Quasispecies: Concept and Implications for Virology (Current Topics in Microbiology and Immunology)

299 Current Topics in Microbiology and Immunology

Editors R.W. Compans, Atlanta/Georgia M.D. Cooper, Birmingham/Alabama T. Honjo, Kyoto · H. Koprowski, Philadelphia/Pennsylvania F. Melchers, Basel · M.B.A. Oldstone, La Jolla/California S. Olsnes, Oslo · M. Potter, Bethesda/Maryland P.K. Vogt, La Jolla/California · H. Wagner, Munich

E. Domingo (Ed.)

Quasispecies: Concept and Implications for Virology

With 44 Figures and 7 Tables

123

Esteban Domingo Centro de Biología Molecular “Servero Ochoa” (CSIC-UAM) Universidad Autónoma de Madrid Cantoblanco 28049 Madrid Spain e-mail: [email protected]

Cover illustration: Amino acid residues in the parvovirus MVMi capsid selected during evolutionary processes in mice. Highlighted in colours are residues of the spike whose replacement conferred the MAR phenotype (red), residues involved in primary receptor recognition (green), and residues conferring hematotropism (yellow). All other capsid residues are colored blue and shown as wireframe, except for the white-space-filling model of the residues defining the spike at the three-fold axis of symmetry.The structure of MVMi viral capsid is shown by the program RasMol (Sayle and Milner-White 1995) and the MVMi coordinates (1MVM) deposited in the PDB (Agbandje-McKenna et al. 1998).

Library of Congress Catalog Number 72-152360 ISSN 0070-217X ISBN-10 3-540-26395-0 Springer Berlin Heidelberg New York ISBN-13 978-3-540-26395-1 Springer Berlin Heidelberg New York This work is subject to copyright. All rights reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September, 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springeronline.com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Product liability: The publisher cannot guarantee the accuracy of any information about dosage and application contained in this book. In every individual case the user must check such information by consulting the relevant literature. Editor: Simon Rallison, Heidelberg Desk editor: Anne Clauss, Heidelberg Production editor: Nadja Kroke, Leipzig Cover design: design & production GmbH, Heidelberg Typesetting: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig Printed on acid-free paper SPIN 11501312 27/3150/YL – 5 4 3 2 1 0

Dedication

Many observations on the great potential for phenotypic change of RNA viruses were made during the twentieth century, and John Holland reviews them in the closing chapter of this volume. There is a very remarkable precedent that concerns the noted Catalan virologist Jordi Casals, a pioneer of virology research who sadly passed away last year. Born Jordi Casals i Ariet in Viladrau, Girona on May 15, 1911 he died in New York City on February 10, 2004 after a productive and distinguished career in the United States. Jordi Casals left Spain in 1936 at the onset of the civil war and occupied positions at Rockefeller Institute for Medical Research in New York, Cornell University Medical College, Rockefeller Foundation, Yale University, and Mount Sinai School of Medicine (see a bibliographical note by Charles Calisher (2004) Arch Virol 149:1264-1266). A survivor of Lassa fever, he was a devoted, meticulous and observant scientist interested in virus classification. He was particularly concerned by confounding antigenic cross-reactions among viruses and viral diversity within a virus group. In a letter addressed to Charles Calisher in 1968 Jordi Casals wrote “A virus or species is a cluster of different individualities grouped around and resembling a prototype or model, rather than a number of strains all identical with a prototype.” (I am indebted to Charles Calisher for sharing this information). Jordi Casals was honored at the recent annual meeting of the Virology Group of the Catalan Society of Biology (Societat Catalana de Biologia) held in Barcelona on 25 October 2004, with a biographical note read by Dr. Albert Bosch. This volume on quasispecies is dedicated to his memory. Esteban Domingo

Preface

High mutation rates and quasispecies dynamics are essential features of RNA viruses. Continuous genetic variation and selection of virus subpopulations in the course of RNA virus replication are intimately related to viral disease mechanisms. Experience has taught that the adaptive potential of viruses must be taken into consideration in designing preventive and therapeutic antiviral strategies. The central topics of this volume are the origins of the quasispecies concept, and the implications of quasispecies dynamics for viral populations. It includes chapters that emphasize general concepts (quasispecies, sequence space, fitness, error catastrophe, lethal defection, adaptive responses, population bottlenecks, etc.) and chapters that deal with population dynamics of specific viruses such as Picornaviruses, Pestiviruses, Arenaviruses, Arboviruses, human immunodeficiency virus, plant viruses and some DNA viruses that display features of RNA genetics. In particular, implications of quasispecies dynamics in vivo are dealt with in several chapters. I thank all authors who have contributed their time and expertise to produce a markedly transdisciplinary volume that should provide a stimulus for future research. Hopefully, as sometimes happens with mutant swarms, the entire volume will be more “fit” than the sum of its chapters! I am also deeply indebted to Dr. Michael B.A. Oldstone for his generous invitation to serve as guest editor for a volume of this prestigious CTMI series. The Springer-Verlag staff and Lucia Horrillo of Centro de Biología Molecular “Severo Ochoa” were decisive to successfully completing this volume. Madrid, April 2005

Esteban Domingo

List of Contents

What Is a Quasispecies? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. K. Biebricher and M. Eigen

1

Quasispecies in Time-Dependent Environments . . . . . . . . . . . . . . . . . . . . . . . 33 C. O. Wilke, R. Forster, and I. S. Novella Viruses as Quasispecies: Biological Implications . . . . . . . . . . . . . . . . . . . . . . . 51 E. Domingo, V. Martín, C. Perales, A. Grande-Pérez, J. García-Arriaza, and A. Arias Virus Fitness: Concept, Quantification, and Application to HIV Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 M. E. Quiñones-Mateu and E. J. Arts Population Bottlenecks in Quasispecies Dynamics . . . . . . . . . . . . . . . . . . . . . . 141 C. Escarmís, E. Lázaro, and S. C. Manrubia Evolutionary Dynamics of HIV-1 and the Control of AIDS . . . . . . . . . . . . . . . . 171 J. I. Mullins and M. A. Jensen Evolution of Virulence in Picornaviruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 S. Tracy, N. M. Chapman, K. M. Drescher, K. Kono, and W. Tapprich Molecular Mechanisms of Poliovirus Variation and Evolution . . . . . . . . . . . . . . 211 V. I. Agol Hepatitis C Virus Population Dynamics During Infection . . . . . . . . . . . . . . . . . 261 J.-M. Pawlotsky Evolutionary Influences in Arboviral Disease . . . . . . . . . . . . . . . . . . . . . . . . . 285 S. C. Weaver Arenavirus Diversity and Evolution:Quasispecies In Vivo . . . . . . . . . . . . . . . . . 315 N. Sevilla and J. C. de la Torre

X

List of Contents

Mutant Clouds and Occupation of Sequence Space in Plant RNA Viruses . . . . . . 337 M. J. Roossinck and W. L. Schneider Parvovirus Variation for Disease: A Difference with RNA Viruses? . . . . . . . . . . . 349 A. López-Bueno, L. P. Villarreal, and J. M. Almendral Transitions in Understanding of RNA Viruses: A Historical Perspective . . . . . . . 371 J. J. Holland Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403

List of Contributors (Addresses stated at the beginning of respective chapters)

Agol, V. I. 211 Almendral, J. M. 349 Arias, A. 51 Arts, E. J. 83 Biebricher, C. K. 1 Chapman, N. M. 193 de la Torre, J. C. 315 Domingo, E. 51 Drescher, K. M. 193

López-Bueno, A. 349 Manrubia, S. C. 141 Martín, V. 51 Mullins, J. I. 171 Novella, I. S. 33 Pawlotsky, J.-M. 261 Perales, C. 51 Quiñones-Mateu, M. E. 83

Eigen, M. 1 Escarmís, C. 141 Forster, R. 33 García-Arriaza, J. 51 Grande-Pérez, A. 51

Roossinck, M. J. 337 Schneider, W. L. 337 Sevilla, N. 315

Holland, J. J. 371

Tapprich, W. 193 Tracy, S. 193

Jensen, M. A. 171

Villarreal, L. P. 349

Kono, K. 193 Lázaro, E. 141

Weaver, S. C. 285 Wilke, C. O. 33

CTMI (2006) 299:1–31 c Springer-Verlag Berlin Heidelberg 2006


What Is a Quasispecies? C. K. Biebricher (u) · M. Eigen Max Planck Institute for Biophysical Chemistry, Am Fassberg, 37077 Göttingen, Germany [email protected]

1

Species and Quasispecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

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Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Selection of the Fittest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

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Sequence Heterogeneity in Virus Populations . . . . . . . . . . . . . . . . . . . . 11

6

The Sequence Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

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The Quasispecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

8

The Error Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

9 9.1 9.2 9.3

Evolutionary Biotechnology . . . . . . . . . Principles . . . . . . . . . . . . . . . . . . . . . . Protein Design . . . . . . . . . . . . . . . . . . . Selection of nucleic acids with a function

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21 21 23 24

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Abstract The concept of the quasispecies as a society formed from a clone of an asexually reproducing organism is reviewed. A broad spectrum of mutants is generated that compete one with another. Eventually a steady state is formed where each mutant type is represented according to its fitness and its formation by mutation. This quasispecies has a defined wild type sequence, which is the weighted average of all genotypes present. The quasispecies concept has been shown to affect the pathway of evolution and has been studied on RNA viruses which have a particularly high mutation rate. They (and possibly the majority of other species) operate close to the error threshold that allows maximum exploration of sequence space while conserving the information content of the genotype. The consequences of the quasispecies concept for the new ‘evolutionary technology’ are discussed.

