a pro-

47

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria

pionic acid or homoacetogenic fermentation. In addition, lactate (+acetate) and ethanol (+acetate) can be fermented in a butyric acid fermentation by Clostridium kluyveri. Chemostat experiments have indicated that at low concentrations lactate and probably also ethanol are mainly consumed by sulfate-reducers. Desulfomicrobium outcompeted Veillonella and Acetobacterium at low acetate concentration. However, it appeared that the Veillonella sp. had a much higher specific growth rate than the sulfate-reducer, 0.30 and 0.17 h–1, respectively. Interestingly, sulfate-reducers are also able to ferment lactate and ethanol. Lactate and ethanol can be oxidized to acetate and hydrogen, provided that the hydrogen partial pressure is kept low by methanogens [99], while Desulfobulbus species are able to ferment lactate and ethanol in a propionic acid fermentation [37, 100, 101]. For wastewater with an excess of sulfate it is to be expected that sulfate-reducing bacteria become predominant over syntrophic fatty acid-degrading consortia, because of their better growth kinetic properties (Table 6). It is obvious that at high sulfate concentrations, sulfate-reducing bacteria grow much faster than the syntrophic consortia.Almost no Ks and Km values for propionate and butyrate Table 6. Specific growth rates (1/day) of sulfate-reducing bacteria and of acetogenic bacteria in co-cultures with hydrogenotrophic methanogens/sulfate reducers, growing on butyrate or propionate. For references see Oude Elferink [81] and Oude Elferink et al. [149] Sulfate-reducing culture Butyrate-degrading strains Desulfoarculus baarsii Desulfobacterium autotrophicum Desulfococcus multivorans Desulfotomaculum acetoxidans Desulfotomaculum strain Gro111 Syntrophomonas sapovorans Syntrophomonas wolfei Syntrophospora (Clostridium) bryantii sporeforming strain FMS2 sporeforming strain FSS7 non-sporeforming strain FM4 non-sporeforming strain B1 Propionate-degrading strains Desulfobulbus elongatus Desulfobulbus propionicus a Desulfococcus multivorans Syntrophobacter fumaroxidans Syntrophobacter pfennigii Syntrophobacter wolinii culture PT culture PW a

Several strains.

0.4 0.67–1.11 0.17–0.23 1.11 1.2–1.3

Syntrophic co-culture – sulfate + sulfate

27

0.6 0.2 0.25 0.31

0.3 0.34

0.24 0.1 1.39 0.89–2.64 0.17–0.23 0.02 0.07 0.06 0.23

0.15–0.17 0.07 0.02–0.10 0.1 0.14

0.18–0.21

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A.J.M. Stams et al.

degradation have been reported. Therefore, a comparison of the growth of syntrophic cultures and sulfate-reducers at low substrate concentrations is not possible. The existence of two subpopulations of propionate-oxidizers in methanogenic sludge was reported [102], a fast-growing one with a µmax of 1.2 day–1 and a Ks of 4.5 mM, and a slow-growing one with a higher affinity (µmax of 0.13 day–1 and a Ks of 0.15 mM). Several researchers investigated the competition for propionate and butyrate between sulfate-reducers and acetogens in anaerobic reactors and in sediment slurries. In most cases syntrophic consortia are easily outcompeted by sulfatereducers [48, 50, 60, 103]. However, in some of these studies no distinction can be made between a direct oxidation of propionate and butyrate by sulfatereducers and an indirect conversion whereby the fatty acids are oxidized to acetate and hydrogen by the acetogenic bacteria followed by hydrogen conversion via sulfate reduction. In this respect it is important to note that sulfatereducers keep the hydrogen partial pressure lower than methanogens, and that propionate- and butyrate-degrading acetogens grow much faster in coculture with hydrogen-consuming sulfate-reducers than with hydrogen-consuming methanogens [104, 105]. Therefore, the reported critical role of sulfatereducers in mediating propionate and butyrate degradation [48, 50, 106, 108] may be that of a hydrogen-consumer or that of a direct propionate or butyrateoxidizer. Findings of Harmsen [108] and Raskin et al. [109] seem to support the direct propionate oxidation by sulfate-reducers. The population dynamics of propionate-oxidizing bacteria in two UASB reactors, one fed with propionate and sulfate and the other with only propionate were studied. In the first reactor the number of Desulfobulbus sp. increased rapidly, and in the second reactor the number of syntrophic propionate oxidizers increased. It seems unlikely that Desulfobulbus acted as a hydrogen scavenger in the first reactor, although Desulfobulbus sp. are able to use H2 as well as propionate, because no syntrophic propionate-oxidizers were enriched in this reactor, and all Desulfobulbus cells were localized on the outside of the granule, not intertwined with other bacteria. Remarkably, Syntrophobacter species are also able to grow on propionate and sulfate [110–113]. The importance of the sulfate-dependent growth of these bacteria is not fully understood 3.4.5 Competition for Sulfate

At low sulfate concentrations the growth of the sulfate-reducing bacteria will be sulfate-limited.Also under conditions of high sulfate concentrations, sulfate limitation may occur due to mass transfer limitation of sulfate into the biofilm. Nielsen [114] reported that sulfate limitation could already occur in a biofilm of a few hundred µm thick when the sulfate concentration in the bulk solution was below 0.5 mM. Under sulfate-limiting conditions aceticlastic sulfate-reducers will have to compete with other sulfate-reducers for the available sulfate. Laanbroek et al. [115] experimented with three bacterial strains, Desulfobacter postgatei, Desul-

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria

49

fobulbus propionicus and Desulfomicrobium baculatum in sulfate-limited chemostats. They found that D. baculatum was the most successful competitor for limiting amounts of sulfate, followed by D. propionicus and then by D. postgatei. The Km for sulfate of D. postgatei is 200 µM [116], a value which is much higher than the reported Ks and Km values for several Desulfovibrio strains (5–77 µM) [117–119]. The affinities for sulfate of Desulfobacter strain AcKo, Desulfotomaculum acetoxidans, Desulforhabdus amnigenus and Desulfobacca acetoxidans are not known. However, if these species have a higher Ks value than other sulfate-reducers, one might speculate that limiting amounts of sulfate would result in an oxidation of compounds like hydrogen, formate and butyrate by sulfate-reducing bacteria, while acetate is used by the aceticlastic methanogens. Competition for sulfate between sulfate-reducing bacteria could explain the results obtained in studies with sulfate-limited reactors, where acetate seemed to be the least favored substrate for sulfate reduction, compared to propionate, butyrate and hydrogen [50, 107, 120] When hydrogen-utilizing sulfate-reducers have the highest affinity for sulfate this would indicate that under sulfate-limiting conditions fatty acids are oxidized in syntrophy with hydrogen-utilizing sulfate-reducers and not directly by Desulfobulbus species. 3.5 Competition Between Sulfate-Reducers and Acetogens in the Absence of Sulfate

The role of sulfate-reducing bacteria in the anaerobic digestion in the absence of sulfate has hardly been investigated. Yet, recent studies showed that sulfatereducing bacteria can be present in methanogenic sludge to upto 15% of the total biomass [109]. It is known that several types of sulfate-reducing bacteria have fermentative or syntrophic capacities. Widdel and Hansen [37] gave an overview of the fermentative and syntrophic growth of sulfate-reducing bacteria. Growth of sulfate-reducers in the absence of sulfate could explain the fast response of methanogenic ecosystems to the addition of sulfate. Some substrates which can be fermented by sulfate-reducers are pyruvate, lactate, ethanol, fumarate and malate, fructose, serine, choline, acetoin and S-1,2-propanediol and propanol + acetate. Sulfate-reducers can also grow as acetogens in the absence of sulfate. Desulfovibrio sp. oxidize ethanol or lactate to acetate when co-cultured with methanogens [99, 121–124]. It has been reported that Desulfovibrio sp. were the main lactate- and ethanol-degrading bacteria in a reactor treating whey in the absence of sulfate [125, 126]. However, others reported that only in the presence of sulfate were Desulfovibrio sp. the dominant lactate degraders, while in the absence of sulfate lactate was fermented according to the usual fermentation pattern of Propionibacterium [48]. Syntrophic formate degradation has been reported for Desulfovibrio vulgaris in association with Methanobacterium bryantii [127], and a Desulfovibrio-like organism could syntrophically degrade alcohols like 1,3-butanediol, 1,4-butanediol, 1-butanol and 1-propanol in the presence of 10 mM acetate and Methanospirillum hungatei [128].

50

A.J.M. Stams et al.

The role of sulfate-reducing bacteria in propionate degradation becomes more intricate by the work of Wu et al. [129, 130]. They were the first to report that the syntrophic conversion of propionate was mainly performed by sulfatereducing bacteria, and they were able to isolate such an organism. This indicates that in the absence of sulfate certain propionate-degrading sulfate-reducing bacteria are able to oxidize propionate in syntrophic association with H2-consuming anaerobes, while in the presence of sulfate they couple propionate oxidation to sulfate reduction. This represents a considerable ecological advantage of this type of sulfate-reducing bacteria over obligate syntrophic propionate-degraders in ecosystems where sulfate is continuously or intermittently available. Interestingly, as mentioned before, several Syntrophobacter species, including S. wolinii [111], S. pfennigii [112], S. fumaroxidans [110, 131], strain HP1.1 [113], were shown to grow on propionate with sulfate. For S. wolinii this finding was very remarkable because S. wolinii grows as an acetogen in the presence of Desulfovibrio G11 [104]. Phylogenetic research, based on 16S rRNA sequences, showed that Syntrophobacter species belong to the Gram-negative sulfate-reducers [108, 132]. Thus far, growth of sulfate-reducers on butyrate in the absence of sulfate but in the presence of methanogens was not yet demonstrated. However, Desulfovibrio sp. were detected in a fixed-bed reactor fed with butyrate without sulfate [133, 134].

4 Inhibition As discussed earlier in this chapter, several substrates and products of anaerobic respiration might have inhibitory effects on the methanogenic consortia in anaerobic digesters. Much of the decrease in methane production caused by intermediate nitrogen oxides of the denitrification process (NO2–, NO and N2O) is due to toxicity of these compounds rather than competition and unfavorable redox conditions. The inhibition mechanism of nitrate and its denitrification products is still largely unknown. The reduction of oxidized nitrogen species for dissimilatory electron dissipation by fermentative bacteria yields ammonia which numerous authors have demonstrated to be toxic to methanogenic consortia. Ammonia is mainly toxic in its un-ionized form (NH3) while the ammonium ion (NH 4+) is much less toxic, and toxicity is therefore dependent upon pH and temperature of the reactor. Fig. 3 shows the effect of temperature and pH on the percentage of total ammonium (NH 4+ + NH3) which appears as NH3 . It is obvious that increasing temperature and pH leads to increased NH3 concentrations in a reactor. If the sludge fed to the reactor simultaneously contains high amounts of proteinaceous material or/and pig manure, large amounts of ammonia are released from the fermentation of amino acids and other nitrogen-rich compounds [135]. Ammonia has been shown to mainly affect acetate-utilizing methanogenic Archaea, and to a lesser degree, hydrogen-utilizing methanogens and syntrophic bacteria [136]. A decrease in pH and an increase in the concentration of

NH3 + H+ ´ NH+4 ) were calculated from enthalpies of formation [150]. Equilibrium constants at the three temperatures were then calculated from Van’t Hoof ’s equation (ln K2/K1 = DH/R(1/T1 –1/T2). Finally, the undissociated fractions at different pH values (f) were calculated from the equation f = 100 ¥ (1+K/10–pH)–1. (---)25 °C; (–––) 37 °C; (––––) 60 °C

Fig. 3. The effect of pH and temperature on the dissociation of H2S and NH3 . DH of the two reactions (H2S ´ HS– + H+, and

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria

51

52

A.J.M. Stams et al.

volatile fatty acids observed in ammonia-inhibited reactors, however, point towards an inhibition of all terminal microorganisms of the anaerobic degradation chain [137]. In two studies on the effects of high ammonia concentrations (7 g NH 4+ – N/L) on methanogenesis from acetate, Blomgren et al. [138] and Schnürer et al. [139] demonstrated that aceticlastic methanogenesis was displaced in favor of syntrophic acetate oxidation in enriched and defined cultures growing with acetate as the only substrate. When the anaerobic processes are inhibited by ammonia, the decrease in pH will counteract the effect of ammonia due to a decrease in the free ammonia concentration. Since anaerobic reactors used in different ammonia toxicity studies have often been operated at different pH values, it is difficult to generalize about the inhibitory concentration as different concentrations of NH3 ammonia are present. In most reactor studies, however, inhibitory concentrations are in the range 1.7–5 g total ammonia-N/L, corresponding to 0.4–1 g NH3-ammonia/L [135, 140, 141]. Several authors have also shown that the biogas process can be adapted to ammonia concentrations above 4 g total ammonia/L without any reduction of the methane yield [135, 142, 143]. Sulfide produced by sulfate-reducing bacteria and by fermentation of sulfurcontaining amino acids has been shown to be inhibitory to the biogas process by several authors [144, 145]. Similar to ammonia, it is generally assumed that the neutral undissociated sulfide is the agent of toxicity since it is only membrane permeable in this form [146]. The pH is therefore also an important determinant of the toxicity, but contrary to ammonia, low pH values and low temperatures favor the undissociated sulfide (Fig. 3) . Much of the published literature on sulfide toxicity does not take pH into consideration, which makes general conclusions about toxicity levels difficult. Since sulfide readily reacts with most metals to form insoluble metal sulfides, the toxicity of sulfide is also related to metal concentrations in the sludge. However, several authors have found that sulfide inhibits the biogas process at concentrations around 50 mg/L [144, 147]. Sulfide and ammonia have been shown to inhibit methanogenesis in thermophilic anaerobic digesters synergistically. A sulfide concentration of only 23 mg/L led to an approximately 40% decrease of the methane production in a digester treating material with a high ammonium concentration [140]. From Fig. 3 it is obvious that optimal conditions for maintaining a low concentration of undissociated H2S and NH3 are occurring at lower pH values for thermophilic digesters than for mesophilic digesters.

5 Conclusion Compared to many other anaerobic environments, anaerobic digesters receiving municipal sludge or animal wastes are generally sparsely exposed to inorganic electron acceptors. Of the large amounts of easily degradable carbon only a tiny fraction is consumed by respiring bacteria. In digesters receiving industrial wastes, however, significant amounts of electron acceptors stimulating anaerobic respiration other than methanogenesis might occur and pose a problem as described in this chapter. In most natural environments, inorganic electron

Metabolic Interactions Between Methanogenic Consortia and Anaerobic Respiring Bacteria

53

acceptors and the corresponding respiration types are confined to distinct zones in a stratified system. A similar zonation can be established in sludge blanket reactors such as UASB reactors. In stirred reactors, however, the maintenance of gradients outside particles is difficult and probably only sporadically occurring; thus, in principle, several types of anaerobic respiration might proceed simultaneously given sufficient amounts of organic electron donors. The interactive pattern of electron acceptors, intermediate products and respiring microorganisms is therefore very complex under these conditions and only partly understood as discussed in this chapter. The role of electron acceptors such as ferric iron and manganese has only been very sparsely studied in anaerobic digesters. Also the role of newly described respiration systems such as humic acid respiration [148] awaits thorough investigation in anaerobic digesters.

6 References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33.

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75. Platen H, Temmes A, Schink B (1990) Arch Microbiol 154:355 76. Oude Elferink SJWH, Akkermans-van Vliet WM, Bogte JJ, Stams AJM (1999) Int J Syst Bacteriol 49:345 77. Oude Elferink SJWH, Maas RN, Harmsen HJM, Stams AJM (1995) Arch Microbiol 164:119 78. Hulshoff Pol LW (1989) The phenomenon of granulation of anaerobic sludge. PhD Thesis, Wageningen Agricultural University, Wageningen 79. Boone DR, Whitman WB (1988) Int J Syst Bacteriol 38:212 80. Widdel F, Pfennig N (1981) Arch Microbiol 129:395 81. Oude Elferink SJWH, Luppens SBI, Marcelis CLM, Stams AJM (1998) Appl Environ Microbiol 64:2301 82. Gottschal JC, Thingstad TF (1982) Biotechnol Bioeng 24:1403 83. Genthner BRS, Davis CL, Bryant MP (1981) Appl Environ Microbiol 42:12 84. Genthner BRS, Bryant MP (1987) Appl Environ Microbiol 53:471 85. Bache R, Pfennig N (1981) Arch Microbiol 130: 255 86. Hutten TJ, Bongaerts HCM, van der Drift C,Vogels GD (1980) Antonie van Leeuwenhoek 46:76 87. Lynd LH, Zeikus JG (1983) J Bacteriol 153:1425 88. Maestrojuán GM, Boone DR (1991) Int J Syst Bacteriol 41:267 89. Nanninga HJ, Gottschal JC (1986) FEMS Microbiol Ecol 38:125 90. Nanninga HJ, Gottschal JC (1987) Appl Environ Microbiol 53:802 91. Braun M, Stolp H (1985) Arch Microbiol 142:77 92. Florencio L (1994) The fate of methanol in anaerobic bioreactors. PhD thesis, Wageningen Agricultural University 93. Cord-Ruwisch R, Ollivier B (1986) Arch Microbiol 144:163 94. Heijthuijsen JHFG, Hansen TA (1986) FEMS Microbiol Lett 38:57 95. Phelps TJ, Conrad R, Zeikus JG (1985) Appl Environ Microbiol 50:589 96. Weijma J, Stams AJM, Hulshoff Pol LW, Lettinga G (2000) Biotech Bioeng 67:354 97. Widdel F, Rouviere PE, Wolfe RS (1988) Arch Microbiol 150:477 98. Zellner G, Bleicher K, Braun E, Kneifel H, Tindall BJ, Conway de Macario E, Winter J (1989) Arch Microbiol 151:1–9 99. Bryant MP, Campbell LL, Reddy CA, Crabill MR (1977) Appl Environ Microbiol 33:1162 100. Laanbroek HJ, Abee T, Voogd IL (1982) Arch Microbiol 133:178 101. Tasaki M, Kamagata Y, Nakamura K, Mikami E (1992) J Ferment Bioeng 73:329 102. Heyes RH, Hall RJ (1983) Appl Environ Microbiol 46:710 103. Scholten JCM, Stams AJM (1995) Antonie van Leeuwenhoek 68:309 104. Boone DR, Bryant MP (1980) Appl Environ Microbiol 40:626 105. McInerney MJ, Bryant MP, Hespell RB, Costerton JW (1981) Appl Environ Microbiol 41:1029 106. Harada H, Uemura S, Monomoi K (1994) Water Res 28:355 107. Mizuno O, Li YY, Noike T (1994) Water Sci Tech 30:45 108. Harmsen HJM (1996) Detection, phylogeny and population dynamics of syntrophic propionate-oxidizing bacteria in anaerobic granular sludge. PhD Thesis, Wageningen Agricultural University, Wageningen 109. Raskin L, Rittmann BE, Stahl DA (1996) Appl Environ Microbiol 62:3847 110. Kuijk van BLM, Stams AJM (1995) Antonie van Leeuwenhoek 68:293 111. Wallrabenstein C, Hauschild E, Schink B (1994) FEMS Microbiol Lett 123:249 112. Wallrabenstein C, Hauschild E, Schink B (1995) Arch Microbiol 164:346 113. Zellner G, Busmann A, Rainey FA, Diekmann H (1996) Syst Appl Microbiol 19:414 114. Nielsen PH (1987) Appl Environ Microbiol 53:27 115. Laanbroek HJ, Geerligs HJ, Sijtsma L,Veldkamp H (1984) Appl Environ Microbiol 47:329 116. Ingvorsen K, Zehnder AJB, Jørgensen BB (1984) Appl Environ Microbiol 47:403 117. Ingvorsen K, Jørgensen BB (1984) Arch Microbiol 139:61 118. Nethe-Jaenchen R, Thauer RK (1984) Arch Microbiol 137:236 119. Okabe S, Nielsen PH, Characklis WG (1992) Biotechnol Bioeng 40:725

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A.J.M. Stams et al.: Metabolic Interactions Between Methanogenic Consortia and … Uberoi V, Bhattacharya SK (1995) Water Eviron Res 67:330 McInerney MJ, Bryant MP (1981) Appl Environ Microbiol 41:346 Tatton MJ, Archer DB, Powell GE, Parker ML (1989) Appl Environ Microbiol 55:440 Traore AS, Fardeau M-L, Hatchikian CE, Le Gall J, Belaich J-P (1983) Appl Environ Microbiol 46:1152 Yadav VK, Archer DB (1988) Lett Appl Microbiol 7:165 Chartrain M, Zeikus JG (1986) Appl Environ Microbiol 51:188 Zellner G, Vogel P, Kneifel H, Winter J (1987) Appl Microbiol Biotechnol 27:306 Guyot J-P, Brauman A (1986) Appl Environ Microbiol 52:1436 Tanaka K (1992) J Ferment Bioeng 73:362 Wu W-M, Hickey RF, Zeikus JG (1991) Appl Environ Microbiol 57:3438 Wu W-M, Jain M, Conway de Macario E, Thiele JH, Zeikus JG (1992) Appl Microbiol Biotechnol 38:282 Harmsen HJM, van Kuijk BLM, Plugge CM, Akkermans ADL, de Vos WM, Stams AJM (1998) Int J Syst Bacteriol 48:1383 Harmsen HJM, Wullings B, Akkermans ADL, Ludwig W, Stams AJM (1993) Arch Microbiol 160:238 Zellner G, Geveke M, Conway de Macario E, Diekmann H (1991) Appl Microbiol Biotechnol 36:404 Zellner G, Geveke M, Diekmann H (1991) Biotechnol Lett 13:687 Angelidaki I, Ahring BK (1993) Appl Microbiol Biotechnol 38:560 Heinrichs DM, Poggi-Varaldo HM, Oleszkiewicz JA (1990) J Environ Engineering 116:698 Poggi-Varaldo HM., Rodriguez-Vazquez R, Fernandez-Villagomez G, Esparza-Garcia F (1997) Appl Microbiol Biotechnol 47:284 Blomgren A, Hansen A, Svensson B (1990) In: Belaich JP, Bruschi M, Garcia J-L (eds), Microbiology and biochemistry of strict anaerobes involved in interspecies hydrogen transfer. Plenum Press, New York, p 225 Schnürer A, Houwen FP, Svensson BH (1994) Arch Microbiol 162:70 Hansen KH, Angelidaki I, Ahring BK (1999) Wat Res 33:1805 Borja R, Sánchez E, Weiland P (1996) Process Biochem 31:477 Hashimoto AG (1986) Agricul Wastes 17:241 van Velsen AFM (1979) Wat Res 13:995 Karhadkar PP, Audic J-M, Faup GM, Khanna P (1987) Wat Res 21:1061 Hilton BL, Oleszkiewicz JA (1988) J Environ Eng 114:1377 O’Flaherty V, Mahony T, O’Kenndey R, Colleran E (1998) Process Biochem 33:555 Parkin GF, Speece RE, Yang CHJ, Kocher WM (1983) J Wat Pol Contr Fed 55:44 Lovley DR, Coates JD, Blunt-Harris EL, Phillips EJP, Woodward JC (1996) Nature 382:445 Oude Elferink SJWH, Visser A, Hulshoff Pol LW, Stams AJM (1994) FEMS Microbiol Rev 15:119 Stumm W, Morgan JL (1981) Aquatic chemistry. Wiley, New York

Received: January 2002

CHAPTER 6

Kinetics and Modeling of Anaerobic Digestion Process Hariklia N. Gavala 1 · Irini Angelidaki 2 · Birgitte K. Ahring 1 1

2

The Environmental Microbiology and Biotechnology Group (EMB), Biocentrum-DTU, bldg 227, The Technical University of Denmark, 2800 Lyngby, Denmark. E-mail: [email protected] Environment and Resources DTU, Bldg 115, The Technical University of Denmark, 2800 Lyngby, Denmark

Anaerobic digestion modeling started in the early 1970s when the need for design and efficient operation of anaerobic systems became evident.At that time not only was the knowledge about the complex process of anaerobic digestion inadequate but also there were computational limitations. Thus, the first models were very simple and consisted of a limited number of equations. During the past thirty years much research has been conducted on the peculiarities of the process and on the factors that influence it on the one hand while an enormous progress took place in computer science on the other. The combination of both parameters resulted in the development of more and more concise and complex models. In this chapter the most important models found in the literature are described starting from the simplest and oldest to the more recent and complex ones. Keywords. Anaerobic digestion, Kinetics, Modeling

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2.1 2.2 2.3 2.4 2.5

Microbial Growth Kinetics Hydrolysis of Biopolymers Acidogenesis . . . . . . . Acetogenesis . . . . . . . Methanogenesis . . . . .

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References

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Abbreviations ATP COD CSTR FBR LCFA NADH NAD+ VFA

adenosine 5-triphosphate chemical oxygen demand continuous stirred-tank reactor fluidized sand-bed reactor long-chain fatty acids reduced form of nicotinamide adenine dinucleotide oxidized form of nicotinamide adenine dinucleotide volatile fatty acids

1 Introduction Anaerobic digestion is one of the main processes used for sludge stabilization. Furthermore, anaerobic digestion is widely used for the treatment of manure, industrial wastewaters and the organic fraction of municipal solid waste. The microbiology of anaerobic digestion is complicated, since it involves several bacterial groups, each performing a separate task of the overall degradation process. So far, up to nine steps have been identified during the anaerobic conversion of organic matter. However, one can distinguish four main steps and three major bacterial groups (Fig. 1): the hydrolytic-fermentative bacteria that hydrolyze and convert the organic compounds to volatile fatty acids with the simultaneous production of hydrogen (H2) and carbon dioxide (CO2), the acetogenic bacteria that convert the above-mentioned acids to acetic acid and finally the methanogenic bacteria that produce methane, either from acetate or from H2 and CO2 . Anaerobic digestion has the advantages of producing small amounts of sludge, requiring less nutrients and energy than an aerobic treatment process whereas the generated biogas can be used as an energy source. Unfortunately, anaerobic systems can be unstable and this instability is usually caused by feed overload or by the presence of an inhibitor or even by inadequate temperature control. This is certainly a factor that limits the applicability of anaerobic digestion. Therefore, appropriate mathematical models need to be developed in order to overcome this problem and also to design and operate efficiently anaerobic systems. Several models have been developed during the last 35 years. In the first studies on anaerobic process modeling, special attention was paid to the description of the final stage of the anaerobic digestion, methanogenesis, which was considered also as the most important step of the overall process. These models were very simple and consisted of a limited number of equations. More complicated models describing two or even more bacterial groups and also including inhibition kinetics, pH calculations and gas-phase dynamics came later. Also much attention has been paid to the modeling of the anaerobic degradation of “synthetic substrates” such as glucose. On the other hand, and despite the difficulties, many successful attempts exist on the modeling of the anaerobic degradation of

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Fig. 1. Bioconversion of organic matter to methane during the anaerobic digestion process

real and complex wastewater. Nowadays, a large number of models can be found in the literature each of them having its own potential and worth. No unified modeling framework for the anaerobic digestion process exists so far. However, an international anaerobic modeling task group was established in Japan in 1997. This group has now formulated a common platform for the establishment of an anaerobic model. In the following sections, kinetics and existing models on anaerobic suspended growth systems are discussed. At first, general microbial growth kinetics is presented and a discussion on hydrolytic kinetics during anaerobic digestion process follows. Only some representative kinetics on acidogenesis, acetogenesis and methanogenesis are included in this chapter since some excellent reviews on this subject have already been published [1–3]. Recently, a review of some of the important models for the anaerobic digestion has been published as well [4]. In this chapter and in order to facilitate the study of the numerous models found in the literature, a classification has been attempted according to the primary key parameter used. Thus five major categories are distinguished: models considering (a) the non-ionized VFA inhibition, b) the total VFA inhibition, c) the different composition of the wastewaters, d) the H2 as regulator of the volatile fatty acids production and e) the un-ionized ammonia inhibition. Due to the complexity of the existing models it could be that some of the models described and especially the recent ones use more than one key parameter. As a

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rule of thumb the first two categories are mostly referring to older models that use only one key parameter and consider the organic content of a wastewater as a whole whereas the third category refers to models that gave much attention to the different composition of wastewaters. On the other hand, most of the models described in fourth and fifth category are more complex and consider more than one key parameter. Finally, a description of the common platform for an anaerobic model that has been established so far by the international anaerobic modeling task group is given.

2 Kinetics of Anaerobic Digestion 2.1 Microbial Growth Kinetics

Cell growth generally involves a respiratory (electron transport phosphorylation, [5]) or a fermentative (substrate-level phosphorylation, [5]) conversion of the substrate to products (catabolism) which releases energy in the form of adenosine 5-triphosphate (ATP). The energy obtained from the catabolic reactions is used for both the synthesis of new cells and the maintenance of old ones (anabolism). Catabolism Anabolism Metabolism

Substrate Æ Microbial products + Energy Substrate + Energy Æ Microorganisms Substrate Æ Microbial products + Microorganisms

In general, the metabolism of the microorganisms is coupled with the production of ATP. The ratio of the ATP mass produced per substrate mass consumed is defined as the ATP yield factor, YATP [6]. Accordingly, the biomass yield factor and the product yield factor are expressed as follows: Biomass yield factor: DX YX/S = 6 DS

(1)

Product yield factor: DP YP/S = 6 DS

(2)

where X, S, P symbolize the amounts of the biomass, the substrate and the product, respectively. Anaerobic degradation gives low biomass yield factors compared to aerobic; this is due to the low energy (ATP) yield of anaerobic metabolism. In particular, the anaerobic biomass yield factor usually lies between 0.05–0.2 g of biomass produced per g of substrate consumed whereas the aerobic one could be as high as 0.5 g of biomass produced per g of substrate consumed. The yield factors could be determined either experimentally or theoretically from the stoichiometry of the biochemical reactions.

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The bacterial growth is often described by a series of mathematical expressions according to the following Eq. (3) rX = µ (S, X) · X

(3)

where rX is the bacterial growth rate and µ symbolizes the specific growth rate of the microorganisms. Monod [7] suggested the following Eq. (4) for the specific growth rate: µmax · S µ = 03 KS + S

(4)

where µmax is the maximum specific growth rate achievable when S
KS and KS symbolizes the saturation constant, meaning the value of the limiting nutrient (substrate) concentration at which the specific growth rate is half its maximum value. By combining the Eqs. (3) and (4) the microbial growth rate is as follows: µmax · S rX = 03 · X KS + S

(5)

whereas the substrate consumption rate, rS , follows Eq. (6). 1 µmax · S rS = 7 · 03 · X YX/S KS + S

(6)

However, Monod’s equation is incapable of predicting the decrease of the biomass concentration that is due to the endogenous respiration and the cell lysis. McCarty [8] developed the following modified Monod equation taking into consideration the endogenous respiration and the cell lysis [Eq. (7)]: µmax · S rX = 03 · X – b · X KS + S

(7)

where b is the specific decay rate (or decay coefficient). In general, the decay coefficient lies around 5% of the maximum specific growth rate. Nevertheless, the methanogens have a relatively low decay coefficient (almost 1% of their maximum specific growth rate) and thus it can be ignored during modeling and simulations of the anaerobic process. Other expressions of the microbial growth rate as a function of the substrate concentration are presented in Table 1. The above expressions are incapable of describing the bacterial growth when an inhibitory factor is present. In anaerobic digestion many factors could inhibit the whole process and especially the methanogenesis step. Intermediate products such as volatile fatty acids and even compounds that are used as substrate could be inhibitory at high concentration. Also hydrogen sulfide (H2S), ammonia (NH3), chlorinated hydrocarbons, aromatic compounds, fatty acids and heavy metals among other compounds are either inhibitory or toxic – depending on their concentration [13]. The most common inhibition types used in anaerobic models are expressed according to Eqs. (8) and (9) and are those of

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Table 1. Expressions of the microbial growth rate as a function of the substrate concentration Equation

Specific growth rate, µ

Moser [9]

Sn µmax · 02 S n + KS

Contois [10]

um · S 04 B·x+S

Grau et al. [11]

µmax · S 02 S0

Chen and Hashimoto [12]

µmax · S 0208 K · S0 + (1 – K) · S

Haldane [14] first used by Andrews [15] and the non-competitive inhibition type, first introduced by Ierusalimsky [16], respectively. 1 µ = µmax · 001 KS I 5+5+1 S KI S KI µ = µmax · 01 · 0 KS + S KI + I

(8)

(9)

where KI is the inhibition constant and I symbolizes the concentration of the inhibitor. Haldane inhibition has been used by several researchers for describing the inhibition caused by either the un-ionized volatile fatty acids (butyrate, propionate, acetate) or the total volatile acids concentration. Other investigators have used the non-competitive inhibition type in order to describe the inhibition caused by either the volatile fatty acids or other toxic substances, e.g., ammonia. Dinopolou et al. [17] studied the inhibition of acidogenesis by volatile fatty acids and they concluded that the non-competitive type describes it better. Mösche and Jördening [18] studied the inhibition of acetate and propionate degradation by propionate (substrate inhibition) and the inhibition of propionate degradation by acetate (product inhibition). They concluded that the substrate inhibition is best described by the Haldane equation whereas the non-competitive type of inhibition describes the product inhibition best. 2.2 Hydrolysis of Biopolymers

Hydrolysis means both the solubilization of insoluble particulate matter and the biological decomposition of organic polymers to monomers or dimers, which can pass the cell membrane. Hydrolysis of organic polymers is usually carried out by extracellular enzymes (hydrolases) and it may or may not be the rate-lim-

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iting step of their bioconversion under anaerobic conditions. However, solubilization is not necessarily an enzymatic process catalyzed by biologically produced enzymes but could take place due to physicochemical reactions as well. It is very difficult to describe the whole process by reliable kinetics since hydrolysis of a complex, insoluble substrate depends on many different parameters such as the particle size, pH, production of enzymes, diffusion and adsorption of enzymes to particles. Hydrolysis of organic polymers is often described by a first-order kinetic model [Eq. (10)] as the enzymatic activity is not directly coupled to the bacterial growth [1, 2]. Nevertheless, it has been reported that a first-order function may be most appropriate for complex, heterogeneous substrates, while other hydrolysis functions may be more appropriate for single homogeneous substrates [19]. However, McCarty and Mosey [20] claimed that hydrolysis could be considered as a microbial process and, besides first-order kinetics, they suggested a “pseudo-Monod” equation with a high saturation constant (5000– 10000 mg/L). rS = Kh · S

(10)

where Kh is the hydrolytic constant. A really interesting approach is described in the study of Vavilin et al. [21] where hydrolysis of complex organic matter is considered as a two-phase process. The first phase is a bacterial colonization, during which the hydrolytic bacteria cover the surface of the solids and its rate depends on the contact area available. Hydrolytic enzymes degrade the solid surface at a constant depth per unit of time during the second phase.According to the aforementioned study the hydrolysis rate is given by Eq. (11). rS = Kh · SF1/3 · S2/3

(11)

where SF is the concentration of influent biodegradable organic matter. The hydrolytic constant Kh of the two-phase model is a function of the ratio between the characteristic sizes of bacteria and particles hydrolyzed according to Eq. (12). ÇB d Kh = 6rmS · 5 · 4 (12) ÇS dS where rmS is the maximum specific hydrolysis rate, ÇB and ÇS are the bacterial and particles densities, respectively, d denotes the depth of the bacterial layer and dS is the current diameter of particles. This approach may be reduced, in some cases, to the Contois kinetics [10] (Table 1) as it predicts exponential growth of the hydrolytic biomass at a high solids-to-biomass ratio and first-order kinetics at a low solids-to-biomass ratio. Information about the hydrolysis of the undissolved part of different wastewaters is presented in Table 2. In all studies first-order hydrolysis kinetics were assumed. However, in the study of Miron et al. [22] on hydrolysis of the components of primary sludge, it was reported that none of them followed first-order hydrolysis. They concluded that hydrolysis still remains the less defined step in the anaerobic digestion process. Furthermore, Schober et al. [23] concluded that

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Table 2. Kinetic constant (d–1) values for the hydrolysis of the undissolved part of different wastewaters Substrate

k h (d–1 )

Temperature (°C)

Reference

Mixture of primary and secondary sludge

0.077 0.150

25 35

Primary sludge (from a domestic wastewater treatment plant) Algae biomass Primary sludge (from a domestic wastewater treatment plant) Secondary sludge

0.007–0.990 35–60

calculations from [1] experimental results from [25] [26]

0.11–0.032 0.4–1.2

20 35

[27] [28]

0.168–0.6

35

[29]

zero-order kinetics describes better the hydrolysis in the acidogenic reactor during the two-stage anaerobic digestion of municipal solid organic wastes. On the other hand,Vavilin et al. [24] reported that Contois kinetics (Table 1) are preferable to the traditional first-order kinetics when considering the optimal design of a two-stage anaerobic digestion system. A wide range of hydrolysis rate constants concerning the hydrolysis of carbohydrates, proteins and lipids has been reported assuming first-order hydrolysis. Some representative values coming from different studies are presented in the following paragraphs. However, one should take into account that the substrate hydrolysis rate depends very much on the origin and the previous acclimation of the anaerobic culture [30–32]. The dependency of the hydrolytic constant on the previous acclimation of the anaerobic culture coming from the study of Gavala et al. [31] is presented in Table 3. Many studies on the hydrolysis of carbohydrates under anaerobic conditions have been made while much attention was paid to the hydrolysis of cellulose in the rumen [33–36]. The study of O’Rourke [28] on the hydrolysis of cellulose in a continuous, lab-scale reactor gives interesting information on the factors that influence the hydrolytic constant assuming first-order hydrolysis. The results of the aforementioned study are presented in Table 4. Proteins are hydrolyzed by extracellular enzymes, the proteases, into polypeptides and amino acids. It has been reported in the past that the protein hydrolysis is a slower process than the carbohydrate hydrolysis [37]. However, Table 3. Dependence of the hydrolytic constant on the previous acclimation of the anaerobic culture [31]. The substrate was a mixture of piggery, olive mill and dairy wastewater Inoculum

Digested piggery wastewater Digested olive-mill wastewater Digested dairy wastewater

Hydrolysis constant (d–1) Undissolved proteins

Undissolved carbohydrates

0.68 0.35 0.24

0.28 0.19 0.13

65

Kinetics and Modeling of Anaerobic Digestion Process

Table 4. Hydrolytic constants (d–1) for cellulose hydrolysis as a function of temperature and solids retention time [1, 28] Temperature (°C)

35 25 20 15

Solids retention time (days) 5

10

15

30

60

1.95 0.29 0.09 –

1.21 0.27 0.14 0.05

0.62 0.27 0.13 0.03

0.38 0.34 0.14 0.10

0.21 0.16 0.10 0.08

Table 5. Representative values of the hydrolytic constant, Kh , for different proteins hydrolysis Substrate

Kh (d–1)

Reference

Casein Gelatin Corn-protein

0.35 0.60 0.04

[38] [38] [39]

the hydrolysis rate depends very much on the solubility of the protein, the pH and the origin of the anaerobic culture. Some representative values of the hydrolytic constant, Kh , (assuming first-order hydrolysis) for the hydrolysis of casein, gelatin and corn-protein under anaerobic conditions, are presented in Table 5. The term “lipids” includes a heterogeneous group of biomolecules which are soluble in organic solvents of low polarity and not in water. The first step of lipids biodegradation under anaerobic conditions is their hydrolysis by a group of esterases, the lipases. For example, hydrolysis of one molecule of a phosphoglyceride results in one molecule each of glycerin and phosphoric acid and two molecules of fatty acids. In the literature, not much information exists on the anaerobic biodegradation of specific lipids. On the contrary, many studies exist on the hydrolysis of lipids when considering them as a homogeneous part of the organic load of a wastewater. The study of O’Rourke [28] on the hydrolysis of the lipid part of the primary sludge in a continuous, lab-scale reactor gives interesting information on the factors that influence the hydrolytic constant assuming first-order hydrolysis. The results are presented in Table 6. Christ et al. [40] studied the hydrolysis of carbohydrate, protein and lipid fractions in different organic waste. The ranges of the hydrolysis constant values are presented in Table 7. For comparison purposes, the results of the literature study of Gujer and Zehnder [1] on the first-order hydrolysis of complex biomolecules are presented in Table 7 as well.

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Table 6. Hydrolytic constants (d–1) for the hydrolysis of the lipid part of the primary sludge as a function of temperature and solids retention time [1, 28] Temperature (°C)

Solids retention time (days) 5

35 25 20 15

10

0.01 0 0 –

0.17 0.01 0 0

15 0.11 0.09 0.02 0

30 0.06 0.07 0.05 0

60 0.04 0.03 0.03 0

Table 7. Kinetic constants (d–1) for carbohydrate, protein and lipid hydrolysis Substrate

Carbohydrates Cellulose Proteins Lipids

Reference [40]

[1]

0.025–0.2 – 0.015–0.075 0.005–0.010

– 0.04–0.13 0.02–0.03 0.08–1.7

2.3 Acidogenesis

During the acidogenesis step the dissolved organic matter is biodegraded mainly to volatile fatty acids and alcohols by a heterogeneous microbial population. The dominant species in anaerobic digesters is the bacteria while small populations of protozoa, fungi and yeasts have been reported as well [41]. It is mainly the obligatory and facultative anaerobic bacteria that carry out fermentative conversion of the substrate to products. Much attention was paid to the acidogenesis of carbohydrates during the last decades and in almost all cases Monod kinetics was assumed. On the contrary, limited data exist on the kinetics of anaerobic degradation of amino acids despite the fact that the pathways of their anaerobic biodegradation and their corresponding products have been extensively studied. In Table 8 representative kinetics concerning the anaerobic biodegradation of glucose, cellulose and starch are reported. Many studies also exist on the production stoichiometry of the various products coming from carbohydrates and/or proteins metabolism. Most important factors that influence the production stoichiometry are the interspecies hydrogen transfer [42–45], the pH [46], the dilution rate [46, 47] and the previous acclimation of the anaerobic culture [32].

Substrate

glucose cellulose

glucose

glucose

glucose starch glucose various carbohydrates

Culture acclimated in

Primary sludge

Synthetic substrate coming from agricultural products

dextrose

Primary sludge

2.76 1.56 0.323 0.3–1.25

1.25

1.25

0.3 0.071

µmax (h–1)

0.14–0.17

0.162

0.27

0.15 0.16

YX/ S (mgVSS/mgCOD)

0.0706 0.591 0.494 24–672

0.0225

0.0225

0.4 0.0368

KS (g/L)

Table 8. Representative values of kinetic constants concerning the acidogenesis of carbohydrates

0.5

2.3 9.8

Doubling time (h)

[51] [52] [2]

37 37

[50]

[49]

[48]

Reference

35

37

37

36.5

Temperature (°C)

Kinetics and Modeling of Anaerobic Digestion Process

67

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2.4 Acetogenesis

In general, two different types of acetogenic mechanisms can be distinguished: (a) acetogenic hydrogenations and (b) acetogenic dehydrogenations.Acetogenic hydrogenations include the production of acetate as a sole end product either from fermentation of hexoses or from CO2 and H2 . Usually the step of acetogenesis in anaerobic digestion refers to acetogenic dehydrogenations and specifically to the anaerobic oxidation of long and short (volatile) chain fatty acids. Obligate proton-reducing or obligate hydrogen-producing bacteria carry out anaerobic oxidation of fatty acids. They are inhibited by even low hydrogen partial pressures and consequently they can survive only in syntrophic association with microorganisms that consume hydrogen such as the acetoclastic methanogens. Many studies have been performed so far on the anaerobic oxidation of long- and short-chain fatty acids. Table 9 includes representative kinetics concerning the anaerobic biodegradation of some long-chain fatty acids while kinetics concerning the bioconversion of propionate and butyrate to acetate are reported in Table 10. 2.5 Methanogenesis

A very limited number of organic compounds are used as carbon and energy sources supporting growth of methanogenic bacteria. So far, CO2 , CO, formic and acetic acid, methanol, methylamines and dimethyl sulfide have been identified as substrates for methanogenesis. Almost the 65–70% of the methane produced in the anaerobic digesters comes from acetate. On the other hand methanogenesis from CO2 and H2 has a significant role as well by keeping a low hydrogen pressure and thus supporting the growth of bacteria which carry out anaerobic oxidation of long- and short-chain fatty acids. Methanogenic bacteria are extremely sensitive to temperature, loading rate and pH fluctuations and they are inhibited by a number of compounds as has already been reported. Many studies exist so far on the isolation and kinetic characterization of specific methanogenic bacteria utilizing acetate and/or hydrogen. For the purposes of this chapter only representative kinetics of methanogenesis from mixed cultures are presented in Table 11.

3 Modeling of Anaerobic Digestion 3.1 Models Using Un-Ionized VFA Inhibition as the Primary Key Parameter

The first model that takes into consideration the inhibition of methanogenesis caused by the volatile fatty acids (VFA) is that of Graef and Andrews [63]. This study considers only one bacterial population, the acetoclastic methanogens. Assuming that all VFA can be represented and expressed in acetic acid units,

105 143 417 3180 1816

37

37

Saturated long-chain fatty acids myristic (C14) palmitic (C16) stearic (C18)

Unsaturated long-chain fatty acids oleic (C18) linoleic (C18)

4620 3720 2000

20 25 35

KS (mgCOD/l)

Products coming from hydrolysis of lipids found in primary sludge

Temperature (°C)

0.44 0.55

0.105 0.110 0.085

0.139 0.171 0.252

µmax (d–1)

0.11 0.11

(mean value) 0.11 0.11 0.11

0.04 0.04 0.04

Y (mgVSS/mgCOD)

Table 9. Representative values of kinetic constants concerning the anaerobic oxidation of long chain fatty acids [2]

0.01 0.01

0.01 0.01 0.01

0.015 0.015 0.015

b (d–1)

[53]

[53]

[28]

Reference

Kinetics and Modeling of Anaerobic Digestion Process

69

35 60 33

35

25 35 35 37 35

166 12 11

499

1144 78 13 298 17

KS

(mgCOD/l)

Temperature

(°C)

0.414 0.45 –

1.2

0.86 0.13

µ max (d–1 )

0.030 0.019 –

–

0.051 0.042 0.047 – –

Y (mgVSS/mgCOD)

b

0.099 – –

–

0.04 0.01 0.027 – –

(d–1)

Methanogenesis from

acetate acetate acetate acetate acetate acetate acetate acetate hydrogen

Culture acclimated in

Municipal wastewater Municipal wastewater Municipal wastewater Municipal wastewater Municipal wastewater Municipal wastewater Glucose Acetate Municipal wastewater

35 25 30 35 36.5 33 37 37 30

Temperature (°C) 0.050 0.054 0.041 0.28

YX/S (mgVSS/mgCOD)

930 356 165 5 20 198 49 0.07–0.109

KS (mgCOD/l)

11–69 mgCOD/l/h

0.25 0.275 0.357 0.49

µmax (d–1 )

[60] [54] [54] [54] [48] [59] [61] [61] [62]

Reference

[58] [59]

[57]

[55] [56]

[54]

Reference

Table 11. Representative values of kinetic constants concerning methanogenesis from acetate and hydrogen in anaerobic mixed cultures

Acetate: propionate: Butyrate = 2:1:1 Butyrate Propionate

mixed acids butyrate propionate

propionate propionate butyrate butyrate propionate (HRT: 14.5d) propionate (HRT: 8.2d)

Propionate Propionate Butyrate Glucose Propionate

Propionate

Kinetics for

Substrate

Table 10. Representative values of kinetic constants concerning the bioconversion of propionate and butyrate to acetate

70 H.N. Gavala et al.

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71

Graef and Andrews developed the following stoichiometry [Eq. (13)] for their conversion to methane. CH3COOH + 0.032NH3 Æ 0.032C5H7NO2 + 0.92CO2 + 0.92CH4 + 0.096H2O (13) where the empirical formula C5H7NO2 corresponds to the composition of methanogenic bacteria. The growth of biomass and the substrate consumption is assumed to follow inhibition kinetics according to Eq. (8). Both the substrate and the inhibitor are the un-ionized VFA (AcH) expressed as acetic acid. The concentration of the unionized form is calculated according to acetate dissociation reaction: AcH ¤ Ac– + H+

(14)

The model includes a total ion balance and takes into consideration three phases, gas, liquid and biological (solid) phase. Methane is considered to be water insoluble, whereas the carbon dioxide produced is partly dissolved and partly escapes to the gas phase. This model has been used to simulate digester start-up and response to organic overload and was able to predict digester failure due to a temporary accumulation of VFA, which lowers the pH and subsequently increases the concentration of un-ionized VFA. The introduction of a first-order equation describing the rate of microorganisms’ death in the model [Eq. (15)] gives us the possibility to predict digester failure due to toxic substances. rK = KT · TX

(15)

where rK is the rate of organisms death due to the toxic substance, KT is the toxicity rate constant and TX is the concentration of the toxic compound. Hill and Barth [64] developed a model describing the animal waste digestion (Fig. 2). Their model considers two microbial groups, the acid-formers and the acetoclastic methane-formers and it is using un-ionized VFA (expressed as acetic acid) inhibition of both microbial groups. Furthermore, it considers a hydrolytic step and ammonia (un-ionized) inhibition of methane-formers. This double inhibition has been incorporated into the growth kinetics of the methanogens according to the Eq. (16). µmax µ = 0000 (16) KS AcH NH3 1+8+8+8 AcH KI, 1 KI, 2 Kleinstreuer and Poweigha [65] and Marsili-Libelli and Nardini [66] published simulation studies of a digester receiving soluble and insoluble organic compounds, respectively. Their models are based on un-ionized VFA inhibition of methanogenesis and the basic steps considered are presented in Fig. 3. Moletta et al. [67] developed a model for the anaerobic digestion of glucose (Fig. 4). It considers two steps: glucose biodegradation to acetate and methanogenesis from acetate and it is based on un-ionized VFA inhibition of both acidogenesis and methanogenesis. The model simulated satisfactorily the methane production during batch experiments with pea bleaching wastewater and with a synthetic medium consisting of sucrose and acetic acid.

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Fig. 2. Block diagram of the Hill and Barth [64] mathematical model

Fig. 3. Block diagram of the Kleinstreuer and Poweigha [65] (a) and the Marsili-Libelli and

Nardini [66] (b) mathematical models

Fig. 4. Flow chart of the Moletta et al. [67] model

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73

Fig. 5. Flow chart of the Smith et al. [68] model

A model developed by Smith et al. [68] considers three steps (Fig. 5) assuming that the insoluble organic material used as feedstock (biomass) consists of two components, a rapidly and a slowly biodegradable one. In the first step, the two insoluble biomass components are converted to soluble intermediates that serve as substrate for volatile fatty acid-producing bacteria during the second step. Finally in the third step, the methanogenic bacteria convert volatile fatty acids to methane and carbon dioxide. Smith et al. used two inhibition types: a) the un-ionized volatile fatty acids inhibition of the methanogenesis step and b) the total volatile fatty acids inhibition of the acidogenesis step. However, the model has not been experimentally verified. Finally, Märkl [69] published a study concerning the quantitative analysis of the modern biogas reactor systems. This study was based on the model of Graef and Andrews [63] and especially on its physicochemical assumptions. 3.2 Models Using Total VFA Inhibition as the Primary Key Parameter

The first model considering total VFA inhibition was that of Hill [70]. This model was developed in order to simulate methanogenesis from animal wastes. The model considers five bacterial groups and four steps (Fig. 6). During the first step complex organic material enters the digester and is converted by extracellular enzymes to soluble, biodegradable organic matter. A set of “biodegradability constants” from another study [71] was used in this hydrolysis stage. During the second step (acidogenesis), the soluble organic matter is biodegraded mainly to butyrate, propionate and acetate. In the third step (acetogenesis) acetate is produced from butyrate and propionate whereas the fourth step (methanogenesis) refers to the methane production from acetate and hydrogen. All five bacterial groups catalyzing the three last steps are assumed to be inhibited by total VFA concentration. This inhibition is expressed both in the growth rate according to Eq. (8) and microbial decay rate according to Eq. (17), which was developed by Hill et al. [72]. bmax b = 04 Kb 1+8 VFA

(17)

where b is the specific decay rate [Eq. (7)], bmax is the maximum specific decay rate and VFA is the concentration of total VFA.

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Fig. 6. Flow chart of the Hill [70] model

Comparison of steady-state predictions with experimental data from sixteen pilot and full-scale biogas plants digesting animal wastes validated the model of Hill (1982). The same model was used for the design of parameters and operating characteristics of swine and poultry [73], beef cattle [74] and dairy cattle manure [75] anaerobic digestion systems. Additionally, Durand et al. [76] used the Hill’s (1982) model for predicting the steady state and dynamic performance of swine manure digesters and a satisfactory agreement between the experimental results and the theoretical predictions was noted. The model of Kalyuzhnyi [77] is the last one of a series of models [78–82] dealing with anaerobic digestion of glucose (Fig. 7). It consists of five steps and considers five bacterial groups: the acidogens that ferment glucose to butyrate, propionate, acetate and ethanol at an experimentally defined stoichiometry with the simultaneous production of hydrogen and carbon dioxide, the obligate proton reducers that convert butyrate and propionate to acetate, the ethanol degrading acetogens, the acetoclastic methanogens and finally the hydrogenotrophic methanogens. All steps are pH-dependent according to the pH function [Eq. (18)] of Angelidaki et al. [83]. Hydrogen inhibition of the acidogenic and both acetogenic steps, acetate inhibition of the butyrate-degrading step and ethanol and butyrate inhibition of both methanogenic steps is taken into con-

Kinetics and Modeling of Anaerobic Digestion Process

75

Fig. 7. Flow chart of the Kalyuzhnyi [77] model

sideration. Model validation has been made using batch kinetic experiments with glucose. 1 + 2 · 100.5(pKl–pKh) F (pH) = 00002 (18) 1 + 10(pH – pK h ) + 10(pKl – pH) where pKl and pKh denote the lower and upper pH values where the microbial growth rates are approximately 50% of the uninhibited rate. 3.3 Models Considering the Different Composition of Wastewater

The models described so far do not take the different compositions of wastewater into account. Specifically, models dealing with the anaerobic digestion of complex wastewaters, such as animal slurries, considered the organic content as a whole, which was hydrolyzed and degraded with an experimentally measured rate. This assumption on the one hand simplifies the models and makes their use easier, but on the other hand limits their applicability since such models are useful only for the anaerobic digestion of the specific wastewater they are developed for. The first model suggesting that the complex biodegradable particulate part of a wastewater is hydrolyzed to protein, carbohydrates and lipids and subsequently to amino acids, simple sugars and fatty acids respectively, is that of Bryers [84]. However, due to the lack of information, the particulate matter, proteins, carbohydrates and lipids are considered as a whole, while the amino acids and simple

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Fig. 8. Flow chart of the Bryers [84] model

sugars are lumped together (Fig. 8). Consequently, in the first step hydrolysis takes place resulting in amino acids, sugars and fatty acids while in the second and third steps intermediates such as propionate, butyrate and acetic acid are derived from amino acids/sugars and fatty acids acidogenesis. The fourth step corresponds to the acetogenesis while the fifth and sixth steps are the methanogenesis from acetate and hydrogen, respectively. The model predicted fairly well the observations in two different experimental systems that treated biomass particulates anaerobically after some parameter optimization concerning initial bacterial compositions. In 1996 a mathematical model for the codigestion of piggery, olive-mill and dairy wastewaters in continuous stirred tank reactors (CSTR) was developed by Gavala et al. [85]. It was assumed that wastewaters consist mainly of carbohydrates and proteins (undissolved and dissolved) and other dissolved organic matter (fatty acids and lipids are the major constituents of the latter). The model considers four steps and three bacterial groups, the acidogens, the acetogens and the acetolytic methanogens (Fig. 9). The model was based on batch kinetic experiments and is capable of predicting adequately the COD and fatty acids dependence on the operating conditions when an anaerobic culture acclimated to a specific wastewater starts being fed a mixture of other agroindustrial wastewaters [86]. Thus, the model can be useful for predicting the short-term response of a digester subjected to feed changes and thus avoiding system fail-

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77

Fig. 9. Flow chart of the Gavala et al. [85] model

ure, as well as for maximizing the co-digestion process efficiency. The changes of anaerobic culture’s biological characteristics during the acclimation process to each one of the three aforementioned wastewaters were determined as well [31]. It was found that a dairy acclimated culture was characterized by the higher total biomass percentage in acidogens, whereas methanogen and acetogens concentrations were higher in the piggery and olive-mill acclimated cultures, respectively. On the other hand, different bacterial species of acidogens had predominated in each culture. This suggests that the composition of the wastewater in different organic compounds (carbohydrates, proteins, lipids etc.) should definitely be taken into account when designing anaerobic digestion processes. In the study of Jeyaseelan [87] a model for anaerobic digestion of municipal wastewater was proposed considering its composition in carbohydrates, proteins, lipids and other organics. However, this study was a theoretical one since no experimental part is involved and the kinetic parameters used were collected from the available literature. Some other models consider the different composition of wastewater as well [88–92]. However, these models use different key parameters than the ones already reported and thus they are presented in the next two sections. 3.4 Models Using H2 as the Primary Key Parameter

The first model using H2 as the key parameter that regulates the production of fatty acids from glucose is that of Mosey [93]. The main topic of this study is the investigation and the expression of the effect that the dissolved hydrogen concentration has on the regulation of the redox potential inside the bacterial cell and subsequently on the produced mixture of volatile fatty acids. The model considers four bacterial groups: a) the fast-growing (minimum doubling times around 30 minutes) acid-forming bacteria that ferment glucose to a mixture of butyrate, propionate and acetate, b) the slow-growing (minimum doubling times of 1.5–4 days) acetogenic bacteria that convert the butyrate and propionate to acetate,c) the slow-growing (minimum doubling times of 2–3 days) acetoclastic methanogenic bacteria that produce methane from acetate and d)

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the relatively fast-growing (minimum doubling times around 6 hours) hydrogen-utilizing methanogens that produce methane from carbon dioxide. The key assumption made by Mosey is that the formation of butyrate, propionate and acetate from glucose is regulated by the availability of hydrogen. The main hypothesis of his study is that the relative availability of the reduced (NADH) and oxidized form (NAD+) of the carrier molecule nicotinamide adenine dinucleotide controls both the overall rate of the conversion and the composition of the mixture of acids formed. The ratio [NADH]/[NAD] is a function of the hydrogen partial pressure in the gas phase and it is expressed according to Eq. (19). [NADH] [NADH] –3 (19) 04 = 1500 · PH2 or 04 = 1.5 · 10 · H [NAD] [NAD] where PH2 is the partial pressure of hydrogen in the gas phase and H is the concentration of hydrogen (ppm by volume) in the digester gas. Eq. (19) applies under the following restrictions: a) the bacteria maintain a constant internal pH value of 7.0 regardless of the variations in the pH value of their growth medium and b) gaseous hydrogen diffuses both freely and rapidly into and out of the bacterial cells. Consequently, the partial pressure of hydrogen inside the bacteria is the same as the partial pressure of hydrogen in the digester gas and the redox potential of the bacteria is the same as the potential of the growth medium. The model considers hydrogen-regulated production of volatile fatty acids from glucose via the Embden-Meyerhof-Parnas metabolic pathway and also hydrogen regulated acetogenesis from butyrate and propionate. It includes yield equations for all the four bacterial groups that are involved in the overall process of glucose fermentation. The yield equations are based on the production of ATP at each step and on the assumption that one mole of ATP provides sufficient energy for the formation of about 10 g of biomass [94]. The metabolic pathways inside acid-forming bacteria are shown in Fig. 10. Steps and reaction rates are shown in Tables 12, 13 and 14. Mosey’s model combines in a very fine way biochemical and microbiological considerations. It is the first model that takes into consideration the varied stoichiometry of the produced fatty acids during the acidogenesis step of glucose. According to this model and during stress situations that usually result in to high H2 concentrations, acidogenesis is diverted to the production of propionate (and butyrate) which then are degraded slowly. This is consistent with the experimentally observed persistency of propionate concentrations during stress situations. Nevertheless, the Mosey model has not been experimentally verified. Thereafter, many models were based on the assumption that H2 is the key parameter that regulates the production of fatty acids from glucose. The study of Pullammanapallil et al. [95] is mainly based on Mosey’s model and additionally it includes a description of the gas phase and acetoclastic inhibition by undissociated fatty acids. Costello et al. [96] developed a model taking into consideration that lactic acid may also be an important intermediate in the anaerobic degradation of glucose. Their model consists of five bacterial groups (Fig. 11) and the main differences between this model and the model of Mosey [93] is that the glucose is con-

Kinetics and Modeling of Anaerobic Digestion Process

79

Fig. 10. Schematic biochemical pathways of the Mosey [93] model. The model of Pullam-

manapallil et al. [95] is based on this scheme as well

verted to a mixture of butyric, acetic and lactic acids with the subsequent degradation of the lactate to propionic and acetic acids. These two steps are inhibited and regulated by hydrogen through the redox reactions of NAD. The conversion of butyrate and propionate to acetate is subjected to hydrogen inhibition as well. pH inhibition functions were applied to each group of bacteria while product inhibition was taken into consideration for the acid-forming, lactic acid and acetogenic bacteria by using non-competitive and competitive inhibition terms. In the study of Costello et al. [97] an attempt at experimental verification of the aforementioned model was made by using independent sets of experimental data. In some cases the model was able to provide a reasonable comparison between the theoretical predictions and the experimental results; however, in most cases an under-estimation of the propionic and butyric acid concentrations as well as an over-estimation of the total biogas flow rate was observed. Keller et al. [98] and Romli et al. [99] used the model of Costello et al. [98] for the prediction and simulation of experimental results coming from a two-stage high-rate anaerobic wastewater treatment system fed with diluted molasses. This system consisted of a continuous stirred tank reactor (CSTR) as the acidi-

Where

RG : kG : KG : XG : [gluc]: [acet]:









d [but] RG 03 = 0460005 [NAD+] dt [NADH] 2 1 + 04 · 1 + 04 [NAD+] [NADH]

d [gluc] RG kG · XG · [gluc] 03 = 046 whereas RG = 004 dt [NADH] (KG + [gluc]) 1 + 04 + [NAD ]

Rate and cells yield equations












[prop]: [but]: Yacet ¢ : Yprop ¢ : Y but ¢ : b:

 concentration of propionic acid (mM). concentration of butyric acid (mM). biomass yield coefficient (20 mg/mM acetate). biomass yield coefficient (10 mg/mM propionate). biomass yield coefficient (20 mg/mM butyrate). decay coefficient (0.2/day).

dXG d [acet] d [prop] [but] ¢ · 77 + Y but ¢ · 72 – XG · b ¢ · 76 + Yprop 7 = Yacet dt dt dt dt




2RG d [acet] 2RG 021 = 046612 – 0460002 2 [NADH] [NAD+] dt [NADH] 1 + 04 1 + · 1 + 04 04 [NAD+] [NAD+] [NADH]




d [prop] 2RG 022 = 0460005 dt [NADH] [NAD+] 1 + 04 · 1 + 04 [NAD+] [NADH]

unregulated rate of uptake of glucose (mmoles/L/d). maximum rate constant (mmoles/mg/d). Michaelis-type constant (mM). concentration of glucose fermenters (mg/L). concentration of glucose (mM). concentration of acetic acid (mM).

C12H12O6 Æ H2O Æ 2CH3COOH + 2CO2 + 4H2 + 4ATP

C12H12O6 + 2H2 Æ 2CH3CH2COOH + 2H2O + 2ATP

C12H12O6 Æ CH3CH2CH2COOH + 2CO2 + 2H2 + 2ATP

Stoichiometric reactions

Table 12. Stoichiometric reactions, rate and cells yield equations for the acidogenesis step during glucose fermentation

80 H.N. Gavala et al.

Where

RB , RP : kB , kP : KB , KP : XB , XP : YB : YP : b:

d [prop] dXP 7 = YP · 04 – XP · b dt dt

kP · XP · [prop] d [prop] RP 77 = 00 whereas RP = 005 dt [NADH] (KP + [prop]) 1 + 761 [NAD+]

d [but] dXB 7 = YB · 02 – XB · b dt dt

d [but] RB kB · XB · [but] 02 = 029 whereas RB = 0228 dt [NADH] (KB + [but]) 1 + 041 [NAD+]

Rate and cells yield equations

unregulated rate of uptake of butyrate and propionate (mmoles/L/d). maximum rate constant for butyrate and propionate (mmoles/mg/d). Michaelis-type constant for butyrate and propionate (mM). concentration of butyrate and propionate utilizers (mg/L). biomass yield coefficient (20 mg/mM butyrate). biomass yield coefficient (10 mg/mM propionate). decay coefficient (0.2/day).

CH3CH2COOH + 2H2O Æ CH3COOH + CO2 + 3H2 + 1ATP

CH3CH2CH2COOH + 2H2O Æ 2CH3COOH + 2H2 + 2ATP

Stoichiometric reactions

Table 13. Stoichiometric reactions, rate and cells yield equations for the acetogenesis step during glucose fermentation

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Table 14. Stoichiometric reactions, rate and cells yield equations for the methanogenesis step during glucose fermentation Stoichiometric reactions

Rate and cells yield equations

CH3COOH Æ CH4 + CO2

d [acet] kA · XA · [acet] 03 = 004 dt (KA + [acet]) d [acet] dXA 7 = YA · 76 – XA · b dt dt

4H2 + CO2 + CH4 + 2H2O

d [H2] kH · XH · [H] 01 = 704 dt KH + [H] d [H2] dXH 8 = YH · 74 – XH · b dt dt

Where

kA , kH : maximum rate constant for acetate and hydrogen (mmoles/mg/d). Michaelis-type constant for acetate (mM). KA : Michaelis-type constant for hydrogen expressed as partial pressure KH: of hydrogen in the digester gas (atm). XA , XH : concentration of acetate and hydrogen utilizers (mg/L). biomass yield coefficient (2.5 mg/mM acetate). YA: biomass yield coefficient (2.5 mg/mM hydrogen). YH: b: decay coefficient (0.2/day). [H2]: concentration of hydrogen gas in the solution (mM). [H]: partial pressure of hydrogen in the gas (atm).

fication reactor and a fluidized sand-bed reactor (FBR) as the methanogenic reactor. They stated – although not giving details – that a physico-chemical reaction system was included in the model to calculate the pH at any time given the concentration of the acidic and basic species in the reactor. The model was pretty well capable to predict the pH, the alkali consumption rate at the acidification reactor, the gas generation and composition and the effluent concentration in organic acids when hydraulic step changes at various organic loading rates were taking place. On the contrary, theoretical predictions were not in good agreement with experimental results when a recycle stream from the second to the first reactor was introduced [98].Also the system responses in combination with the model predictions during pH changes and shock loads are described in Romli et al. [100, 101]. In the study of Ruzicka [102] an extension of Mosey model is proposed taking into account the fact that in some cases formation of higher acids than butyrate takes place during saccharide fermentation. Other modifications also are suggested in Ruzicka [103] and are based on hydrogen transport across the cell membrane as well as on reaction phenomena in the cell involving hydrogen and NAD. This interesting approach, mentioned by the author as a “nonequilibrium concept”, could explain the contradictory experimental observations how, on the one hand, small amounts of hydrogen could completely inhibit glucose cleavage and how glucose uptake could proceed even in a medium fully saturat-

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Fig. 11. Schematic biochemical pathways of the Costello et al. [96] model. The studies of Keller et al. and Romli et al. [98, 99, 101] are based on this scheme as well

ed with hydrogen, on the other. Unfortunately, no experimental validation of this model exists so far. The model of Batstone et al. [92] was developed in order to simulate the anaerobic degradation of complex wastewaters such as the slaughterhouse effluent. It is a complex model consisting of nine generic biological groups and three enzymatic groups (Fig. 12). In this study the catabolic reactions are as follows: the lipids, particulate proteins and carbohydrates are hydrolyzed by exo-enzymes to long-chain fatty acids and simple sugars, soluble proteins and carbohydrates, respectively. Acetic acid is coming from the further biodegradation of longchain fatty acids via b-oxidation whereas acidogenesis of proteins results in acetate, propionate, butyrate and valerate. The pathway of protein degradation is assumed to be via coupled Stickland reactions. The soluble carbohydrate degradation follows H2 regulation of VFA formation as suggested by Mosey [93] and the pathways are the same as used by Costello et al. [96], thus resulting in acetate, butyrate and lactate. Subsequently, the lactate is biodegraded to propionic and acetic acids. All the aforementioned volatile fatty acids are biodegraded to acetate and finally the acetoclastic and the H2-utilizing methanogens produce

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Fig. 12. Schematic biochemical pathways of the Batstone et al. [92] model

methane. The degradation rates of the substrates are subjected to hydrogen and pH inhibition. The model includes physico-chemical reactions determining the pH of the liquid phase and the gas-liquid transfer of carbon dioxide. Concentrations of saline and free organic components as well as ammonia and carbon dioxide are calculated using acid-base equilibrium relationships. The model was validated by performing experiments with a two-stage, high-rate anaerobic treatment plant that treated wastewater from a pig slaughterhouse [104]. 3.5 Models Using NH3 as the Primary Key Parameter

Ammonia is considered as a major inhibitor of the methanogenesis process especially when animal wastes are being digested. The inhibition is mainly caused by the un-ionized form of ammonia and thus it is very much dependent on the pH value. Detailed description of the ammonia chemistry, diffusion and release from liquid manure can be found in the review of Ni [105]. The first model using un-ionized ammonia inhibition is that of Hill and Barth [64] already described earlier in this chapter. During the last decade many models have been developed including ammonia inhibition as one of the key parameters and most of them focus on the anaerobic digestion of animal wastes. Kiely et al. [106] developed a model for anaerobic digestion of piggery slurry, primary sewage

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sludge and the organic fraction of municipal solid waste. The model was based on those of Hill and Barth [64] and Moletta [67] (Fig. 4). It consisted of two stages (hydrolysis/acidogenesis producing acetate and methanogenesis) and considers un-ionized VFA inhibition of both steps and un-ionized ammonia inhibition for the methanogenesis step [Eq. (16)]. The model was validated using experimental results from lab-scale continuous stirred-tank reactors and despite its simplicity the theoretical results fitted satisfactorily the experimental ones. Digesters fed with substrate with a high ammonia content, such as manure, exhibit a self-regulation of the pH and self-resistance on un-ionized ammonia toxicity that can be described as follows: when free ammonia concentration exceeds the toxicity threshold, inhibition of methanogenesis occurs which leads to an accumulation of VFA with a subsequent decrease of pH and thus reduction of the free ammonia concentration. This mechanism tends to stabilize the process at a certain volatile fatty acids concentration and pH level. The first model that mentioned this mechanism is that of Angelidaki et al. [83]. It is a complex model (Fig. 13) consisting of four microbial groups: the glucose fermenting acidogens, the propionate degrading acetogens, the butyrate degrading

Fig. 13. Flow chart of the Angelidaki et al. [83] model including pH and ammonia regulation

scheme

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acetogens and the acetoclastic methanogens. The primary substrates are soluble and insoluble carbohydrates whereas a fraction (almost 32%) of the total ammonia is bound to the insoluble fraction and is released during hydrolysis. The stoichiometry of the microbial reactions is based on the study of Hill [70] with minor modifications to some coefficients. Equilibrium relationships for ammonia, carbon dioxide and pH as well as gas phase dynamics and temperature effects are included. Total VFA inhibition of hydrolysis, total acetate inhibition of acetogenesis and free ammonia inhibition of methanogenesis is assumed in the model. The type of inhibition used is the non-competitive one [Eq. (9)]. For the last two steps, acetogenesis and methanogenesis, the effect of the pH on the microbial growth rate was described by a Michaelis pH function, normalized to give a value of 1.0 as center value [Eq. (18)]. The model was validated with thermophilic (55 °C) laboratory scale anaerobic digestion experiments with cattle manure where the feed concentration of ammonia was increased from 2.5 to 6.0 g-N/l. Experimental results and theoretical simulations showed on the one hand that the process was inhibited but stabilized at a lower level of methane production on the other. The above-mentioned model was extended in order to be able to simulate the anaerobic treatment of cattle manure with olive oil mill effluent [107]. Two more bacterial groups were added to the model: the lipolytic and the long-chain fatty acids-degrading bacteria (Fig. 14). Subsequently, the model was extended once more for simulating the anaerobic bioconversion of complex substrates to bio-

Fig. 14. Extension of the Angelidaki et al. [83] model in order to include lipid and protein

anaerobic biodegradation [91, 107]

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gas [91]. Hydrolysis of undissolved proteins and two more bacterial groups (the amino-acid- and valerate-degrading acidogens and acetogens, respectively) are also considered (Fig. 14). Specifically, the substrate in this model is defined by its organic and inorganic composition. The organic part consists of carbohydrates, proteins, lipids and volatile fatty acids. The inorganic part includes ammonia-N, phosphate-P, carbonate-C, hydrogen sulfide, anions and cations (i.e., Ca2+, Mg2+, and K+). The latter plays an important role in determining the pH and buffer balance of the process. Product inhibition of VFA degradation to acetate was further included along with inhibition of all the bacterial groups by the longchain fatty acids. The model was validated with thermophilic (55 °C) experiments in (a) laboratory-scale CSTRs receiving a mixture of cattle manure and glycerol trioleate or gelatin and (b) full-scale reactors receiving a mixture of cattle manure, bentonite-bound oil and a proteinaceous wastewater coming from bone extraction. Another model that considers the ammonia inhibition of acetate conversion to methane is that of Siegrist et al. [89, 90]. This model was developed in order to describe the dynamic behavior of the mesophilic anaerobic digestion of sewage sludge according to the reaction scheme shown in Fig. 15. Gujer and Zehnder [1] first developed this reaction scheme. The model consists of five microbial groups: the acidogens that ferment the amino acids and sugars to

Fig. 15. Reaction scheme for anaerobic digestion of domestic sewage sludge [1, 89, 90]

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Fig. 16. Schematic biochemical pathways of the Vavilin et al. [88] model

butyrate, propionate and acetate, the microorganisms that perform the b-oxidation of fatty acids to hydrogen and acetate, the acetogens that produce acetate from butyrate and propionate and finally the acetogenic and hydrogen-utilizing methanogens. The model includes equilibrium relationships for ammonia, carbon dioxide and pH and gas phase dynamics as well. The propionate and acetate degradation and hydrogen consumption are subjected to pH dependence. Additionally, propionate and fatty acid degradation have non-competitive inhibition terms for increased hydrogen pressure and acetate concentration whereas for acetate conversion a non-competitive inhibition term for free ammonia is included. The model is verified with load variation experiments in laboratory and full-scale digesters. Vavilin et al. [88] developed a model named “methane” in order to describe the anaerobic digestion of complex organic matter. According to Fig. 16, the model considers hydrolysis of the biodegradable organic matter to dissolved organic substrate by hydrolytic enzymes released by acidogenic bacteria. Acetate- and propionate-producing bacteria with the simultaneous release of hydrogen, ammonia and carbon dioxide consume the dissolved organic substrate, which is a mixture of carbohydrates, proteins and lipids. Propionate is biodegraded to acetate, which is used for the production of methane along with

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the hydrogen. Simultaneously, the sulfate-reducing bacteria, resulting in the release of hydrogen sulfide, use the propionic and acetic acids. The inhibition effect of non-ionized molecules of ammonia, sulfide and propionate was taken into consideration as well. The innovation brought by this model is the consideration of two more groups of bacteria than the ones already reported in this chapter: the bacteria that perform sulfate reduction coupled with the utilization of acetate and propionate, respectively. Also, the aforementioned model takes into account that the metabolism of the carbohydrates, proteins and lipids is regulated by the hydrogen partial pressure and thus the latter controls the relative production of propionate and acetate. This is accomplished by the consideration of two different groups of acidogens: the propionate- and the acetate-producers. The model “methane” was used successfully for the simulation of the results of batch experiments on anaerobic digestion of food industry wastewater. In 1996, the same scientists developed a theory describing the hydrolysis as a twophase process. The model “methane” was used to compare the results of four types of hydrolytic kinetics during anaerobic digestion of swine waste, sewage sludge, cattle manure and cellulose. Experimental results from different studies were used for this purpose [21]. 3.6 Recent Developments on Anaerobic Digestion Modeling: Anaerobic Modeling Task Group Work Presentation

An international anaerobic modeling task group was established in Japan in 1997 in order to formulate a common platform for the establishment of an anaerobic model (http://www.awmc.uq.edu.au/admtg/tg). This task group has now formulated an anaerobic model and the key assumptions of this generic model are presented below. Conversion processes taken into account are biological and physico-chemical ones and the carbon flow chart is presented in Fig. 17. Acidogenesis, acetogenesis and methanogenesis are the three main biological steps whereas the degradation of complex particulate matter is considered as a combination of an extracellular, partly non-biological disintegration step and a extracellular biological hydrolysis step. All extracellular steps follow first-order kinetics [Eq. (10)] and substrate uptake and biomass growth and decay proceed according to Eqs. (6) and (7), respectively. The stoichiometric yields of volatile fatty acids during the acidogenesis step of carbohydrates are considered to be constant with no hydrogen regulation function suggested so far. Additionally, Stickland pathways are considered for the estimation of products yield from amino acids. pH influence on all biological processes is included according to the empirical function proposed by Angelidaki et al. [Eq. (18)]. Hydrogen inhibition of acetogenesis from long-chain fatty acids (LCFA), propionate, butyrate and valerate and for hydrogenotrophic methanogenesis is proposed along with non-competitive free ammonia inhibition of acetolytic and hydrogenotrophic methanogenesis. Physico-chemical processes such as liquid-liquid transformations (ion association/dissociation) and gas-liquid transfers of CO2, CH4 and H2 are included in the model since they are considered very important for anaero-

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Fig. 17. Flow chart of the generic anaerobic model as suggested by the international anaerobic modeling task group

bic systems. The IWA Anaerobic Digestion Model No 1 was presented during the 9th World Congress on Anaerobic Digestion [108]. However, discussions about the final version of the model are still under way.