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C. K. Biebricher · M. Eigen

1 Species and Quasispecies The taxonomic classification of living organisms by Carl von Linné (1707– 1778) was a milestone in biology. According to the view of his time, the living world was divided into different species that preexisted since the creation of the world and would persist until its end. Linné determined the systematic kinship of species by similarities in their anatomical build. A species was considered a society of individuals that are able to generate fertile offspring. This definition remained valid after the view of invariable species was shattered: Since Darwin it has been accepted that species can go extinct and new ones are formed by diversification. In the first half of the twentieth century, this view took on a theoretical fundament with the Mendelian view of biological species and the Neodarwinistic synthetic theory. With the advent of the molecular biology in the second half of the twentieth century, interest focussed on organisms that have a much simpler, ‘vegetative’ propagation mechanism, like Prokarya. Obviously, the biological concept of species stated above does not apply to them, even less to forms of life such as viruses that have no cellular organisms. There is no gene pool to select from and no continuous shuffling of gene alleles. Nevertheless, these forms of life have been classified with reasonable results. Obviously, the reproducing individuals in prokaryotic taxons and virus populations must lead to a society that shares many properties with the ‘classical’ species; it is called a ‘quasispecies’. Its emergence and its properties are described in this article.

2 Growth The purpose of a theory is not to describe how processes occur in nature in detail, but rather to understand why certain regularities can be observed. Theory is no alternative to experiment, but theory and experiment support one another, theory by interpreting experimental results and suggesting further meaningful experiments, which then can be used to refine the theory. However, theory needs reduction to the most important parameters while neglecting less important contributions. It is necessary to find suitable experimental conditions where complexity is low enough to verify theoretical predictions. The different reproduction mechanisms among the superkingdoms in nature cause difficulties; hence one must find the fundamental property of reproduction. Fisher [85], in deriving the laws of population dynamics of

What Is a Quasispecies?

3

a Mendelian population, was fully aware of this difficulty. He assumed that the population contains a sufficient number of males to fertilize all females, regarded only the female part of the population and obtained Malthusian, i.e. exponential, growth. The potential for exponential growth is an inherent property of life, even though it is rarely observed in nature because growth is rapidly limited by a shortage of resources. Indeed, processes such as leavening, where exponential spread can be observed, have always served as a metaphor for life. Exponential growth can be easily observed in the superkingdom of Prokarya. For further considerations, horizontal gene transfer phenomena such as transformation, infection, transfection, and conjugation should be disregarded. An experimental system that allows the study of exponential growth is the growth of a bacterial culture in a defined nutrient medium under controlled conditions. Bacteria grow by metabolizing nutrients from the medium, and divide after a certain time τ to two daughter cells. While the τ values vary from one bacterium to another, the average τ values for large populations of a species can be determined with high precision; the population density can be preferentially derived from macroscopic properties of the population, e.g. by turbidity measurements. After the time t, the initial concentration of bacteria has grown to c(t) = c(t

= 0) · eAt

(1)

where the growth rate A = ln 2t |τ (Table 1 lists the symbols for the parameters used in this article). Dynamical processes are best described by differential equations. The rate of population increase is directly proportional to the number of parental cells and their specific growth rate parameter: dc|dt

= Ac .

(2)

In experimental measurements, this equation is only strictly observed at large dilution. At higher concentrations, the consumption of resources, in particular of oxygen, slow down bacterial growth until the growth curve eventually levels in to a maximal concentration. While this behaviour can be described by the logistical equation, we shall use for our arguments the simple form. A specially designed apparatus, the chemostat, was invented to avoid large concentration increases by pumping in fresh medium at the constant rate Φ and removing a balancing volume of bacterial culture [93]. The dynamical equation then becomes dc|dt

= (A − Φ)c .

(3)

If A − Φ > 0, the population increases, if A − Φ < 0, the population decreases. When A − Φ = 0, the bacterial concentration should be constant,

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C. K. Biebricher · M. Eigen

Table 1 Parameters Growth rate parameter of type i
¯ = k Ak xk ) Average growth rate parameter of the population (A Mortality or decomposition rate parameter of type i Excess reproduction rate of type i (Ei = Ai − Di )
Average excess reproduction rate of the population (E¯ = k Ek xk ) Number of generations Probability of precise reproduction of sequence i (Qii = q¯ νi ) Probability of producing type i while replicating sequence k Rate parameter of precise (excess) production of sequence i: (Wii = Qii Ai − Di ) Rate parameter of production of sequence i by erroneous copying of sequence k (Wik = Qik Ak ) ci Population density of type i dik The Hamming distance between two sequences i and k, i.e., the number of positions at which both genomes differ. q¯ Average fidelity of single digit reproduction (insertion of correct nucleotide) 1 − q¯ Probability of single error production
xi Fraction of type i in the total population (type frequency) (xi = ci | k ck ) x˜i type frequency of i in the evolution steady state F i Fitness value of species i; Fi = Ai |Am εi Average error rate per sequence i (εi = νi (1 − q¯ )) νi Genome length of type i σ¯ m Average superiority of master sequence over competitor mutants (¯σm = Am | (Dm + E¯ k
=m ), requiring σm >1) τ Duplication time of a bacterium Φ Flux rate of growth medium in reactor Ai ¯ A Di Ei E¯ N Qii Qik Wii Wik

but measurements show irreproducible changes in the bacterial concentration. Experiments with finite populations never observe deterministic laws exactly: inevitable statistical fluctuations of the A values lead to concentration fluctuations that are not compensated because A is independent of the concentration. Another experimental device, the turbidostat, keeps the turbidity of the bacterial culture and thus the bacterial concentration constant. A nonbiotic system of studying exponential growth is RNA replication [111] in a cell-free system, which is even capable of Darwinian evolution [86]. An RNA strand serving as template replicates, producing a complementary replica or minus strand, which can itself replicate again to produce plus strands. This cross-catalysis leads to exponential growth of the RNA [8, 9]. Direct measurement of the exponential growth is difficult,

What Is a Quasispecies?

5

Fig. 1 Growth profile of replicating RNA [121]. A mixture containing 0.75 mM ATP, CTP, GTP and UTP, 1 µM RNA polymerase from Escherichia coli and 3 µM thiazole orange was inoculated with RNA species EcorpG at different concentrations and the fluorescence of the probes was measured in a multichannel fluorimeter at an excitation wavelength of 488 nm and an emission wavelength of 515 mm.

because exponential growth requires that the replicase enzyme that catalyses the replication is present in large excess of the growing RNA. As in measuring the bacterial population with turbidity, only populations above a boundary give a measurable signal. The solution is that one measures growth at different initial concentrations (Fig. 1). If the population is diluted by the factor Fdil , the growth curve appears to be shifted on the time axis by the difference ∆t and one calculates the replication rate to be A = ln Fdil |∆t [9, 121] (Fig. 1).

3 Selection of the Fittest Populations are rarely composed of absolutely identical individuals, but contain usually types that can be distinguished by certain criteria. In the past, the criteria were obtained by visual inspection, e.g. Mendel distinguished the types of his pea population by the color of the flowers and the shape

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of the seeds. In the first half of the twentieth century, a large number of different variants of the fruit fly Drosophila melanogaster were investigated that were distinguished by the visual differences in the appearance at the larval or imago stages. Bacterial types can be distinguished by inspection of the colonies they form on solid nutrient media. Types classified by such criteria are called phenotypes. In contrast to our lack of quantitative predictions of evolutionary events, the molecular basis of transmitting phenotypic traits to offspring is well understood: The information needed for morphogenesis and function is encoded into the genotype, the nucleotide sequence in each organism’s genome. The key step in reproduction is replication of the genome. The enormously complex process of decoding the genotypic program resulting in a phenotype with certain properties is called expression. Let us assume that the different types in a population ignore one another and that each individuum grows as if it were alone, by observing the growth of different types, e.g. species, of bacteria in a medium that contains nutrients
in vast excess. Each of them is present at a relative concentration xi = ci | k ck and grows with the specific fecundity Ai . As in the previous examples, the total concentration of bacteria shall be kept constant by removing the
excess production of offspring caused by the average growth of the types A¯ = k Ak xk by appropriately diluting the medium. The relative population of each type then changes with the rate dxi (t)|dt

¯ = {Ai − A(t)}x i (t) .

(4)

¯ Note that equation (4) is nonlinear because A(t) is time-dependent. Types ¯ whose growth rate exceeds the average are enriched while types with Ai < A(t) are depleted in the population. It is obvious that the value for Ai of a type i is a good measure of its ‘fitness’ in Darwin’s sense. The enrichment of fitter types in the population raises the average growth rate, and more and more types fall below the average, until eventually the population reaches a maximum growth rate where only the master type m, the type with the maximal fitness, survives: ¯ → Amax A(t)

xm → 1

xi
=m → 0 .

(5)

Natural selection is thus indeed an immediate consequence of autocatalytic reproduction [36]. The following conditions must be fulfilled: 1. One type cannot be converted into another, e.g. because the types belong to different species. 2. The growth of one species is not influenced by the presence of another. 3. The selection proceeds far from chemical equilibrium.