4 Conclusions A systematic classification of the mathematical models describing the anaerobic digestion process in suspended growth systems was made in this chapter. This effort focused on the overview of the most important models found in the literature so that the reader can acquire the knowledge of how the mathematical modeling of the anaerobic digestion developed throughout the years. It is a wellknown fact that anaerobic digestion is a complex process affected and regulated by many factors. Also, anaerobic digestion is applied to the treatment of a wide

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range of wastes/wastewater with significant differences in their composition. In order to have appropriate mathematical models that combine simplicity and accuracy and are applicable to almost every type of waste/wastewater, an integrated approach should focus on the a) formulation of a common platform, which will take into account all the important parameters and factors influencing the process, b) classification of the wastes/wastewater based on their different composition in organic and inorganic compounds and c) formulation of a model for each category selecting each time the key elements from the common platform, e.g., NH3 effect should be taken into account in case of wastewater with high concentration of NH3 or proteins; on the other hand the introduction of the NH3 effect into a mathematical model concerning anaerobic digestion of dairy wastewater will only add complexity with no practical advantage. In this way, it will be possible to fully exploit the information and knowledge gained through decades of scientific research on the area of anaerobic digestion process.

5 References 1. Gujer W, Zehnder AJB (1983) Water Science and Technology 15:127 2. Pavlostathis SG, Giraldo-Gomez E (1991) Water Science and Technology 24:35 3. Pavlostathis SG, Giraldo-Gomez E (1991) Critical Reviews in Environmental Control 21:411 4. Lyberatos G, Skiadas IV (1999) Global Nest: the International Journal 1:63 5. Gottschalk G (1986) Nutrition of bacteria. In: Bacterial metabolism. Springer, Berlin, Heidelberg, New York 6. Gottschalk G (1986) Bacterial fermentations. In: Bacterial metabolism. Springer, Berlin, Heidelberg, New York 7. Monod J (1949) Ann Rev Microbiol 3:371 8. McCarty PL (1966) Kinetics of waste assimilation in anaerobic treatment. In: Developments in Industrial Microbiology. American Institute of Biological Sciences, Washington DC 9. Moser H (1958) Carnegie Institute Washington Publ. No. 614: 10. Contois DE (1959) Journal of General Microbiology 21:40 11. Grau P, Dohanyos M, Chubota J (1975) Water Res 9:637 12. Chen YR, Hashimoto AG (1978) Biotechnology And Bioengineering Symp 8:269 13. Bitton G (1994) Anaerobic digestion of wastewater and sludge. In: Wastewater microbiology. Wiley-Liss, New York 14. Haldane JBS (1930) Enzymes. Longmans, London 15. Andrews JF (1969) J Sanit Engng Div Am Soc Civ Engrs SA1:95 16. Ierusalimsky ND (1967) Bottle-necks in metabolism as growth rate controlling factors. In: Powell EO, Evans CGT, Strange RE, Tempest DW (eds), Microbial Physiology and Continuous Culture, 3rd International Symposium. Her Majesty’s Stationery Office, London 17. Dinopoulou G, Sterritt RM, Lester JN (1988) Biotechnol Bioeng 31:969 18. Mosche M, Jordening HJ (1999) Water Res 33:2545 19. Eastman JA, Ferguson JF (1981) J WPCF 53:352 20. McCarty PL, Mosey FE (1991) Water Science and Technology 24:17 21. Vavilin VA, Rytov SV, Lokshina LYa (1996) Bioresource Technol 56:229 22. Miron Y, Zeeman G, van Lier JB, Lettinga G (2000) Water Res 34:1705 23. Schober G, Schaefer J, Schmid-Staiger U, Troesch W (1999) Water Res 33:854 24. Vavilin VA, Rytov SV, Lokshina LYa, Rintala JA, Lyberatos G (2001) Water Res 35:4247 25. Pfeffer JT (1968) J Water Pollution Control Federation 40:1933

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H.N. Gavala et al. Pfeffer JT (1974) BioTechnol and Bioengineering 26:771 Foree EG, McCarty PL (1969) Proc 24th Ind Waste Conf, Purdue University 13 O’Rourke JT (1968) PhD thesis, Stanford University, California Ghosh S (1981) BioTechnol And Bioengineering Symp 11:301 Doyle O, O’Malley J, Clausen E, Gaddy J (1983) Kinetic improvements in the production of methane from cellulosic residues. In: Energy from biomass and wastes. 7:546 Gavala HN, Skiadas IV, Lyberatos G (1999) Water Science and Technol 40:339 Gavala HN, Lyberatos G (2001) BioTechnol and Bioengineering 74:181 Colberg PJ (1988) Anaerobic microbial degradation of cellulose, lignin, oligolignols and monoaromatic lignin derivatives. In: Zehnder AJB (ed), Biology of Anaerobic Microorganisms. Wiley, New York Stack RJ, Cotta MA (1986) Applied and Environmental Microbiology 52:209 Pavlostathis SG, Miller TL, Wolin MJ (1988) Applied and Environmental Microbiology 54:2566 Pavlostathis SG, Miller TL, Wolin MJ (1988) Applied and Environmental Microbiology 54:2660 Heukelekian H (1958) Basic principles of sludge digestion. In: McCabe J, Eckenfelder WW (eds), Biological Treatment of Sewage and Industrial Wastes. Reinhold, New York Nagase M, Matsuo T (1982) BioTechnol and Bioengineering 24:2227 Greco RL, Coto JM, Dentel SK, Gosset JM (1983) Technical report. Environmental Engineering department, Cornell University, Ithaca, New York Christ O,Wilderer PA,Angerhofer R, Faulstich M (2000) Water Science and Technol 41:61 Toerien DF, Hattingh WHJ (1969) Water Res 3:385 Iannotti EL, Kafkewitz D, Wolin MJ, Bryant MP (1973) J Bacteriol 114:1231 Miller TL, Wolin MJ (1973) J Bacteriol 116:836 Thauer RK, Jungermann K, Decker K (1977) Bacteriol Rev 41:100 Cohen A, van Gemert JM, Zoetemeyer RJ, Breure AM (1984) Process Biochemistry (December) 228 Zoetemeyer RJ, van den Heuvel JC, Cohen A (1982) Water Res 16:303 Kisaalita WS, Lo KV, Pinder KL (1989) Biotechnol and Bioengineering 34:1235 Ghosh S, Klass DL (1978) Process Biochemistry 13:15 Pohland FG, Ghosh S (1971) Biotechnol And Bioengineering Symp 2:85 Ghosh S, Pohland FG (1974) J WPCF 46:748 Noike T, Endo G, Chang J-E, Yaguchi J-I, Matsumoto J (1985) BioTechnol and Bioengineering XXVII:1482 Huang CJ (1983) The effect of dilution rate on the kinetics of anaerobic acidogenesis. Proceedings of the thirteenth Annual Biochemical Engineering Symposium, Reilly PJ (ed) Novak JT, Carlson DA (1970) J WPCF 42:1933 Lawrence AW, McCarty PL (1969) J WPCF 41:R1 Massey ML, Pohland FG (1978) J WPCF 50:2205 Heyes RH, Hall RJ (1983) Applied and Environmental Microbiology 43:710 Lin C-Y, Sato K, Noike T, Matsumoto J (1986) Water Res 20:385 Ahring BK, Westermann P (1987) Applied and Environmental Microbiology 53:429 Kaspar HF, Wuhrmann K (1978) Applied and Environmental Microbiology 36:1 Smith PH, Mah RA (1966) Applied Microbiology 14:368 Aguilar A, Casas C, Lema JM (1995) Water Res 29:505 Robinson JA, Tiedje JM (1982) Applied and Environmental Microbiology 44:1374 Graef SP, Andrews JF (1973) AIChe symposium series. Water 70:101 Hill DT, Barth CL (1977) J WPCF October:2129 Kleinstreuer C, Poweigha T (1982) BioTechnol and Bioengineering XXIV:1941 Marsili-Libelli S, Nardini M (1985) Environ Technol Lett 6:602 Moletta R, Verrie D, Albagnac G (1986) Water Res 20:427 Smith PH, Bordeaux FM, Goto M, Shiralipour A, Wilkie A, Andrews JF, Ide S, Barnett MW (1988) Biological production of methane from biomass. In: Smith WH, Frank JR (eds), Methane from Biomass. A Treatment Approach. Elsevier, London

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69. Markl H (1999) Modeling of biogas reactors. In: Rehm HJ, Reed G (eds), Biotechnology. Wiley-VCH, Weinheim 70. Hill DT (1982) Transactions of the ASAE 25:1374 71. Hill DT (1983) Agricultural wastes 5:1 72. Hill DT, Tollner EW, Holmberg RD (1983) Agricultural wastes 5:105 73. Hill DT (1983) Agricultural wastes 5:153 74. Hill DT (1983) Agricultural wastes 5:205 75. Hill DT (1983) Agricultural wastes 5:219 76. Durand JH, Iannotti EL, Fischer JR, Miles JB (1988) Biological wastes 24:1 77. Kalyuzhnyi SV (1997) Bioresource Technol 59:249 78. Kalyuzhnyi SV, Davlyatshina MA (1997) Bioresource Technol 59:73 79. Kalyuzhnyi SV, Davlyatshina MA, Varfolomeev SD (1994) Applied Biochemistry And Microbiology 30:162 80. Kalyuzhnyi SV, Davlyatshina MA, Varfolomeev SD (1994) Applied Biochemistry And Microbiology 30:20 81. Kalyuzhnyi SV, Gachok VP, Davlyatshina MA, Varfolomeyev SD (1993) Applied Biochemistry And BioTechnol 39:601 82. Kalyuzhnyi SV, Gachok VP, Sklyar VI, Varfolomeev SD (1991) Applied Biochemistry And BioTechnol 28–29:183 83. Angelidaki I, Ellegaard L, Ahring BK (1993) BioTechnol and Bioengineering 42:159 84. Bryers JD (1985) BioTechnol and Bioengineering XXVII:638 85. Gavala HN, Skiadas IV, Bozinis NA, Lyberatos G (1996) Water Science and Technol 34:67 86. Lyberatos G, Gavala HN, Stamatelatou A (1997) Nonlinear Analysis 30:2341 87. Jeyaseelan S (1997) Water Science and Technol 35:185 88. Vavilin VA, Vasiliev VB, Ponomarev AV, Rytov SV (1994) Bioresource Technol 48:1 89. Siegrist H, Renggli D, Gujer W (1995) Mathematical modelling of anaerobic mesophilic processes in a digester. In: International meeting on anaerobic processes for bioenergy and environment. Colle – Colle, Copenhagen, Denmark 90. Siegrist H, Renggli D, Gujer W (1993) Water Science and Technol 27:25 91. Angelidaki I, Ellegaard L, Ahring BK (1999) BioTechnol and Bioengineering 63:363 92. Batstone DJ, Keller J, Newell RB, Newland M (2000) Bioresource Technol 75:67 93. Mosey FE (1983) Water Science and Technol 15:209 94. Bauchop T, Elsden SR (1960) J of General Microbiology 23:457 95. Pullammanapallil P, Owens JM, Svoronos SA, Lyberatos G, Chynoweth DP (1991) AIChe Annual Meeting 43 96. Costello DJ, Greenfield PF, Lee PL (1991) Water Res 25:847 97. Costello DJ, Greenfield PF, Lee PL (1991) Water Res 25:859 98. Keller J, Romli M, Lee PL, Greenfield PF (1993) Water Science and Technol 28:197 99. Romli M, Keller J, Lee PL, Greenfield PF (1994) Advances in Bioprocess Engineering 379 100. Romli M, Keller J, Lee PL, Greenfield PF (1994) Water Science and Technol 30:35 101. Romli M, Keller J, Lee PJ, Greenfield PF (1995) Process Safety and Environmental Protection 73:151 102. Ruzicka M (1996) Water Res 30:2440 103. Ruzicka M (1996) Water Res 30:2447 104. Batstone DJ, Keller J, Newell RB, Newland M (2000) Bioresource Technol 75:75 105. Ni J (1999) J of Agricultural Engineering Research 72:1 106. Kiely G, Tayfur G, Dolan C, Tanji K (1997) Water Res 31:534 107. Angelidaki I, Ellegaard L, Ahring BK (1997) Water Science and Technol 36:263 108. Batstone DJ, Keller J, Angelidaki RI, Kalyuzhny SV, Pavlostathis SG, Rozzi A, Sanders WTM, Siegrist H,Vavilin VA (2001) 9th World Congress on Anaerobic Digestion,Antwerpen, September 2–6 Proceedings of the Workshop on ADM 1

Received: April 2002

CHAPTER 6

Molecular Biology of Stress Genes in Methanogens: Potential for Bioreactor Technology Everly Conway de Macario · Alberto J. L. Macario Wadsworth Center, Division of Molecular Medicine, New York State Department of Health; and Department of Biomedical Sciences, School of Public Health, The University at Albany (SUNY), Albany, New York 12201-0509, USA. E-mail: [email protected]

Many agents of physical, chemical, or biological nature, have the potential for causing cell stress. These agents are called stressors and their effects on cells are due to protein denaturation. Cells, microbes, for instance, perform their physiological functions and survive stress only if they have their proteins in the necessary concentrations and shapes. To be functional a protein shape must conform to a specific three-dimensional arrangement, named the native configuration. When a stressor (e.g., temperature elevation or heat shock, decrease in pH, hypersalinity, heavy metals) hits a microbe, it causes proteins to lose their native configuration, which is to say that stressors cause protein denaturation. The cell mounts an anti-stress response: house-keeping genes are down-regulated and stress genes are activated. Among the latter are the genes that produce the Hsp70(DnaK), Hsp60, and small heat protein (sHsp) families of stress proteins. Hsp70(DnaK) is part of the molecular chaperone machine together with Hsp40(DnaJ) and GrpE, and Hsp60 is a component of the chaperonin complex. Both the chaperone machine and the chaperonins play a crucial role in assisting microbial proteins to reach their native, functional configuration and to regain it when it is partially lost due to stress. Proteins that are denatured beyond repair are degraded by proteases so they do not accumulate and become a burden to the cell. All Archaea studied to date possess chaperonins but only some methanogens have the chaperone machine. A recent genome survey indicates that Archaea do not harbor well conserved equivalents of the co-chaperones trigger factor, Hip, Hop, BAG-1, and NAC, although the data suggest that Archaea have proteins related to Hop and to the NAC alpha subunit whose functions remain to be elucidated. Other anti-stress means involve osmolytes, ion traffic, and formation of multicellular structures. All cellular anti-stress mechanisms depend on genes whose products are directly involved in counteracting the effects of stressors, or are regulators. The latter proteins monitor and modulate gene activity. Biomethanation depends on the concerted action of at least three groups of microbes, the methanogens being one of them. Their anti-stress mechanisms are briefly discussed in this Chapter from the standpoint of their role in biomethanation with emphasis on their potential for optimizing bioreactor performance. Bioreactors usually contain stressors that come with the influent, or are produced during the digestion process. If the stressors reach levels above those that can be dealt with by the anti-stress mechanisms of the microbes in the bioreactor, the microbes will die or at least cease to function. The bioreactor will malfunction and crash. Manipulation of genes involved in the anti-stress response, particularly those pertinent to the synthesis and regulation of the Hsp70(DnaK) and Hsp60 molecular machines, is a promising avenue for improving the capacity of microbes to withstand stress, and thus to continue biomethanation even when the bioreactor is loaded with harsh waste. The engineering of methanogenic consortia with stress-resistant microbes, made on demand for efficient bioprocessing of stressor-containing effluents and wastes, is a tangible possibility for the near future. This promising biotechnological development will soon become a reality due to the advances in the study of the stress response and anti-stress mechanisms at the molecular and genetic levels. Advances in Biochemical Engineering/ Biotechnology, Vol. 81 Series Editor: T. Scheper © Springer-Verlag Berlin Heidelberg 2003

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Keywords. Stress, Stress genes, Methanogens, Anti-stress mechanisms, Multicellular struc-

tures

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

1.1 1.2 1.3 1.4 1.5

Terminology, Stress, Heat-Shock Proteins, Chaperones, and Anti-Stress Mechanisms . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . . . . . . . . Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . Life on Earth, Evolution, and Stressors . . . . . . . . . Methanogens . . . . . . . . . . . . . . . . . . . . . . .

2

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2.1 2.2 2.3 2.4

Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strategies and Methods for Study and Utilization of Stress Genes

3

Molecular Chaperones . . . . . . . . . . . . . . . . . . . . . . . . . 103

3.1 3.2 3.3 3.4

Definition and Functions . . . Interactions . . . . . . . . . . . Families . . . . . . . . . . . . . Mechanisms and Manipulation

4

Stress Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

4.1 4.2 4.3

Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5

Stress Genes and Molecular Chaperones in Archaea . . . . . . . . . 106

5.1 5.2 5.3 5.4

Overview . . . . . . . . . Evolution . . . . . . . . . Structure . . . . . . . . . Expression and Regulation

6

The Hsp70(DnaK) Chaperone Machine in Methanogens

6.1 6.2 6.3 6.4 6.5 6.6 6.7

Components . . . . . . . . . . . . . . . . . . . . . . . Expression . . . . . . . . . . . . . . . . . . . . . . . . Stressor-Response Relationships . . . . . . . . . . . . Other Stressors Pertinent to Methanogenic Bioreactors Factors that Modify the Stress Response . . . . . . . . Other Methanogens . . . . . . . . . . . . . . . . . . . Co-Chaperones . . . . . . . . . . . . . . . . . . . . . .

. . . . .

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98 99 100 100 101

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

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102 102 102 103

103 104 104 105

106 107 109 109

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109 110 114 114 116 119 123

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7

The Hsp60 (Chaperonin) System in Methanogens . . . . . . . . . . 123

7.1 7.2

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Structure and Potential for Bioreactor Technology . . . . . . . . . . 124

8

Other Stress or Stress-Related Molecules, Genes and Proteins, and Anti-Stress Mechanisms in Methanogens . . . . . . . . . . . . 125

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osmolytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . TrkA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prefoldin or GimC . . . . . . . . . . . . . . . . . . . . . . . Small Heat-Shock Proteins (sHsp) . . . . . . . . . . . . . . . PPIase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proteases . . . . . . . . . . . . . . . . . . . . . . . . . . . . Putative Stress Genes and Proteins Found in Fully Sequenced Genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. . . . . . .

125 125 125 127 128 129 129

. . . . 130

9

Other Manifestations of the Stress Response . . . . . . . . . . . . . 130

9.1 9.2 9.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Thermoprotectants . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Multicellular Structures . . . . . . . . . . . . . . . . . . . . . . . . 131

10

Perspectives and Applications . . . . . . . . . . . . . . . . . . . . . 138

10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . Diversity of Methanogens . . . . . . . . . . . . . . . . . . Dynamics of Methanogenic Subpopulations in Bioreactors Diversity of Stressors . . . . . . . . . . . . . . . . . . . . . Diversity of Response . . . . . . . . . . . . . . . . . . . . Diversity of Methanogens: A Source of Useful Microbes? . Cooperation Between Molecules and Between Cells . . . . Proteases as Builders . . . . . . . . . . . . . . . . . . . . . Intrinsic Stress Resistance . . . . . . . . . . . . . . . . . .

11

Conclusion and Perspectives

12

References

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138 138 138 141 141 142 145 145 146

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1 Introduction 1.1 Terminology, Stress, Heat-Shock Proteins, Chaperones, and Anti-Stress Mechanisms

Terms will be defined in the text when pertinent. A few are introduced here to abate the sense of awe some uninitiated readers might experience when they encounter specialized words for the first time. This ought to make the Chapter more “user friendly.” Stress is a word applied in many fields of intellectual endeavor and does not need a lengthy explanation. It will be used in this Chapter to indicate an altered status of the cell caused by an agent (stressor) of a physical, chemical, or biological nature [1]. This status is the stress response, which manifests itself by an increase in the products of a series of genes (stress genes). These produce the stress proteins, also called for historical reasons, heat-shock proteins, abbreviated Hsp [2]. While the genes’ names are written in italics, those of their products are in Roman characters. A gene, when active, is transcribed to messenger RNA (mRNA), which in turn is translated in the ribosome into a linear series of amino acids to form a polypeptide or protein. Thus, when one measures intracellular levels of the mRNA from a given gene, an estimate of the degree of activity of that gene is obtained. mRNA levels may increase in a cell also via other mechanisms, but these will be explained in the text. A protein is synthesized as a string of amino acids but to become functionally competent it has to fold into a three-dimensional configuration, i.e., it has to accommodate itself to what is called the native configuration for that particular protein. There are many occasions in which this functional configuration is partially or totally lost, a process named protein denaturation. Denatured proteins must be renatured, or eliminated, lest they cause serious problems to the cell. The major effect of stressors is protein denaturation. Consequently, the stress response is made of events unchained by protein denaturation, and many of these events aim at counteracting it. Some Hsp are molecular chaperones [2]. They assist other polypeptides to acquire their native configurations, or to regain them if they have been partially denatured. Proteins that are denatured beyond repair are eliminated by proteases, i.e., enzymes that digest the damaged molecules [3–5]. There are several groups of Hsp, but the two most studied are those that constitute the molecular chaperone machine and the chaperonin system [6–11]. Both will be explained in the text. NOTE. This review was completed in January 2000. Since then other genomes have been sequenced and new publications have appeared. Most relevant is a genome survey in search of archaeal co-chaperones, a discussion of which was added to this article, after completion of the original manuscript. Other updates may be found in Frontiers of Bioscience, Vol. 5 and 6, Special Issue, Anti-stress mechanisms in Archaea, at: http://www.bioscience.org/current/special/macario.htm; Genome Res. 12:532–542, 2002; and Crit. Rev. Biochem. Mol. Biol., December 2002

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Stress genes may be active in the absence of recognizable stress. This physiological activity is called basal or constitutive to distinguish it from that induced by stressors. The two gene activities, constitutive and stress-induced, are important to the cell. They have to be properly regulated, so the cell can perform all its functions under physiological conditions, and can react speedily and efficiently in the face of stress, and survive. It follows that the gene-regulatory mechanisms are as important to the cell as the stress genes themselves. Gene regulation is mediated by proteins called transcription and regulatory factors that are the product of other genes. Some of these factors interact with critical DNA sequences named cis-acting sites or elements, usually located near the genes they regulate. There are other components of the stress response beyond stress genes and chaperones. A few will be discussed in this Chapter, such as simple compounds that protect the cell against stressors and are called thermoprotectants, and multicellular structures [12]. The latter structures allow cells to survive adverse conditions as they are shielded inside a large body surrounded by extracellular material. It should be easy to envisage from the above general considerations how important anti-stress mechanisms are for biomethanation. The microbial cells involved in the bioconversion of wastes in bioreactors are constantly exposed to stressors. If the cells are not prepared to withstand these attacks by stressors they will die, or at least cease to function. There is a very promising future for the use of stress genes and proteins in the optimization of biomethanation technology.A rational application of these antistress means must be based on adequate knowledge of the mechanisms involved in transcription and regulation of stress genes. The aim of this Chapter is to present in a simplified manner part of what is known about the stress response in a group of microbial cells that are key to biomethanation. 1.2 Objectives

This Chapter, as stated above, will introduce the topic defined in the title to nonspecialists in molecular biology or stress. Data will be presented in Tables with only a brief explanation in the text, in which selected references will be quoted for consultation by those interested in more details. Moreover, recent reviews on the stress genes and proteins will be quoted extensively because they contain practically all the information available at the end of 1999 [8, 12, 13]. In addition, the few pertinent publications that have appeared since then will be discussed. The information on stress genes and proteins will be discussed mainly to indicate how it might be pertinent to methanogenesis and, more specifically, to the designing, monitoring, improving, and controlling of anaerobic methanogenic bioreactors (digestors).

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1.3 Scope

An attempt will be made to cover all aspects of the stress response, and the stress genes and proteins that might be applicable to bioreactor technology, focusing on the methanogens. However, the emphasis will lie on the systems that are better known, namely the molecular chaperone machine and the chaperonins. In addition, a rather detailed discussion of multicellular structures that play a critical role in resistance to stressors will be presented. Likewise, we will focus on two organisms, Methanosarcina mazeii and Methanosarcina thermophila, whose stress response and molecular chaperonemachine genes are the best studied among methanogens [12, 14]. They are key for methanogenesis in bioreactors and in many other ecosystems of biotechnological relevance [15–19]. M. mazeii has an optimal temperature for growth (OTG) of 37°C and is a key element for mesophilic digestion, while the other, M. thermophila, has an OTG of 50°C and plays a central role in thermophilic bioreactors. This Chapter is based on an extensive search of written and electronic literature, and on consultations with colleagues, but only a minimal list of essential references will be quoted and work done in our laboratories will be primarily surveyed.As stated above, there are comprehensive reviews available that may be used by the reader to advantage, and that allow us to simplify this Chapter, and to direct its focus on matters that are, or might be, relevant to methanogenesis and bioreactor technology. 1.4 Life on Earth, Evolution, and Stressors

Life on Earth today resulted from the evolution of organisms that survived through the changes in temperature, pH, oxygen levels, etc. that the planet experienced since the most primitive life form appeared. Since these environmental changes, and many others that must have occurred during the millennia, are cell stressors, one is inclined to think that stress genes have played a decisive role in determining which organisms survived. It would seem reasonable to think that life forms endowed with the best anti-stressor means (among which stress genes and proteins are paramount) would be those that withstood the stressful conditions when they appeared more or less suddenly, until the conditions either changed back to a more benign character or the organism adapted itself to the new situation. In the latter case, the stressful conditions were no longer such. They became normal, physiological conditions for the adapted organism. This mechanism may be one of the reasons why we see today such a variety of habitats that are optimal for their aboriginal organisms [20], but may be deadly to foreigners. This is a crucial concept while defining stressors. An agent, for instance a certain level of temperature, may be a potent stressor for a given organism while it may be optimal for the growth and physiology of another [1]. The same applies to acidity, alkalinity, salinity, etc. It is with these ideas in mind that one may now look at the list of common cell stressors displayed in Table 1.

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Table 1. Cell stressors a

Type

Name description b

Physical

Heat; several types of irradiation, including ultraviolet light; pressure; sound H2O2; oxygen-derived free radicals or reactive oxygen species (ROS); anaerobiosis to aerobiosis shift; hypoxia-anoxia Alkalinity; acidity; pH shift Changes in the concentration of salt, sugars, other osmolytes (hyper- or hypoosmotic shock) Starvation: multiple; specific (carbon, glucose, nitrogen, phosphate, nitrate) Puromycin; tetracycline; nalidix acid Ethanol; methanol; butanol; propanol; octanol Cadmium; copper; chromium; zinc; tin; aluminum; mercury; lead; nickel Lindane; diazinon; paraquat; thiram; tributyltin

Oxygen pH Osmotic Nutritional Antibiotics Alcohols Metals Insecticides, pesticides Mechanical Other

a b

Compression; shearing Benzene and derivatives; phenol and derivatives; mutagens; ammonia; arsenite; arsenate; amino acid analogues; nicotine; anesthetics; desiccation

Reproduced modified from reference [1] with permission from the copyright owner. These agents cause stress in cells from the three phylogenetic domains, Bacteria, Archaea (both prokaryotes), and Eucarya (eukaryotes).

1.5 Methanogens

All living cells have been classified into three main lines of evolutionary descent based mostly on comparative analyses of the sequences of the small subunit of ribosomal RNA (rRNA) [21–24]. The lines or phylogenetic domains are: Archaea (formerly archaebacteria), Bacteria (eubacteria), and Eucarya (eukaryotes). Methanogens belong to the Archaea, and consequently they are prokaryotes with many characteristics that distinguish them from the other prokaryotes, the bacteria. Methanogens are very important organisms for a number of reasons. The most pertinent to this Chapter is that they are key elements in the bioconversion of organic matter with the generation of methane gas, i.e., biomethanation [17, 19, 25]. They are widespread, diverse, and capable of metabolizing a range of different substrates. Therefore, they are useful in waste treatment under a variety of conditions, and have the potential for application in the bioconversion of a range of materials in many different locations in our planet.A major objective of this Chapter is to indicate possibilities for improving methanogens, and to suggest means to do it, so they become very resistant to stressors and thus able to function efficiently even in very inhospitable environments.

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2 Stress Genes 2.1 Definition

In a broad sense, stress genes are those that work during and after a stressor hits a cell differently as compared with the pre-stress situation [1]. Stress genes begin to be transcribed, or increase their transcriptional activity, in response to the stressor’s impact on the cell. In contrast to other cellular genes, the stress genes are activated upon stress. Non-stress genes are down-regulated or shut off by stress. In agreement with a broad definition, stress genes form a large group encompassing those that produce the molecular chaperones and chaperonins (the most representative of the group) [2] and many others [12, 26]. Among the latter are those that participate in the formation of multicellular structures, of which we know very little despite their obvious importance in cell survival and function. 2.2 Classification

The stress genes and their proteins (Hsp) are classified according to the molecular mass of the latter as shown in Table 2. The Hsp70 and Hsp60 families are the best known in Archaea, including methanogens [8, 12, 27, 31, 32]. There is also information on the components of the molecular chaperone machine other than Hsp70(DnaK), namely the Hsp40(DnaJ) and GrpE proteins, particularly in M. mazeii and M. thermophila, as we shall discuss later in this Chapter. While these gene/protein families are very important and their study should continue if one wants to develop means to improve methanogens for biotechnologic purposes, another important group shown in Table 2 is “Other”. Under this heading, there Table 2. Classification of Hsp into families according to their molecular mass a

Family Name (synonyms within parentheses) b

Mass (kDa)

Found in methanogens

Heavy (High M.W.; Hsp100) Hsp90 Hsp70 (Chaperones) Hsp60 (Chaperonins) Hsp40 (DnaJ) Small Hsp (sHsp) Other (proteases, etc.)

100 or higher 81–99 65–80 55–64 35–54 <35 Various

No c No Yes Yes Yes Yes Yes

a b c

Reproduced from reference [2] with permission from the copyright owner. For additional information on the various families see references [7, 10, 11, 27–36]. Not yet investigated, or investigated but not yet found, or found but incompletely characterized (see also references [8, 37]).

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are several gene/proteins that are surely crucial for cell survival in the face of stress but that are not yet well understood [12]. This group offers a great potential for research and as a source of genes and molecules to engineer more resistant methanogens as we shall see in other sections of this Chapter. 2.3 Evolution

The evolutionary history of stress genes and proteins is extremely interesting from both the theoretical and practical standpoints. The topic has been reviewed in detail [8, 12, 32, 38–40], and we shall not deal with it here again. We encourage the reader to consult the references quoted, and others cited in them, to become acquainted with the evolution of these important genes, and to understand why they are useful for constructing evolutionary trees, and how they can be used for such a purpose. What are the practical dividends of understanding stress gene/protein evolution? It provides a comprehensive picture of their presence and variations in many organisms, and thus their functions and adaptations in a variety of circumstances. Thus, it helps us to understand the functions of the stress proteins as a whole, and those of their specialized domains, which in turn is instrumental to designing strategies for engineering more efficient genes and molecules that will “fortify” methanogens pertinent to bioreactors. 2.4 Strategies and Methods for the Study and Utilization of Stress Genes

Because of the diversity of methanogens mentioned above, including the capacity to grow in a wide variety of ecosystems, the methods for studying them are very varied [21, 25, 41, 42]. It follows that the methods for manipulating the stress genes and proteins from methanogens are also diverse and may be quite complex. A wide array of species possibly also exists, with many families, but this type of diversity has not yet been fully assessed because only a limited number of isolates have been thoroughly characterized.We shall come back to this topic in the course of the Chapter to show the array of possibilities that are available for discovering useful methanogens and for obtaining useful genes and proteins.

3 Molecular Chaperones 3.1 Definition and Functions

A molecular chaperone is a protein that assists others to fold correctly, refold if partially denatured (unfolded) by stressors, and translocate to the cell’s locale (e.g., the periplasm in bacteria, or the mitochondria and the endoplasmic reticulum in eukaryotes) where they reside and function [6, 9, 43]. They are also

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implicated in the degradation of intracellular proteins that are damaged or abnormal to the extent that they might aggregate and precipitate, and thus cause serious problems to the cell [3–5]. In addition to the functions listed above, molecular chaperones have other activities, the list of which grows rapidly as more research is done on these important molecules. For example, they participate in the regulation of their own synthesis [44–46] and in the disaggregation and refolding of partially denatured polypeptides [47–49], and some of them interact with nucleic acids [50]. Furthermore, it has been suggested that molecules other than proteins (e.g., lipids) can play a role as chaperones in protein folding [51]. In this Chapter, the molecular chaperones that are the product of stress genes will be treated in more detail. However, others that are less well studied will also be mentioned to stimulate the curiosity of the reader and, hopefully, to promote research aiming at clarifying their functional role. This should provide a wider basis for biotechnologic developments than that furnished by the analysis of the classical molecular chaperones exclusively. 3.2 Interactions

Molecular chaperones usually exercise their functions by interacting with other molecules that are chaperones themselves, or that act as co-chaperones or co-factors, forming complex multimolecular assemblies [6, 8, 9, 35, 43, 52]. Examples are the molecular chaperone machine formed by the chaperones Hsp70(DnaK), Hsp40(DnaJ), and GrpE; and the thermosome or chaperonin complex, as we shall see later in this Chapter. Also, the molecular chaperone machine may interact with chaperonins in the process of de novo polypeptide folding. Thus, in the chaperoning process and in the regulation of their own synthesis, chaperones do not act alone but in association with teammates. It follows that if one aims at, for example, controlling the levels of stress proteins in a cell to make it more resistant to stressors over periods of time longer than usual, one must understand their self-regulatory circuits and the array of other molecules involved in this mechanism. 3.3 Families

As mentioned above, the Hsp, many of which are molecular chaperones, are classified into families according to their molecular mass, see Table 2. It is important to bear in mind that while many molecular chaperones are stress proteins in the sense that their levels are increased by stress, others are not. Also, the reverse is true, namely, many stress proteins are not molecular chaperones. Their levels increase in response to stress, or they are more active as a consequence of stress, but they do not assist other proteins to fold, re-fold, or mobilize; they have other functions.

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3.4 Mechanisms and Manipulation

It is well known that stress proteins, including those that are molecular chaperones, increase in quantity inside the cell in response to stressors. This increase is one of the landmarks of the stress response [1]. There are several mechanisms by which the concentration of a given protein may be increased in a cell, for example, increases in the rate of transcription of the gene that encodes the protein, life-time of the mRNA, efficiency of mRNA translation, and life-time of the protein itself. Hence, there are at least four points to consider when designing strategies to increase the cell’s resistance to stressors. One may focus on transcription initiation and elongation and try to improve these processes to make them faster and more efficient. Or one may try to do the same with the process of translation at the level of the ribosome. In addition, one may aim at decreasing the degradation rate of the mRNA, or the protein, and thus lengthen their life spans. Obviously, a combination of these approaches seems the most promising strategy, however complicated it might be.

4 Stress Response 4.1 Definition

Now that we have introduced the stressors, stress genes and proteins, and the molecular chaperones, it is timely to define the stress response. This is a complex series of events unchained by a stressor upon hitting a cell [1]. The stress genes, proteins, and molecular chaperones are the main players in these events. They increase in concentration and/or activity to counteract the effects of the stressor. The central, most damaging effect of stressors is on cellular proteins that become denatured, i.e., they lose their native configuration [1, 12]. Hence, the stress response aims at avoiding protein denaturation, renaturing (refolding) partially damaged molecules, and degrading those damaged beyond repair. 4.2 Characteristics

A stress response manifests itself in several ways [1, 12]: (i) (ii) (iii) (iv)

Most, if not all, house-keeping genes are down-regulated or shut off; The stress genes are activated; Many cellular proteins become denatured in various degrees; The cell may change its motility and move more or less than before stress; (v) The cell membrane and wall may change to become more resistant to the stressor, more or less permeable to certain compounds, ions, etc.;

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(vi) The cell may aggregate with others via synthesis of an intercellular connective material and build multicellular structures of various degrees of complexity; and (vii) Other phenomena may occur, ranging from alterations in the intracellular osmolytes through changes in the electrolytes and other components. All the above events, or some of them, may occur in a cell in response to a given stressor, but down-regulation of housekeeping genes and activation of stress genes are by definition essential landmarks. These two main components are evidenced by a change in the levels of the proteins that these genes encode: the products of the housekeeping genes diminish, even become undetectable, whereas the products of the stress genes increase. Thus, in monitoring microbes in a bioreactor to check their functional status and potential for recovery if stressed, one may measure the levels of representatives of these two groups of proteins, or their respective mRNAs. This in fact ought to be a straightforward, relatively simple approach to the monitoring and controlling of bioreactors to prevent, or at least anticipate, failure with disruption of a waste-treatment operation. 4.3 Stressors

Anything endowed with the capacity to elicit a stress response is a stressor [1]. The most common or best studied are listed in Table 1. It is clear from the list that stressors are ubiquitous. They can be found in air, soil, water, all kinds of foods and nutrients, and in a great variety of natural or manufactured materials. Many of them reach the microbes in methanogenic bioreactors with the influent or are produced in the bioreactor itself [17, 53–56]. It is therefore not surprising that bioreactor malfunction is a rather frequent occurrence, even when the mechanical and most chemical conditions seem to be adequate. It follows that monitoring bioreactors ought to include testing the degree of stress of the microbes by measuring manifestations of the stress response.

5 Stress Genes and Molecular Chaperones in Archaea 5.1 Overview

This topic has been reviewed recently [12]. It will, therefore, not be necessary to treat it again here in any detail, except in what pertains directly to biomethanation. The study of stress genes in Archaea began very recently by comparison with their counterparts in organisms of the other two domains, Bacteria and Eucarya. The earliest studies on the stress response in Archaea may be traced back to the late 1980s, but it was only in 1991 that an archaeal stress gene, hsp70(dnaK), was cloned and sequenced for the first time [57]. The same year, a chaperonin gene

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was also cloned and sequenced [7]. Since then, a few others have been sequenced, some as part of genome sequencing projects [12]. In parallel, functional studies in vitro have been carried out to elucidate how and when these genes are expressed and how they respond to stress. Nevertheless, very little is known about these genes in comparison with those from bacteria and eukaryotes. A lot of work remains to be done before one can think of using archaeal genes, or their products, for improving methanogens pertinent to bioreactor technology. 5.2 Evolution

The evolution of archaeal stress genes, hsp70(dnaK) in particular, has attracted the attention of a number of investigators. Considerable information exists on the evolution of hsp70(dnaK), which has revealed very interesting features [8, 32, 39, 40]. For example, the gene is absent from several archaeal species, including some methanogens, Table 3, seemingly in contradiction of the generally acknowledged fact that this gene is very important, if not essential, for life, especially for surviving stress. In fact, the discovery that the gene has a discontinuous distribution among Archaea may be considered one of the major contributions of researchers working with Archaea to the fields of evolution, stress Table 3. Occurrence, or lack thereof, of the hsp70(dnaK) gene among Archaea and represen-

tatives of thermophilic and hyperthermophilic bacteria a Organism

OTG b (°C)

hsp70 (dnaK)

Genome size (Mb)

Demonstrated by:

37 37 37 37 37 37 50

Yes Yes Yes Yes Yes Yes Yes

2.8 n.d.d n.d. n.d. 2.7 2.7 2.7

S, N, W, seq.c N N N N S S, N, seq.

Methanospirillum hungateii

37

No

n.d.

S

Methanobacterium thermoautotrophicum DH

65

Yes

1.7

seq.

37 37 85

No No No

n.d. n.d. 1.7

S, W S, P S, seq.

ARCHAEA Methanosarcina mazei S-6 mazei JC3 mazei LYC sp. JVC acetivorans C2A barkeri thermophila TM-1

Methanococcus voltae vannielii jannaschii Methanothermus fervidus

85

No

n.d.

S, P

Methanopyrus kandleri

100

No

n.d.

S, P

Haloarcula marismortui

45

Yes

n.d.

seq.

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E. Conway de Macario · A. J. L. Macario

Table 3 (continued)

Organism

OTG b (°C)

hsp70 (dnaK)

Genome size (Mb)

Demonstrated by:

Yes Yes

n.d. n.d.

seq. S, P

Halobacterium cutirubrum halobium

45 45

Thermoplasma acidophilum

55

Yes

1.7

seq., P

Sulfolobus solfataricus

70

No

3.1

S, P

Sulfolobus sp.

70

No

n.d.

S

Archaeoglobus fulgidus

83

No

2.2

seq., P

Desulfurococcus mobilis

85

No

n.d.

S, P

Thermococcus tenax

88

No

n.d.

S, P

Pyrococcus furiosus horikoshii woesei abyssi

100 100 100 100

No No No No

2.0 1.7 n.d. 1.8

seq. seq. S, P seq.

Pyrobaculum aerophilum

100

No

2.2

seq.

Aeropyrum pernix K1

100

No

1.7

seq.

Thermus thermophilus

70

Yes

n.d.

seq.

Thermomicrobium roseum

70

Yes

n.d.

seq.

Thermotoga maritima

80

Yes

n.d.

seq.

Aquifex aeolicus pyrophilus

83 83

Yes Yes

n.d. n.d.

seq. seq.

BACTERIA

a b c d e f

Reproduced from reference 12 with permission from the copyright owner. OTG, optimal temperature for growth. S, N, and W, Southern, Northern, and Western blotting, respectively; P, PCR; seq., sequencing of gene or genome. n.d., not determined. R. Weiss, personal communication. S.A. Fitz-Gibbon, personal communication.

response, and molecular chaperones in general. Several of the critical questions posed by this finding have been discussed recently [8, 12]. The finding has had a significant impact on our views regarding the evolutionary conservation of the gene, its role in cell physiology and survival, and its substitute in those species that do not have it (but still need to maintain an optimal set of functional proteins via folding and refolding under “normal” conditions and in the face of stress). Also very interesting is the evolution of the gene encoding the Hsp60 archaeal chaperonin, of which one, two, or three representatives may be found depending

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on the species [31, 38]. The proteins encoded in these chaperonin genes have received different names: chaperonin subunit 1 and 2, a and b (and g in case there were three in the same organism), or TF55 and TF56. Comparative analyses of amino acid sequences using computer programs that reveal phylogenetic relationships suggested that multiple subunit species arose several times, independently, during archaeal evolution. 5.3 Structure

The analyses of sequences mentioned above have revealed other features of interest. For example, the variation in the number of chaperonin subunits depending on the species is intriguing. It may have important consequences on the structural details of their assembly when they build up the chaperonin complex (for details on this complex, including three-dimensional reconstructions, see [12]). These structural details may in turn play a decisive role on the way the complex functions as a chaperoning machine. A complex formed by a single type of subunit may work differently from complexes formed by two, or three, types of subunits. This remains to be determined and points to another area in which research with archaeal chaperonins will most likely have reverberations on biomethanation technology. No doubt, the engineering of chaperonin complexes with the proper structure will help cells to make functionally competent proteins (such as the enzymes needed for methanogenesis) even under stress. 5.4 Expression and Regulation

Very little is known about the expression of the genes that produce the components of the chaperone machine, the chaperonin complex, and other stress proteins and molecular chaperones in Archaea. The topic has been recently reviewed [12], and the reader is encouraged to consult this article in order to understand its relevance to the art and science of fortifying cells, to make them resistant to stress. Some detailed information is available for the methanogens M. mazeii and M. thermophila, which will be discussed in subsequent Sections of this Chapter.

6 The Hsp70(DnaK) Chaperone Machine in Methanogens 6.1 Components

As mentioned above, some methanogens lack the hsp70(dnaK) gene, and the genes for the other components of the machine, Hsp40(DnaJ), and GrpE, Table 3. The latter two genes seem to always accompany hsp70(dnaK) [8]. If hsp70(dnaK) is missing, hsp40(dnaJ) and grpE are also absent in the genome. This parallelism has been demonstrated whenever enough sequence data have been available.

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Fig. 1. hsp70(dnaK)-locus genes of the methanogens for which sequences are available that include genes up- and downstream of hsp70(dnaK). The genes are represented by rectangular boxes from the 5¢ to the 3¢ end (left to right) with their names above their respective boxes in the locus on top (dnaK and dnaJ are used instead of hsp70(dnaK) and hsp40(dnaJ) for simplicity). The figures within the boxes indicate the number of amino acids encoded. The lines joining the boxes represent the intergenic regions with their lengths, in base pairs, shown underneath. The sequences of M. thermophila TM-1 grpE and trkA are still incomplete (what is available would encode 53 and 401 amino acids, respectively). Accession numbers and other details are provided in Tables 3–6. Reproduced from references [12] and [14] with permission from the copyright owners

Thus, one may conclude that the product of hsp70(dnaK) cannot operate without the products of the other two. Needless to say, this conclusion from sequencing data alone must translate into functional inferences, and impact on the way one plans to engineer microbes to make them stronger in the face of stress. Manipulation of only hsp70(dnaK) would not suffice. The other two genes should also be included, so the three of them would be expressed in a coordinated fashion for their products to act in unison, namely, to form a balanced chaperone machine, functionally efficient. The hsp70(dnaK), hsp40(dnaJ), and GrpE genes from methanogens that have been cloned and sequenced are listed in Tables 4, 5, and 6, respectively. The first hsp70(dnaK) loci to be fully sequenced in methanogens (and in the whole domain Archaea) are depicted in Fig. 1. 6.2 Expression

Expression of the Hsp70(DnaK) molecular chaperone machine genes in response to the stressor heat has been studied in M. mazeii S-6, Fig. 2, and to a lesser extent in M. thermophila TM-1 [14, 59–64]. Data in Fig. 2 demonstrate that the genes in M. mazeii S-6 respond to heat shock and, thus, that they are in this regard similar to counterparts in the organ-

X60265

Y17862

Methanosarcina mazei S-6

Methanosarcina thermophila TM-1

c

b

1833/610

1860/619

1791/596

Base pairs/amino acids encoded

aacttttatcta (-60)/ tctttttt (+38)/ agtgaggataaa (-7)

Palindrome; distinctive features upstream

Up- and downstream repeats; stemloops

Downstream repeats; stemloops

n.r./n.r./gaggtg (-8) c aaactttaattaa (-79)/ inverted repeats/ aggatataa (-5)

Other structures

Promoter/terminator/ RBS b

Heat-shock inducible

Heat-shock inducible

n.r.

Expression

Data extracted from reference [12] with permission from the copyright owner. RBS, ribosome-binding site. n.r., not reported; (–) and (+) refer to position of center of sequence upstream from the translation start codon or downstream from the translation stop codon, respectively.

AE000894

Methanobacterium thermoautotrophicum DH

a

Accession number

Organism

Table 4. hsp70(dnaK) genes in methanogens a

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X60265

AJ010152

Methanosarcina mazei S-6

Methanosarcina thermophila TM-1

X74353

Methanosarcina mazei S-6

c

b

aaacctgcact (-55)/tcttttt (+30), t-rich region/ atgacagggaa (-11)

aaacctgttcaca (-100)/t-rich region/ aacagggaatctg (-8)

acatttttttatt (-63) /n.r./ aggtg (-9) c

Promoter/terminator/ RBS b

Inverted repeat upstream; t-rich region downstream

Up- and downstream repeats; stemloops

Up- and downstream repeats; stemloops

Other structures

n.r.

Heat-shock inducible

n.r.

Expression

630/209

525/174

Base pairs/amino acids encoded

aactatttataga (-69)/ inverted repeat (+76)/atggg (-11)

aaafftttatata (-87)/ n.r/aggtg (-7) c

Promoter/terminator/ RBS b

Up- and downstream repeats; stemloops

Upstream repeats, stemloops

Other structures

Heat-shock inducible

n.r.

Expression

Data extracted from reference 12 with permission from the copyright owner. RBS, ribosome binding site. n.r., not reported; (–) and (+) refer to position of center of sequence upstream from the translation start codon or downstream from the translation stop codon, respectively.

AE000894

Methanobacterium thermoautotrophicum DH

a

Accession number

Organism

Table 6. grpE genes in methanogens a

c

b

1167/388

1170/389

1131/376

Base pairs/amino acids encoded

Data extracted from reference 12 with permission from the copyright owner. RBS, ribosome binding site. n.r., not reported; (–) and (+) refer to position of center of sequence upstream from the translation start codon or downstream from the translation stop codon, respectively.

AE000894

Methanobacterium thermoautotrophicucm DH

a

Accession number

Organism

Table 5. hsp40(dnaJ) genes in methanogens a

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Fig. 2. Northern blots with M. mazeii S-6 total RNA (10 µg/lane) showing an increase in the transcripts of hsp70(dnaK) (A), hsp40 (dnaJ) (B), and grpE (C), and a decrease in the transcript of orf16 (D), in response to heat shock. (E) dot blot showing a decrease in the transcript of orf11-trkA in response to heat shock. Hybridizations were done in all cases with radiolabeled probes specific for the respective genes. In A, B, and D, I the gel is stained with ethidium bromide showing the ribosomal RNAs, 23S and 16S, while II is the corresponding Northern blot. Lanes A: total RNA from M. mazeii S-6 cells maintained at the optimal growth temperature of 37°C, i.e., non-heat-shocked cells. Lanes B–C, or B–D: total RNA from cells heatshocked at 45°C for increasing time periods, from 15 to 60 min. The sizes of the transcripts in A–D are indicated in kilobases (kb). Transcripts were detected for all the genes in non-heatshocked cells. Heat shock caused an increase in the transcripts of hsp70(dnaK), hsp40(dnaJ), and grpE. The reverse occurred for orf16, and orf11-trkA. The latter two genes overlap, and are cotranscribed, whereas the other genes are transcribed monocystronically. Reproduced from references [59, 60, 62, 64] with permission from the copyright owners

isms of the other two phylogenetic domains. However, the M. mazeii genes resemble eucaryal homologues in as much as the message is monocistronic, in contrast to the bacterial counterparts. The latter are transcribed polycistronically into an mRNA molecule at least as long as the sum of the lengths of the three genes [12, 65]. In contrast, the mRNAs from the M. mazeii genes are distinct from one another, and their individual lengths are about the same as those of the respective genes.

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The other two genes in the M. mazeii S-6 hsp70(dnaK0 locus, orf16 and orf11trkA, Fig. 1, respond differently [62, 64, 66]. Their transcripts decrease instead of increasing, after heat shock, Fig. 2. 6.3 Stressor-Response Relationships

In order to be able to develop methanogens with improved stress resistance – that can be used for bioconversion of wastes in harsh, changing environments – one must first understand the range of conditions within which the stress genes are able to respond, and also, one must learn how the cell responds at the extremes of that range. Data in Fig. 3 demonstrate that the three genes of the chaperone machine triad in M. mazeii S-6 (OTG 37°C) respond very well at temperatures between 45 and 60°C, even though cell viability is considerably diminished already at 55°C [63]. The conclusion from the data is that while an increase in temperature above certain limits kills many M. mazeii S-6 cells, the heat-shock response of the chaperone machine triad is still in operation, at least with respect to increasing the genes’ transcripts. This observation suggests that in a seriously damaged microbial population, with many of its members dying, there are still many cells capable of mounting a sizable stress response. The data also suggest that by improving the cells’ ability to mount a stress response, using genetic engineering procedures for example, one will increase the size of the surviving, functional subpopulation, and thus insure continuity in the bioconversion process and avoid bioreactor failure. It must be mentioned, however, that it remains to be established whether damaged cells still capable of mounting a stress response as measured by an increase in the stress genes’ transcripts, are also capable of proceeding with the entire pathway of protein biogenesis and produce functional stress proteins. This is a very promising area of research pertinent to biomethanation technology. 6.4 Other Stressors Pertinent to Methanogenic Bioreactors

The list of cell stressors that can affect methanogens is long, as one may infer from the sample listed in Table 1. Examples of stressors pertinent to industrial and other effluents and environments are heavy metals and sound. Cadmium (Cd++) and sound do elicit a stress response in M. mazeii S-6, as shown by the data in Figs. 4 and 5, respectively [58]. It is likely that other methanogens, and other microbes pertinent to methanogenesis in bioreactors, are also susceptible to be stressed by Cd++ and sound. It follows that methanogens and the other components of methanogenic consortia ought to be able to withstand stress caused by heavy metals and sound to a degree above and beyond that of cells in other, less stressful environments.

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Fig. 3. Response of the M mazeii S-6 genes grpE, hsp70(dnaK), and hsp40(dnaJ) to heat shock

at various temperatures demonstrated by slot-blotting. The levels of mRNA for grpE, hsp70(dnaK), and hsp40(dnaJ) (top three panels) are represented by vertical bars expressed in the OD. X mm units given by the densitometer. The respective slot blots (10 µg/slot of total RNA) are shown at the foot of the bars, while the heat-shock temperatures are indicated in the horizontal axis at the bottom of the figure (°C). Hybridization was done with the respective labeled probes. The culture density is shown in the bottom panel. The OD660 was determined at time 0 (open bars) and at 30 min (hatched bars), in cultures maintained at 37°C or heatshocked during this 30-min period at the temperatures indicated at the foot of the bars. Reproduced from reference [63] with permission from the copyright owner

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dnaK

% area of peak

grpE dnaJ

a

b

c

d

Fig. 4. Response of the M. mazeii S-6 genes grpE, hsp40(dnaJ), and hsp70(dnaK) to the stres-

sors cadmium (Cd++) and heat. The bars represent levels of mRNA determined by slot-blotting with probes for the grpE, hsp40(dnaJ), and hsp70(dnaK) genes. The total RNAs were from cells grown at 37°C (i.e., the optimal temperature for growth for M. mazeii S-6) in medium without Cd++ (a), and in medium with 5 or 27 mM CdCl2 (b and c, respectively); and from cells grown in medium without Cd++ but heat-shocked at 45°C for 30 min (d). Note that the mRNAs from the three genes increased after heat shock by comparison with the levels before heat shock (constitutive or basal levels; compare a vs. d). Likewise, the presence of Cd++ in the medium also induced an increase in the three mRNAs. This effect was more marked with 27 than with 5 mM CdCl2; compare a vs. b and c; and b vs. c. Reproduced from reference [58] with permission from the copyright owner

Another compound very pertinent to methanogenic bioreactors is ammonia. Bioconversion of wastes from humans and other animals with feces and urine is inhibited when the ammonia present in these wastes reaches certain levels [53, 54]. Data in Fig. 6 demonstrate that ammonia induces a stress response in M. mazeii S-6 as evidenced by the increase in the levels of the mRNAs from the molecular chaperone machine genes [67]. Interestingly, ammonia also induces a response by the adjacent gene trkA. The response of this gene seems to be tightly regulated in view of its strict dose dependency. Overall, the results show that concentrations of ammonia over 20-fold higher than that which is adequate for physiological growth of M. mazeii S-6 cause stress in this methanogen (see also Sects. 6.6 and 8.3). 6.5 Factors that Modify the Stress Response

It is well established that a number of cellular activities and physiological functions are associated with, or are dependent on, the cell cycle and the growth

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Fig. 5. Response of the M. mazeii S-6 hsp70(dnaK) gene to the stressors heat and sound. The levels of hsp70(dnaK) mRNA were determined by slot blotting (A) and were measured by densitometry (B). RNAs were from cells cultured at the optimal temperature for growth, i.e., 37°C (slot-blots 1 and 3, counting from the top down); or from cells heat-shocked at 45°C for 30 min (slot blot 2); or from cells maintained at 37°C and exposed to sound (90 decibels) for 15, 30, 60, and 120 min (slot blots 4, 5, 6, and 7, respectively). Notice the increase of hsp70(dnaK) mRNA after heat shock (compare peaks 1 vs. 2), and after sound stress (compare peaks 3 vs. 4–7). Reproduced from reference [58] with permission from the copyright owner

phase. Thus, growth phase affects many cellular properties. Among these is the stress response. In M. mazeii S-6, both the basal (constitutive) and heat-shock induced levels of the mRNAs from the molecular chaperone machine genes are affected by growth phase, as illustrated by the data in Fig. 7, which pertain to the hsp70(dnaK) gene [63]. The highest levels of this gene’s mRNA were induced by heat stress in cells in early stationary phase, as compared to the levels induced in cells in the exponential and late-stationary phases. The highest basal levels of mRNA were observed in cells in late stationary phase. This, together with the diminished response to heat stress, suggests that cells in late stationary phase are stressed. The degree of stress gene activity in late-stationary phase in the absence of an added stressor is higher than in the other two phases. Concomitantly, in late stationary phase, the capacity to respond to an added stressor is impaired.

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Fig. 6. Effect of ammonia on grpE, hsp70(dnaK), hsp40(dnaJ) and orf11-trkA mRNA levels in M. mazeii S-6. Total RNA was extracted from single cells cultivated in medium with the standard concentration of NH4Cl (i.e., 1 g/L; lanes A), or from cells incubated for 30, 60, or 180 min in medium containing either 10 (lanes B, C, and D, respectively) or 25 (lanes E, F, and G, respectively) g/L of NH4Cl, and electrophoresed (10 µg/lane) in a denaturing gel. The upper portion of each panel represents the gel stained with ethidium bromide showing the 23S and 16S rRNAs, whereas the bottom portion displays the respective Northern blot hybridized with probes for grpE (top left panel), hsp70(dnaK) (top right), hsp40(dnaJ) (bottom left) and orf11trkA (bottom right). The sizes of the rRNAs, and those of the hybridization bands in kilobases (kb) are indicated to the right. Reproduced from [63] with permission from the copyright owner

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dnaK 37°C

45°C 1 2 3

Fig. 7. Response of hsp70(dnaK) to heat shock in M. mazeii S-6 cells at different growth phas-

es. Top panel. Slot blots of total RNA (10 µg/slot) from cells in exponential (1), and early (2) and late (3) stationary phases, before (37°C) and after (45°C) a heat shock at 45°C for 30 min, hybridized with a probe for hsp70(dnaK). Bottom panel. Densitometric readings (OD. X mm) of the slot blots shown in the top panel, before and after heat shock (open and hatched bars, respectively). Reproduced from reference [63] with permission from the copyright owner

The observations describe above are critical for developing strategies to monitor the performance of microbes in bioreactors, and to control and improve the microbial populations so that they are always capable of responding to stressors, and to proceed with methanogenesis, despite changes in growth rates and environmental factors. 6.6 Other Methanogens

The studies referred to above were done using M. mazeii S-6, which is a key organism in mesophilic methanogenic ecosystems, including bioreactors [19, 68–73]. There are, in addition, data pertaining to M. thermophila TM-1 (OTG 50°C), which is important for methanogenesis in many thermophilic ecologic niches and bioreactors [15, 17, 18, 71, 74, 75]. The effect of heat-shock of various durations on the chaperone machine genes of M. thermophila TM-1 was determined. Illustrative data for

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hsp70(dnaK) and hsp40(dnaJ) are shown in Fig. 8, top panel [14]. Cells were heat-shocked at 60 or 65°C for 15, 30, or 60 min. Gene transcripts were minimal before stress, but after a 60-min heat shock at 60°C they increased. A similar trend was already evident after a 15-min heat shock at 65°C, and the levels of transcripts were still higher after longer heat shocks by comparison with cell heat-shocked at 60°C. The mRNA from hsp40(dnaJ) increased little under all the conditions tested, suggesting that this gene is regulated differently as compared with hsp70(dnaK). It is clear from the results in Fig. 8 that 65°C is a critical temperature for M. thermophila TM-1. The cells suffer a stress and mount a stress response after only 15 min of exposure at 65°C. It may be concluded that assessing the levels of hsp70(dnaK) transcripts will provide a sensitive indicator of TM-1-cell stress in a bioreactor with temperatures above 60°C. By the same token, the data also indicate that the bioreactor temperature must be maintained below 60°C to insure the well being of M. thermophila TM-1 (and the same is probably true for other methanogens that have OTGs similar to TM-1). The response of M. thermophila TM-1 to ammonia stress was also studied [14]. An increase in the mRNA from hsp70(dnaK) was induced by all three ammonia doses tested: 5, 10, and 25 g/L in the series of experiments shown in Fig. 8, bottom panel. Interestingly, the effect of 5 g/L correlated with exposure length: no mRNA increase after a 10-min exposure, clear increase after 30 and 60 min, and clear but slightly lower increase after 120 min. A dose-dependent response to ammonia stress was also observed for the trkA gene, Fig. 8, bottom panel, lower section.A clear increase in this gene’s transcript was produced by the 5 g/L-10 min dose. Longer exposure times caused transcript increases that were less and less marked as the times increased. A different pattern was observed for the 10 g/L dose. The trkA mRNA augmented progressively as the incubation time with ammonia increased from 10 to 60 min, but a 120-min exposure caused about the same effect as 60 min. The highest dose tested, 25 g/L, caused the same increase in the trkA mRNA levels at all the incubation times tested with ammonia, except for the 30-min exposure, which produced a slight but evident higher increase.

Fig. 8. Top panel. Response of M. thermophila TM-1 to heat shock. Northern blotting of total RNA (10 µg/lane) from TM-1 hybridized with a probe for hsp70(dnaK), top and middle sections, or for hsp40(dnaJ), bottom section. Lanes 1 and 5 contained RNA from cells maintained at 50°C. The other lanes contained RNA from cells heat-shocked for 15 min (lanes 2 and 6), 30 min (lanes 3 and 7), or 60 min (lanes 4 and 8), at 60°C (top section) or at 65°C (middle and bottom sections). The left half of each section displays the gel stained with ethidium bromide to show the 16 and 23 rRNAs, while the right half shows the respective Northern blot with the size of the hybridization bands in kilobases (kb). Bottom panel. Response of M. thermophila TM-1 to increasing concentrations of ammonia assessed by slot blotting to determine levels of hsp70(dnaK) and trkA transcripts after incubation with the stressor for various time periods. Total RNA was extracted from exponentially-growing cells and 10 µg of RNA/slot was used. The blots were hybridized with biotin-labeled probes for hsp70(dnaK) and trkA (top and bottom sections, respectively). In each section the slot blots are at the bottom, and the vertical bars above the slot blots represent the respective densitometric readings. Reproduced from reference [14] with permission from the copyright owner

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These data, together with those obtained with M. mazeii S-6 and described in the previous Sect. 6.4, indicate that the stress response to the stressor ammonia is dose dependent, and that it must be tightly regulated. The levels of ammonia in the medium in which the cells grow, and the length of time during which the cells are exposed to this compound, play critical roles in determining the degree of the stress response to ammonia (and probably have distinctive effects on the response to other stressors that may act simultaneously in real-life situations). The implications for bioreactor management and technology of the data discussed above are manifold. For example, the level of stress caused by ammonia can be monitored by assessing the levels of mRNAs from one or more stress genes, including trkA. Also, it is clear from the data that ammonia can be a powerful cell stressor, and that its effects are more pronounced as the dose and exposure times increase. Lastly, a time of exposure to elevated levels of ammonia as short as 2 h will cause severe cell stress. The effects of relatively long heat shocks on both Methanosarcina species are illustrated by data in Fig. 9 [63]. The results show that, contrary to bacteria [12], M. mazeii S-6 and M. thermophila TM-1 (and most likely many other methanogens) can withstand heat shocks longer than 15–30 min without a decrease in the stress response as measured by the levels of the hsp70(dnaK)locus gene products. Even after a 3-h heat shock, the M. mazeii S-6 hsp70(dnaK) mRNA is quite increased.

Fig. 9. Response of the hsp70(dnaK) gene from M. mazeii S-6 and M. thermophila TM-1 to heat shocks of various durations. Left panel. Northern blots of total RNA (10 µg/lane) extracted from S-6 cells before heat shock (lane 0, in both sections), or after a heat shock at 45°C for the length of time indicated in the horizontal axis, in minutes (min) or hours (h). Hybridization was done with a probe for hsp70(dnaK). The size of the hybridization bands in kilobases (kb) is indicated to the right. Right panel. Total RNA (20 µm/lane) from M. thermophila TM-1 was run in a denaturing gel and stained with ethidium bromide to show the 23S and 16S rRNAs (top section). The respective Northern blot obtained with a probe for hsp70(dnaK) is shown below (bottom section). Lanes from left to right are: RNA from cells maintained at 50°C (lane 0, non-heat-shocked) and RNAs from cells heat-shocked at 60°C for 1, 2, or 3 h (lanes 1, 2, and 3, respectively). The size of the hybridization bands in kilobases (kb) is shown to the right. Reproduced from reference [63] with permission from the copyright owner

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The capacity of methanogens to respond efficiently to temperature elevations above their optima, even if they are exposed to these temperatures for hours, is a useful and encouraging feature. It shows that the need to correct the temperature of a bioreactor before it causes irreversible cell damage is not of extreme urgency. Likewise, it also shows that any measure that would improve the cell’s stress response, even slightly, will add time to the period during which corrections to the bioreactor conditions can be made. This time gain should greatly facilitate bioreactor operation, and reduce the frequency of total, irreversible failures. 6.7 Co-Chaperones

In view of the discontinuous distribution of the chaperone machine among Archaea and the occurrence of chaperonins in these organisms (see following Section), it is pertinent to ask whether Archaea have the co-chaperones – also named chaperone co-factors – known to coexist, and interact with the machine and the chaperonins in bacteria and eukaryotes. Examples of cochaperones are the bacterial trigger factor (TF), and the eucaryal Hop, Hip, BAG-1, and NAC. A recent survey of five fully sequenced archaeal genomes, including one with the machine and four lacking it, but all containing chaperonins, showed absence of conservation of the genes encoding the co-chaperones [75a]. There were no genes readily identifiable by common genome-searching methods as being the homologues of the five co-chaperones listed above. However, two families of molecules were identified that might be related to Hop and to one of the subunits of NAC. These results, which open the road to a more detailed analysis of the chaperoning mechanisms in Archaea as compared to those of bacteria and eukaryotes, are available in the Internet at: http://www.bioscience.org/2001/v6/d/macario/fulltext.htm

7 The Hsp60 (Chaperonin) System in Methanogens 7.1 Examples

Extensive descriptions and discussions of the archaeal chaperonin system are available in the literature [7, 10, 12, 76, 77]. Therefore, only aspects directly or indirectly pertinent to methanogens will be touched in this chapter. Examples of chaperonins in methanogens are listed in Table 7. The functions of these genes’ products have not been elucidated in any detail either in vivo or in vitro. However, extrapolating from what is known from studies in other archaeal, non-methanogenic species, and in eukaryotes and bacteria (references in [12]), it may be said that the chaperonins of methanogens are likely to play a critical role in de novo protein biogenesis. They may also play a role during the stress response, and in the cell recovery after stress, but these roles although probable have not yet been demonstrated.

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Table 7. A sample of hsp60 (chaperonin) genes in methanogens a

Organism

Chaperonin (accession number)

Base pairs

Promoter

Terminator

RBS b

Methanobacterium thermoautotrophicum

Chaperonin (a-subunit) (mt0794)

1617

n.r. c

n.r

n.r

DH

Chaperonin (mt0218) 1659

n.r.

n.r.

n.r.

Methanococcus jannaschii

Chaperonin (U67542)

1626

n.r.

n.r.

n.r..

Methanococcus thermolithotrophicus

MTTS (AB015435)

1632

tttatata (–75) d

t-rich region (+20)

n.r.

Methanopyrus kandleri

Thermosome (Z50745)

1635

tttaaata c-rich region (–60), atgc (–42)

a b c d

aggtgat (+18)

Data extracted from reference 12, with permission from the copyright owner. RBS, ribosome binding site. n.r., not reported. (–) and (+) refer to position of center of sequence upstream from the translation start codon or downstream from the translation stop codon, respectively.

7.2 Structure and Potential for Bioreactor Technology

The chaperonins form multimeric complexes of comparatively very large size (thousands of kDa) with a spheroidal or cylindrical shape, and with a central cavity that serves as a protected chamber inside which polypeptide folding is thought to occur [12]. The implications for biotechnology and bioreactors are significant. The physiological performance of methanogens is tied to a protein balance within the normal range. Protein balance here is understood as the entire set of proteins in a cell, which is composed of many functionally distinct subsets. Each subset must be maintained within the normal range of number of molecules (concentration), and each molecule must be kept with its structural and conformational integrity (native configuration). Most of these properties are maintained by the concerted action of the molecular chaperone machine, the chaperonins, and other molecules including proteases. It follows that research ought to be directed towards the elucidation of how the chaperonin system in methanogens with different OTGs, and pertinent to anaerobic digestion of wastes (e.g., M. mazeii S-6 and M. thermophila TM-1, and others) assembles itself and functions under normal circumstances, and under stress. Regulation of the chaperonin genes ought to be clarified in order to manipulate them in a way that will maintain protein biogenesis during stress to ensure continuous biomethanation.