What Is a Quasispecies?

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In reality, populations do not change solely by differences in fecundity. Mortality rates Di also influence the composition since types with higher mortality also get depleted in the population. In this case, fitness is correlated with the excess reproduction rate Ei = Ai − Di . Not only maximizing the reproduction rate, but also minimizing the mortality rate is then important for selection. A practical application of this selection process is the preparation of pure bacterial cultures from a natural specimen by the use of selective media. In nature, the selection process we described is much more complicated. Fecundity and mortality rates are often not time-independent. Under realistic conditions, say in an ecosystem, the types compete for nutrients, or interact one with another by mutually influencing their fecundities and mortalities, most conspicuously in a predator–prey relationship. The fecundity and mortality rates of one species depend on the presence of other types and change with time. The situation is further complicated by variations of the environmental conditions. Calculation of selection values or fitness values is then very difficult. When working under defined conditions with the RNA replication system described above, it was possible to predict precise selection values. At low RNA concentrations, with nucleoside triphosphate substrates and replicase in vast excess, the RNA species with the highest growth rate is selected as described above. When the RNA concentration reaches the replicase concentration, however, the growth characteristic as well as the selection drastically changes. Since an RNA strand must bind a replicase molecule for replication, exponential increase of the RNA concentration is suddenly replaced by linear growth when the template concentration reaches the enzyme concentration. Free RNA strands, both of plus and minus polarity, accumulate, which can react one with the other by formation of a double helix that is unable to replicate. With the onset of the linear phase it is no longer the RNA species with the highest growth rate that wins, but rather the one with the highest rate of replicase binding. At still higher RNA concentrations, minimizing the loss rate by double strand formation also becomes important. Eventually, often a stable ecosystem is formed where different RNA species occupy constant parts of the population. The rather complicated but quite instructive calculation of the selection values can be found in the literature [12, 13].

4 Mutation Selection among preformed and invariable types alone does not suffice for Darwinian evolution. Evolution needs the formation of new types by a process

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called mutation. While in Mendelian populations the offspring usually is of a different type than the parents because the different parental gene alleles are reshuffled, ‘vegetatively’ growing populations such as bacteria and viruses have been assumed to produce identical offspring. New types are created by mutation, i.e. by misincorporation of nucleotides during the replication process. Evolutionary innovation is driven in all biological species by mutation. It is straightforward to introduce mutation rates Wji describing the rate of producing type j during reproduction of type i. However, in many cases mutation rates are too low to be realistically described by deterministic rate coefficients, even in large populations. When writing down an equation with rate coefficients, one has to be aware that the parameters are merely averaged probabilities. Before further conclusions can be drawn, one has to estimate whether there is a reasonable probability that the pertaining mutation will take place at all. How do we modify the equation to take mutation into account? Reproduction can proceed with fidelity, producing offspring of the parental type (Wii ) or it can erroneously produce offspring of another type (Wji , i
= j). The production rates of each type i are composed by the rates of fidelity reproduction of the same type and the rates of erroneous reproduction from other types [32]. The equation considering selection and mutation then is  Wik xk (t) (6) dxi |dt = {Wii − E¯ (t)}xi (t) + k
=i

where E¯ (t) is the average excess growth rate of the total population. If the mutation terms become negligible, i.e. at high fidelity reproduction, equation (6) is converted to the selection equation (5). The reproduction rate with fidelity can also be written as Wii = Qii Ai − Di , where Qik , the mutation probability is the probability of producing type i in a reproduction round of type k. When Di can be neglected, which can often be obtained by providing suitable environmental conditions, then equation (6) converts to  ¯ dxi |dt = {Qii Ai − A(t)}x Qik Ak xk (t) . (7) i (t) + k
=i


There we have two mutation terms: (i) the mutational gain k
=i Qik Ak xk , producing type
i by replication of other types, and (ii) the mutational loss (1 − Qii )Ai xi = i
=k Qki Ai xi , producing mutants in reproduction. How can Qik values be measured? With the sequencing machines available today, it is in principle possible to screen the genotypes of the offspring of a single reproduction cycle, but, since mutations are very rare, millions of sequence determinations would be needed to get values with some statistical significance. In the past, one had to work with phenotypic markers.

What Is a Quasispecies?

9

The challenge is determining a small number of mutants in a vast excess of wild type. Luria and Delbrück [84] have invented an ingenious method to measure mutations by observing the formation of mutants resistant to phage T1 (type r) in a sensitive wild type (type s) population. Since the wild type population has to be created, one needs a conditional lethal mutation, i.e. under one set of conditions, the ‘permissive’ one, the bacteria grow normally while under ‘nonpermissive’ conditions growth is inhibited. Mutation can be observed only under permissive conditions. Since the initial population already contains mutants and since more than a single reproduction round takes place, the accumulation of mutants with time has to be observed. When performing several independent experiments, the absolute number of resistant bacteria in the offspring was found to scatter widely, depending on whether the mutation was formed early or late during growth, showing the stochastic and undirected nature of the mutation. From the kinetic profiles of mutant accumulation after further growth, one can directly calculate Qrs values, where r is the viable, resistant and s the lethal, sensitive phenotype. The above equation can be simplified. Mutations are rare (Qss ≈ 1), wild type bacteria are in large excess (xs ≈ 1) and the growth of the total population ¯ ≈ As ), and we obtain depends only on the wild type (A dxr |dt

= {Ar − As }xr (t) + Qrs Ar .

(8)

When the mutation is entirely neutral, the first term cancels and the linear increase of the type frequency gives the mutation rate Wrs = Qrs Ar [93]. Otherwise one observes an exponential and a linear growth component and one can determine the mutation probability and, from the exponential part, the selection rate Ar − As . Generally, we can introduce classical fitness values Fi for a type i by dividing its growth rate by the rate of the predominant wild type w and divide the time by the growth rate to obtain the generation number N. The equation, which is valid only for a population dominated by the wild type where the contributions from other mutants to the mutational gain are negligible, then reads dxi |dN

= {Fi − 1}xi + Qiw .

(9)

Simplifications introduced by experimental conditions always limit the validity of the interpretation. When the time periods for observing the accumulation of phenotypic mutations was extended to several weeks by working in the turbidostat [31], non-reproducible, erratic patterns appeared, comprising periods where the relative mutant increased linearly as described above, but also periods where the relative mutant concentration decreased. How can

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the periods of decrease be understood? Obviously, the model chosen for the analysis was too simplistic: there were not only two types in the solution. Other mutations not changing the phenotype also occurred and, if one happened to be advantageous, it was enriched in the population and eventually replaced the old wild type together with its mutant spectrum. A new mutant spectrum was then built around the new wild type. Furthermore, the measured mutation probabilities were rather high since a degenerative mutation has been chosen. Resistance to phage T1 is simply achieved by abolishing the phage receptor, which is not needed by the host cell under these conditions. Hundreds of different mutational events may lead to destruction of a gene, causing a specific phenotypic change while only one (or a very few if pseudorevertants are possible) leads to the restoration of a lost gene. The found mutation probability was thus the sum of many specific mutation probabilities that led to the same mutant phenotype. Benzer [5, 6] succeeded in adapting this technique to map mutations within a gene of bacteriophage T4 by measuring the probabilities to restore wild type function by recombination after double infections. A surprisingly precise map of the gene was obtained. Two mutants not able to recombine to wild type were judged to be identical, and Benzer thus also obtained type frequencies. Benzer noted that type frequencies scattered for different loci, and called the loci with particularly high type frequencies ‘hot spots’. This name suggests that type frequencies are particularly enhanced at loci with high mutation rates, but this conclusion is not justified because Benzer’s measurements were taken from a snapshot of the mutant distribution at a certain time. Other factors also contribute to the type frequency, most notably error propagation by replication of the mutant genomes. As previously seen, only a kinetic study of the mutant spectrum can clarify the contributions of mutation and error propagation. Measurements of error rates of replicating enzymes by determining the increase of the number of revertants from a lethal phage mutant in vitro [42, 43, 45, 79, 78] restricted the growth to one replication round to avoid error propagation. Strictly seen, these error rates only apply to the specific mutation tested in the experiment, because error rates are not uniformly distributed within the sequence. Average enzyme fidelities q¯ , i.e. the probability of inserting the correct nucleotide in the incorporation of a single nucleotide, and average error rates 1 − q¯ , i.e. the probability of producing a mismatch in incorporating a nucleotide can be estimated only by statistical analysis of several such experiments. With an average fidelity of q¯ , the probability of obtaining a precise copy of a sequence with the chain length ν is calculated to be Qii = q¯ ν [37]. This suggests that at sufficiently large chain lengths, the probability of making a precise copy can become small. While the probabilities of

What Is a Quasispecies?

11

making specific mutations are always small in comparison to the probability of making correct offspring, the summation of the huge number of mutants may add up to a majority, causing Qii << 1. This will cause a fundamental difference to the notion of a population dominated by a single type.