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8 Other Stress or Stress-Related Molecules, Genes and Proteins, and Anti-Stress Mechanisms in Methanogens 8.1 Examples

A number of molecules different from those discussed in the preceding Sections appear, or increase in concentration, in response to stressors [12]. Illustrative examples of molecules that have been studied are listed in Table 8. These molecules are integral parts of the stress response as the molecular chaperones and chaperonins. Similar genes, and other pertinent examples have been identified in the genomes of methanogenic species that have been fully sequenced, as shown in Table 9. Also, some methanogenic species have a cell envelope with extraordinary stability [42, 78]. 8.2 Osmolytes

Some of the molecules listed in Table 8 (e.g., inositol compounds) are not proteins and participate in maintaining the internal osmotic pressure. Compounds of this sort are called osmolytes and they come into action when the osmolarity of the medium surrounding the cell increases or decreases (osmotic shock) [13, 79–81]. These compounds are of paramount importance for cells that inhabit hypersaline environments, or that suddenly encounter such environments, for example when the influent of a bioreactor contains unusual concentration of salts. In addition, the enzymes that participate in the synthesis import of osmolytes are also stress proteins in as much as they must be active during stress, and must produce the osmolytes to protect the cells from osmotic stress. 8.3 TrkA

TrkA has been identified in M. mazeii S-6, M. thermophila TM-1, and in other methanogens whose genomes have been fully sequenced [14, 62, 66]. It is a protein member of the Trk K+ transport system in Escherichia coli and other bacteria [82, 83]. By analogy, the archaeal homologue is assumed to be also involved in the transport of this cation. TrkA is involved in maintaining the K+ balance of the cell [82, 83]. The internal K+ balance of methanogens in anaerobic bioreactors may be affected by factors in the immediate environment. Ammonia is one of these factors, known to inhibit methanogenesis [55, 56]. Ammonia may reach inhibitory levels when a bioreactor is fed with protein-rich wastes or swine manure, for example [53]. The unionized ammonia causes a pH increase. An intracellular pH increase will result in a K+ efflux coupled to a H+ influx in order to counteract the pH increase [55, 84]. TrkA is involved in this counter transport. As described in a previous Section, the trkA of both M. mazeii S-6 and M. thermophila TM-1 respond to

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Table 8. Other examples of stress, or stress-related, genes and proteins that have been found

and studied in methanogens a Gene/protein

Protein mass Organism (kDa)

Presumed function

Inducer

Crx protein trio

40.8, 42.3, and 42.9

Methanobacterium bryantii

Copper or general resistance

Copper

Betaine transporter n.r. b

Methanosarcina thermophila TM-1

Maintains internal ionic balance

Osmotic stress

Inositol compounds

n.r.

Methanococcus igneus

Maintains internal ionic balance

Osmotic stress

TrkA

44.1

Methanosarcina mazei S-6

Maintains internal K + balance

Ammonia

Prefoldin or

14–23 c

Methanococcus jannaschii;

Protein folding

n.r

Methanobacterium thermoautotrophicum

n.r.

n.r.

n.r.

GimC

Small heat-shock protein (sHsp)

n.r.

Methanococcus jannaschii

RNA stabilization, thermotolerance

ClpB

n.r.

Methanosarcina acetivorans

Affects growth and n.r. survival at hightemperatures, involved in proteolysis

PPIase (peptidyl prolyl cis-trans isomerase)

19.4–31 d 16 or 42 d

Methanococcus thermolithotrophicus,

Accelerates rate limiting step in protein folding

n.r.

Proteasome

24 and 22 e

Methanosarcina hermophila TM-1, Thermoplasma acidophilum

Protein degradation

n.r.

25.8 and 22.3 e a b c d e

Data extracted from reference 12 with permission from the copyright owner. See also Table 9. n.r., not reported. Six subunits within the indicated size range in eukaryotes but only two subunits in Archaea. Depends on the method used. a- and b -subunit, respectively.

ammonia stress as shown by an increase of its mRNA. Since it may be assumed that this gene’s product, TrkA, is involved in maintaining a physiological level of intracellular K+ also in methanogens, and since this cation is essential for the molecular chaperone machine to function [refs. in 62, 66, 67], one may hypothesize that trkA is a stress gene. It probably plays a major role in cell physiology and survival in bioreactors. More research on this interesting topic with many

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Table 9. Stress related gene/protein-homologues identified in sequenced genomes from methanogens a

Organism

Gene/protein

ID b

Methanococcus jannaschii

Heat-shock protein X Heat-shock protein 31 DNA repair protein 45 DNA repair protein RAD51 DNA repair protein RAD2 PPIase PPIase Proteasome a-subunit Proteasome b-subunit Survival protein

MJ1682 MJ0285 MJ0869 MJ0254 MJ1444 MJ0278 MJ0825 MJ0591 MJ1237 MJ0559

Methanobacterium thermoautotrophicum DH

Heat-shock protein X Heat-shock related protein X Heat-shock protein class I DNA repair protein rad2 DNA repair protein rad51 DNA repair protein radA DNA repair protein rad32 PPIase PPIase B Proteasome, a-subunit Proteasome, b-subunit Survival protein (SurE)

MTH569 MTH1817 MTH859 MTH1633 MTH1693 MTH541 MTH1383 MTH1125 MTH1338 MTH686 MTH1202 MTH1435

a b

Excluding the Hsp70(DnaK) chaperone machine and the Hsp60 (chaperonin) family. Reproduced from reference 12 with permission from the copyright owner. ID, identification number in genome project.

potential applications for the monitoring and control of bioreactors should be done. This research will lay the foundations for using the trkA gene to fortify cells and make them more resistant to the ammonia and other stressors, which may also provoke imbalances of intracellular electrolytes. 8.4 Prefoldin or GimC

Another multimeric complex named prefoldin or GimC seems to be involved in protein folding in eukaryotes and in Archaea, including methanogens [85–87]. Six subunits have been identified in eukaryotes, but only two have been found in Archaea. The role of this complex in the stress response is unclear. The genes coding for the subunits do not seem to be activated by stressors. Nonetheless, we will discuss prefoldin in this Chapter because of its probable participation in protein folding in vivo. Very little is known in this regard but current research will soon add to our knowledge of this “chaperone machine” and may unveil functions

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that are essential for survival during stress, and/or for recovery after stress. Both these functions are important for methanogens in bioreactors. The representative from Methanobacterium thermoautotrophicum named MtGimC has been studied in some detail [86]. It is a complex of 87 kDa made of two different subunits, a and b. Preliminary studies have indicated that the complex is a hexamer consisting of two a and four b subunits. The a subunit is the equivalent of the eukaryotic subunits Gim2 and Gim5, while b is the homologue of the eukaryotic Gim 1, 3, 4, and 6 subunits. A preliminary in vitro search for possible chaperone functions of MtGimC showed that it: (i) Formed a complex with unfolded actin, and bound this substrate with relatively low affinity; (ii) Suppressed aggregation of unfolded hen lysozyme (14 kDa); (iii) Prevented aggregation of chemically unfolded bovine mitochondrial rhodanese (30 kDa) and glucose dehydrogenase (39 kDa); (iv) Formed complexes with non-native dihydrofolate reductase (DHFR; 23 kDa) and firefly luciferase (62 kDa); (v) Stabilized non-native actin for at least 15 min, which allowed transfer of actin to TRiC (the eukaryotic chaperonin complex) for folding in the presence of ATP; and (vi) Prevented aggregation of unfolded rhodanese (as mentioned above) and allowed its folding by the bacterial chaperonin GroEL. While the above series of observations demonstrate a certain degree of participation of MtGimC in preventing the aggregation of partially denatured polypeptides, and in assisting folding by way of interaction with TRiC in the case of actin or GroEL for rhodanese, how much the results reflect in vivo, physiologically meaningful situations remains to be seen. Further analysis in vitro, and new studies in vivo should be done to elucidate the functions of GimC in methanogens, its mechanism of action, preferred substrates, and activity (or lack thereof) during stress. One may anticipate that important information is going to emerge from these analyses, which will be very useful in understanding the intracellular situation of stressed methanogens and in thinking of ways for coping with it so the cell will be able not only to survive, but also to continue the bioconversion pathway unabated. 8.5 Small Heat-Shock Proteins (sHsp)

The sHsp are currently the focus of active investigation in organisms of the three phylogenetic domains, including the methanogens [12]. An sHsp from Methanococcus jannnaschii has been purified and crystallized [33]. Like other stress proteins, it forms a large multimeric complex. It protects other proteins from heat denaturation and prevents aggregation of partially denatured polypeptides in vitro [34]. More research is necessary to determine the role of this, and other, sHsp in vivo. Such research should provide the basis for designing strategies to use sHsp and/or their genes for improving the mechanisms

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against protein denaturation in methanogens, and thus their resistance to stressors. In this regard, it is noteworthy that sHsp form large complexes, as the chaperonins for example do. These complexes are seemingly essential for the chaperones in general to exercise their function of assisting other proteins to fold and refold. One of the aims of research in this area, with direct implications for biomethanation technology, should be the elucidation of how the multimeric structures form, what stressors do tend to damage these structures, and what keeps them from being disrupted by stressors. Obviously, information on these areas will help in identifying the most damaging stressors, and in developing means to avoid their accumulation in a bioreactor, and tools, genetic and otherwise, to strengthen the stability of the multimeric complexes. 8.6 PPIase

PPIase (for peptidyl prolyl cis-trans isomerase) is an enzyme that is found in many organisms of the three phylogenetic domains [12]. It mediates peptidylprolyl isomerization, an important step in protein folding. There are various forms of the enzyme, similar to each other, that have been grouped into three families: cyclophilins (Cyp), FK506-binding proteins (FKBPs), and parvulins [29, 30, 88–90]. A PPIase from Methanococcus thermolithotrophicum that belongs to the FKBP family has been characterized in some detail [88]. The genes encoding other PPIases have been found in the genomes of other methanogens, as seen in Table 9. The observations described above demonstrate that methanogens possess a complex battery of tools, including PPIases, to generate and maintain a balanced set of proteins within the ranges of concentrations and configurations required for growth and survival. Study of PPIases will help in understanding protein folding in methanogens pertinent to bioreactors, and will pave the way to devising means for protecting the folding machinery from damage due to stressors. 8.7 Proteases

Proteases constitute a large group of enzymes, some of which should be considered under the umbrella of stress.We will not discuss them here in any detail but refer to reviews available in the literature [3–5, 91, 92]. Suffice it to say that proteases are involved in the degradation of abnormal proteins lest they interfered, or might interfere, with the trafficking of normal proteins and other functions inside the cell. Abnormal protein in this context means molecules that are partially or completely unfolded due to stress or to some structural alteration (mutation, or post-synthetic modification that went wrong). These abnormal molecules tend to misfold, aggregate, and build up precipitates. If these are too large, they will be an obstacle to the physiological movement of molecules inside the cells, and cause a disturbance in many functions. Hence, abnormal molecules must be refolded into a correct configuration or, if this is impossible, they must be eliminated. Molecular chaperones participate in folding, refolding, dissolving aggregates, and degradation. For the latter purpose, some molecular chaperones

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present the abnormal polypeptide to the protease for digestion. When all the preventive and corrective measures aiming at keeping the proteins in the correct concentrations and configurations fail, or are overwhelmed owing to an excess of substrate for the chaperoning systems, proteases are called into action. These enzymes degrade the abnormal polypeptides and, in doing so, they not only rid the cell of aggregates, but they also generate building blocks (i.e., amino acids) for the synthesis of new protein molecules. Proteases are surely also involved in the construction of multicellular structures (see Section below), a process that requires the action of many molecules in addition to proteases. The formation of multicellular structures requires also the migration of these molecules towards the cell’s outside, as we shall discuss in a subsequent Section of the Chapter. Interestingly, proteases tend to form multimeric complexes. One example is the proteasome, which is a large multimolecular machine similar to the chaperonin complex [91–95]. 8.8 Putative Stress Genes and Proteins Found in Fully Sequenced Genomes

The availability of full genome sequences has opened the doors to computerassisted searches for stress genes, or candidate (putative) stress genes, which encode proteins likely to play a role in the stress response but which have not yet been isolated and tested in the laboratory. A sample of these genes/proteins found in two genomes from methanogens (Methanococcus jannaschii and Methanobacterium thermoautotrophicum) that have been sequenced is displayed in Table 9. Excluded from the list are the genes for the members of the Hsp70(DnaK) chaperone machine and those for the Hsp60 (chaperonin) system, both groups already discussed in previous Sections (see Tables 4–7). It is important to re-emphasize that the chaperone machine genes are not present in the genome of M. jannaschii, as discussed previously (see Table 3), whereas the chaperonin genes do occur in this methanogen and in M. thermoautotrophicum. The functions of the genes/proteins listed in Table 9 remain to be determined. This is a challenging task for the near future made attractive because of the availability of the clones that contain the genes, and the promise of information useful to devise strategies and tools for improving methanogens so that they will develop increased resistance to stressors.

9 Other Manifestations of the Stress Response 9.1 Introduction

In addition to the components of the stress response described in the preceding Sections of this Chapter, which have been identified in methanogens and other Archaea, there are other pertinent molecules, anatomical structures, and events that must be discussed [12]. These are either induced or are associated in some

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meaningful way with the stress response, the molecular chaperoning process, the development of stress resistance also called thermotolerance or stress tolerance [26], or the recovery of cells after stress. What follows is a brief account of several of these stress-related molecules, structures, and phenomena that are important for the survival and functioning of methanogens, and that have potential for the devising of means to improve bioreactor performance despite changing environmental conditions. 9.2 Thermoprotectants

Cells produce compounds that somehow improve their thermotolerance. Some of these are sugars and simple molecules such as di-myo-inositol phosphate (DIP) and cyclic diphosphoglycerate (cDPG). They have been demonstrated, for instance, in the hyperthermophilic methanogens Methanopyrus kandleri (OTG 100°C) and Methanothermus fervidus (OTG 85°C). A more detailed discussion on the functions and possible mechanism of action may be found in recent articles with pertinent bibliography [13, 81, 96]. See also Table 8. 9.3 Multicellular Structures

A few methanogenic species have the ability to build multicellular structures, either by themselves (single-species structure) or in association with one or more different species (multispecies structure) [17, 25, 42, 97, 98]. These structures may be formed in response to stressors and confer more resistance to them by comparison with the isolated cells growing as independent, free units. Morphologically, the multicellular structures appear as flat sheets of one or very few cell-diameters in thickness, or as globular masses vaguely spheroidal in shape with diameters equivalent to many (e.g., 10–20) cell diameters. The cells are kept together by an intercellular connective material, whose components are not yet fully elucidated, and that ought to be considered elements of the stress response as a working hypothesis for future research (see below). Examples of single-species multicelluar structures are produced by M. mazeii S-6, which can be flat (named lamina) or globular (named packet), as illustrated in Fig. 10 [97, 98]. The packet morphotype is considerably more resistant to mechanical, chemical, and physical stressors, and to antibiotics, than the singlecell morphotype (AJLM and ECdeM, unpublished data). For instance, induction of a heat-shock response measurable by an increase in the mRNAs from the molecular chaperone machine genes (as shown in Figs. 2 and 3, for example) requires higher temperatures and longer exposure times in packets than in single cells. Illustrative data for the grpE gene are presented in Fig. 11 [60]. The single-cell morphotype showed a more pronounced response than that of the packets to a heat-shock at 45°C for 30 or 60 min. In fact, the packets showed a response only after a heat shock of 60 min. Other multicellular structures directly pertinent to anaerobic methanogenic bioreactors are the globular multispecies consortium termed granule [17], and

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Fig. 10. Multicellular structures formed by M. mazeii S-6. Packets (A) and lamina (B) are displayed along with the single-cell morphotype (C), for comparison (see references [97, 98]). The diameter of the single cells is 1–3 µm, and the magnification factor is the same for the three panels. The photographs were taken with phase contrast optics of wet samples from live cultures between glass slide and cover slip. Reproduced from reference [12] with permission from the copyright owner

the biofilm [72, 99, 100]. They are usually composed of a variety of methanogens and bacteria interlaced in a food web. Histological thin sections of a granule from a thermophilic bioreactor are shown in Fig. 12, where the spheroidal shape may be inferred from the visible segment of the outer profile [101]. Methanosarcinal packets can be seen in panel A, while panel B shows laminar structures formed by M. thermophila TM-1 as demonstrated by the antigenic fingerprinting method. The methanogens form colonies of various shapes and sizes that are lodged in the supporting scaffolding provided by the intercellular connective material as represented in the model shown in Fig. 13 [101]. Little is known about the mechanism of granule formation (granulogenesis) at the molecular and genetic levels, or about the biochemistry and synthesis of the components of the intercellular connective material. Also, the functions of this material, beyond the obvious mechanical support for cells, are largely unknown. These functions are surely more complex than just providing a scaffold for the growth of cellular colonies. They must also include insulation, transport of nutrients and catabolites in opposite directions, concentration of micronutrients, passive barrier or active defense against agents of various kinds (chemical, physical, and biological such as antibiotics), and others that future research ought to discover. Granules have an inner communication network made of a small tubes [101], as shown in Fig. 14. These tubes can conceivably be the route for nutrients to reach the cells inside the granule, and the way of escape for catabolites and other cellular products away from the cells. There is some information about the composition of the intercellular connective material in Methanosarcina packets [102, 103], but beyond that there is not much that would allow the developing of means to manipulate this material

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Fig. 11. Heat resistance of a multicellular structure formed by a methanogen as compared with its own single-cell phenotype. Primer-extension mapping of the transcription initiation site for M. mazeii S-6 grpE. A radiolabeled oligonucleotide primer complementary to bases 57 through 77 within the grpE coding region was used with 10 µg of total RNA from single cells (lanes 1 to 3) or packets (lanes 4 to 6) per test. Single cells and packets were grown at 37°C (lanes 1 and 4) or heat shocked at 45°C for 30 (lanes 2 and 5) or 60 (lanes 3 and 6) min. The primer-extended products were electrophoresed in a 6% acrylamide sequencing gel in parallel with the products of a sequencing reaction that was done with the same primer and the dideoxychain-termination method (lanes G, A, T, and C). These lanes show the complementary (anti-sense) strand sequence. The coding (sense) strand sequence and the initiation site (asterisk) are shown on the left. Reproduced from reference [60] with permission from the copyright owner

at the molecular and genetic levels for biotechnologic purposes. This is a very important area for investigation since efficient methanogenesis in bioreactors depends on the presence of a stable population of microbes retained in position within the granule, and inside the bioreactor, in the appropriate spatial relationship with one another. This three-dimensional distribution of different species is key to the metabolic interactions between them, as required by the food web leading to waste bioconversion with generation of methane.

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Fig. 12. Spheroidal multicellular structure (granular consortium, or granule) formed by methanogens associated with bacteria in a thermophilic (50°C), anaerobic, methanogenic bioreactor, as seen in a thin histological section. (A) Cross section of the granule showing the cortex and medulla (see reference [101]) and a large island of methanosarcina packets (arrows). Hematoxylin-eosin (magnification ¥800). (B) Another section of the same granule in which the presence of Methanosarcina thermophila TM-1 (optimal temperature for growth, 50°C) is demonstrated with a antibody probe for TM-1 by immunofluorescence. The methanosarcina cells are arranged mostly in laminae (see Fig. 10; magnification ¥4000). Reproduced from reference [12] with permission from the copyright owner

Most likely, granules are among other things a mechanism to protect the cells from stressors, as may be inferred from the data mentioned above, obtained with methanosarcinal packets. Also, because of their large size and weight as compared with individual cells, the granules will not be washed away by the circulating bioreactor contents, and thus they will maintain a steady functional profile.

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Fig. 13. Computer-assisted three-dimensional representation of a granular consortium like

the one shown in the preceding figure, depicting the aggregates or bundles formed by methanogens. Methanobacterium thermoautotrophicum, surface (SC) and inner (IC) colonies; Methanosarcina thermophila packets (P) and laminae (L); Methanosaeta (Methanothrix) rods in bundles of more or less intertwined filaments (Mx); Methanobrevibacter arboriphilus (Ma), clouds that appear in cross-section as lawns of variable density; and Methanobrevibacter smithii (Ms), thin clouds that look like sparse lawns in cross-section. The filaments formed by the Methanosaeta rods are shown for the sake of clarity in only two areas but they are more generalized. Reproduced from reference [101] with permission from the copyright owner

The structure of the granule is complex (see Figs. 12–14) [75, 101, 104], including well defined zones, such as the cortex and the medulla, and subzones that probably represent functionally specialized areas. In addition, there are the small tubes (Fig. 14), which provide still another proof that a granule is a complex anatomic structure with a complicated physiology, seemingly well equipped to withstand stressors. It is then important to realize that understanding how a granule forms and maintains its integrity as a functional unit in an environment as full of stressors as the bioreactors influents is essential for the developing of means to monitor and control biomethanation, and to correct it when the bioreactors malfunction. The same type of considerations apply to the biofilm [70, 72, 73, 99, 100, 105], an example of which is presented in Fig. 15. Fortification of cells to withstand stressors should, therefore, also include improvements in their granule- or biofilm-formation ability. In this regard, all the molecules that form the intercellular connective material and the enzymes that synthesize as well as those that translocate them to the cell’s outside should be considered components of the stress response.As such, they should be targets for investigations aiming at developing means to improve granulogenesis, and biofilm formation, and, thereby methanogenesis.

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Fig. 14. Superficial, histological thin section of a granule like the one shown in Fig. 12, passing through the cortex. Visible are circular openings that are cross-sections of the tubes that crisscross the granule (possibly communicating different zones of it between themselves and with the immediate surroundings of the granule [101]). Hematoxylin-eosin (magnification ¥800). Reproduced from reference [12] with permission from the copyright owner

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Fig. 15. Example of a microbial consortium in the form of biofilm made of methanogens and associated, syntrophic bacteria visualized by scanning electron microscopy (SEM). The biofilm was attached to the substratum (curler-type polypropylene) in a fixed-bed anaerobic methanogenic bioreactor processing synthetic waste water containing acetate, propionate, and butyrate at 35 °C. The samples were collected from the top (A and B), middle (C and D) and bottom (E and F) of the bioreactor 57 days after its inoculation with sludge from another digestor treating municipal sewage. Discernible are cells that were identified as related to M. mazeii S-6 (single cells, 1), Methanosaeta (Methanothrix) soehngenii (2), Methanospirillum hungatei (3), and Desulfovibrio sp. (4). M. mazeii occurred as single cells (best visible in B) and as laminae (see Fig. 10). The exopolymer of the intercellular connective material in the laminae appeared as filaments, as illustrated in D. Scale bars (in µm) are shown at the rightbottom corner of each panel. Reproduced from reference [73] with permission from the copyright owner

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10 Perspectives and Applications 10.1 Introduction

The study of the stress response, stress genes and proteins, other components of the stress response, and molecules and phenomena pertinent to resistance against stressors and recovery after stress is essential to deal with stressors, counteract them, and avoid or abate their effects. Stressors of many kinds reach bioreactors with the influent, or are produced inside the bioreactors. It is therefore of paramount importance to develop means to deal with the problems caused by stressors. As repeatedly stated in this Chapter, before preventive and corrective means can be developed, information from basic and applied research is needed. This research should focus on several topics, some of which will be dealt with in the following portions of this Chapter. 10.2 Diversity of Methanogens

In the preceding Sections we have referred to methanogens in bioreactors and focused chiefly on M. mazeii S-6 and M. thermophila TM-1. These two Methanosarcina species are key for methanogenic bioconversion in meso- and thermophilic environments, respectively. However, other methanogens also occur in bioreactors, Fig. 13. In fact, there is considerable diversity of methanogenic species, strains, and immunotypes in bioreactors as demonstrated as early as 1988, Fig. 16 [69]. An important conclusion drawn from these and subsequent findings is that the study of the stress response, stress genes and proteins, and anti-stress mechanisms should be extended to other methanogens, in addition to methanosarcinas (see also Sect. 10.6). 10.3 Dynamics of Methanogenic Subpopulations in Bioreactors

Qualitative and quantitative analyses using immunologic and other complementary methods have revealed that the population of methanogenic organisms in bioreactors (and several other ecosystems) is composed of subpopulations, each of these representing a different species [15, 18, 69–71, 74, 75, 100]. Subpopulations have also been identified within a single species. Time-course studies have demonstrated that methanogenic subpopulations change in distribution and in size (number of organisms in each subpopulation), during bioreactor operation [70, 75]. An illustrative study is displayed in Fig. 17. A few species of methanogens identified in a bioreactor fed with sulfite evaporator condensate were followed over a period of 14 months [70]. Some stressful manipulations were done to the bioreactor during this period. Methanogenic subpopulations were assessed by qualitative and quantitative methods at different time points. The results showed that:

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Fig. 16. Diversity of methanogens in bioreactors. Methanogens identified in a series of 14 different bioreactors (DIGESTOR A-N) with antibody probes and the antigenic fingerprinting method using indirect immunofluorescence and the quantitative slide immunoenzymatic assay, SIA. The variety of methanogens occurring in these bioreactors as a group and in each one of them is evident from the total number (14) of species identified and the range of species found in the individual bioreactors (from one, bioreactor K, up to 8, bioreactor B). In most cases the methanogens found were not identical to the reference species, neither were they of the same immunotype within each species identified.Abbreviations are: Mx., Methanothrix; Msp., Methanospirillum; Mbr., Methanobrevibacter; Mc., Methanococcus; Mb., Methanobacterium; Ms., Methanosarcina. Reproduced from reference [69] with permission from the copyright owner

(i) The subpopulations differed in size at the beginning, thus adding an extra dimension (quantitative) to the diversity already evident from the variety of species present; (ii) The subpopulations closely related to Methanobrevibacter smithii ALI and M. mazeii S-6 were the most abundant at the beginning, while the subpopulations closely related to Methanobacterium formicicum MF, Methanobrevibacter arboriphilus AZ, and M. arboriphilus DC, were the smallest;

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Fig. 17. Diversity and dynamics over time (months) of methanogenic subpopulations in bioreactors subjected to manipulations known to cause cell stress (e.g., change in pH, nutrients’ availability, and configuration of functional space). Samples A and D (abscissa) were taken from different levels inside the chamber at the beginning, when the bioreactor reached stable conditions, i.e., steady flow of substrate and yield of biogas. Seven months later, sample F was obtained at a time in which modifications in pH and substrate chemical oxygen demand (COD) were introduced. Immediately thereafter, configuration changes were also made, and seven months later, samples H and J were collected, from different levels. Methanogens were identified and quantified in each sample as follows: Methanobacterium formicicum MF (a); Methanobacterium arboriphilus AZ (b) and DC (c); Methanobrevibacter smithii ALI (d) and PS (e); a rod related to Methanosarcina thermophila TM-1 (f); Methanosarcina barkeri W (g); and Methanosarcina mazeii S-6 (h). Each bar represents the number (arithmetic mean ± range; n = 2) of organisms per species identified – that in most cases were not identical to the reference organism. In g and h, open and closed bars represent the packets and single-cell morphotype, respectively, of M. mazeii S-6 (see Fig. 10). The wavy lines at the top of the bars for samples H and J in panel f indicate abundance beyond the quantifiable by the method used. Reproduced from reference [70] with permission from the copyright owner

(iii) Some subpopulations increased after seven months (e.g., MF, DC, ALI, and Methanobrevibacter smithii PS), while others remained the same (AZ) or decreased (Methanosarcina barkeri W, and S-6); (iv) Seven months after stressful manipulations of the bioreactor performed within a short interval, which had caused a decrease in all methanogens (data not shown), practically all the subpopulations had recovered; (v) The two Methanosarcina species recovered to some extent but only in the form of packets, namely the phenotype most resistant to stressors; (vi) The presence of Methanosarcina packets at the end of the observation period was even more striking if one considers that at the beginning there were virtually only single cells, and suggests that stressors along the way selected against single cells and perhaps induced them to form multicellular structures.

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Efforts to improve bioreactor operation and yield ought to take into account the diversity of methanogens involved and their time-course dynamics in terms of quantity, described above. Those species more productive of methane will have to be targeted first for improving their anti-stress machinery using genetic engineering procedures, or stress-gene inducers that are not harmful, such as drugs that mimic physiologic stress-gene inducers. These drugs could be added to the influent at doses predetermined to induce stress genes without secondary, unwanted effects on the cells. 10.4 Diversity of Stressors

A major goal of future research aiming at improving bioreactor technology should be the identification of stressors that might affect methanogens and other pertinent microbes. A list of representative stressors for all kinds of cells is displayed in Table 1, but only a minority of them have actually been tested with methanogens, as shown in Table 10. These stressors are relevant to bioreactor technology because they are found in relatively high levels in the effluents from many factories, homes, farms, and other man-made sources that require anaerobic bioconversion in bioreactors. 10.5 Diversity of Response

Another important task for the near future will be that of characterizing in detail the response to the different stressors that are relevant to methanogenic biotechnology. It is well established that a series of components of the stress response are the same for any stressor. These are the basic or common components, which are those discussed in this Chapter for the most part. It is very likely, however, that the stress response has, in addition to the basic components, other elements Table 10. Examples of stressors, other than heat, tested with methanogens a

Stressor

Organism

Hyperosmolarity

Methanococcus igneus, Methanococcus thermolitholitrophicus, Methanosarcina thermophila TM-1, Methanosarcina mazei S-6

Pressure

Methanococcus thermolithotrophicus, Methanococcus jannaschii

Ethanol

Methanococcus voltae

Copper

Methanobacterium bryantii

Cadmium

Methanosarcina mazei S-6

H2O2

Methanococcus voltae

Ammonia

Methanosarcina mazei S-6, Methanosarcina thermophila TM-1

Sound

Methanosarcina mazei S-6

a

Data extracted from reference [12] with permission from the copyright owner.

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that are specific for each stressor or family of similar stressors. It will be extremely interesting and useful to identify at least some of the stress-response components that are specific for each of the stressors most relevant to biomethanation technology. They could be genes/proteins, signal transducers, membrane sensors or receptors, gene-activating and gene-repressing factors, molecules for signaling the formation of multicellular structures, etc. Also, the mechanism of action of some of these molecules may differ depending on the stressor. A case in point would be a gene activator that would induce a stress gene by one mechanism (e.g., using a heat-shock cis-acting element) if the stressor is heat, and by another if the stressor is a heavy metal (e.g., by interacting with a metal element in the DNA instead of binding to a heat-shock element). One can also hypothesize that the response to stress by ammonia implicates DNA elements and transcription and regulatory factors that are different from those used in the response to a heat shock, at least for the activation of the trkA gene. 10.6 Diversity of Methanogenes: A Source of Useful Microbes?

A rational approach to the improvement of bioreactor technology includes the manipulation of relevant genes to construct better methanogens, more resistant to stressors and also more efficient bioconverters. This must be based on knowledge provided by basic and applied research on the molecular biology and biochemistry of the various components of the stress response, as outlined throughout this Chapter up to this point. A second avenue towards assembling a very efficient and resistant microbial population inside a bioreactor is the search for “good” microbes in natural ecosystems. If one or more are found with the characteristics required, they could be used for bioconversion in bioreactors, or as a source of useful genes. The diversity of methanogens we have mentioned several times before probably reflects their universality [106]. They can be found in a wide variety of ecologic niches. An idea of the ubiquity of methanogens is provided by data in Table 11 [AJLM and ECdeM, unpublished data]. In it, we have listed the sources of methanogenic isolates recorded as tested and identified immunologically in our laboratories between 1981 and 1986, and the number of isolates from each source. The variety of sources is evident and encompasses ecologic niches with very different characteristics. We have also identified methanogens in other ecosystems, different from those mentioned in Table 11, such as the Antarctic continent [107], deep subterranean aquifers [108], and temperate marine waters [109], just to mention a few. In each ecosystem explored, whenever it was possible to study at least a few isolates, species diversity was evident, and within species a diversity of immunotypes was usually discovered. For example, 46 methanogens isolated from human feces were all identified as Methanobrevibacter smithii, but they were distributed into at least seven groups with distinctive antigenic mosaics demonstrable with a panel of six monoclonal antibodies [110]. These findings show that the diversity of methanogens, even within a single species, is quite remarkable, and that with the appropriate tools

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Table 11. Methanogenic isolates from various ecosystems identified by antigenic fingerprinting during the period 1981–1986

Isolate Source Country

Ecosystem

USA USA USA USA USA The Netherlands Germany; USA Germany Germany USA Germany; France USA United Kingdom Japan; USA; France Germany; USA; France Canada; Germany; Japan; New Zealand; USA

human feces dental plaque animal feces rumen herbivores cockroach digestive tract marine ciliate marine sediments hot spring swamp peat lands soil fresh-water sediments landfills waste-water sludge bioreactors (digestors)

Total studied

Antigenically identifiable Yes

No

67 14 16 11 2 1 14 1 1 5 4 5 12 5 39

67 12 11 9 2 0 12 1 1 1 3 5 11 5 33

0 2 5 2 0 1 2 0 0 4 1 0 1 0 6

undetermined

21

17

4

TOTAL

218

190 (87%)

28 (13%)

(e.g., a panel of calibrated antibody probes) this diversity can be demonstrated fairly easily. Another source of methanogens for possible use in biotechnology is the sea. In one study of the water column of the Chesapeake Bay, we demonstrated several species, Fig. 18. Some were related more or less closely to the reference organisms available in culture collections but others were not [109]. The deeper the water layer, the more abundant were the methanogens less similar to the known species. The diversity of methanogens demonstrated by the antigenic fingerprinting method is phenotypic. It might not reflect to the last detail structural diversity at the genome level. Nevertheless, phenotypic diversity does suggest genomic differences, particularly functional ones. It shows that even if the gene contents of different genomes are very similar, their functional patterns are not. Genes active in one phenotype may be inactive in another. Thus, there is diversity in the pattern of regulatory mechanisms. What are the regulatory genes involved in determining the phenotypes? Which are the most useful phenotypes? An important endeavor in the near future should be the identification of useful phenotypes, and of the genes involved in producing them. It will then be possible to search for the “good” microbes, stress resistant and efficient for biomethanation, or to make them by means of genetic engineering procedures. It is evident from the data described above, more recently confirmed by others with different methods [111], that microbial diversity is probably enormous,

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Fig. 18. Diversity of methanogens in an aquatic ecosystem. Methanogens were isolated from the water column of Chesapeake Bay (USA) and characterized by antigenic fingerprinting and other methods. Samples for isolating the microbes were collected from three different layers of the water column (abscissa). Twelve, eight, and thirteen isolates from the upper, middle (pycnocline), and lower layer, respectively, were characterized and identified. Each isolate is represented by a square with the number inside indicating the species-strain most closely related, as follows: 3, Methanosarcina barkeri MS; 6, Methanosarcina barkeri R1M3; 18, Methanosarcina mazeii S-6; 19, Methanosarcina barkeri W; 20, Methanosarcina thermophila TM-1; and 0 (zero), unrelated to the known reference methanogens. The striped bars indicate the percentage of methanogenic isolates that were unrelated to the reference organisms, namely they were novel methanogens, not yet available in the pure-culture collections. The relative abundance of isolates related to the Methanosarcinae is noteworthy, as is the increased abundance of novel methanogens with increasing water-column depth. Data extracted from reference [109] with permission from the copyright owner

and that what we have so far uncovered is only a very minimal portion of it. We have seen only the tip of the iceberg, as it were. Hence, there is hope that a search for methanogens in nature will yield abundant dividends in terms of species useful for methanogenic biotechnology, endowed with the necessary resistance to the stressors that usually threaten bioreactor stability and efficiency. This search for naturally “good” organisms can be complemented with genetic engineering to make them optimal not only to withstand stress but also to proceed through the methanogenic pathway with speed and efficiency.

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10.7 Cooperation Between Molecules and Between Cells

Stress proteins like the molecular chaperones function as members of a team or molecular machine with several interconnected and interacting parts. Members of a machine, for example the Hsp70(DnaK)-Hsp40(DnaJ)-GrpE molecular chaperone machine, interact with each other, and also with other molecules and machines [6, 9, 43, 49, 52]. Chaperonins and sHsp also assemble into large multimeric complexes [7, 11, 33, 76, 77]. It is obvious that natural selection has favored these complexes, and one must infer that they are functionally better than the sum of the separate activities of their single components. Alternatively, one might think that multimerism is a requirement for the functioning of certain types of molecules as multicellular communities (tissues, organs, and their primitive prokaryotic counterparts) would be for cells. A parallelism to molecular multimerism seems to occur with the tendency to form multicomponent (communities, tissues, organs) structures by cells. These forms of association for function seem to be far more effective under physiologic circumstances and in the face of stress than solitary molecules or cells. Molecular machines, tissues and organs, and microbial consortia, all appear to be landmarks of evolutionary success. The main conclusion one may draw from these observations is that strategies for optimizing bioreactor technology ought to include the development of means that enhance the formation of multicomponent machines at the molecular and cellular levels. 10.8 Proteases as Builders

Proteases are essentially destructive in as much as they degrade molecules into smaller parts [4, 5, 91]. However, this process may be essential in some instances for building complex multicellular structures. Enzymatic digestion of molecules by proteases frees the space occupied by the initial, larger whole, when the smaller parts have been used up or removed (e.g., washed away with fluids in circulation, or engulfed by cells). The freed space can then be occupied by another component of the complex, this time a more appropriate one for that particular location. Alternatively, the voided space may remain empty of solids and thus become a vesicle or tube for storage or circulation, respectively. It is likely that proteases take an active role in the formation of the tubes that crisscross the granular microbial consortia discussed earlier in this Chapter, and shown in Fig. 14. There can be little doubt that this internal circulatory system is essential for the survival of cells inside the structure, or at least for distribution of nutrients within it. It also is a convenient way for removal of catabolites and for delivery of products from one cell (or colony) to another (see Fig. 13), or to the outside (e.g., methane). The observations discussed above suggest that proteases should also be the target of basic and applied research that would provide the basis for engineering more efficient microbial consortia. For example, a consortium should have a circulation network commensurate with its size, and the needs and metabolic

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activities of all its cellular constituents, regardless of the location of these constituents in the whole structure. 10.9 Intrinsic Stress Resistance

The molecules of organisms that have high or very high OTG, or that grow under high hydrostatic pressure, are able to function under these extreme conditions (as compared to those that are optimal for humans, for instance). The molecules are endowed with intrinsic stress resistance; the mechanisms implicated in this resistance are only now beginning to be examined [13, 112, 113]. This is an interesting point for investigation, potentially useful for methanogenic biotechnology.

11 Conclusion and Perspectives Stress proteins, molecular chaperones, formation of functional multimeric structures by molecules and cells, thermoprotectants, ion transporters, and other anti-stress mechanisms ultimately depend on the presence of genes properly regulated, capable of responding to the attack of stressors. Hence, elucidation of the gene regulatory mechanisms is an important step towards optimizing biomethanation. Tools to study gene regulation and to manipulate genes in methanogens are being developed [114–117]. Likewise, means to manipulate microbial cells, including methanogens and syntrophs, using antibodies and related techniques are available [118]. The perspectives for rapid progress are, therefore, promising. If the study of stress genes, proteins, and other anti-stress mechanisms, and the identification of novel microbial species continue, it will be possible in the near future to optimize the biological component of bioreactors. Furthermore, it will be possible to monitor bioreactor function to anticipate failure, and to repair it in case of malfunction, by removing unwanted microbes and/or introducing those pre-selected or pre-engineered (genetically) to meet the requirements for stress resistance and optimal biomethanation. It has been demonstrated that it is possible to introduce a microbial species in a granular consortium to add to it a lacking metabolic ability [119]. The consortium was thus endowed with the capacity to bioconvert a substrate that could not be metabolized prior to the microbial graft. This procedure is easy to perform and has a promising future as a means to build consortia with stress-resistant microbes tailored to bioconvert specific substrates. Biofilms and granules constructed on demand with stress-resistant microbes, which are also efficient for biomethanation of specific substrates (e.g., a certain type of waste), should be a reality in the first decade of the third millennium. Acknowledgement. Work in the authors’ laboratories has been supported over the years by

grants from NYSERDA, DOE, and NSF. We thank our collaborators of yesterday and today, too numerous to mention by name (many appear in the list of references), for their help. We also thank the members of the Photo-Art unit of this Center for their excellent assistance with graphs and photographs.

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CHAPTER 6

Molecular Ecology of Anaerobic Reactor Systems J. Hofman-Bang 1 · D. Zheng 2 · P. Westermann 1 · B. K. Ahring 1 · L. Raskin 3 1

2 3

Environmental Microbiology and Biotechnology, Biocentrum DTU, The Technical University of Denmark, Building 227, 2800 Lyngby, Denmark. E-mail: [email protected]; E-mail: [email protected]; E-mail: [email protected] Alpha Therapeutic Corporation, Los Angeles, CA 90032, USA. E-mail: [email protected] Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA. E-mail: [email protected]

Anaerobic reactor systems are essential for the treatment of solid and liquid wastes and constitute a core facility in many waste treatment plants. Although much is known about the basic metabolism in different types of anaerobic reactors, little is known about the microbes responsible for these processes. Only a few percent of Bacteria and Archaea have so far been isolated, and almost nothing is known about the dynamics and interactions between these and other microorganisms. This lack of knowledge is most clearly exemplified by the sometimes unpredictable and unexplainable failures and malfunctions of anaerobic digesters occasionally experienced, leading to sub-optimal methane production and wastewater treatment. Using a variety of molecular techniques, we are able to determine which microorganisms are active, where they are active, and when they are active, but we still need to determine why and what they are doing. As genetic manipulations of anaerobes have been shown in only a few species permitting in-situ gene expression studies, the only way to elucidate the function of different microbes is to correlate the metabolic capabilities of isolated microbes in pure culture to the abundance of each microbe in anaerobic reactor systems by rRNA probing. This chapter focuses on various molecular techniques employed and problems encountered when elucidating the microbial ecology of anaerobic reactor systems. Methods such as quantitative dot blot/fluorescence in-situ probing using various specific nucleic acid probes are discussed and exemplified by studies of anaerobic granular sludge, biofilm and digester systems. Keywords. rRNA, rDNA, PCR, Biofilm, UASB, Granular sludge

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2.3 2.3.1 2.3.1.1 2.3.1.2 2.3.2 2.3.2.1 2.3.2.2 2.3.2.2.1 2.3.2.2.2 2.3.2.2.3 2.3.2.3 2.3.2.3.1 2.3.2.3.2 2.3.2.3.3 2.3.2.3.4 2.3.3 2.3.4 2.3.4.1 2.3.4.2 2.3.4.3 2.3.4.3.1 2.3.4.3.2 2.3.4.3.3 2.3.4.3.4 2.3.5 2.4 2.5 2.6 2.7

Oligonucleotide Probes . . . . . . . . . . . . . . . . Probe Design . . . . . . . . . . . . . . . . . . . . . Probe Specificity . . . . . . . . . . . . . . . . . . . Target Accessibility . . . . . . . . . . . . . . . . . . Quantitative Slot (Dot) Blot Hybridization . . . . . Hybridization Stringency . . . . . . . . . . . . . . Quantification . . . . . . . . . . . . . . . . . . . . . Interpreting the Quantification Results . . . . . . . Sensitivity . . . . . . . . . . . . . . . . . . . . . . . Variation . . . . . . . . . . . . . . . . . . . . . . . Factors that May Interfere with Quantification . . . Membrane Saturation . . . . . . . . . . . . . . . . Target Accessibility . . . . . . . . . . . . . . . . . . Co-Extracted Substances . . . . . . . . . . . . . . . In Vitro Transcribed rRNA . . . . . . . . . . . . . . Reverse Genome Sample Probing . . . . . . . . . . Whole Cell or in Situ Hybridization . . . . . . . . . Hybridization Stringency . . . . . . . . . . . . . . Cell Fixation . . . . . . . . . . . . . . . . . . . . . . Signal Enhancement . . . . . . . . . . . . . . . . . Indirect Assays . . . . . . . . . . . . . . . . . . . . Enzyme-Labeled Oligonucleotides . . . . . . . . . . Multi-Probe and Multi-Labeling . . . . . . . . . . . Amplification of the Target Sequence . . . . . . . . Solution-Based Hybridizations (Molecular Beacons) FISH and Reporter Systems . . . . . . . . . . . . . FISH and Antibody Probes . . . . . . . . . . . . . . FISH and Microautoradiography . . . . . . . . . . Peptide Nucleic Acid Probes . . . . . . . . . . . . .

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rRNA-Based Analyses of Anaerobic Reactors . . . . . . . . . . . 180

3.1 3.1.1 3.1.2 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.3 3.3.1 3.3.2

Biofilm Reactors . . . . . . . . . . . . . . Biofilm Formation . . . . . . . . . . . . . Biofilm Composition and Dynamics . . . . Granular Sludge Reactors . . . . . . . . . Granular Sludge . . . . . . . . . . . . . . . Microbial Composition of Granules . . . . Structure of Granular Sludge . . . . . . . . The Granulation Process . . . . . . . . . . Continuously Stirred Tank Reactors (CSTR) Microbial Composition in CSTRs . . . . . Microbial Dynamics in CSTRs . . . . . . .

4

Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . 197

5

References

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1 Introduction Most anaerobic microbial processes are characterized by close association of numerous functional groups of microorganisms. The understanding of anaerobic processes has improved greatly during recent decades with advances made in microbial physiology, biochemistry, ecology, kinetics, and mathematical modeling. These contributions have led to an expansion of anaerobic processes by introducing better designs and operational controls. However, the understanding of anaerobic processes is far from complete. Understanding the microbial ecology in anaerobic reactor systems requires (1) identification and classification of microorganisms, (2) quantification of microbial abundance, and (3) quantification and identification of activity. Morphology and other microbial traits have previously been used for identification and quantification of microbial populations. Grotenhuis et al. [1] microscopically counted cell numbers of methanogens and identified aceticlastic methanogens based on morphology, and hydrogenotrophic methanogens by visualizing autofluorescence at 420 nm. Morphology and ultrastructure have also been used extensively in scanning or transmission electron microscopy studies to show the location of certain microorganisms in anaerobic granules [2, 3]. Information gained from morphology-based techniques is, however, ambiguous and limited since most microorganisms are small in size, and simple in morphology and ultrastructure. In the absence of special morphological features or autofluorescence, physiological and biochemical traits have been used for identification. Furthermore, enrichments on defined substrates have been helpful to identify prevalent species in anaerobic granules [4], and Most Probable Number (MPN) estimates have been used frequently for quantification of different trophic groups of anaerobic microorganisms [1, 4]. These methods are, however, cultivationdependent and therefore limited by the ability of microorganisms to grow under laboratory conditions. It is well known that only a very small fraction of the microorganisms in nature is culturable by present cultivation techniques, because of unrecognized nutrient and growth conditions, or the interruption of intrinsic interdependencies such as syntrophic interactions [5]. Amann et al. estimated that the culturability ranges from 0.001% in seawater to 15% in activated sludge [6]. Culturing may be especially difficult for anaerobes due to their low growth rates and fastidious nutritional and environmental requirements. Recently, more direct methods have been developed for identification, quantification, and localization of microorganisms in environmental samples. Immunology techniques utilize monoclonal or polyclonal species-specific antibodies to detect and even quantify the abundance of cultivable microorganisms in environmental samples [7–9]. In combination with electron microscopy, antibodies have been used to localize microorganisms in sections of anaerobic granules [1]. The major disadvantages of immunotechnology are the need for axenic cultures or defined co-cultures to produce the specific antibodies, and the high

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specificity that limits the detection to the species or subspecies level [10–12]. In addition, cross-reactions often might cause a problem [13, 14]. Adsorption to cross-reacting cells can broaden the specificity of an antibody [15], but this approach is limited by the number of species that are used for the specificity test. Molecular phylogeny, which employs nucleic acid sequences to document the history of evolution, has provided a new basis for the direct identification and quantification of microorganisms [16]. Nucleic acid-based methods allow microbial community characterization without cultivation. So far, ribosomal RNA (rRNA) and ribosomal DNA (rDNA) have been the most commonly used target nucleic acids in microbial ecology studies. This chapter focuses mainly on the use of rRNA- and rDNA-based methods for the study of anaerobic reactor systems. In addition, some other molecular approaches are discussed briefly. We first present the fundamentals and principles of different nucleic acidbased techniques to study anaerobic reactor systems. The second part of the chapter reviews literature in which rRNA- and rDNA-based techniques have been applied to studies of anaerobic bioreactors.

2 Nucleic Acid-Based Analysis of Anaerobic Bioreactors This section provides an overview of the most widely used or potentially applicable rRNA- and rDNA-based methods, but also presents studies in which functional diversity has been investigated by analyses of expressed messenger RNA. When available, we have used examples from anaerobic bioreactor work. 2.1 Background

Studying microbial ecology requires identification of microorganisms, based upon a comprehensive classification system that ideally should reflect the evolutionary relatedness of organisms [5].As pointed out by numerous authors, traditional classification systems based on phenotypic characteristics (morphology, physiology, and structure of cell components) offer little information on evolutionary relatedness and require cultivation for identification [5, 17]. In the mid-1960s, Zuckerkandl and Pauling pointed out that molecular sequences could document evolutionary history [18]. Due to the pioneering work of especially Carl Woese, the rRNAs have become the most commonly used molecules for phylogenetic analyses. rRNA or the corresponding rDNA are particularly suitable as evolutionary chronometers [19–21] since (1) they are key elements of the cells and are functionally and evolutionarily homologous for all organisms; (2) they are very conserved in overall structure; (3) their regions of different conservation levels allow phylogenetic analysis and design of probes and primers;

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(4) they are very abundant in most cells (103 to 105 copies) [6], and are easily recovered and detected; (5) the small subunit (SSU) rRNA (16S and 16S-like rRNA) and the large rRNA of the large subunit (LSU) of the ribosome (23S rRNA and 23S-like rRNA) are sufficiently long for statistically significant comparisons; and (6) their genes have so far not been shown to be transferable among organisms. Using 16S rRNA comparative sequence analysis, Woese and colleagues developed the first universal phylogenetic tree, which reflects the evolutionary relatedness of all organisms, grouping them into three domains: Eucarya, Archaea, and Bacteria [22–24]. 16S rRNA sequences are most commonly used for molecular ecology investigations since a huge number is available through the Ribosomal Database Project II (16,277 aligned and 30,322 unaligned 16S rRNA sequences in June, 2000) [25]. However, 23S rRNA-based analyses should become more common when more sequence data become available, since 16S rRNA comparisons sometimes fail to resolve very closely related species [20, 21, 26]. Internal transcribed spacer (ITS) regions that separate rRNA genes may provide additional information for resolving very close phylogenetic relationships [27]. A complication related to the use of rRNA as a target for the quantification of population abundance is the limited information currently available on the number of rRNA operons present on microbial genomes. Information on the level of gene redundancy present in the rRNA operons has recently been catalogued in the Ribosomal RNA Operon Copy Number Database (rrndb) [28]. Besides the ribosomal RNA genes, other gene sequences have been used for phylogenetic analyses, including genes for the elongation factor Tu, and F1F0ATPase b-subunit [29]. A phylogenetic analysis allows the identification of a microorganism based only on a molecular sequence, eliminating the need for cultivation. In other words, a sequence can be retrieved from an environmental sample, sequenced, and compared to known sequences for identification of the corresponding organism [19]. If the retrieved sequence is new, characteristics associated with housekeeping functions of the cells (e.g., characteristics of ribosomes, DNA replication machinery, biosynthetic pathways and their regulation mechanisms) can be inferred from closely related species [5, 17]. The metabolic diversity of cells, however, is more variable than reflected by the housekeeping functions, due to events such as lateral transfer of metabolic genes and symbiotic fusions [17]. Thus, caution has to be taken when metabolic characteristics of a newly identified microorganism are inferred from characteristics of close phylogenetic relatives. Based on “signature” sequences of specific groups of microorganisms, probes can be designed and used to identify and quantify these microorganisms in complex microbial ecosystems. The strategies based on rRNA sequences analysis for characterizing a microbial community are summarized in Fig. 1. It should be noted that many of these strategies are also applicable to other genes.

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Fig. 1. Strategies based on rRNA sequence analysis for characterization of microbial communities without cultivation (arrows indicate the interconnected use of methods, experimental materials, and information in the study of microbial ecosystems. RT-PCR: reverse transcription to produce DNA from RNA, followed by PCR. DGGE: denaturing gradient gel electrophoresis. RFLP: restriction fragment length polymorphism. Modified from [5, 6]

2.2 Retrieving Nucleic Acid Sequences

Methods for retrieving nucleic acid sequences from environmental samples are mainly used to detect and identify microorganisms, although some quantitative methods are being developed, especially for populations present in low numbers. The sequences obtained should ideally represent the diversity present in the sample, but all methods introduce at least some bias and may not identify all populations present as discussed below. The process starts with the extraction of nucleic acid (DNA or RNA) from a sample. Extracted DNA can be randomly digested and cloned (shotgun cloning). Subsequently, the clones are screened for rRNA genes using dot/colony blot hybridization. More commonly, however, the rRNA genes in DNA extracts are specifically amplified by PCR, or rRNA genes are produced by reverse transcription from rRNA in RNA extracts followed by PCR (i.e., RT-PCR). Next, PCR or RT-PCR products are cloned or separated by gel electrophoresis (denaturing gradient gel electrophoresis [DGGE], temperature gradient gel electrophoresis [TGGE], terminal restriction fragment length polymorphism [T-RFLP]). The rDNA clone library or the DNA bands from the electrophoresis gel can be sequenced and the obtained sequences are deposited in sequence databases.

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Subsequently, phylogenetic trees showing the diversity of the corresponding environmental sample can be constructed by comparative sequence analysis. Details of these methods and their applications can be found in a number of reviews [5, 6, 17, 19, 20, 26, 27, 30, 31]. Brief descriptions of the methods and of some factors that affect the overall results are discussed below. 2.2.1 Nucleic Acid Isolation

Ideally, a sample should be representative and free from bias, especially when quantification is the objective. It should also contain sufficient biomass for subsequent analyses. In addition, the presence of materials that interfere with nucleic acid recovery or manipulation (e.g., humic substances) should be avoided or such compounds should be removed from the sample if possible. If the sample is collected for RNA extraction, nuclease activity should be reduced as much as possible [32]. Measures such as quick freezing, storing at –80°C, avoiding thawing the sample before extraction, and preventing the introduction of foreign nucleases should be practiced. Several methods have been published in the recent years for extraction of intact RNA from environmental samples, such as manure [33], sediment, soil, and water samples [34], sediment and microbial mat samples [35], and rumen fluid [39]. Recovering RNA or DNA quantitatively from all cells in a complex community without bias can be difficult. In general, mechanical lysis methods have shown less bias than enzymatic lysis methods, leading to the recovery of intact high molecular weight nucleic acids [36]. Ibrahinm et al. reported on a rapid method for extracting high purity rRNA from manure [33]. The bead-beating-based method involves citrate buffered, low-pH phenol and chloroform extractions. This method effectively disrupts the cell wall of cells that are difficult to break such as Gram-positive bacteria and methanogens. Citrate has been shown to strongly inhibit RNases [37]. Humic acids were removed from the samples by repeated washes with low-pH citrate prior to cell disruption. Moran and coworkers used a low-pH, hot-phenol extraction method and subsequent gel filtration with Sephadex G-75 spin columns for sediment, soil, and water samples [34]. Since they used lysozyme to open the cells, this method may introduce a bias, since lysozyme is not equally effective for all types of cells. Alm and Stahl compared different lysis solutions and subsequent vortexing in low-pH phenol and chloroform to extract RNA from sediments and microbial mats [38]. Use of a guanidine thiocyanate/b-mercaptoethanol lysis solution resulted in an efficient recovery of intact, high purity rRNA. When they increased the ratio of lysis buffer to sample volume from a 1:1 to 5:1, an order of magnitude more rRNA was extracted. Adding the sample to the lysis buffer while mixing, instead of the opposite also proved to be important. This extraction method left the final extract with considerable amounts of organic contamination. Raskin and coworkers used a low-pH, hot phenol, bead-beating extraction method to isolate RNA from rumen fluid [39]. They demonstrated that

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the total amount of RNA recovered per gram of rumen fluid neither changed significantly with the duration of the beating, nor with the amount of beads used. However, the amount of RNA recovered from Gram-positive Ruminococcus cells increased significantly when the beating period was extended. In the same study, the total amount of RNA recovered in replicate extractions showed very high variability. The efficiency of RNA recovery was also investigated [39]. The study demonstrated proportional recovery of RNA for a specific target population. The study also demonstrated loss of RNA in the phenol phase and during precipitation/rinse/resuspension steps of the extraction process. Yu and Mohr developed a fast method (one hour) to simultaneously extract DNA and RNA [36]. Bead beating combined with 2 M ammonium acetate precipitation of proteins in the presence of DEPC (diethyl pyrocarbonate) resulted in intact rRNA and non-sheared DNA. No phenol or chloroform was used. By adding RNase or 200 mM NaOH, either DNA or RNA was extracted. The nucleic acids were of a sufficient quality to perform PCR and RT-PCR. This method has been adapted to extract intact DNA or rRNA from municipal solid waste and cow manure without any washing prior to extraction in our laboratory. The resuspended nucleic acids obtained after extraction are colorless, indicating that humic substances and other impurities are removed effectively during the extraction procedure. Humic substances coextracted with nucleic acids may interfere with subsequent enzymatic reactions (such as PCR) [27]. They can also interfere with membrane hybridizations (see below). A number of methods has been developed for removing the humic substances. These include polyvinylpolypyrrolidone (PVPP) adsorption, gel purification, and dilution [27, 34, 35]. Furthermore, DNA is often present in RNA extracted from environmental samples [38, 40]. The influence of DNA on membrane hybridization is discussed below. When necessary, DNase can be used to remove DNA, although concerns of partial degradation of RNA due to impurities in commercial DNase should be considered [40]. 2.2.2 PCR Reaction

The polymerase chain reaction (PCR) can be used to amplify DNA sequences from environmental samples. The PCR products can be analyzed by techniques such as DGGE (denaturation gradient gel electrophoresis), TGGE (temperature gradient gel electrophoresis), T-RFLP (terminal restriction fragment length polymorphism), or SSCP (single stranded conformation polymorphism), which have the potential to separate the PCR products originating from different DNA sequences representing populations in the original samples. The PCR products can also be cloned and subsequently sequenced to allow identification of populations. For details about PCR, the reader is referred to a review by Steffan and Atlas and “The PCR application manual, 2nd ed. [41, 42]. Since the amount of DNA produced by PCR ideally increases exponentially during the amplification, errors occurring early in the process will result in biased results [6].

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Factors that cause bias of PCR are: – “Universal” primers or other specific primers are designed based on sequence information available in databases (obtained from cultured organisms and clones) [5]. However, primers targeting multiple groups of organisms may not amplify all target genes since the primer sites are not completely conserved. – Bias can be caused by an inappropriate annealing stringency, which results in amplification of genes that are not intended to be amplified [27]. – There is some evidence that PCR does not amplify all rRNA sequences in the sample to the same extent (preferential amplification) [6, 27]. – Contaminating sequences from chemicals and enzymes can be erroneously included in the analysis. – Chimeric sequences are often produced [27] due to the presence of partial fragments of rDNA in DNA extracts, partially reverse transcribed DNA when performing RT-PCR, or premature PCR products acting as primers in a subsequent PCR cycle [6]. 2.2.3 Cloning

Cloning can produce large amounts of DNA segments originally isolated from environmental samples. The DNA fragments can be produced after digestion with restriction enzymes of the DNA extracted from a sample (i.e., shotgun cloning), or after PCR or RT-PCR (if RNA is the template). Compared to cloning after PCR, shotgun cloning introduces less bias and produces clones of multiple genes at the same time [5]. Cloning after PCR is rapid and convenient, but can be biased [5, 27]. The bias can be introduced during the PCR step as discussed above or during cloning. For instance, the use of rare-cutting restriction enzymes during cloning might also cut amplified rDNA [6]. In addition, it is possible that different rRNA gene fragments are cloned with different efficiencies. 2.2.4 rDNA Sequences

Cloned DNA fragments can be sequenced to study the phylogenetic diversity of the microbial community from which the sample was originally obtained. The resulting sequences can be compared to sequences in databases to identify the closest phylogenetic relatives. There are a number of problems that need to be taken into consideration when using this technique. First, when the retrieved sequences exhibit high similarity to sequences available in databases (98% to 99% identity), it is difficult to rule out PCR errors [27]. Secondly, it is difficult to convert the rRNA similarity to the nomenclature level of species or genus [6]. In general, more than 97% 16S rRNA identity indicates that two sequences belong to the same species, which typically corresponds to DNA:DNA hybridization values above 70%. The definition of a similar threshold for genera is not as clear, but 16S

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rRNA differences greater than 5–7% may be used to support a new genus [43]. Finally, the heterogeneity of 16S rRNA genes increases the complexity. It has been observed that some species express different 16S rRNA genes at different growth stages, or multiple 16S rRNA genes are expressed at the same time [27]. For example, sequence heterogeneity was found in Paenibacillus polymyxa [45], strains of the Mycoplasma mycoides cluster [44], and in Clostridium paradoxum [45a]. 2.2.5 Community Fingerprints

Several fingerprinting techniques, such as DGGE, TGGE, RFLP, T-RFLP, and SSCP, have been developed to screen clone libraries, to estimate the level of diversity in environmental samples, to follow changes in community structure (e.g., trace one or more populations over time) and to compare diversity and community characteristics in various samples. These techniques usually involve gel electrophoresis that can separate different DNA segments of a community rDNA library. DGGE (denaturing gradient gel electrophoresis) separates DNA fragments of equal length (obtained after PCR of DNA extracted from an environmental sample) into distinct bands on a chemical denaturing gradient polyacrylamide gel. PCR amplification of the 16S rRNA gene utilizing conserved primers targeting either the V3 or the V8 +V9 variable regions is normally used to produce a 300–500 bp fragment. Larger fragments are typically not used as the DGGE technique cannot resolve these into distinct bands [46]. One of the primers used has a GC-clamp consisting of a 30 nucleotide GC-rich 5¢ end, which maintains the two denaturated single stranded DNA fragments together in the denaturing gel. As the double stranded DNA migrates through the gel experiencing increasingly higher denaturant concentrations, the double stranded DNA separates into two single strands at a specific point and the migration stops due to the larger volume of the denaturated molecule kept together by the GC clamp. The DGGE technique has been used to characterize the microbial diversity in different environments such as activated sludge [47], sediments [46], lake water [8], hot springs [48], soils [49, 50], biofilm [51]. DGGE has been used to monitor changes in complex communities [48, 52–55] and to identify microorganisms present in wall painting [56]. The banding pattern reveals the community components at best semi-quantitatively due to the possible bias caused by PCR and difficulties to quantify the amount of DNA associated with a band. An advantage of the technique is that it can resolve the microbial diversity of up to 15 different species by optimizing the denaturing gradient concentration in the gel. By using narrow gradients, rDNAs that differ in only one bp can be separated in DGGE [46]. A drawback of the technique is that the reproducibility is not optimal; one DNA fragment may generate more than one band on the gel and a DNA sample analyzed on two different gels may not generate the same band pattern [57]. In addition, it is possible that a band in a DGGE gel may contain different sequences with similar denaturation characteristics.

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Alternatively, the obtained rDNA can be digested with restriction enzymes and analyzed on an agarose gel. This technique is called restriction fragment length polymorphism (RFLP) [58] or amplified ribosomal DNA restriction analysis (ARDRA) [59]. The banding pattern obtained has been used to identify different genotypes of microorganisms [60] and to monitor population changes in environmental samples [61, 62]. The RFLP patterns have also been used to deduce the phylogeny of axenic cultures of microorganisms [63–67]. Compared to cloning, DGGE and RFLP are faster and less laborious, and bands of interest can be cut out, extracted from the gels, and cloned and sequenced directly. SSCP (single-strand conformation polymorphism) is based on the separation of the double stranded DNA PCR product by NaOH prior to non-denaturing polyacrylamide gel electrophoresis. The single stranded DNA forms secondary structures (analogous to the cloverleaf structure of tRNAs). Scheinert and coworkers employed this technique to differentiate between 15 Mycoplasma species and to analyze a mixed sample of six different species based upon analysis of the spacer region between the 16S rRNA and the 23S rRNA gene [68]. The advantage of the technique is that even point mutations can be detected as a change of conformation in the secondary structure of the single stranded DNA. In the study of the Mycoplasma species, the variable size of the PCR product of the spacer region (280–1300 bp) further discriminated the populations while the 16S rDNA DGGE technique might not have been able to do so since the gel band pattern would have been too compressed. A critical parameter in the SSCP technique is to control the temperature in the gel tank to reduce smearing. As the available sequence data of the rRNA spacer region is limited compared to the 16S rRNA sequence databases, the SSCP technique is mainly used to differentiate between different cloned PCR products. 2.2.6 Quantification Based on Sequence Retrieval

Theoretically, the abundance of a population can be inferred from the frequency of a particular sequence appearing in the sequence collection obtained from an environmental sample. For instance, the microbial community structure of an anaerobic fluidized-bed reactor (treating wine distillation wastewater) was characterized by PCR and sequencing [69]. The PCR was conducted using three pairs of primers specific for the three domains. The authors obtained 460 and 96 clones from Bacteria and Archaea, respectively. Of these 556 clones, 76% were Bacteria, 10% corresponded to Methanobacterium formicicum, 4% represented Methanosarcina frisius, 8% were Methanosarcina barkeri, and 2% represented other Archaea. Within the bacterial domain, there were 6% high G + C Grampositives, 4 % Planctomyces, 33% low G + C Gram-positives, 4% Spirochaetes, 12% delta Proteobacteria, 2% gamma Proteobacteria, 1% beta Proteobacteria, 2% alpha Proteobacteria, 26% Cytophaga-Flexibacter-Bacteroides, and 7% green non-sulfur bacteria. As discussed by the authors, this method has many shortcomings when used as a quantitative tool. First, the primers were designed based on previously isolated cultures, thus it is not free from the well-known cul-

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tivation-derived limitations. Secondly, bias can be introduced during PCR and nucleic acid extraction as previously discussed. 2.2.7 Quantitative PCR

Recently, a number of quantitative PCR methods have been developed that have the potential of detecting low-level populations in environmental samples. One of these methods is competitive PCR, in which an internal standard is added to the sample. The sample and the internal standard are amplified using the same pair of primers. The corresponding assumptions are: The gene of interest and the internal standard are equally accessible to primers after denaturation; both templates have the same efficiency to hybridize to the primer and to be extended by the polymerase; substrate exhaustion affects the extension of both templates equally [70]. However, in a competitive PCR experiment [70], the authors observed a bias that was strongly dependent on the number of replication cycles. They demonstrated that reannealing of genes progressively inhibited the formation of template-primer hybrids. Taqman PCR is another quantitative method exploited for detection of lowlevel populations. This method takes advantage of the 5¢ to 3¢ exonuclease activity of the Taq DNA polymerase [71]. A probe targeting one strand of the PCR template is labeled at the 5¢ end, and its 3¢ end is phosphorylated to prevent extension. During PCR, the Taq polymerase extends the ordinary primer along the template strand. When it meets the probe that binds to the template strand, it cleaves the 5¢ terminal nucleotide and produces mono- or oligonucleotides, which are shorter than the original probe. In the first Taqman study [71], the probe was labeled with 32P. Autoradiography after TLC (thin layer chromatography) was needed to detect the hydrolyzed probes. The original Taqman method was modified [72] to allow rapid analysis by labeling the probe with a fluorescent dye at the 5¢ end and with a quencher at the seventh nucleotide from the 5¢ end. The dye fluoresces when the probe is cleaved between the dye and the quencher (Fig. 2). Therefore, there is no need for post-amplification separation. Subsequently, Taqman PCR was demonstrated to be quantitative since the intensity of fluorescence was proportional to the amount of PCR product, and under appropriate conditions, to the initial number of the templates [73]. However, it is necessary to assume that the efficiency of the PCR to amplify DNA from an environmental sample is similar to the efficiency of the PCR used for constructing the standard curve, before this method can be used to quantify populations in environmental samples. Since this assumption may not always be valid, bias can occur. 2.2.8 Summary

Despite the potential biases of the various methods discussed above, retrieving 16S rRNA sequences directly from environmental samples allows the investigation of microbial communities without cultivation. The use of these techniques

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Fig. 2. Taqman PCR. Modified from [72]

has revealed that the microbial diversity is much greater than was anticipated based on cultivation studies. 2.3 Oligonucleotide Probes

The first step of oligonucleotide probe hybridizations consists of probe design as illustrated in Fig. 1. The probes are used in various types of hybridizations to detect, quantify, and localize the target sequences or cells in a sample, in a nucleic acid extract, or in a clone library. For more information on environmental application of nucleic acid hybridization, several reviews are available [10, 39, 74, 75]. This section focuses on probes rationally designed, using the phylogenetic framework provided through comparative analysis of sequences available in databases [76]. In particular, the large collection of 16S rRNA sequences makes it possible to design a nested set of 16S rRNA-targeted oligonucleotide probes with different levels of specificity. By comparing aligned 16S rRNA sequences, unique regions can be found that are shared only by the target population(s). Empirically designed probes have traditionally been generated from a genomic recombinant library or simply are the total genomic DNA obtained from a target organism [76]. These probes have not been used much in microbial ecology research in the last decade because of limitations with nesting and quantification. An obvious advantage of oligonucleotide probes is that targets differing in a single nucleotide can be discriminated under appropriate experimental conditions. A few applications of empirically designed probes have been published. DeLong et al. [12] studied the correlation between growth rates of Escherichia coli, the average ribosome contents, and the fluorescence conferred by hybridization probes. They observed that with decreasing growth rates the

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hybridization signal quickly approached the limit of detection of epifluorescence microscopy or flow cytometry. Using oligonucleotides carrying multiple labels both in the hybridizing probe and in a non-complementary tail did not significantly increase the sensitivity [77]. A possible way to identify cells with low metabolic activity, i.e., low amount of ribosomes, is by applying polynucleotide probes carrying multiple fluorescent reporter molecules [78]. In this study a polynucleotide probe (ca. 200 to 300 nucleotides in length) was generated by transcription of a cloned probe sequence from the 23S rRNA gene from Pseudomonas stutzeri. The probe was selected to target the variable domain III region in the 23S rRNA molecule. Whole-cell hybridization proved that the polynucleotide probe was superior to the oligonucleotide probes for in situ detection of cells with low cellular rRNA contents. Also, the larger probes could differentiate between two closely related organisms Pseudomonas stutzeri and Pseudomonas diminuta. Heuer and coworkers utilized digoxigenin-labeled probes targeting the 16S rRNA molecule to fingerprint the microbial community in rhizosphere samples taken from potato plants [79]. Briefly, PCR amplified 16S rDNA genes (the V6 region) were separated by temperature gradient gel electrophoresis (TGGE), cloned, sequenced and used to probe dot blotted 16S rDNA amplified from bacterial isolates. To optimize the specificity of the probes, flanking conserved regions were removed. One truncated probe hybridized to three different bacterial isolates all with different electrophoretic mobility in the TGGE. Subsequent sequencing of these isolates revealed an identical V6 region but different V7 and V8 regions explaining the above observations. Also, the polynucleotide probes were shown to discriminate between targets differing only by two nucleotides. The advantage of polynucleotide probes compared to oligonucleotide probes is their higher specificity. Even though the specificity of oligonucleotide probes is sufficient for many applications, more specific probes are sometimes needed to discriminate between two closely related organisms. Several groups of organisms have been identified which share almost identical 16S rRNA sequences but among which DNA:DNA hybridization values are lower than 70% [80]. 2.3.1 Probe Design 2.3.1.1 Probe Specificity

The specificity of a newly designed probe has to be tested before it can be used with confidence. The specificity can be checked using rRNA databases such as the CHECK_PROBE software provided in the Ribosomal Database project (http://www.cme.mwu.edu/RDP/html/analyses.html) [25] and the Oligonucleotide Probe Database (OPD) [81]. Alternatively, the BLAST network service available from the National Center for Biotechnology Information at http://www.ncbi.nlm.nih.gov can be used. Due to the limited collection of rRNA sequences compared to the total estimated prokaryotic diversity, there is a possibility that some yet undiscovered sequences are targeted by probes designed

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for other organisms [27]. As pointed out by Ward et al., “probes should be regarded as tools subject to refinement” [27]. Probe nesting is another way to check the specificity of a probe [39]. Theoretically, the sum of quantification results from an environmental sample at one taxonomic level (e.g., family) should equal the quantification result of the taxa at one higher level (e.g., order). If this is observed, all the probes used are probably specific. If the population size obtained for, e.g., an order, however, is larger than the sum of populations recognized at the family level, an unknown family not targeted by the family probes might exist [39]. 2.3.1.2 Target Accessibility

This needs to be considered when probes are used for in situ or whole cell hybridization, since the higher order structure of the target rRNAs in this type of hybridization is intact and since rRNAs are associated with ribosomal proteins [6]. Fixation can denature the higher order structure. However, since the influence of fixation is hardly predictable, there is no easy estimation of accessibility to the target [6]. Some of the 16S rRNA and 23S rRNA sites that have been successfully targeted were summarized by Amann and coworkers [6, 82].A more systematic study of target accessibility was reported by Frischer and co-workers [83]. Five probes each consisting of 12 nucleotides were designed to target the 515, 786, 1063, 1341, and 1369 sites of Escherichia coli 16S rRNA. Hybridization signals of all the five probes were equal in hybridization to cell blotted membrane, but different in whole-cell hybridization. Only probe 1341 gave a good signal in whole-cell hybridization. Probe 515, which targeted a ribosomal protein-binding site, showed moderate signals, but the inhibition by the proteins seemed to be outcompeted by a longer probe. The study showed that probe 786, which targeted a loop site, gave a moderate signal for unknown reasons. Targeting the self-complementary sites did not seem to be a problem since probe 1369, which targeted a less self-complementary site, gave a lower signal than probe 1341, which targeted a higher self-complementary site. Another study also showed that targeting highly structured sites with probes consisting of 30 nucleotides resulted in good signals [84]. More recently, Fuchs et al. convincingly conducted a systematic study on the accessibility of 16S rRNA target sites in E. coli by probing with more than 200 probes along the 16S rRNA molecule showing regions with high accessibility and other regions with low accessibility [85]. Fuchs et al. also showed improved accessibility to otherwise low accessibility regions by applying unlabeled helper oligonucleotides binding next to the labeled probe’s target site [86]. 2.3.2 Quantitative Slot (Dot) Blot Hybridization

In general, the quantitative slot (dot) blot hybridization involves the application of rRNAs extracted from environmental samples on membranes together with a dilution series of RNA from an axenic culture (reference RNA). The membranes

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are then prehybridized, hybridized with the probes, and washed. Usually membrane hybridizations are conducted at low stringency (high salt concentrations and low temperatures). The washing step is then used to remove all excess nonspecific binding probe. The signals from the environmental samples are quantified by comparison with the reference rRNA. The abundance of a specific population is expressed as a percentage of the total rRNA determined by a universal probe targeting all rRNA. Alternatively, results can be reported in terms of µg of rRNA per sample volume or weight. 2.3.2.1 Hybridization Stringency

The washing conditions are critical in order to distinguish between target sequences and sequences with one or more mismatches. The specificity is usually controlled by temperature. The optimum washing temperature (Tw) is recommended to be equal to, or slightly higher than, the dissociation temperature (Td) which is the temperature at which 50% of the duplexes remain intact during a specified washing period [87]. Another closely related parameter is the melting temperature (Tm) which is defined as the equilibrium temperature where half of the duplexes are dissociated [76, 87]. Thus, Tm is defined for equilibrium conditions and is time-independent, whereas the Td value is timedependent. There are a number of empirical equations that can be used to estimate the Tm and Td of a duplex [76, 87]. These equations provide guidelines for probe design, especially when certain Td values are needed (e.g., the design of a number of probes for simultaneous in situ hybridization experiments) [39]. However, since the Td is a function of numerous factors [76, 87] such as duplex structure and length, sequence, nucleotide content, number and type of mismatches, terminal unpaired bases, as well as the hybridization and washing conditions, the experimental determination of Td is highly recommended. 2.3.2.2 Quantification 2.3.2.2.1 Interpreting the Quantification Results

When interpreting the quantification results obtained from membrane hybridization experiments, it is important to consider that rRNA abundance does not equal cell abundance, since the number of rRNA molecules in each cell lies within a very broad range (103 to 105) [6]. However, since the cellular rRNA content of a cell often is correlated to its growth rate [12], the abundance of the rRNA is an indication of the metabolic activity. Since one genotype can be related to several phenotypes, derivation of specific physiological activities from rRNA abundance should be carried out with caution. Although the absolute abundance of a population in terms of mass of rRNA per weight or volume of a sample is desirable, this type of quantification result should also be carried out with caution due the high variability of RNA recovery in the extraction process as previously discussed.