5 Sequence Heterogeneity in Virus Populations The experimental observation of mutant spectra with normal organisms is difficult. Their genome sizes are huge, their obtainable population sizes are small and DNA replication is highly accurate. With RNA viruses, which have small genomes, large population numbers and especially high mutation rates [27, 28, 26], observation becomes feasible. Still easier is the observation of mutant spectra in replicating RNA populations, since the phenotypic expression of the RNA type is reduced to directing the replicase to replicate it as efficiently as possible [8, 9, 101]. The first method of sequencing RNA involved digesting radioactively labelled RNA with specific RNases and separating the resulting oligonucleotides by two-dimensional electrophoresis [103]. A highly reproducible spot pattern called fingerprint was obtained, where the radioactivity of single spots related one to another as integer multiples, clearly indicating a defined sequence of the population. This is in agreement with a population dominated by the wild type. As the sequence analysis of RNA phage Qβ (ν = 4216 bases) was progressing in the laboratory of Charles Weissmann [14], the phage was recloned by making a population from a single plaque. Surprisingly, the fingerprint pattern of the resulting clone deviated from the former wild type [25]. Was it a rare mutant that was picked by chance? Obviously not: fingerprinting several clones derived from single plaques revealed frequent deviations from the wild type fingerprint, but the deviations were in different places. Passaging the clones resulted in reappearance of the ‘wild type’ fingerprint. Apparently, the phage RNA population is highly heterogeneous, but has a defined average of the sequence, the wild type sequence [25]. Subsequent studies with eukaryotic viruses [96, 26] confirmed this view. An average mutation rate q¯ = 3 × 10−4 was estimated [2] by calculating the frequency of mutant and revertant clones in reversion and competition experiments. This rate is quite high and suggests that a large proportion of the virus progeny contains errors. Indeed, the specific infectivity of most RNA viruses was found to be quite low: only a fraction of virions produce plaques. The majority of them may have lethal mutations, but this is not yet proven.

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Reverse transcription of phage RNA and cloning at the DNA level revealed that the phage population does contain many lethal or nearly lethal phages that fail to produce a plaque (Biebricher, unpublished observations). While each mutation may be rather rare and cannot be propagated, the chance of picking a lethal mutant is substantial because there are so many of them. When viruses from a single clone are amplified, they produce mutants. The mutants compete with each other, until eventually a stable steady state mutant spectrum is obtained, where the contributions from mutation and selection balance each other for every mutant. When starting from a different mutant of the same population, the same steady state is obtained. When determining a virus sequence, it is thus instrumental to work with a reproducible, equilibrated mutant spectrum. It is formed whenever a large population grows for sufficient time. On the other hand, when picking small parts of the population, in the most extreme part a single individuum as in the plaque test, one obtains irreproducible results: different mutants are picked, often with substantially lower fitness values than the wild type [29] (Muller’s ratchet [87]; see the chapter by C. Escarmís et al., this volume).

6 The Sequence Space Mutants are ordered by aligning their sequences with the wild type sequence (Fig. 2). A quantitative kinship relation can be given with the Hamming distance [54], i.e. the number of bases different in their sequences. When doing so, one often observes that certain mutants share several base exchanges and form a clan within the population. The clan resulted from error propagation derived from a common ancestor, because it is highly unlikely that several identical base exchanges take place in a single reproduction cycle. One-dimensional alignment cannot adequately display this kinship of one mutant to another. The adequate topography of mutant distribution is the sequence space, a hypercube [33, 107] with at least as many dimensions as the chain length of the genome, ν. The Hamming distance between two mutants is then the minimum number of steps to go from one sequence to the other. For nucleic acids, which are composed of four different nucleotides, one would require a 2ν-dimensional space. In the full sequence space, each mutant would occupy two positions because the replication mechanism produces the complementary sequence, occupying the antipodal corner of the hypercube and having the maximal Hamming distance of 2ν. If one wants to avoid two mirror landscapes, one can neglect the complement by assuming a (2ν − 1)-dimensional

What Is a Quasispecies?

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sequence space. A hypercube can of course not be imagined geometrically and projections on paper are very difficult to survey for larger ν, but the mathematical relations can be used for understanding evolutionary pathways [33]: 1. The storage capacity is very high. For a viral RNA with a genome length of 104 nucleotides, it would be 106000 , an astronomically large number. 2. Distances between corners remain small, the maximal Hamming distance is 2ν. 3. The connectivity is very high: at each corner one has 2ν directions to chose. It is obvious that the small distances between any pair of corners does not bring any advantage if one tries to go to a certain target corner by a random walk. Without guidance in choosing the right direction, one gets lost in the vast space. Guidance is provided by fitness differences. Instead of representing the sequence space by a cube, we span a 2ν-dimensional hyperplane and add as ‘vertical’ coordinate the selection values for each locus. We obtain a ‘fitness’ landscape that has some properties in common with landscapes on Earth. Selection values are not distributed at random in the sequence space but form coherent mountains with several peaks. Gradients provide guidance: while gravity directs to lower heights, selection drives uphill. How are the landscapes formed? Genotypes express into their corresponding phenotypes, and a global evaluation of their reproduction and survival successes determines their selection values. Even though expression of viruses is simpler than expression of multicellular organisms, its complexity is still much too high for making accurate calculations, but experimental data give us valuable insight. Probably the best understood virus is the levivirus Qβ [11]. It contains three major cistrons and at least one minor one, caused by the occasional read through a stop codon. The viral RNA folds into a highly compact structure. Much is known about the secondary structure of the RNA [4]. Folding and refolding of the secondary and tertiary structure provides a highly sophisticated regulation of the gene translation resulting in a ‘phage clock’: the linear nucleotide sequence precisely determines a cascade of chemical reactions leading eventually to reproduction. The sophistication is so high that hardly any nucleotide position can be replaced without consequences for the selection value. The proof is the experiment described above: even mutants that appear to be neutral since they form normal plaques eventually return to a reproducible wild type after sufficient time for selection to do its work. Arguments that much of the sequence is not important for fitness because they neither change the amino acid sequence nor the secondary structure [62]

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What Is a Quasispecies? Fig. 2 Sequences of representative subclones of quasispecies populations of the replicating RNA species MNV-11 in the linear growth ˆ A inserted, ø: nucleotide deleted, n: number of clones. phase (top) and the exponential growth phase (bottom) [101]. A:

15

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are thus not in agreement with the experimental data. If these arguments were true, passaging of the virus would lead to random drift of the sequence [66] and not to the reselection of a wild type mutant distribution. Expression involves much more than translation and secondary structure: 1. The tertiary structure includes a large repertoire of intramolecular nucleotide interactions in addition to those directing the secondary structure [19]; 2. Site-directed mutagenesis experiments [95, 94, 67, 118] showed that structural dynamics of the RNA folding is of high importance for the expression of the virus: during replication and translation, extensive refolding of the genome is crucial; 3. Plus and minus strands have different roles in expression; nevertheless, there are structural constraints for both strands (See the chapter by E. Domingo et al., this volume, for further discussions of possible multiple cis-acting and trans-acting interactions among components of the mutant spectra of RNA viruses). Work with replicating RNA has corroborated this argument. As mentioned, expression of short replicating RNA species is reduced to a fraction of the expression of a viral RNA: translation and its regulation is irrelevant and only replication by the extraneously added replicase matters. Nevertheless, defined sequences of the various replicating RNA species are obtained. Even in this model system, extensive amplification of a mutant, no matter whether isolated from a natural population or generated artificially, results in the ultimate reselection of the optimal wild type mutant distribution. The mutant spectrum of the replicating species MNV-11 has been studied in greater detail after extensive amplification to obtain mutant distributions in the quasispecies equilibrium [101] by retrotranscribing and cloning on the DNA level RNA strands picked at random from the RNA population. When the RNA population was grown in the exponential growth phase (where replicase is present in excess of RNA), the overall replication rate decided the selection and the population was dominated by three mutants which were equally represented. When RNA template was in excess of replicase, the mutant spectrum was dominated by a single master sequence which was identical to the sequence of the average population (Fig. 2). Under these conditions, not only the replication rate, but also the competition of RNA for enzyme decides the selection success. Even small changes of the external conditions altered the spectrum [101].

What Is a Quasispecies?

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7 The Quasispecies We can also build a population landscape by assigning to the vertical dimension the type frequency for each possible genotype in sequence space. As discussed earlier, type frequency values can be easily measured and mutation probabilities and selection values are normally calculated from measuring the temporal change of the population landscape. Relative population values can be determined by picking members of the population at random and ordering them by sequence, as in Benzer’s experiments. We find that they are closely grouped together; as in a landscape on earth, they form more or less coherent mountain regions containing several peaks. How do the population landscape and the fitness landscape compare? – Obviously, the two landscapes are not congruent! Therefore, Darwin’s theory is not the trivial tautology ‘survival of the survivor’. In model systems like RNA replication, it was not necessary to derive fitness values by studying the dynamics of the evolutionary process itself, but they could be calculated from physical and chemical parameters. – The population landscape is poorly correlated with mutation rates, and it is not possible to derive mutation rates from type frequencies. The population dynamics is controlled by selection and mutation and two of the parameters must be known to calculate the third. – The shape and extension of the population landscape is strongly dependent on population size, while the fitness landscape is independent of population size. – Fitness landscapes are ‘rugged’: since many single mutations are lethal, many steps lead precipitously to zero fitness. This does not inhibit evolution severely: the more dimensions there are, the higher the probability of finding one with substantial fitness is. – Population landscapes are less rugged because the mutation term smoothes them; there is no nonpopulated locus in the immediate neighborhood of a highly populated one. – The population space does not monotonously decrease with increasing Hamming distance to the master sequence. As in a natural landscape on earth, hills with smaller elevations group around the highest peak. The fitness landscape is also not monotonous: two deleterious mutations can compensate each other, e.g. by restoring a base pair in a stem region or by correcting a frame shift.

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– Hills in the population landscape comprise individuals of close kinship. The population is thus divided into subpopulations that compete one with another. – The highest mountain peak in the fitness landscape is the master m. It is not necessarily the highest peak in the population landscape because of the contribution of the mutational gain term. If two or more species are present, a corresponding number of isolated mountains are found. This property allows us to extend the notion of the species barrier, defined in Mendelian populations with unsuccessful interbreeding, to non-Mendelian populations: coherent regions in the population landscape, sufficiently far apart one from another in sequence space to make mutational jumps from one region to the other extremely unlikely, define different species. Another suitable metaphor for the population landscape is a cloud, denser in the interior, fuzzy and fluctuating at the periphery. The periphery is the arena of evolutionary progress: a newly found fitness maximum can lead to a shift in the cloud to the new optimum or to a bifocal cloud. When the centres of the latter float apart in sequence space to their new fitness optima, the cloud eventually is separated by a new species barrier: a new species has been formed. Peaks in the fitness landscape far outside the species barrier are without consequences: they cannot be reached by a mutational event. This formation mechanism for new species, first proposed by Darwin, makes it inevitable that the fitness landscape remains largely unexplored. This is illustrated by the fact that the sequences of ribosomal [124, 70] and transfer RNA [38] still show evidence that they departed from the progenote, the ancestor cell. Crick [22] argued that in such central features of the expression as translation, a once designed optimum could not be changed without destroying most of the accumulated information. It cannot be known whether the solutions found were the only or at least the optimal ones, but from our biological knowledge this seems unlikely: the fixed solution is probably merely a ‘frozen accident’. The opposite phenomenon is also observed. The coliphages of the genus Leviviridae can be grouped into two main groups. Their structure and organization are so similar that it seems inconceivable that they did not have a common phylogenetic origin, but their sequences do not reflect any relationship. Apparently, a wide-spread flat fitness landscape allowed the sequences to drift widely apart. It is quite clear that a particularly high heterogeneity does not allow the conclusion on a particularly high mutation rate; often it is rather a measure of how much variability the expression can tolerate.

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Under realistic conditions, the population cloud in the sequence space is in constant movement due to changes in the environmental conditions (See the chapter by C.O. Wilke et al., this volume). This rapid adaptation does not have to wait for a rare advantageous mutation to happen. Instead, the mutants best adapted to the environment are rapidly enriched by selection and shift the center of the cloud to another position. Instead of creating advantageous mutants from the master sequence, adaptation starts with the selection of hills that are closest to the new optimum. When conditions can be kept constant, the population eventually reaches its optimal composition: a steady state is reached (d˜xi = 0) where all mutants retain a fixed proportion of the population x˜i because the selection term and the mutational gain balance one another. As seen with the sequence determination of viruses, this steady state is highly reproducible. No matter at which point of the sequence space (within the species barrier) the evolution process starts, one ends up with the same steady state distribution. This ‘reference’ population has reproducible properties and a defined sequence and therefore resembles a species. It is called quasispecies. Its wild type is not a predominant type, but rather the gravity centre of the steady state mountain in the population landscape. It may, but does not need to, coincide with the master type. The steady state concentration of a type can be approximated by neglecting the mutational gain term [32] to x˜i

W − E˜

= ˜ ii ˜ k
=i Ei − Ek
=i

(10)

This approximation holds only when type i belongs to a master-dominated population, where the fitness of the master far exceeds any other fitness value. However, this conditions is not fulfilled in real mutant distributions, which contains mutants in a continuous fitness range from lethal to almost neutral mutations. Analysis requires an exact solution of the system of differential equations [Eq. (6)], which despite their inherent nonlinearity is possible [115, 64, 39]. Its maximum eigenvalue (which is the only stable one [39]) describes the quasispecies distribution. Of course, evolution does not know a permanent steady state. A change of external conditions changes the wild type. Furthermore, a new fitness hill may be hit by chance in the still unexplored part of the sequence space. The steady state is then replaced by a new rapid evolution process until a new steady state is found. Evolution thus proceeds via ‘punctuated equilibria’ [49]. What is the target of ‘Selection of the fittest’? The type, the clan or the (quasi)species? This depends on what is investigated. If selection among

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species is investigated, not the fittest type, but the whole wide-spread quasispecies distribution is the target of selection. Within the quasispecies distribution, the clans represented by hills in the population space compete one with another. Within the clan, the types compete for reaching their maximum representation. (Measurements and implications of fitness variations of viruses are covered in the chapter by M.E. Quiñones-Mateu, E.J. Arts, this volume.)

8 The Error Threshold Balancing of the focussing force of selection and the diversifying force of mutation is only possible within certain bounds: the latter must not exceed a certain threshold for maintaining the information content of the quasispecies. According to Eq. (10), a master sequence should disappear whenever its selection rate value Wmm becomes equal to or smaller than E¯ k
=m . Under constant external conditions, Wmm is a constant, but in nature external conditions usually vary. Moreover, in addition to the kinetic parameters Am and Em , Wmm depends on the fidelity probability Qmm , which itself depends on νm , the chain length of the sequence to be reproduced. The larger νm , at given reproduction mechanisms, the smaller Qmm = q¯ νm (with q¯ < 1). If the average superiority of the master over its competitors is introduced as σ¯ m = Am |(Dm + E¯ k
=m ), Eq. (10) can be written as x˜m = (¯σm q¯νm − 1)|(¯σm − 1), and the error threshold relation is obtained [32]: 1 − q¯ m ≤

ln σ¯ m . νm

(11)

Above the critical error rate given by Eq. (11), the master type and with it the information contained in the sequence will be lost. To maintain the information, the mutation loss 1 − Qmm must be compensated by at least an average superiority σ¯ m >1. Though Eq. (10) is an approximation, relation (11) generally holds surprisingly well, even for wide-spread quasispecies distributions. In all studied cases with single-stranded RNA viruses, the critical product νm (1 − qm ) turned out to be in the vicinity of 1. Therefore, σ¯ m must clearly exceed 1 to obtain a natural logarithm ln σ¯ m that is not too far from 1. In contrast, σ¯ m -values in Mendelian species are near unity, because large parts of the genome do not contribute to the immediate survival under the pertaining conditions. For these species, the average error rate per sequence i, εi , is very small and the term ln σ¯ m can be approximated by ε. For the cases studied, ε turned out to be between 10−2 and 10−3 .

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The disintegration of information at the error threshold behaves like a firstorder phase transition [113, 114, 34]. In a numerical simulation carried out by Swetina and Schuster [113], where all mutants were assumed to have a uniform fitness of one tenth of the master, the relative population of the master x˜m dropped with increasing error rate to quite small values, until, at the error threshold, an instability occurred and all types in the population, including the master, became equally populated. While a defined wild type sequence could be determined below the error threshold, it suddenly disappeared at the error threshold. The chosen example of a single master sequence surrounded by uniformly less fit mutants is quite instructive, but unrealistic in nature. In reality, much of the sequence space cannot be accessed because nonviable mutants do not produce progeny. Hence, at the error threshold, the mutant distribution cannot evaporate into the whole sequence space. Instead, the cloud spreads out so much into the lethal area that fewer and fewer progeny are produced, resulting in the eventual annihilation of the population. This has been shown to happen with viral populations after the error rate was artificially raised by the addition of mutagenic drugs [80, 109, 23, 35, 50] (see E. Domingo et al, this volume, for discussion of error catastrophe as an antiviral strategy).

9 Evolutionary Biotechnology 9.1 Principles A new research field of increasing impact is evolutionary biotechnology. It is an application of the Darwinian principles of generation of diversity and selection with the aim of deriving novel molecules with desired properties [65]. The latter are improved by further cycles of amplification and selection (Fig. 3), whereby natural selection is replaced by artificial selection. Two general strategies can be chosen: rational and irrational design [15, 116, 92]. Rational design is a well-tested method that is suitable for well-understood systems. It is frequently used to modify protein properties [75, 44, 102] by site-directed, not random mutations. It is evolutionary in the sense that it involves evaluation and selection of successful mutants. Exploration of fitness is restricted to the mountain itself and its close surroundings. The irrational design is a gunshot method: we try to obtain success by playing a lottery game. The advantage of this method is that it does not depend on prior knowledge of how to achieve a desired function. The whole

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Fig. 3 General flow diagram for evolution experiments using artificial selection by a biassed process.

sequence space is used for exploration. This approach is very old: nearly all information on food and drugs accumulated in the history of mankind has been gained using this method, even though essentially by serendipity. A systematic exploration requires high-throughput methods and the invention of efficient screening methods. The extension to chemically synthesized polymers composed by suitable nonbiotic chemical residues (combinatorial chemistry [18, 77, 83]) is also new, as is the application of evolution strategies to technical processes [99]. Unfortunately, the expectation of success drops sharply with increasing complexity. Once a positive result has been obtained, the neighbourhood of its locus in sequence space is explored. For most applications, both strategies have severe limitations. A mixed approach is better: shotgunning is restricted to areas in sequence space with