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2.3.2.2.2 Sensitivity

The sensitivity of membrane hybridizations can be as low as 0.1% when an isotope-labeled probe is used [6, 27]. Based on observations in our laboratory, this sensitivity can be obtained by proper loading (highest loading without saturating the membrane, see [40]) and high radioactivity of the probes. This has the consequence that populations having a rRNA abundance below 0.1% of the total rRNA in a sample cannot be detected by membrane hybridization. However, low-abundance populations can be detected with the aid of PCR. 2.3.2.2.3 Variation

By comparing different types of commercially available membranes, [32] it was shown that the detectability and local variation differed significantly from one type of membrane to another. The local variability of a membrane (Type I) ranged from 10 to 50%, and was believed to be the primary cause for variation in quantification results. Several other types of variability during membrane hybridizations were tested [39]. It was demonstrated that variability introduced by denaturation/dilution (Type II) and prehybridization/hybridization (Type III) were not statistically significant. The washing step (Type IV), however, could introduce significant variation. Therefore, it was recommended to apply each sample in triplicate to compensate for the Type I variation. Samples and reference rRNA series should also be washed in the same tube or beaker to avoid Type IV variation. 2.3.2.3 Factors that May Interfere with Quantification 2.3.2.3.1 Membrane Saturation

Membrane saturation can be one of the many reasons that a non-linear hybridization response is observed [39, 40]. The saturation of Marga Charge membranes (MSI, Westborough, MA) was determined to be around 150 ng nucleic acid/slot using 32P-labeled E. coli rRNA [40]. 2.3.2.3.2 Target Accessibility

Accessibility of probes to the 16S rRNA sequence can be different from site to site, even in membrane hybridizations [32]. This study showed that the hybridization signal increased as the denaturation conditions increased (in terms of temperature, time, and the concentration of glutaraldehyde) for the target site 628 ( E. coli 16S rRNA numbering). However, for site 1392, higher levels of denaturation resulted in lower signals. It was hypothesized that a higher

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level of denaturation might result in loss of signal because site 1392 has relatively low levels of secondary structure [32]. In another study, McMahon et al. [88] also showed that the effect of denaturation conditions on membrane hybridization signals was target site-dependent. The difference in accessibility along the 16S rRNA could therefore cause bias in quantification if different probes are used. 2.3.2.3.3 Co-Extracted Substances

The presence of as little as 5.6 µg humic substances in 10 ng RNA can lower hybridization signals [38].Although DNA does not contribute much to non-specific binding during membrane hybridization [38], high concentrations of DNA (higher than 10 ng in 10 ng RNA) can reduce hybridization signals [40]. Since a total of 20 ng nucleic acids (DNA and RNA) is much lower than the saturation limit of the membrane [40], the inhibition is caused by mechanisms other than saturation. However, since hybridization signals are reduced for both specific and universal probes, results expressed in terms of percentage should not change significantly due to the presence of inhibitory compounds, but detectability is reduced. 2.3.2.3.4 In Vitro Transcribed rRNA

An advantage of the oligonucleotide probe method to previously used antibody methods is that a probe can be designed and synthesized without the availability of an axenic culture. However, there is still a requirement for pure culture rRNA for probe characterizations, for specificity studies, and for standards during quantitative membrane hybridizations. In vitro transcribed RNA or rcDNA (obtained by reverse transcription) was suggested as a substitute for native RNA by Ward et al. [27] and this method has been used in some studies [89–92]. However, the behavior of the in vitro transcribed rRNA and rcDNA was not compared to native rRNA in those studies. Polz and Cavanaugh [93] and McMahon et al. [88] both agreed that in vitro transcribed rRNA can be used to determine the Td values of the native rRNA, although there is some disagreement between the two studies. Polz and Cavanaugh found that transcribed rRNA resulted in 2 to 3 °C higher Td values compared to native rRNA using probes S-D-Bact-0338-a-A-18 for Bacteria and S-S-V.ang-0219-aA-20 for Vibrio anguillarum. McMahon et al. found, however, that transcribed rRNAs have the same Td values as the native rRNA for probes S-*-Synb0222-a-A-19, S-S-S.fum-0464-a-A-19, S-F-Synm-0700-a-A-23, and S-*-Univ1390-A-a-18. Both studies demonstrated bias when the transcribed rRNAs were used for quantification by membrane hybridization, even though Polz and Cavanaugh claimed that the bias was not statistically significant due to the high variability of the hybridization signals. This will restrict the usage of in vitro transcribed RNA for absolute quantification of microbial population abundance.

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2.3.3 Reverse Sample Genome Probing

The reverse sample genome probing technique was developed by Voordouw and coworkers in the mid-1990s [95]. Instead of using a probe targeting a genus or a family, genomic DNAs from reference strains are immobilized on a membrane. DNA extracted from an environmental sample is then isotope-labeled by nick translation and used as probes. This method is well suited to study diversity of microbial groups like sulfate-reducing bacteria, syntrophs or methanogens. rRNA probing detects how many known and unknown species containing the probe sequence are present in a sample while the reverse sample genome probing detects specific species or strains. To carry out the same task by rRNA probing, many hybridizations would need to be performed using different probes. The DNA probe is much more specific because it contains thousands of genes. A prerequisite, however, is a proper selection of the reference strains to minimize cross-hybridization to closely related strains. Each DNA reference spot only detects other genomes present if they share enough homology. This technique has been shown to discriminate down to the species level [96]. Voordouw and coworkers have used the technique to study diversity in oilcontaining environments [96, 97]. Twenty-six sulfate-reducing bacteria (16 belonging to the genus Desulfovibrio) plus other reference species were used to probe oil field samples to detect environmental nitrate- and sulfate-reducing bacteria. Hybridization signals were shown to specific Desulfovibrio species but not to others. Thereby the authors were able to identify and to quantify the relative amount of the different species present in a single hybridization event. A problem with this technique is that the DNA extracted from the samples may originate from dead cells. Also, the generation time of specific populations has to be considered to ensure that environmental changes are reflected in the bacterial populations. rRNA probing may detect changes in population sizes and activities that occur within days, while the reverse sample genome probing can do the same on a week scale, but in higher detail. 2.3.4 Whole Cell or in Situ Hybridization

Amann et al. define whole cell hybridization as hybridization performed with morphologically intact cells, and in situ hybridization as whole cell hybridization targeting cells in their natural habit [6, 82]. The term “fluorescence in situ hybridization (FISH)” is used for both whole cell and in situ hybridizations. The perspectives of whole cell and in situ hybridizations are discussed below. 1) FISH can show the three-dimensional spatial distribution and morphology of uncultured cells. For instance, Harmsen et al. [90, 98] used fluorescently labeled probes to reveal the internal structure of anaerobic granules. This technology was also used to show the dense aggregation of Paracocci in a denitrifying biofilm [99], as well as locations of Nitrosomonas and Nitrobacter in a nitrifying biofilm [100]. With careful design of probes and their fluorescent

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labeling, the distribution of at least seven different types of microorganisms can be shown on one slide at the same time [101]. 2) Following FISH labeling, target cells can be counted and concentrations can be expressed in terms of cell numbers. Flow cytometry in combination with FISH can also be used to count a large number of cells in a short time [10]. 3) Since the number of ribosomes in each cell is assumed proportional to its growth rate, quantification of the signal from each cell may be used to infer its growth rate. This approach was used to detect the in situ growth rate of sulfate-reducing bacteria in a biofilm [102]. The results should, however, be interpreted with caution, since it is not known if the relation between the ribosome number and the growth rate is similar for cells under different conditions [6]. The sensitivity of FISH can be very high, 1 in 106 cells (compared to 0.1% in membrane hybridization and 1 in 103 for cloning). 4) Probe specificity can be controlled even for very complicated communities by using multiple probes with different color labels that target the same organism at different sites in their 16S or 23S rRNA sequences. 2.3.4.1 Hybridization Stringency

In contrast to membrane hybridizations, FISH is usually conducted at a relatively high stringency, i.e., a stringency that can differentiate target from non-target cells. The wash step is merely used to remove excess probes. Although temperature could be used as the parameter for controlling stringency [103], salt or formamide concentrations are more often used [99, 104]. This is more convenient since only one temperature and hence, one oven is needed for hybridization reactions at different stringencies. The optimal stringency for FISH is determined in a similar way as a Td study. Target cells (perfect match) as well as non-target cells with a few mismatches should be used for stringency tests. Hybridizations are conducted at a number of stringencies. The average hybridization intensities obtained for a large number of cells (e.g., 100 to 200 cells) are plotted versus the stringencies in terms of formamide or salt concentration. The optimal hybridization stringency is determined as the point where the signals from non-target cells are low while those from target cells are still strong (see [104] and [99] for examples). Using the same hybridization and wash solutions employed for membrane hybridization, Amann and coworkers carried out a Td study with whole cells using a 32P-labeled probe [105]. It was shown that there was no significant difference in Td values between the two methods. Empirical equations are available for the conversion of stringencies expressed in terms of temperature, salt concentration, and formamide concentration [76]. These, again, can be used to predict the necessary stringency for whole cell hybridization, but are not a substitute for experimental examinations.

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2.3.4.2 Cell Fixation

The first step of FISH is fixation, which permeates the cell wall and cell membrane to allow the penetration of probes. Insufficient fixation can be a cause of low signal [6]. It is recommended to check the permeability of the cell walls before further studies involving hybridization with well-labeled universal or domain-specific probes are carried out [6]. Fixatives can be ethanol or methanol, aldehydes, enzymes, or heat [6]. In general, paraformaldehyde (PFA) and 50% ethanol offer good results for both Gram-positive and Gram-negative cells for fluorescently labeled oligonucleotide probes [106]. PFA is often preferred for the fixation of Gram-negative cells, since PFA might cross-link the thick Gram-positive cell wall to such an extent that the probe cannot pass through the wall. The 50% ethanol fixation is less used for Gram-negative cells, since the stability of ethanol-fixed Gram-negative cells is low. Heating or ethanol-formalin (v/v = 9:1) fixation also have proven to be suitable for Gram-positive cells [107]. Some of the Gram-positive cells may need lytic enzymes, hydrophobic solvents (toluene or diethyl ether), or acids for proper fixation [82]. PFA solution (4%) has proven to be a good fixative for most of the Archaea tested in a study by Burggraf et al. [108]. However, 4% PFA was too mild in some cases due to the rigid cell wall of Methanopyrus kandleri, Methanothermus fervidus, Methanobacterium thermoautotrophicum, and Halococcus morrhuae [108]. Sørensen et al. [109] found that 4% PFA fixation of Methanosarcina mazeii resulted in disruption of the cells. They demonstrated satisfying fixation results by washing the M. mazeii cells in saline-formaldehyde (1.6% formaldehyde and 0.85% NaCl). Fixation time also plays an important role. In a study by de los Reyes et al. [104], one minute fixation in 4% PFA is optimal for mycolic-acid-containing Gordona amarae, Rhodococcus rhodochrous, and Mycobacterium semegmatis. Longer fixation caused excessive cross-linking of the proteins in the cell wall preventing the access of probes. A number of fixation methods, including PFA fixation, ethanol/formaldehyde fixation, solvent extraction using chloroform/methanol, acid methanolysis, and acid hydrolysis, were evaluated for mycolic-acid-containing actinomycetes and some other Gram-positive and Gram-negative cells [110]. It was demonstrated that the optimum fixation was species-dependent. The wide variety of cell wall types complicated a proper fixation of all cells in a complex community by a single treatment method. It was suggested that different fixation methods should be used corresponding to the cell wall properties of the populations to be detected [106]. 2.3.4.3 Signal Enhancement

Besides poor fixation, low signals can be the result of non-complementarity between the probe and the target, ineffective probe labeling, non-optimal hybridization conditions, low ribosome numbers, or low accessibility of the target site.

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Several methods can be used to enhance the signal from cells having a low ribosome content, and thus increase the sensitivity of detection [see below]. It should be noted that none of these methods can improve the signal quantitatively. The sensitivity of detection can alternatively be improved by using instruments such as CCCD (cooled charged-coupled device) cameras, which can detect very low levels of emitted light, and confocal laser scanning microscopy (CLSM), which can exclude out-of-focus fluorescence. 2.3.4.3.1 Indirect Assays

In this technique the oligonucleotide probe is labeled with digoxigenin. After hybridization, the digoxigenin is detected by a binding protein labeled with a fluorescent dye or an enzyme. The signal increase by fluorescently labeled binding proteins is limited, since the molar fluorescent dye/protein ratio cannot exceed 3. Enzyme-labeled binding protein has the advantage that hybridization is detected by the precipitation of suitable substrates, eliminating interference from background fluorescence or autofluorescence. The major problem with indirect assays is the limited permeability of the cell wall to relatively large fluorescent dyes or enzyme-labeled binding proteins. Usually, enzymatic or more rigid fixation methods are needed, even for Gram-negative cells [111, 112]. 2.3.4.3.2 Enzyme-Labeled Oligonucleotides

Enzyme-labeled oligonucleotides are formed by covalent linking of the oligonucleotide probe to an enzyme. After hybridization, the probe is detected by the precipitate formed from suitable substrates. Similar to the indirect assay, autofluorescence and background fluorescence do not interfere with this method.An improvement compared to indirect assays is the lack of problems with non-specific binding of the binding proteins. A study by Urdea et al. (1988) showed that enzyme-labeled oligonucleotide probes had lower detection limits than fluorescently labeled probes and that their sensitivity was comparable to 32P-labeled probes [113]. Since the probe is smaller compared to binding proteins of indirect assays, this method can be used for most of the Gram-negative cells and some Gram-positive, ethanol-fixed cells. Lysozyme treatment of Gram-negative cells is also suitable for this method [114], while SDS might be used for treatment of Archaea [114]. In a study of the bacterial community in the gut of an earthworm Fischer et al. (1995) used fluorescence-, peroxidase- (enzymelabeled), and digoxigenin- (indirect assay) labeled probes [115]. The authors showed that the peroxidase- and digoxigenin-labeled probes were limited by the requirement of enzymatic fixation, diffuse images of stained cells, and interference with DAPI.

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2.3.4.3.3 Multi-Probe and Multi-Labeling

In general, the multi-probe method using several monolabeled fluorescent probes that target the same cell can increase the hybridization signal [103, 105]. However, limitation of specific target sites in a cell restrict the signal increase of this method. Alternatively, a probe can be labeled with several fluorescent dyes. But for unknown reasons, this method does not result in a significant improvement of signal intensity [6]. 2.3.4.3.4 Amplification of the Target Sequence

Amplification of the target sequence before detecting has been shown effective in detection of weak hybridization signals [27]. In situ PCR with a fluorescent primer was used to localize the single prokaryotic cells in a complex community [116]. Non-specific amplification, however, might be a potential problem associated with this method. 2.3.5 Solution-Based Hybridizations (Molecular Beacons)

When performing membrane hybridization or FISH, it is necessary to remove excess probe from the hybridization mixture before the detection step. This requires immobilization of cells or nucleic acids on solid surfaces (slides or membranes), which may lower the sensitivity of hybridization due to non-specific binding of probes to the surface [117]. It also prohibits real-time monitoring of nucleic acid synthesis and location of specific nucleic acids in living cells [117]. Furthermore, these hybridization methods are labor intensive and cannot be automated. The use of solution hybridization techniques, in contrast, offers advantages such as fast kinetics and the suitability for automatic analysis [74]. Molecular beacon techniques eliminate the need of removing excess probe after hybridization, and thus provide the feasibility for quick and automated hybridization. A molecular beacon is a probe that contains a stem-and-loop structure (Fig. 3) [117]. The loop part consists of a sequence that is complementary to the target sequence, while the stem part consists of two short sequences (arms) located at each end of the probe and complementary to each other. One of the arms is end-labeled with a fluorescent dye, while the end of the other arm contains a quencher. The quencher is selected so that its absorption spectrum overlaps the emission band of the fluorophore.When the molecular beacon is closed, the fluorescent dye and the quencher are held closely together by the stem. As a result, no fluorescence is emitted. When the molecular beacon hybridizes to a target, or when the temperature is higher than the Tm of the stem, the fluorescent dye is spatially removed from the quencher and the fluorophore emits light upon exitation (Fig. 3).

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Fig. 3. Concept of the molecular beacon technique. Modified from [117, 118]

Tyagi and Kramer [117] showed that the hybridization was very fast and that the reaction rate increased as a function of the beacon concentration, the target concentration, the salt concentration, and the temperature. The molecular beacon technique was found to be very specific when oligonucleotides were used as a target. Almost no hybridization was observed to targets with one mismatch or one deletion. Molecular beacons, therefore, can be used for detection of living cells or for real-time monitoring of PCR reactions. In addition, it is possible to detect a number of different populations in the same reaction tube if molecular beacons are labeled with different types of fluorescent dyes. In a comparative study of membrane hybridizations with oligonucleotide probes and solution hybridizations with molecular beacons, Schonfield et al. [118] obtained similar results for the detection of 16S rRNA. However, several problems were encountered by the authors. Firstly, the specificity of molecular beacons was not as high as reported by Tyagi and Kramer [117]. Only an 80% signal decrease was observed when one mismatch occurred in the target sequence. Secondly, denaturation had to be carried out carefully. Chemical denaturants may not be applicable, since they denature the molecular beacons if they are not removed from the solution before hybridization. In addition, denaturants cause high background fluorescence interfering with the detection of molecular beacons. Schonfield et al. denatured the RNA by heating (95°C for 5 min) followed by overnight hybridization at 39°C. Thirdly, the post-hybridization mixture needed to be centrifuged to remove particles that otherwise interfered with the detection of the molecular beacons. Nevertheless, this study showed that the molecular beacon technique reduced the total time needed for analysis of a sample from 3–4 days to 12 hours, and it was possible to use the technique for detecting 16S rRNA in environmental samples.

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2.4 FISH and Reporter Systems

Direct hybridization methods (e.g., FISH) are only possible when the abundance of target nucleic acids is sufficiently high. Detecting genomic DNA or mRNA using in situ hybridizations is therefore difficult or impossible since the amount of target is too low. The amount of target can be increased by in situ RT-PCR applied to whole, permeabilized cells. By this technique, it is possible to address the questions regarding gene expression under different growth conditions and the influence of metabolites and chemicals on the different pathways at the single cell level. This approach is very laborious when compared to direct studies with well known model systems like Echerichia coli, Saccharomyces cerevisiae, and Bacillus subtilis, but at the current state of technology it is the only possibility. To our knowledge the only report describing gene expression in anaerobic reactor systems on the mRNA level is by Lange et al. [119]. This paper describes the expression of the heat shock gene dnaK in Methanosarcina mazei S-6 under different stress situations in situ. Paraformaldehyde-fixed cells were permeabilized by lysozyme and heat treatment, and the cellular DNA was removed by DNAse treatment.By means of a semi-nested RT-PCR protocol,DIG-labeled primers were used to amplify the dnaK gene product. Detection of the dnaK reporter molecule was based on the HPPN/Fast Red detection system and the binding of antidigoxigenin-AP conjugate. Key parameters in this technique are the permeabilization of the cells and the number of cycles in the RT-PCR step. Analyses of different ecosystems by this technique, therefore, have to be carefully and individually optimized as especially Gram-positive bacteria and many archaeal species have a cell wall that is difficult to permeabilize.As opposed to traditional mRNA analysis techniques, e.g., Northern blotting, the in situ RT-PCR technique may reveal heterogeneous gene expression in microbial populations [120], providing a more detailed picture of the physiological state of populations. A few reporter systems have been developed in organisms present in anaerobic reactor systems. In the methanogenic archaeon Methanococcus maripaludis, genetic manipulations are now possible, as a naturally occurring plasmid in Methanococcus has been modified to include the genes encoding the puromycin resistance marker and the reporter gene lacZ encoding a b-galactosidase [105, 121, 122]. A uidA b-glucuronidase reporter gene has been used in Methanococcus voltae [123]. By fusing the nifH promotor region to the lacZ coding region, mutational analysis showed the presence of a regulatory palindromic sequence repressing the nifH gene expression. In the genus Methanosarcina, a plasmid from Methanosarcina acetivorans was reported to be able to replicate in 9 of 11 Methanosarcina strains with high transformation efficiency [124]. Lange and Ahring have utilized the plasmid found by Metcalf to construct a reporter system based on the heat shock promotors of dnaK and grpE in Methanosarcina mazei S-6 and the lacZ marker gene. The resistance marker was the puromycin cassette. This system showed a nice correlation between the amount of stress and the activity of b-galactosidase. However, after several transfers of the transformants, the presence of the promotor-lacZ cassette could no longer be confirmed due to the size of the plas-

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mid (12 kb), but the strains retained their resistance towards the original level of puromycin [125]. These findings should encourage new investigations to construct smaller plasmids to allow routine genetic manipulations in the genus Methanosarcina. Also, other resistance markers are needed as the puromycin marker is one of a few shown to work in methanogens [126–128]. In the extremely halophilic archaeon Haloferax alicantei, a b-galactosidase protein was purified and the sequence of the N-terminal part of the protein was determined which facilitated the cloning of the gene [108]. b-Galactosidases from other organisms do not function in the halophiles due to salt concentrations above 4 M NaCl. Since plasmids already have been found in the halophilic Archaea, in vivo studies of gene expression in this group of organisms is now possible [127]. Future gene candidates to be used as reporter genes may be the luxA and the luxB gene system; these genes have been expressed in the anaerobe Clostridium perfringens under the transcriptional control of the a-toxin promotor region under anoxic conditions [129]. However, this gene system has only limited relevance in anaerobic prokaryotes, as the lux protein complex needs oxygen to produce light. The green fluorescent protein (GFP) also requires oxygen to mature into a functional fluorescent protein [130]. Errampalli et al. [131] have recently reviewed the applications of the GFP protein as a molecular marker in environmental studies, so we refer to this paper for further reading. The GFP may be valuable as a reporter gene in anaerobic laboratory systems if organisms transformed with the GFP gene can be detected by fluorescence when subsequently exposed to oxygen [130]. It will, therefore, not be possible to detect the GFP reporter system in situ, but ex situ as a tool to monitor gene expression and to characterize promotor regions. Further work has to be done on the applicability of the GFP in anoxic and anaerobic systems. An unusual blue protein Ambineela, has been isolated from the archaeon Acidianus ambivalens [132]. Spectrum analyses of this protein show two emission bands around 395 nm and 625 nm. The protein does not require any transition metals in order to display this blue color. Ambineela might be a possible reporter gene candidate in archaeal species for flow cytometric measurements or absorbance measurements in axenic cultures. 2.5 FISH and Antibody Probes

Molecular studies of anaerobic reactor systems started using antibodies raised against different microbial groups and labeled with fluorochromes [133–139]. The technique was used in phylogenetic studies to construct 2D pictures of the spatial distribution of different groups of microorganisms in granular sludge and flocs. Several drawbacks are, however, associated with the production and use of antibodies in microbial ecology studies [10]. In order to raise the desired antibodies, axenic cultures are required to induce antibody production by the animals implying that only known organisms can be targeted. The antibodies are less specific than oligonucleotide probes and antibodies raised against one organism may target different epitopes on the cell leading to putative cross-reac-

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tivity to other non-target cells. Furthermore, probing with antibodies does not discriminate between dead and living cells. An advantage of antibody probing is that cells having different metabolic activities are equally visualized. The difference in metabolic activity, on the other hand, is one of the problems of oligonucleotide probing when targeting the 16S rRNA molecule. The cells of a population do not contain an equal amount of ribosomes [27, 119, 120] and as the oligonucleotides normally are quite small, only a certain amount of fluorochrome can be targeted to each cell. If the ribosome content in the cell, therefore, is low, the fluorescent signal cannot be distinguished from the background fluorescence. To circumvent this problem, antibody probing can be combined with fluorescent oligonucleotide probes targeting the 16S rRNA molecule. The antibody probe and the oligonucleotide probe can be labeled with different colors and by applying different filters when examining the samples in the microscope, only cells labeled by both probes are visible. Raskin and coworkers have successfully applied both antibody and oligonucleotide probes in situ for the characterization of Gordona species in activated sludge and anaerobic digesters [140, 141]. Gordona species are slow growing filamentous bacteria commonly found in activated sludge foam causing operational problems in wastewater treatment plants. Quantification of Gordona species in activated sludge samples and in anaerobic digester samples by fluorescent antibodies showed that Gordona species biomass accounted for 10–28% of the volatile suspended solids in a full-scale activated sludge system and for 8–19% in anaerobic digester sludge. Quantification using traditional Gram staining and filament counting for the same activated sludge sample resulted in significantly lower values of 2–10% [142]. Analyzing seven full-scale wastewater treatment plants with antibody probes or FISH targeting Gordona species showed that the antibody probing technique led to estimates of a higher number of Gordona species than when FISH was used [141]. Simultaneous staining with antibody probes and FISH showed that some branched filaments were clearly stained by the antibodies, but gave a low signal when using the FISH probe. The ribosome content of the cells, therefore, indicated that individual cells differed in metabolic activity. These findings point to the difficulties when estimating the number of species and the metabolic state of the species in an environmental sample. Quantifying the number of Gordona species by membrane hybridization targeting 16S rRNA and FISH targeting 16S rRNA, expressed as biomass by measuring cell length and calculating the cell volume, gave different results. As observed in other studies [143], relative cell numbers, therefore, cannot be reliably extrapolated from rRNA abundance levels. Although both immunostaining and FISH might yield non-specific signals from non-target microorganisms, the possibility of misidentifying the same non-target microorganisms by two independent methods should be low. The simultaneous application of a polyclonal antibody serum and an oligonucleotide probe targeting 16S rRNA also offers the advantage of detecting slow growing or metabolically less active microorganisms while maintaining high phylogenetic specificity in complex environments [140].

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2.6 FISH and Microautoradiography

To identify and to evaluate the metabolic activity of a single cell simultaneously, microautoradiography (MAR) has successfully been used in combination with FISH. MAR is used to study the microscale distribution of radiolabeled compounds. The radiolabeled compound is taken up by either active or passive transport across the cell membrane. After appropriate treatment, the radiolabeled cell sample is placed in contact with a layer of radiosensitive emulsion. The emulsion is then developed after days or weeks of exposure to the radioisotope by means of standard photographic procedures. Subsequent microscopy of the cell sample will show cells covered with silver grains if the radiolabeled compound has been taken up or metabolized. Cells on the developed slide can then be investigated further by FISH to correlate the phylogenetic affiliation and metabolic activity. Nielsen et al. and Ouverney et al. have combined FISH and MAR protocols to correlate cell identity and function in activated sludge reactors [144–147]. The uptake of 14C- or 3H-labeled substrates (acetate, glucose, ethanol, glycine, leucine, and oleic acid) in bulking sludge was investigated in seven industrial or municipal activated sludge treatment plants. The authors concluded that the taxonomic variability assessed using FISH with probes specific for type 021 N, Thiothrix and Leucothrix is high since only some of the filaments morphologically identified as type 021 N hybridized with the 021 N probe. Moreover, no filaments took up all the tested substrates, and type 021 N from the various treatment plants varied in their uptake capabilities. The study demonstrates that strain differences with regard to substrate utilization are likely to occur among bacteria within the same genus. Some parameters to obtain good results with the MAR technique are: – The choice of radioactive tracer: image resolution will decrease if high energy isotopes are used. – The selection of incubation conditions: amount of biomass and isotope, ratio of “hot” to “cold” substrate, presence of electron acceptors and incubation time. – The sample handling and fixation: the sample must be washed thoroughly to remove excess isotopes. – Other staining procedures: various cell stains such as Gram and Neisser staining for identification of filamentous microorganisms and fluorescent nonspecific stains such as Acridine orange or DAPI targeting DNA may be combined with MAR. The dye CTC may be used to discriminate between viable and dead cells. The limitation of the MAR technique is low resolution unless cryosectioning of the sample is conducted and 3D distribution of non-fluorescent silver grains in the emulsion is accurately analyzed by confocal laser scanning microscopy.

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2.7 Peptide Nucleic Acid Probes

Peptide nucleic acids (PNA) are artificial oligoamides reported for the first time by Nielsen et al. [148]. PNA are capable of forming not only double-stranded duplexes, but also triple-stranded complexes with poly- or oligonucleotides. Due to their neutral backbone, PNA can hybridize to nucleic acids in the absence of the counterions needed to stabilize nucleic acid duplexes. Thus, PNA probes exhibit superior hybridization characteristics compared to DNA probes under conditions where double-stranded DNA is electrostatically unstable, e.g., at low salt conditions. PNA probes have been used for a wide range of applications and the advantages of PNA over DNA are numerous: rapid hybridization, hybridization independent of the salt concentration, resistant to nuclease and protease attack, more specific and shorter probes can be used for higher sensitivity [149]. Prescott and Fricker [149] have used PNA oligonucleotide probes for in situ detection of Eccherichia coli in water. They targeted the PNA biotinylated probes towards the V1 region of the E. coli 16S rRNA molecule and the specific detection was carried out in less than 3 hours. The specificity of the PNA probe against E. coli was confirmed by comparative dot-blot analyses using the genera Klebsiella, Enterobacter, and Citrobacter. Work done by Perry-O’Keefe et al. [150] has shown the superior hybridization conditions under which PNA probes can detect DNA target sequences compared to DNA probes. Investigations of parameters like hybridization rate and salt concentrations showed that PNA probes hybridized much faster under a wide range of salt concentrations. Because the PNA molecule is not charged, pre-gel hybridization can be performed with subsequent transfer to nylon membrane and fast detection. Another new method of detection of nucleic acids is the application of PNA probes together with the BIAcore biosensor system. The principle behind this system is the detection of PNA hybridizing to DNA through signaling to a surface plasmon resonance unit. Specific hybridization occurs in 10 min within a flow stream at room temperature [151]. This fast and reproducible technique is promising for the detection and quantification of rRNA extracted from environmental samples, as it is much faster than the traditional techniques employed today. The problem with the secondary structure of the rRNA may be overcome by an addition of formamide which will inhibit double strand and hydrogen bond formation, thereby reducing the secondary structure of the rRNA but still allowing the PNA probe to bind to its target sequence.

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3 rRNA-Based Analyses of Anaerobic Reactors 3.1 Biofilm Reactors 3.1.1 Biofilm Formation

Anaerobic biofilm development was monitored by Araujo et al. [152]. They investigated biofilm formation with respect to the methanogens and the properties of biofilm formation on hydrophilic and hydrophobic surfaces. FISH and confocal laser microscopy were used to quantify the microbial composition and to elucidate the spatial distribution of microbes in the biofilm. Table 1 outlines the probes used in this study. In this experiment, axenic cultures of Methanobacterium formicicum, Methanosaeta concilli and Methanosarcina barkeri were grown together for a month before introduction into a sterile, anaerobic chemostat connected to a modified Robbins device (MRD) [153]. The system was operated at 30°C ± 3°C and the biofilm was grown on either polypropylene or glass discs inside the MRD. The carbon sources were methanol, formate and acetate. Samples ware taken on days 0, 2, 7, and 9. Even though the setup was supposed only to analyze for methanogens, contaminants were observed when samples were taken. During the experiment, the chemostat microbial community changed from 100% Archaea at day 0 to contain 40% Bacteria within 2 days.After 7 days the Bacteria constituted 80% of the total microbial community. The number of Methanosarcina barkeri and Methanosaeta concilii decreased from about 15% day 0 to close to 0% day 9. The number of Methanobacterium formicicum cells was reduced from 80% to 10% within 9 days. The findings were not reflected in the composition of the biofilm growing on the discs. After 7 days, the bacterial population had only increased to about 15%. Methanobacterium formicicum predominated in all the biofilms ranging from 40% to 80% after 9 days. Methanosaeta concilii cells were found at low numbers up to 2.4% while Methanosarcina barkeri was not detected. No difference in biofilm colonization was observed between the polypropylene and the glass discs. In a second experiment crushed granular sludge was used as seed in the chemostat and the biofilm in the MRD was grown on polypropylene discs at 33°C ± 2°C. Initially, the inoculum in the chemostat consisted of 13% sulfatereducing bacteria (SRB) and 93% Archaea mostly Methanobacterium species. Methanosarcina species accounted for 5%.After eleven days the SRB population had decreased to 1.7% although the bacterial population still constituted 13%. Since no sulfate was present, the SRB population probably functioned as proton reducers in syntrophic association with hydrogen and formate-consuming methanogens. From day zero to day eleven Methanobacteriaceae increased from 74% to 85% while Methanosarcinales decreased from 11% to 4%. Microscopic observations of the biofilm by confocal laser microscopy showed areas where no growth occurred, and areas up to 9 µm where growth occurred. This was

T

M

M

M

M

Reactor type

Fixed-bed anaerobic bioreactor

Fixed-bed anaerobic bioreactor

Modified Robbins device reactor

Fixed-bed anaerobic bioreactor

FISH, CLSM

FISH Dot blot

FISH

Glucose

FISH Dot blot

Methanococaceae Methanobacteriaceae Methanomicrobiales Methanosarcinales

S-F-Mcoc-1109-a-R-20 S-F-Mbac-1174-a-R-22 S-O-Mmic-1200-a-R-21 S-O-Msarc-860-a-R-21

Virtually all organisms Virtually all Bacteria Virtually all Archaea Virtually all Archaea Virtually all Eucarya Methanococaceae Methanobacteriaceae Methanomicrobiales

Virtually all Archaea All sulfate-reducing bacteria

S-D-Arch-0915-a-R-20 S-*-Srb-0385-a-R-18

S-*-Univ-1392-a-R-15 S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-D-Arch-344-a-R-20 S-D-Euca-0502-a-R-16 S-F-Mcoc-1109-a-R-20 S-F-Mbac-0310-a-R-22 S-O-Mmic-1200-a-R-21

Virtually all bacteria

Unknown Desulfovibrio vulgaris sp.

Virtually all bacteria All sulfate-reducing bacteria Unknown Desulfuromonas sp. Unknown Desulfovibrio vulgaris sp.