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better chances of achieving success, using whatever information is available. Furthermore, the lottery has fortunately not only one major win but many minor wins. In further evolutionary optimization, it is then possible to climb to the highest hill on the mountain. Suitable strategies can reduce the number of trials and reduce the risk of getting trapped in local optima [47], which is inevitable whenever the fitness landscape contains several, well-isolated mountains [20]. Instrumental for any shotgunning method to work are highly efficient screening methods able to spot successful types in a large number of blanks and a procedure to amplify them. Except for nucleic acids (see below), amplification is usually achieved by the synthesis of a new population with more sequential constraints. 9.2 Protein Design A typical mixed approach is the exploration of the fitness landscape around the mountainous part several steps deep into sequence space. These loci are already too far apart to be hit by a mutational event in a population of finite size. Mountainous parts of the fitness landscape are quite rugged. Many single mutations are lethal and lead to a drop into a precipice with no rescue. At other positions, the mountains can send ridges far into the sequence space. This part of the sequence is less stringent and the cloud becomes thin and large, while at the parts of the sequence critical for function the cloud becomes quite compact: lethal mutations interrupt transgression to regions that are farther apart. Exploration of the wider surroundings of the mountain in the fitness landscape is possible by artificially raising the error rate, e.g. by randomizing critical parts of the sequence [58, 60, 30]. Isolated peaks that are unlikely to be reached by natural mutation can thus be reached by jumping over broad canyons of lethal mutations. Such experiments [59, 89, 30] have revealed surprising extensions of the fitness landscape farther out in sequence space. Apparently, mountains in the fitness landscape are more extended than the corresponding mountains in the population landscape, in particular at limited population sizes. A fairly thorough exploration of the fitness landscape is only possible if oligomers of rather short chain lengths are used. Oligomers are poor in function, however. Binding of an antigen to an antibody is often determined by only a few amino acids of the antigen. Libraries of chemically synthesized oligopeptides [100] were thus screened for binding to certain monoclonal antibodies [90]. Often, several isolated mountains in the fitness landscape have been found: oligopeptides unrelated to the amino acid sequence of the

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antigenic part of the antigen were found to bind to monoclonal antibodies nearly as efficiently [68]. In some cases, it was possible to find ridges in the fitness landscape connecting one peak with another [57]; in other cases, the peaks appeared to be well isolated. An ingenious rational approach to create proteins with entirely new functions is the development of catalytic antibodies [73, 105, 74, 106]. The immune system of mammals provides an immunoglobulin repertoire as high as 107 specificities, which can be raised by another three orders of magnitude during the process of affinity maturation. The trick was to synthesize chemically stable analogues that structurally resemble a transition state at an intermediate distance from the chemical reaction that is desired to be catalyzed and use them as a hapten to provoke an in vivo immune response in an animal. Several monoclonal antibodies produced from hybridomas screened for binding the hapten were able to catalyze the desired reaction by several orders of magnitude. While the successful antibodies did not reach the efficiency nor the specificity of optimized enzymes, they were highly stereospecific. This method was limited to a few reactions. A broader application range has been the screening of large libraries of antibodies for a function, e.g. by replacing the hybridoma technique by a display approach. Screening is particularly efficient when the protein is displayed on the surface of a phage [97, 108, 110, 46] because the phage population not only provides clones of a certain protein phenotype, but also carries the corresponding genotypic information to reproduce and to amplify it. However, natural antibodies that contain four rather large polypeptide chains are nearly impossible to display. Single-chain antibody analogues were used [51, 98, 56] that have a conserved part responsible mainly for the structure and a variable, partly randomized part for isolating a binding pocket. The display techniques have been extended to molecular systems such as ribosome display [55, 1] and mRNA display [123, 76]. Since the bottleneck of transformation is avoided, much larger libraries are possible. Single-chain antibodies were also replaced by other protein alternatives that display a variable region on a rigid scaffold [7]. 9.3 Selection of nucleic acids with a function Display techniques limit the library to about 109 types. Much larger libraries of up to 1015 types can be created with nucleic acids. Furthermore, efficient in vitro amplification procedures for DNA [88, 104] and RNA [69, 52] are available. Selection from randomized or partially randomized RNA sequences, followed by iterative cycles of amplification and selection, has facilitated the design of nucleic acids with new properties, e.g., RNA variants that grow with

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certain RNA polymerases [10, 121], ‘aptamers’ that bind ligands with high affinity and specificity [117, 41, 91, 72, 119, 71, 63] or ‘ribozymes’ [125, 53, 48] that catalyze certain chemical reactions, e.g. alkylation [122, 120], RNA ligation [40, 21], amino acid transfer [61, 81], and polynucleotide kinase activity [82]. Evolutionary optimization of natural ribozymes or extending their catalytic repertoire to similar reactions by coupling the wanted activity with a selective advantage has also been highly successful [3, 24]. Similar experiments with DNA single strands [16] have shown that even DNA strands can catalyze specific reactions. However, the catalytic efficiency of nucleic acids does not reach those of proteins due to the lack of side groups that allow efficient acid-base or redox catalysis. Ribozymic self-incorporation of a coenzyme [17] has opened the possibility of introducing residues capable of redox reactions. One of the current challenges of evolutionary biotechnology is designing procedures for coupling nucleic acid amplification with efficient in vitro expression into protein [112] followed by purification of the protein and evaluation of its function.

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Amstutz P, Forrer P, Zahnd C, Pluckthun A (2001) In vitro display techniques: novel developments and applications. Curr Op Biotechnol 12:400–405 Batschelet E, Domingo E, Weissmann C (1976) The proportion of revertant and mutant phage in a growing population, as a function of mutation and growth rate. Gene 1:27–32 Beaudry AA, Joyce GF (1992) Directed evolution of an RNA enzyme. Science 257:635–641 Beekwilder MJ (1996) Secondary structure of the RNA genome of bacteriophage Qβ. Dissertation, University of Leiden, the Netherlands Benzer S (1959) On the topology of the genetic fine structure. Proc Natl Acad Sci U S A 45:1607–1620 Benzer S (1961) On the topography of the genetic fine structure. Proc Natl Acad Sci U S A 47:403–415 Beste G, Schmidt FS, Stibora T, Skerra A (1999) Small antibody-like proteins with prescribed ligand specificities derived from the lipocalin fold. Proc Natl Acad Sci U S A 96:1898–1903 Biebricher CK (1983) Darwinian selection of RNA molecules in vitro. Evol Biol 16:1–52 Biebricher CK (1987) Replication and evolution of short-chained RNA species replicated with Qβ replicase. Cold Spring Harbor Symp Quant Biol 52:299–306

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10. Biebricher CK, Orgel LE (1973) An RNA that multiplies indefinitely with DNAdependent RNA polymerase: selection from a random copolymer. Proc Natl Acad Sci U S A 70:934–938 11. Biebricher CK, Eigen M (1988) Kinetics of RNA replication by Qβ replicase. In: Holland JJ, Ahlquist R, Domingo E (eds) RNA genetics. Vol I RNA-directed Virus Replication. CRC Press, Boca Raton FL, pp 1–21 12. Biebricher CK, Gardiner WC, Eigen M (1985) Kinetics of RNA replication: competition and selection among self-replicating species. Biochemistry 23:6550–6560 13. Biebricher CK, Gardiner WC, Eigen M (1991) Quantitative analysis of selection and mutation in self-replicating RNA. In: Peliti L (ed) Biologically inspired physics. Plenum Press, New York, pp 317–337 14. Billeter MA, Dahlberg JE, Goodman HM, Hindley J, Weissmann C (1969) Sequence of the first 175 nucleotides from the 5 terminus of Qβ RNA synthesized in vitro. Nature 224:1083–1086 15. Breaker RR, Joyce GF (1994) Inventing and improving ribozyme function: rational design versus iterative selection methods. Trends Biotechnol 12:268–275 16. Breaker RR, Joyce GF (1995) A DNA enzyme with Mg2+ -dependent RNA phosphoesterase activity. Chem Biol 2:655–660 17. Breaker RR, Joyce GF (1995) Self-incorporation of coenzymes by ribozymes. J Mol Evol 40:551–558 18. Brenner S, Lerner RA (1992) Encoded combinatorial chemistry. Proc Natl Acad Sci U S A 89:5381–5383 19. Brion P, Westhof E (1997) Hierarchy and dynamics of RNA folding. Annu Rev Biophys Biomol Struct 26:113–137 20. Carothers JM, Oestreich SC, Davis JH, Szostak JW (2004) Informational complexity and functional activity of RNA structures. J Am Chem Soc 126:5130–5137 21. Chapman KB, Szostak JW (1995) Isolation of a ribozyme with 5 → 5 ligase activity. Chem Biol 2:325–333 22. Crick FHC (1968) The origin of the genetic code. J Mol Biol 38:367–379 23. Crotty S, Cameron CE, Andino R (2001) RNA virus error catastrophe: direct molecular test by using ribavirin. Proc Natl Acad Sci U S A 98:6895–6900 24. Dai XC, Demesmaeker A, Joyce GF (1995) Cleavage of an amide bond by a ribozyme. Science 267:237–240 25. Domingo E, Sabo D, Taniguchi T, Weissmann C (1978) Nucleotide sequence heterogeneity of an RNA phage population. Cell 13:735–744 26. Domingo E, Biebricher CK, Eigen M, Holland JJ (2001) Quasispecies and RNA virus evolution: principles and consequences. Landes Bioscience, Georgetown TX 27. Drake JW (1993) Rates of spontaneous mutation among RNA viruses. Proc Natl Acad Sci U S A 90:4171–4175 28. Drake JW, Holland JJ (1999) Mutation rates among RNA viruses. Proc Natl Acad Sci U S A 96:13910–13913 29. Duarte E, Clarke D, Moya A, Domingo E, Holland J (1992) Rapid fitness losses in mammalian RNA virus clones due to Muller’s ratchet. Proc Natl Acad Sci U S A 89:6015–6019 30. Dube DK, Black ME, Munir KM, Loeb LA (1993) Selection of new biologically active molecules from random nucleotide sequences. Gene 137:41–47 31. Dykhuizen DE, Hartl DL (1983) Selection in chemostats. Microbiol Rev 47:150–168

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CTMI (2006) 299:33–50 c Springer-Verlag Berlin Heidelberg 2006


Quasispecies in Time-Dependent Environments C. O. Wilke1 (u) · R. Forster2 · I. S. Novella3 1 Section of Integrative

Biology and Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX 78712, USA [email protected] 2 Digital Life Laboratory, California Institute of Technology, MC 136–93, Pasadena, CA 91125, USA 3 Department of Medical Microbiology and Immunology, Medical University Ohio, 3055 Arlington Ave., Toledo, OH 43616, USA

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

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Virus Evolution in Time-Dependent Fitness Landscapes . . . . . . . . . . . . 35

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Adaptation to Two Alternating Hosts . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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Adaptation of Mutation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

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Co-evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Abstract In recent years, quasispecies theory in time-dependent (that is, dynamically changing) environments has made dramatic progress. Several groups have addressed questions such as how the time scale of the changes affect viral adaptation and quasispecies formation, how environmental changes affect the optimal mutation rate, or how virus and host co-evolve. Here, we review these recent developments, and give a nonmathematical introduction to the most important concepts and results of quasispecies theory in time-dependent environments. We also compare the theoretical results with results from evolution experiments that expose viruses to successive regimes of replication in two or more different hosts.

1 Introduction From the point of view of a virus, the world is in a state of constant change. For sustained existence, the virus has to move from host organism to host organism, and each new host differs somewhat from the previous one. Some viruses

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even change their host species on a regular basis. For example, arboviruses alternate between infections in arthropods and infections in vertebrates. Within a host, the world is not necessarily constant either. Viruses infecting higher organisms find themselves under constant attack from an adaptive immune system, whose ability to fight any particular virus mutant grows dramatically after exposure to the mutant. Fortunately—for the virus, that is—the large class of RNA viruses is more than prepared for this world of change. Their high replication rates, combined with exceptionally high mutation rates, allow them to explore vast numbers of mutations, which leads to rapid adaptation in the face of environmental variation. For example, it has been estimated that HIV-1 produces all possible single- and double-point mutants and a good fraction of the triple mutants in every single patient every day (Perelson et al. 1997). The theoretical framework in which RNA viruses are typically modeled is quasispecies theory, which was put forward by Eigen and co-workers in the 1970s (Eigen and Schuster 1979; Eigen et al. 1988, 1989) (see also chapter by Biebricher and Eigen, this volume). Quasispecies theory describes how a virus population reacts to the two forces of selection and mutation, and how these two forces counterbalance each other. Mathematically, quasispecies theory is equivalent to the theory of mutation–selection balance, which has been developed parallel to quasispecies theory in the population-genetics literature (Bürger 2000). Eigen and co-workers (Eigen and Schuster 1979; Eigen et al. 1988, 1989) defined the quasispecies as the stable mutant configuration that arises when mutation and selection are in perfect equilibrium. With this definition, the theory seemed to apply only to static environments and an infinite population size. Indeed, it has been argued that since viral populations are finite, quasispecies theory may not realistically reflect their behavior, because perfect equilibrium between mutation and selection can never be achieved in the real world (Jenkins et al. 2001; Holmes and Moya 2002). However, a number of theoretical studies have shown that understanding the infinite-population dynamics is crucial for developing a more detailed finite-population model (van Nimwegen et al. 1999a; Wilke 2001a) and that all the effects predicted by quasispecies theory can in principle also be observed in finite populations (Nowak and Schuster 1989; van Nimwegen et al. 1999b; Wilke et al. 2001b, Wilke 2001b). In recent years, substantial progress has been made in extending quasispecies theory to dynamic environments. This work can be subdivided into two groups, studies on virus evolution in time-dependent fitness landscapes (Nilsson and Snoad 2000, 2002a, 2002b, Wilke et al. 2001a; Wilke and Ronnewinkel 2001) and studies on the interaction between virus and the host’s

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immune system, or virus–host co-evolution in general (Kamp and Bornholdt 2002; Kamp et al. 2002; Brumer and Shakhnovich 2004). In this chapter, we put most emphasis on the former type of studies, but will also give a brief overview on the latter. We discuss the relationship of theory to experimental results in the individual sections where appropriate.

2 Virus Evolution in Time-Dependent Fitness Landscapes In this section, we consider situations in which the virus’s environment changes due to external factors but is not in return influenced by any of the virus’s adaptive moves. Throughout this chapter, we use for this case the terminology of “adaptation to a time-dependent fitness landscape.” As an example, consider a virus that is grown alternatingly on different hosts (see also the next section). From a theoretical point of view, it is particularly interesting to study changes in the fitness landscape that are periodic (that is, situations in which the environment changes over and over again in exactly the same pattern), because the theory that has been developed for static fitness landscapes can— after a suitable mathematical transformation—to a large extent be directly applied to periodic fitness landscapes (Wilke et al. 2001a). A number of exact results and simple principles have been derived for periodic fitness landscapes. We will review these results in the following paragraphs, and will consider nonperiodic changes briefly at the end of this section. Before we can talk in depth about periodically changing (or oscillating) fitness landscapes, we have to introduce some terminology. We refer to the time it takes for the fitness landscape to go through one cycle of changes as the oscillation period. For example, if the fitness landscape alternates between environment A for 100 generations and environment B for 100 generations, then the oscillation period is 200 generations. We denote the oscillation period by T. The inverse of the oscillation period, 1/T, is called the oscillation frequency. The shorter the oscillation period, the higher the oscillation frequency. We refer to a special point in time within one oscillation period as the phase. We typically express the phase in fractions of one complete oscillation cycle. For example, in the above example, the switch from A to B occurs at phase 1/2, and the switch from B to A occurs at phase 1, which by definition is equivalent to phase 0. One important result for oscillating fitness landscapes is that as long as we look at the system under study only at a single phase (say, for example, at the beginning of the oscillation, phase = 0), the virus population behaves as

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if the fitness landscape were static (Wilke et al. 2001a). After an initial transient period, the virus population develops a stable distribution of mutants, a quasispecies. The only difference from a static fitness landscape is that this equilibrium depends on the phase. At phase 1/2, the equilibrium distribution of mutants may be totally different from the distribution at phase 0, but at any given phase, the equilibrium distribution will always be the same. In theory, the population reaches this equilibrium only after infinitely many oscillation cycles have passed, but in practice, more than a couple of hundred cycles are rarely necessary to obtain equilibration, and frequently equilibration occurs after only a handful of cycles. We can also calculate accurately what happens when the oscillations are very fast or very slow. The general result is the following: under very fast changes, the population behaves as if the fitness landscape did not change at all, but instead was the average of the changing landscape (Wilke et al. 2001a; Wilke and Ronnewinkel 2001). That is, if for example two traits are alternatingly selected for, then above a certain oscillation frequency, the population will simply acquire both traits at the same time. For very slowly changing landscapes, the population essentially does not realize that the environment is changing, and climbs the local peak that best represents the current environment. If the environment changes slowly but continuously, then the population will track this local optimum, and always stay optimally adapted (Nilsson and Snoad 2002b). If, however, the environment suddenly requires a completely different adaptation, then the population may be thrown off track and suddenly find itself hopelessly ill-adapted and unable to cope with the new environment (Ronnewinkel et al. 2001). The behavior at intermediate oscillation frequencies interpolates between the two extremes. As the oscillation frequency goes up, the population ceases to track the changes accurately, and behaves more as if it were only experiencing the average fitness landscape. An interesting phenomenon that occurs at intermediate oscillation frequencies is the phase shift. The term “phase shift” means that there is a substantial lag between the point in time at which a change in the fitness landscape takes place and the point in time at which the population shows response to this change. If we measure this lag in units of the oscillation period, then the lag goes to zero as the oscillation period increases. However, when the oscillation period decreases, then the phase shift can assume substantial values, as the population is constantly trying to keep up with the changes in the landscape (Nilsson and Snoad 2002b). To summarize, we can compare the evolutionary dynamics in a periodic fitness landscape with the effect of a low-pass filter on an audio signal (Wilke et al. 2001a; Nilsson and Snoad 2002b): A low-pass filter replaces high-frequency signals with their average intensity, and lets low-frequency signals pass unal-

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tered. Moreover, for intermediate- to high-frequency signals, a low-pass filter introduces a time lag (phase shift) that depends on the frequency of the signal. The general predictions for a fitness landscape whose changes are aperiodic or irregular are very similar to those for a periodically changing fitness landscape. Again, evolution essentially acts as a low-pass filter on the changing fitness landscape. The population will not be able to follow changes in the fitness landscape that happen on a short time scale, and will see only the average effect of these changes. However, the population will typically be able to track a slowly changing landscape accurately and stay optimally adapted. One significant difference from periodic fitness landscapes is the case of very fast, irregular changes. In this case, it can happen that the population loses track of the advantageous regions in the fitness landscape altogether and becomes completely ill adapted. This loss of adaptation can be considered as the dynamic equivalent of the classic (static) error threshold. The static error threshold sets an upper bound to the maximum mutation rate that a population can sustain without losing all genetic information (Eigen and Schuster 1979). The dynamic error threshold differs from the classic error threshold in that it sets a lower bound on the minimum mutation rate with which a population can still follow the changes in the fitness landscape (Nilsson and Snoad 2000).