Target organisms

S-D-Bact-0338-a-R-18

S-Ss-Pt2–647-a-R-19

S-D-Bact-0338-a-R-18 S-*-Srb-0385-a-R-18 S-Ss-Pt1–647-a-R-19 S-Ss-Pt2–647-a-R-19

Probe technique Probes used

Acetate, butyrate, FISH, CLSM formate + Ethanol and methanol

Methanol, formate, acetate

Lactate

Glucose

Carbon sources

Table 1. Various 16S rRNA probes used in environmental studies

Raskin et al. 1995 Raskin et al. 1996

Araujo et al. 2000

Kane et al. 1993

Amann et al. 1999

Reference

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T

M

M, T

M

M, T

Reactor type

Anaerobic chemostat

Solid waste digester

Sewage sludge digester

One-phase, two-phase

Table 1 (continued)

Sewage sludge

Sewage sludge

Municipal waste

Glucose

Carbon sources

Dot blot

FISH

Dot blot

S-*-Univ-1390-a-R-18

S-D-Arch-0915-a-R-20 S-D-Euca-0516-a-R-16 S-F-Mcoc-1109-a-R-20 S-F-Mbac-0310-a-R-22 S-O-Mmic-1200-a-R-21 S-O-Msarc-860-a-R-21 S-F-Msar-821-a-R-24 S-G-Msar-0821-a-R-21 S-F-Msae-0825-a-R-23

S-D-Bact-0338-a-R-18

S-U-Univ-1392-a-R-15

S-G-Dsbb-0660-a-R-20

S-O-Msarc-860-a-R-21 S-F-Msar-0821-a-R-24 S-F-Msae-0825-a-R-23 S-F-Dsv-0687-a-R-16 S-G-Dsbm-0221-a-R-20 S-G-Dsb-0129-a-R-18 S-*-Dscoc-0814-a-R-18

Probe technique Probes used

Virtually all organisms

Virtually all Archaea Virtually all Eucarya Methanococales Methanobacteriales Methanomicrobiales Methanosarcinales Methanosarcinaceae Methanosarcina sp. Methanosaeta

Virtually all bacteria

Virtually all organisms

Methanosarcinales Methanosarcina Methanosaetaceae Desulfovibrionaceae Desulfobacterium Desulfobacter Desulfococcus+Desulfosarcina + Desulfobotulus Desulfobulbus

Target organisms

Raskin et al. 1995

Raskin et al. 1994

Reference

182 J. Hofman-Bang et al.

CSTR digester: Biogas plant

Sewage sludge digesters

M

Cow manure + lipids

Dot blot

S-S-S.sap-0181-a-R-20 S-S-S.bry-0181-a-R-21

S-S-S-wol-0180-a-R-21

S-G-Synm-0126-a-R-19

S-F-Synm-0700-a-R-23

S-G-Dsbb-0660-a-R-20

S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-D-Arch-344-a-R-20 S-D-Euca-0502-a-R-16 S-F-Mcoc-1109-a-R-20 S-F-Mbac-0310-a-R-22 S-O-Mmic-1200-a-R-21 S-O-Msarc-860-a-R-21 S-F-Msar1414-a-R-21 S-G-Msar-0821-a-R-21 S-F-Msae-0825-a-R-23 S-G-Dsb-0804-a-R-18 S-F-Dsv-0687-a-R-16 S-G-Dsbm-0221-a-R-20 S-G-Dsb-0129-a-R-18 S-*-Dscoc-0814-a-R-18

Strain FSS7 Strain FMS2 Syntrophospora bryantii Syntrophomonas sapovorans Syntrophomonas wolfei subsp. wolfei Syntrophomonas wolfei subsp. LYB Syntrophomonas sapovorans Syntrophomonas wolfei subsp. wolfei Syntrophomonas wolfei subsp. LYB Syntrophomonas wolfei subsp. wolfei Syntrophomonas wolfei subsp. LYB Syntrophomonas sapovorans Syntrophospora bryantii

Virtually all bacteria Virtually all Archaea Virtually all Archaea Virtually all Eucarya Methanococaceae Methanobacteriaceae Methanomicrobiales Methanosarcinales Methanosarcinaceae Methanosarcina sp. Methanosaetaceae Desulfobacter group Desulfovibrionaceae Desulfobacterium spp. Desulfobacter spp. Desulfococcus multivorans Desulfosarcina variabilis Desulfobotulus sapovorans Desulfobulbus spp. Hansen et al. 1999

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T

M

T

T

M, T

T

Reactor type

CSTR reactor

Temp. phase digester

CSTR digester

Lab-scale digester

Temp. phased digester

Table 1 (continued)

Activated sludge

Municipal waste

Municipal wastewater Paper mill wastewater

Municipal solid waste

Sewage sludge

Glucose

Carbon sources

Dot blot

Dot blot

Dot blot

Selected Desulfotomaculum sp. Selected Desulfotomaculum sp. Selected Desulfotomaculum sp.

S-*-Dtm(c)-0428-a-R-19 S-*-Dtm(cd)-0216-a-R-19 S-*-Dtm(bcd)-230-a-R-18

S-D-Arch-0915-a-R-20 S-F-Mcoc-1109-a-R-20 S-F-Mbac-0310-a-R-22 S-O-Mmic-1200-a-R-21 S-O-Msarc-860-a-R-21 S-F-Msar1414-a-R-21 S-G-Msar-0821-a-R-21 S-F-Msae-0825-a-R-23 S-G-Msae-0733-a-R-22

Virtually all Archaea Methanococaceae Methanobacteriaceae Methanomicrobiales Methanosarcinales Methanosarcinaceae Methanosarcina sp. Methanosaetaceae Methanosaeta

Virtually all organisms

Selected Desulfotomaculum sp.

S-*-Dtm(be)-0152-a-R-20

S-U-Univ-1390-a-R-18

Selected Desulfotomaculum sp.

Desulfotomaculum

Methanosarcina Methanosaeta Methanomicrobiales Methanobacteriales

Target organisms

S-*-Dtm(a)-0229a-R-18

S-G-Dtm-0229-a-R-18

S-G-Msar-0821-a-R-24 S-G-Msae-0381-a-R-22 S-O-Mmic-1200-a-R-21 S-F-Mbac-0310-a-R-22

Probe technique Probes used

Zheng and Raskin, 2000

Hristova et al. 2000

Fernandez et al. 2000

Reference

184 J. Hofman-Bang et al.

M

M

UASB

UASB

S-O-Mmic-1200-a-R-21 S-F-Mbac-0310-a-R-22 S-F-Mcoc-1109-a-R-20 S-O-Msarc-860-a-R-21 S-G-Msar-0821-a-R-24 S-G-Msae-0332-a-R-22 S-S-M.con-0381-a-R-22 S-S-M.the-0396-a-R-22

FISH

S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-F-Mbac-1174-a-R-22 S-F-Mcoc-1109-a-R-20 S-O-Mmic-1200-a-R-21 S-G-Msar-0821-a-R-21 S-F-Msae-0825-a-R-23 S-*-Srb-0385-a-R-18 S-F-Dsv-0687-a-R-16 S-G-Dsbb-0660-a-R-20 S-G-Dsbm-0221-a-R-20 S-G-Dsb-0804-a-R-18 S-S-D.amn-0454-a-R-20 S-*-Dscoc-0804-a-R-18

S-D-Arch-0915-a-R-20

Dot blot

Sulfate-rich mill Dot blot paper waste water

Ethanol, waste water Glucose Glucose+ propionate

S-G-Msae-0781-a-R-22 S-G-Msae-0332-a-R-22 S-G-Msae-0322-p-R-22 S-S-M.con-0381-a-R-22 Zheng et al. 2000

Virtually all bacteria Elferink et al. 1998 Virtually all Archaea Methanobacteriaceae Methanococaceae Methanomicrobiales Methanosarcina Methanosaeta Gram-negative sulfate-reducing bacteria Desulfovibrionaceae Desulfobulbus Desulfobacterium Desulfobacter Desulfohabdus amnigenus Desulfococcus, Desulfobacterium Desulfosarcina, Desulfobacter, Desulfobotulus

Methanomicrobiales Methanobacteriaceae Methanococaceae Methanosarcinales Methanosarcina Methanosaeta Methanosaeta concilii Methanosaeta thermophila

Virtually all Archaea

Methanosaeta Methanosaeta Methanosaeta competitive probe Methanosaeta concilii

Molecular Ecology of Anaerobic Reactor Systems

185

T

M

Reactor type

UASB

Table 1 (continued)

Propionate + sulfate

Carbon sources

S-*-Univ-1390-a-R-18 S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-S-SYN7–0177-a-R-23 S-S-S.wol-0223-a-R-19 S-S-MPOB-0222-a-R-19 S-S-S.pfe-0460-a-R-21 S-F-Dsv-0687-a-R-16 S-G-Dsbb-0660-a-R-20 S-F-Msae-0825-a-R-23 S-O-Mmic-1200-a-RA-21 S-F-Mbac-0310-a-R-22

Dot blot FISH

S-S-S.pfe-0460-a-R-21 S-S-S.wol-0223-a-R-19 S-Ss-SYN7–0177-a-R-23 S-F-Mbac-1174-a-R-22 S-F-Mcoc-1109-a-R-20 S-O-Mmic-1200-a-R-21 S-G-Msar-0821-a-R-21 S-F-Msae-0825-a-R-23

S-*-Sbac-0222-a-R-19

Probe technique Probes used

Virtually all bacteria Virtually all Archaea Syntrophic propionate-oxidizer SYN7 Syntrophobacter wolinii MPOB Syntrophobacter fpennigii Desulfovibrio Desulfobulbus Methanosaeta Methanomicrobiales Methanobacteriaceae

Virtually all organisms

Syntrophobacter fumaroxidans Syntrophobacter pfennigii Desulforhabdus amnigenus Syntrophobacter fpennigii Syntrophobacter wolinii Syntrophic propionate-oxidizer SYN7 Methanobacteriales Methanococales Methanomicrobiales Methanosarcina Methanosaeta

Target organisms

Harmsen et al. 1996 a

Reference

186 J. Hofman-Bang et al.

M

M

M, T

T

M, T

UASB

UABS

UASB

UASB

UASB

Dot blot

FISH

Sucrose, VFAs

Sucrose+VFAs +Yeast extract S-D-Arch-0915-a-R-20 S-F-Mbac-1174-a-R-22 S-Ss-TGP-0690-a-R-20

Dot blot

PCR

S-D-Bact-0338-a-R-18

S-D-Arch-0915-a-R-20 S-G-Msae-0757-a-R-18 S-F-Mbac-1174-a-R-22

S-S-D.amn-0454-a-R-20

S-D-Bact-0338-a-R-18 S-*-Sbac-0222-a-R-19

S-G-Msae-0825-a-R-23 S-O-Mmic-1200-a-R-21 S-F-Mbac-0310-a-R-22

S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-S-MPOB-0222-a-R-19 S-S-Spfe-0460-a-R-21

FISH

Alcohol distillery FISH Sucrose+VFAs Dairy milk Food brewery Bean jam Potato waste Municipal sewage

Paper mill wastewater

Sucrose+sulfate VFAs+sulfate

Virtually all Archaea Methanobacteriaceae Strain SI

Virtually all bacteria

Virtually all Archaea Methanosaeta Methanobacteriaceae

Virtually all bacteria Syntrophobacter fumaroxidans Syntrophobacter pfennigii Desulforhabdus amnigenus Desulfohabdus amnigenus

Virtually all bacteria Virtually all Archaea MPOB Syntrophobacter fpennigii alias KOPROP Methanosaeta Methanomicrobiales Methanobacteriaceae

Sekiguchi et al. 1998

Imachi et al. 2000

Tagawa et al. 2000

Elferink et al. 1997

Harmsen et al. 1996 b

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187

T

M, T

M

Reactor type

UASB

UASB

Table 1 (continued)

Propionate, butyrate

Sucrose, VFAs

Carbon sources

Dot blot

CLSM FISH

S-*-R6B-7–0980-a-R-18 S-*-R1B-16–0980-a-R-18

S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-*-Srb-0385-a-R-18

S-D-Bact-0338-a-R-18 S-D-Arch-0915-a-R-20 S-F-Mbac-1174-a-R-22 S-O-Mmic-1200-a-R-21 S-F-Msar-1414-a-R-21 S-F-Msae-0825-a-R-23 S-G-Dsbb-0660-a-R-20 S-*-MUG28–0701-a-R-20 S-*-GNSB-0633-a-R-20

Probe technique Probes used

Virtually all bacteria Virtually all Archaea Gram-negative sulfate-reducing bacteria Clostridium-like bacteria Clostridium-like bacteria

Virtually all bacteria Virtually all Archaea Methanobacteriales Methanomicrobiales Methanosarcinaceae Methanosaetaceae Desulfobulbus rDNA clone MUG28 rDNA clones in green non-sulfur bacteria

Target organisms

Hofman-Bang et al. 2001

Sekiguchi et al. 1999

Reference

188 J. Hofman-Bang et al.

Molecular Ecology of Anaerobic Reactor Systems

189

explained by the syntrophic growth of the SRB population in conjunction with the methanogens. The high number of Methanobacteriales found may be explained by the competitive advantage over Methanococcales and Methanosarcinales, when hydrogen and formate kinetics are compared. Amann et al. [155] investigated the role of sulfate-reducing bacteria (SRB) in the establishment and development of a biofilm. This was done by comparative sequence analysis and FISH in biofilm targeting selected SRB populations. Biofilms were grown on coverslips and reached a thickness of 10 µm. Two clones were identified as delta-Proteobacteria. One was closely related to Desulfuromonas acetoxidans. The other was closely related to Desulfovibrio vulgaris. Specific probes constructed to target these two SRB did not hybridize to any cells in the biofilm. If specific probes constructed on the basis of the two cloned 16S rDNA genes were used, single cells could be identified. If the PCR primer S-*-Srb-0385-a-R-18, used to construct the clone library, however, was used as a fluorescent probe, only one of the populations could not be identified. The general observation was that the biofilm formation was patchy and that the identified cells were gathered in colonies. The authors also concluded that the different results obtained with the FISH probes demonstrate that a better insight in the SRB diversity is needed to construct more specific probes. 3.1.2 Biofilm Composition and Dynamics

Raskin et al. [154] studied the competition and coexistence of sulfate-reducing bacteria (SRB) and methanogens in anaerobic biofilms. Four biofilm reactors, MA, MB, SA, SB, were operated on glucose as sole carbon source with (SA, SB) or without sulfate (MA, MB). After eleven months of operation, sulfate was added to MB and omitted from SB. MA and SA were maintained as control reactors. Probing of samples taken throughout the experiment using the probes shown in Table 1 was carried out. Reactor MA contained 25% methanogens. Specific probing showed that Methanobacteriales accounted for 12% and Methanosarcinales, Methanomicrobiales, Methanococcales each occurred in the range of 4 –5 %. Prior to sulfate addition, SRB comprised a significant fraction of the community in the methanogenic reactors. Desulfovibrio and Desulfobacterium genera were present in high amounts (16% and 2.8%, respectively). As previously mentioned, these findings may be explained by the capability of several SRB to grow syntrophically on lactate, ethanol, propionate, and pyruvate. After the addition of sulfate to the MB reactor, sulfate reduction started after 6 hours, reaching steady state levels within a few days. Desulfovibrio and Desulfobacterium sharply increased to 26% and 7.7%, respectively. These levels decreased subsequently to 20% and 3.5%, followed by a slow increase to 35% and 4.5%, respectively. Desulfobulbus and Desulfobacterium spp. increased shortly after the addition of sulfate, but later fell to the levels observed before sulfate addition. Desulfosarcina, Desulfococcus, and Desulfobotulus spp. all fell below the levels observed before the sulfate addition (from 2% to 0.5%). 100 days after the sulfate addition, the acetate levels increased in the reactor for about 100 days.

190

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This change in acetate was not accompanied by detectable population changes, thus emphasizing the need for a careful evaluation of the concept “steady-state” in anaerobic reactors. Even though the methane production decreased to undetectable levels after the addition of sulfate, the methanogenic populations still constituted about 8%. Obviously, the ribosome content was maintained for months in the methanogens despite the inactivity of their primary metabolic pathways. After the sulfate was removed from the inflow of reactor SB, methanogenic populations slowly increased to steady-state levels dominated by Methanobacteriales (18%). The relative abundance of other methanogens remained fairly constant. Following sulfate removal, Desulfovibrio and Desulfobacterium populations decreased to levels comparable to the control reactor (MA). These observations show that hydrogen is the key substrate when the metabolism shifts from sulfidogenesis to methanogenesis.After several hundred days of operation without sulfate, the microbial community structure and function of the SB reactor was similar to that of the methanogenic control reactor MA. Similar to observations from other sulfate-rich environments, the SRB were shown to outcompete methanogens in high-sulfate biofilms. Also, the presence of Desulfovibrio and Desulfobacterium spp. only varied a factor 2 as a function of the presence or absence of sulfate. Moreover, Desulfosarcina, Desulfococcus, and Desulfobotulus spp. turned out to be better adapted to the biofilms without sulfate. The content of ribosomes in methanogens only slowly decreased upon sulfate addition although methane could not be detected. Wagner et al. [143] have made similar observations when probing denitrifying bacteria in wastewater treatment plants. It therefore seems reasonable to correlate microbial responses to environmental perturbations to increased ribosome content, while it is more problematic to correlate decreasing microbial activity to decreasing ribosome content. 3.2 Granular Sludge Reactors 3.2.1 Granular Sludge

Granular sludge consists of conglomerates of anaerobic microorganisms, which are still visible as granules after settling and is considered a major form for immobilization of microorganisms in anaerobic wastewater treatment systems [156]. Similar to biofilms, granular sludge provides minimized mass transfer limits, optimal micro-environment, and protection for microorganisms such as methanogens and syntrophic bacteria. Granules typically form in upflow anaerobic sludge blanket (UASB) reactors, although they also might be found in other anaerobic systems, such as expanded granular sludge blanket (ESGB) reactors [157, 158], upflow sludge bed filters (UBF) [2, 159], compartmentalized UASB reactors [160, 161], anaerobic migrating blanket reactors (AMBR) [162], anaerobic baffled reactors (ABR) [163], and anaerobic sequencing batch reactors (ASBR) [164]. Granular sludge has been used successfully to treat wastewaters

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having varying COD contents. The loading can be as high as 20 g COD/L [165], or as low as 1 g COD/L, such as for domestic wastewater [166]. Anaerobic granular sludge reactors can be operated at mesophilic, thermophilic, and even psychrophilic conditions. After acclimation, granular sludge can treat wastewater containing refractory, toxic, or xenobiotic compounds [167–174]. Even though UASB reactors are usually operated at neutral pH, granules can adapt to high pH (8.1–8.5) [175] or low pH (6.0) [176]. UASB reactors have also been used to treat sulfate rich wastewater (SO42–/COD >1) [177]. To further optimize the process, it is of great interest to understand the microbial species composition, and structure of granular sludge, as well as the granulation processes. 3.2.2 Microbial Composition of Granules

Zheng and Raskin [178] used membrane hybridization with radioactively labeled oligonucleotide probes to quantify methanogens in granules from two lab-scale UASB reactors. The two reactors were fed synthetic wastewater (COD = 4 g/L) containing glucose or glucose/propionate as the only energy source. In both reactors, the granular sludge contained around 40% archaeal 16S rRNA of which aceticlastic methanogens, Methanosarcinales were dominating. Quantification by a species-specific probe revealed that Methanosaeta concilii was the predominant species among the Methanosarcinales. This finding was consistent with the observation that Methanosaeta-like filaments often are dominating in granules. Among the hydrogenotrophic methanogens, Methanobacteriaceae were dominant in the glucose-fed reactor (about 5%). In the glucose/ propionate-fed reactor, Methanobacteriaceae and Methanomicrobiales each made up around 5% of the microbial populations. Zheng and Raskin also analyzed granules sampled at different heights from a full-scale UASB reactor treating wastewater from a corn wet milling plant [178]. The archaeal 16S rRNA constituted between 30 to 50% of total 16S rRNA. The highest numbers of Archaea were found in granules sampled from the top of the reactor. Methanosarcinales and Methanobacteriaceae were the two dominant methanogenic groups. Methanomicrobiales constituted less than 5% while Methanococcaceae were almost absent. Within the Methanosarcinales, which constituted between 7.4 and 27.9%, only 2.2 to 2.8% were Methanosaeta concilii, and less than 1% were Methanosaeta thermophila and Methanosarcina species. The authors concluded that genera such as Methanolobus, Methanococcoides, Methanohalophilus, Methanohalobium, and Methanosalsus within Methanosarcinales may be present in high numbers in these granules. The population changes of propionate-oxidizing bacteria after granule formation in potato-processing industry wastewater adapted to different substrates was investigated by Harmsen et al. [90]. Very low amounts of propionate-utilizing, sulfate-reducing bacteria Desulfobulbus spp. (around 2% of total 16S rRNA) and the syntrophic propionate degrader strain SYN7 (less than 1% of total 16S rRNA) were found in the inoculum. After adaptation, the Desulfobulbus spp. increased up to 35% in the granules fed propionate and sulfate, while SYN7

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increased to around 10% in the granules fed only propionate. The universal probe used in this study underestimated the amounts of bacterial 16S rRNA, which may have introduced significant biases [179]. Sekiguchi et al. [180] analyzed the microbial diversity of two types of methanogenic granular sludge, sampled from a mesophilic (35°C) and a thermophilic (55°C) UASB reactor treating synthetic wastewater containing sucrose, propionate, and acetate. Clone libraries of 16S rDNA were constructed using a prokaryote-specific primer set, followed by partial sequencing of the cloned rDNAs. It was found that 19% of the clones from the mesophilic granules and 22% from the thermophilic granules were closely related to methanogens, while the rest were Bacteria.A major group of bacterial clones from the mesophilic granules showed homology to the delta subclass of the Proteobacteria (27%) harboring syntrophic bacteria and sulfatereducing bacteria. The bacterial clones from the thermophilic granules, however, were mainly Thermodesulfovibrio spp. (19%), green non-sulfur bacteria (18%) and low G+C Gram-positive bacteria (18%). The authors also found that the microbial diversity of the thermophilic granules was lower compared to the mesophilic granules. The population changes in granular sludge in a sucrose-fed five-compartment AMBR system were monitored when staging was established [181]. Using probes for methanogens and Archaea, the authors demonstrated that the highest amounts of methanogenic 16S rRNA was found in the middle compartment, where 42% of the total 16S rRNA belonged to Archaea. Methanosaeta accounted for 32% of the 16S rRNA, Methanobacteriaceae for 8%, and Methanomicrobiales for 2%. Throughout the process, Methanosaeta spp. were always the predominant aceticlastic methanogens. Even though acetate concentrations in the side compartments were as high as 600 mg/L, Methanosarcina spp. always amounted to less than 1%. With respect to hydrogenotrophic methanogens, Methanobacteriaceae occurred in highest amounts followed by Methanomicrobiales. Methanococcaceae were almost absent. Interestingly, syntrophic bacteria such as Syntrophomonas and Syntrophobacter, and sulfate-reducing bacteria Desulfobulbus occurred in similar amounts in the different compartments even after the staging was established. Microbial diversity of syntrophic bacteria in granular sludge in UASB reactors was studied by Hofman-Bang et al. [182]. Microbial enrichment was conducted in a UASB reactor at 32°C fed with butyrate and propionate. After three months of stable operation, the granular sludge was divided between three new UASB reactors fed with butyrate and propionate. One reactor was kept as a control reactor, a second reactor was fed 10 mM sulfate, and in a third reactor the temperature was increased by 10°C. After three months of operation, the granular sludge from each reactor was again divided into two new UASB reactors fed with either butyrate or propionate and operated for six weeks. Community fingerprinting (DGGE) from the six reactors showed a low number of bands indicating a bacterial enrichment consisting of 3–5 different species. Bacterial clone libraries were constructed from each reactor and 15–30 clones from each library were fully sequenced. Phylogenetic analysis indicated that bacterial clones were 85.9%–99.7% homologous to members of the Gram-negative d-Proteobacteria and the Gram-positive Syntrophomonas cluster.

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3.2.3 Structure of Granular Sludge

The microbial structure of granular sludge has been studied using fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes. Granules collected from a full-size UASB reactor treating potato-processing wastewater were shown to have a layered structure [90]. The thick outer layer of the granules was dominated by Bacteria and contained very few methanogens. Desulfobulbus spp. were present in this layer. The inner layer of the granules had two types of microcolonies arranged in concentric circles. One type only contained methanogens and the other type consisted of Bacteria tangled with filamentous methanogens. Two lab-scale reactors were inoculated with the granules, one was fed propionate (methanogenic) and the other was supplied with propionate and sulfate (sulfidogenic). After 8–12 weeks of adaptation, the granules in the methanogenic reactor had lost the thick outer bacterial layer. Hybridization with bacterial probes revealed two types of microcolonies yielding strong and weak signals, respectively. Microcolonies yielding strong signals were found to contain syntrophic propionate oxidizers and methanogens. Granules that adapted in the sulfidogenic reactor had established a new outer layer dominated by Desulfobulbus. Harmsen et al. observed three layers in granules that were originally obtained from a system treating sugar beet wastewater and then adapted to sucrose for six months [90]. The external layer contained mainly Bacteria. The middle layer consisted of syntrophic microcolonies containing propionate-degraders and Methanobrevibacter spp. (detected by the Methanobacteriaceae-specific probe and morphologically similar to Methanobrevibacter). In this layer, transmission electron microscopy and FISH revealed microcolonies of Methanosaeta spp. located next to the syntrophic microcolonies. The core contained inorganic material with large cavities and some methanogens were detected by an Archaea-specific probe. In the same study, the authors showed that granules from a system fed a sugar beet wastewater that were adapted to a mixture of VFA (butyrate:propionate:acetate = 42:32:24), created similar structures as the sucrose-adapted granules, except for an additional thick layer between the external and the middle layer rich in Methanosaeta spp. and Methanosarcina spp. microcolonies. The authors explained this extra layer by the high acetate concentration in the feed. High concentrations of acetate are inhibitory to syntrophic propionate degradation due to the unfavorable thermodynamic conditions. Methanosaeta spp. and Methanosarcina spp. present in the thick layer could remove the acetate before it reached the syntrophic microcolonies. Since the feed contained only butyrate, propionate, and acetate, the authors concluded that bacteria in the external layer were mainly butyrate degraders. Granules from a mesophilic and a thermophilic lab-scale UASB reactor were analyzed by Sekiguchi et al. [183]. Granules from both reactors had an outer layer dominated by Bacteria and an inner layer dominated by Archaea and an unstable center. Methanosaeta spp. were the predominant Archaea in both types of granules. In granules from the mesophilic UASB reactor, some Methanobacteriaceae cells were observed together with Bacteria. Some Methanomicrobiales

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cells were also detected that were spread in the granules. In granules from the thermophilic reactor, some Methanobacteriaceae and Methanosarcina cells were detected. The authors also tried to locate Bacteria that were dominating in the clone libraries from their previously mentioned study [180]. In mesophilic granules, Desulfobulbus cells were found in the outer layer of the granules. Cells closely related to Syntrophobacter were shown to form microcolonies together with Methanobacteriaceae cells in the mesophilic granules. A probe targeting green non-sulfur bacteria revealed filamentous cells on the surface of the thermophilic granules. 3.2.4 The Granulation Process

The potentials for Methanosaeta concilii and propionate-degrading syntrophic consortia to serve as nuclei for granulation were tested by monitoring the granulation processes from non-granular sludge in two laboratory-scale upflow anaerobic sludge blanket reactors treating synthetic wastewater [184]. The influent of one reactor contained glucose as the only energy sources, while the influent of the other reactor contained glucose and propionate. The two reactors were started following the recommended procedures and the effluent acetate concentrations were maintained below 200 mg/L. Quantitative membrane hybridizations and FISH were used to monitor the changes of microbial communities and to investigate the cell aggregate structures during the granulation processes. Methanosaeta concilii demonstrated good settling ability and a large population developed in the microbial community. The increase in population size was correlated with the significant increase in cell aggregate sizes at the early stage of granulation. Methanosaeta concilii cells were found to serve as backbones in the small cell aggregates having other archaeal and bacterial cells attached to them. They remained dominant in larger cell aggregates and in the mature granules. These findings support the hypothesis that Methanosaeta serves as nuclei for granulation. Syntrophic propionate-oxidizing bacteria, on the other hand, exhibited poor settling capability and were easily washed out from the system. Their contribution to granulation, therefore, is probably minimal. 3.3 Continuously Stirred Tank Reactors (CSTR) 3.3.1 Microbial Composition in CSTRs

Zheng and Raskin reevaluated the probes available for detection of Methanosaeta species in mesophilic and thermophilic anaerobic digesters [178]. Generally Methanosaeta species are detected with the S-F-Msae-0825-a-A-23 probe. However, some of the Methanosaeta spp. 16S rRNA sequences have a deletion in the target site of this probe at position 838 (based on E. coli numbering). To circumvent the problems with this probe, new probes targeting the genus Methanosaeta according to Table 1 were constructed and tested on anaerobic

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digesters. Also, specific probes for Methanosaeta concilii and Methanosaeta thermophila were constructed. Three probes targeting the genus Methanosaeta were tested against reference strains with or without mismatches. The probes SG-Msae-0733-a-A-22 and S-G-Msae-0781-a-A-22 were not able to discriminate between target and non-target sites since the optimal wash temperatures were too close to each other. A third probe, S-G-Msae-0332-a-A-22 was designed and shown to be specific to the genus Methanosaeta. To evaluate the probes, environmental samples from various mesophilic and thermophilic digesters were analyzed for the presence of Methanosaeta spp. In general, the two new probes detected a higher level of Methanosaeta than the old S-F-Msae-0825-a-A-23 probe. This could indicate putative deletions in target sites of some Methanosaeta species. Moreover, the amount of Methanosaeta spp. in the digesters decreased when acetate concentrations increased, and increased when acetate concentrations decreased. Methanosarcina increased with increasing acetate concentrations which is in agreement with previous findings[185]. Hansen et al. [186] quantified the syntrophic fatty acid-b-oxidizing bacteria in a mesophilic biogas digester treating cow and swine manure. Syntrophic fatty acid b-oxidizing bacteria were found in the Gram-positive Syntrophomonadaceae cluster. This family currently contains three genera, Syntrophomonas, Syntrophospora, and Thermosyntropha as well as two lost strains FSM2 and FSS7 [187–190]. Probing of samples from the digester showed that members of Methanomicrobiales were the most abundant hydrogenotrophic methanogens constituting 10% of the prokaryotes. Syntrophomonadaceae were estimated to comprise 0.2–1% and probing for the different syntrophic genera showed that only members of the genus Syntrophomonas degrading butyrate were present. Methanosarcina spp. were the only methanogens present apart from the Methanomicrobiales. This is consistent with previous studies demonstrating that Methanosarcina spp. are the main acetate utilizers in Danish biogas plants [191]. The relatively low numbers of butyrate degrading bacteria imply that these have high metabolic rates corresponding to the low energy yield of VFA oxidation. Raskin et al. [192] quantified the abundance of sulfate reducing bacteria (SRB) and methanogens in twenty-one single-phase and two-phase full-scale anaerobic sewage sludge digesters by oligonucleotide probing of 16S rRNA. It was determined that methanogens in well-functioning mesophilic, single-phase digesters accounted for 8–12% of which Methanosarcinales and Methanomicrobiales constituted the majority. Methanobacteriales and Methanococcales only played a minor role. The SRB were present in significant amounts. Desulfovibrio and Desulfobulbus spp. dominated the community while Desulfobacter and Desulfobacterium were less abundant. Desulfosarcina, Desulfococcus and Desulfobotulus spp. were not detected. Even though sulfate was present in small amounts in the sludge, the relative high level of SRB found in most of the digesters indicates that SRB still can compete with the methanogens for available electrons and/or with protonreducing syntrophs for fermentation products such as propionate, butyrate, lactate, or ethanol [192].

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Hristova et al. [194] constructed genus- and subgenus-specific probes for Gram-positive SRB. Thermophilic anaerobic digesters were probed to evaluate the probes and to quantify the abundance of Desulfotomaculum spp. Desulfotomaculum spp. accounted for about 2% of the total bacterial population at the start up of a thermophilic CSTR digester inoculated with mesophilic digester sludge and cow manure. After 31 days of operation the amount had decreased to 0.3%. Probing against Desulfovibrio, however, indicated that this genus accounted for 1.7%. This may be explained by the Desulfovibrio being more competitive then Desulfotomaculum under the low sulfate concentrations found in the reactor. Probing another thermophilic digester operated for a longer period showed Desulfotomaculum at a stable level of 2%. However, the sulfate concentration was two times higher than in the start-up reactor, and the long operation time may have stabilized the digester allowing Desulfotomaculum to gain foothold. Godon et al. [195] have determined the microbial diversity in an anaerobic digester treating wine distillation waste by extensive sequencing of clone libraries of 16S rDNA. 579 clones were partially sequenced and analyzed by operational taxonomic unit (OTU) phylogenetic analysis; 146 OTUs were found of which 133 belonged to Bacteria, 6 to Archaea and 7 to Eucarya. The bacterial clones were distributed among at least eight of the major groups of the Bacteria domain. Despite the large bacterial diversity, the 20 most frequent bacterial OTUs represented 50% of the total clones. 3.3.2 Microbial Dynamics in CSTRs

Ahring and coworkers investigated the archaeal population dynamics in a digester treating cow manure during a temperature shift from 55°C to 65°C [196]. Thermophilic anaerobic digestion of complex organic wastes is usually carried out at 50–55°C. Previous studies demonstrated that an increase in the operational temperature to 65°C caused a disturbance of the biogas process reflected in an increase in VFA concentrations [191]. This indicates that the populations participating in the terminal part of the biogas process are much more sensitive to a temperature increase than the fermentative populations responsible for VFA production. Probing of samples from the digester showed that the archaeal population increased significantly as a consequence of the temperature increase and that the bacterial population decreased correspondingly. Analyses of the archaeal population showed that the Crenarchaeota increased dramatically at the cost of the Euryarchaeota after the temperature increase. Methanosaetaceae were never detected, Methanosarcinaceae disappeared and Methanococcus increased to a relatively high amount after the temperature change. These findings indicate that our knowledge about the diversity in thermophilic reactors is limited and that much more work is needed to elucidate the population dynamics at elevated temperatures. Fernandez et al. [67] conducted glucose perturbation studies on CSTR digesters under mesophilic conditions to evaluate the relationship between stability and community structure. Two digesters were studied; one (HS) was inoc-

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ulated with fluid from a bioreactor operating for 200 days on glucose and the other (LS) was inoculated with fluid from a bioreactor operating for 60 days on glucose. The effect of shock loading the digesters with glucose was monitored by morphological analysis, 16S rRNA probing, restriction analysis of amplified rDNA, and partial 16S rDNA sequencing. The HS reactor was characterized by good replicability, a high proportion of spirochete-like and short thin rod morphotypes, a dominance of spirochete-related 16S rDNA genes, and a high percentage of Methanosarcina-related 16S rRNA. The LS reactor was characterized by higher morphotype diversity dominated by cocci, a predominance of Streptococcus-related and deeply branching spirochete-related 16S rDNA genes, and a high percentage of Methanosaeta-related 16S rRNA. In the HS reactor, the glucose shock caused a dramatic shift in the relative abundance of fermentative bacteria, resulting in a temporary displacement of spirochete-related ribotypes by Eubacterium-related ribotypes followed by a return to the pre-shock community structure. The LS reactor was less affected, and the Streptococcus-related organisms still dominated after the glucose shock, although changes in the relative abundance of some of the members were detected by morphotype analysis.

4 Concluding Remarks During the last decade, environmental microbiology has changed markedly as a consequence of the exploitation of molecular biology methods for answering questions such as which organisms are active and where and when do they show activity. The number of isolated microbes has increased markedly and based upon molecular analyses, researchers are able to improve experimental designs and to isolate new bacteria. Also, as our knowledge of microbial metabolism is increasing, we are coming closer to be able to answer questions related to what microbes are doing in situ. Molecular microbiology has increased our field of vision and our resolution capability. Now, traces of DNA from one gene are often sufficient to assign the bacterium from which the DNA originated in a phylogenetic context. Whole genomes are being sequenced using the same effort needed for sequencing of a single gene a few decades ago. Sequence information can now be used to link metabolism, phylogeny, and ecology. Sequence databases are growing exponentially in size and bioinformatics, therefore, will inevitably play an increasing role in microbial ecology. From an evolutionary point of view, massive amounts of DNA sequences and powerful computers have made it possible to conduct phylogenetic analysis on these data and to broaden our view of how life on Earth evolved. In the foreseeable future, genome sequence analysis will to a higher degree link 16S rRNA/23S rRNA gene sequences to metabolic functions of whole bacterial families. To a large degree, the different techniques developed for microbial diversity screening discussed in this chapter answer the questions of who is present. To a limited extent, we are able answer questions on who is where since homogeneous cell accessibility and probe specificity still are two key problems

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in in situ probing. Furthermore, we still need to develop methods to determine activity levels in situ (i.e., who is doing what in which location at which point in time?). For instance, interactions and interdependence among different microbial populations are probably much more pronounced than generally assumed. The metabolic expression pattern of a single microbial cell probably differs from the neighboring cell yielding a multi dimensional patchwork. Several major constraints, therefore, have to be overcome, and several new concepts have to be adapted and resolved to fully exploit the use of molecular methods in understanding complex ecosystems such as anaerobic reactors.

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