3 Adaptation to Two Alternating Hosts As an example for a periodic fitness landscape, we consider here the situation where a virus population has to adapt to two alternating hosts. Such a situation is common, for example in arboviruses, which alternate between rounds of replication in arthropod vectors and rounds of replication in vertebrate hosts. One question that is frequently asked in this context is whether the virus can adapt to both hosts at the same time, or whether adaptation to one host is typically accompanied by loss of adaptation to the other host. We can study this question in the framework of a very simple model. Assume that a virus strain has one gene that is advantageous in host one, and neutral in host two, and a second gene that has the reversed properties. We refer to this strain as the divided strain. Further, assume that there is also a second virus strain which has a single gene that conveys the advantageous functions of each of the two genes in the corresponding hosts. We refer to this strain as the fused strain. In addition, both the divided and the fused strain can suffer from point mutations, and do so at the same rate per site. In the case of the divided strain, a single point mutation will affect either of the two genes (and thus the

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two functions) separately, whereas in the fused strain, point mutations affect both functions simultaneously. Note that while we talk about separate genes throughout this chapter, this model can also apply to separate functions carried out by a single gene. In this case, the divided strain corresponds to a gene that can adapt to either function, but not to both at the same time, whereas the fused strain corresponds to a gene that can adapt to both functions at the same time. These differences in the strains can be caused by pleiotropic effects. Throughout this section, we consider populations of finite size N, whereas the general results presented in the previous section are derived from infinitepopulation theory. By and large, these general results also apply to finite populations. The main difference is that (a) in finite populations, genotypes can truly go extinct, whereas all genotypes are always present in infinite populations, and (b) that the quasispecies dynamic becomes a stochastic process, subject to fluctuations and chance events. Figure 1 demonstrates how the divided strain will evolve if it is grown alternatingly for 30 generations, each on one of the two hosts. When the divided strain grows on host one, the second gene is not under selection, and therefore slowly deteriorates due to mutation accumulation. When the virus is transferred to the second host, then gene two comes under selection and rebounds quickly, while gene one starts to deteriorate. Figure 2 illustrates this dynamic by visualizing the mutant cloud that forms at different points in time. When gene one is under selection, then only a small number of sequences

Fig. 1 Frequencies of error-free genes as a function of time. Parameters are oscillation period T = 60, per-site mutation rate u = 0.02, length of a single gene of the divided strain ldiv = 5, selective advantage of functional gene s = 1, population size N = 1,000

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Fig. 2 Population structure of either the divided or fused strain at various time points. Gray levels indicate the fraction of sequences at the given mutational distance from the respective error-free sequence. Parameters are oscillation period T = 60, per-site mutation rate u = 0.02, length of a single gene of the divided strain ldiv = 5, length of the fused gene lfused = 8, selective advantage of functional gene s = 1, infinite population size

acquire mutations in this gene, while at the same time the majority of the population acquires mutations in gene two. The image reverses when gene two starts to come under selection. Then, only a small number of sequences acquire mutations in gene two, but almost all sequences acquire mutations in gene one. Note that the fused strain, grown under the same conditions, fully retains both functions at all times, and accumulates only a small number of mutations in any case (Fig. 2). If we compete the divided with the fused strain in a changing environment, which one will prevail? There is no simple answer to this question. The answer depends on a number of factors, the two most important of which are the time scale on which the changes in the environment occur and whether mutation pressure creates any additional trade-offs between the divided strain and the fused type. For example, if the environment changes very quickly (such as when the host changes every generation), then the divided strain will maintain both functions at all times, just as the fused type does. Therefore, in this case the only difference between the two strains is the extent to which they suffer from mutation pressure. If we assume that their per-site mutation rate u is the same, then the mutation pressure translates into the number of sites in the genome that are under selection. The strain with the larger number of sites under selection will suffer from a higher mutational pressure and lose against the other strain (Wilke and Adami 2003). Thus, if the fused gene can perform both functions with fewer sites under selection than the two genes of the divided strain, then the fused strain has an advantage. A similar argument applies when the environment changes very slowly. However, in this case the

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selection pressure is different, because the virus has sufficient time to adapt fully to one host before it is exposed again to the other host. During this long period of exposure to a single host, the decisive factor is now whether the single gene of the divided strain that is needed for this host suffers from a higher mutational pressure than the fused gene. It is probably reasonable to assume that the two genes of the divided strain each have fewer selective sites than the single fused gene, but that the fused gene has fewer selective sites than the two former genes together. Under this

Fig. 3 Fused strain invades a population of the divided strain. A single fused genotype is introduced into the population at t = 0 and reintroduced at later times whenever all fused genotypes have been lost from the population. The 25th introduced genotype goes to fixation. Parameters are oscillation period T = 60, per-site mutation rate u = 0.006, length of a single gene of the divided strain ldiv = 5, length of the fused gene lfused = 8, selective advantage of functional gene s = 1, population size N = 1,000

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assumption, the fused strain will be advantageous when the environment changes quickly, while the divided strain will be advantageous when the environment changes slowly. Figure 3 shows how the fused strain can invade a population of the divided strain if the environmental changes occur on a sufficiently fast time scale, while Fig. 4 shows how the divided strain can invade a population of the fused strain if the environment changes slowly. In experiments with digital organisms (self-replicating computer programs that mutate and evolve; Wilke and Adami 2002) that were adapting

Fig. 4 Divided strain invades a population of the fused strain. A single divided genotype is introduced into the population at t = 0, and reintroduced at later times whenever all divided genotypes have been lost from the population. The 31st introduced genotype goes to fixation. Parameters are oscillation period T = 600, per-site mutation rate u = 0.006, length of a single gene of the divided strain ldiv = 5, length of the fused gene lfused = 8, selective advantage of functional gene s = 1, population size N = 1,000

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to a periodically changing fitness landscape, trajectories very similar to the one shown in Fig. 3 were observed for moderately to rapidly changing fitness landscapes, but not for very slowly changing fitness landscapes (Li and Wilke 2004). These observations imply that genes or part of genes can indeed evolve to be fused or divided, even if the system under study does not a priori provide fused or divided genes. In the digital organisms, separation or fusion of genes was not imposed from the outside, unlike in the case of the simple toy model discussed in this section, and evolved presumably through changes in the amount of epistatic interactions among the different parts of the digital organisms’ genomes. Several groups have experimentally tested the effect of replication in two different hosts on the evolution of viral quasispecies. Alternating acute replication of vesicular stomatitis virus (VSV), eastern equine encephalitis virus (EEEV), and Dengue virus in cells of insect and mammalian origin led to fitness increase in both cell types (Novella 1999; Weaver et al. 1999; Chen et al. 2003). The difference in behavior between VSV and EEEV is that for VSV, insect and mammalian cells represent similar fitness landscapes for replication, whereas for EEEV a trade off can be observed: adaptation to one cell type results in loss of viral fitness in the other. Similar results are observed for adaptation to cells of insect and avian origin. Viruses that grow alternatingly on these two cell types produce adaptations to both environments in spite of trade-offs (Cooper and Scott 2001). However, while acute insect replication does not involve trade-offs for acute mammalian replication, persistent replication of VSV in insect cells does result in trade-offs for replication in mammalian cells (Novella et al. 1995), and evolution during alternation between acute mammalian replication and persistent insect replication is dominated by the persistent phase (Zárate and Novella 2004). Additional work has been done using different types of mammalian cells (BHK-21, MDCK, and HeLa) as hosts for VSV replication. In these experiments, VSV consistently adapted to both of the alternating cell types independently of the existence of tradeoffs and independently of whether the host switch occurred periodically or randomly (Turner and Elena 2000). All these experimental results are in qualitative agreement with the theory, in that they show that there is no simple rule of how viruses behave that are grown alternatingly in two different hosts. They may either adapt to both hosts at the same time or adapt to one host at the expense of adaptation to the other. Which of the two cases occurs depends on the time spent in each of the two hosts as well as on the details of the fitness landscape in the two hosts.

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4 Adaptation of Mutation Rate In the context of time-dependent environments, it is natural to ask what the optimal mutation rate is for the viral population. In a static environment, once a virus has reached the global optimum in the fitness landscape, all mutations are deleterious. In this case, it pays for the virus to reduce the mutation rate as much as possible, as a lower mutation rate translated into a lower mutational load—and thus higher average fitness—for the offspring virions. In a timedependent environment, on the other hand, a zero or near-zero mutation rate is detrimental. A virus needs a sufficiently high mutation rate in order to be able to follow changes in the fitness landscape. It is clear that the mutation rate at which the virus is optimally capable of tracking the fitness landscape will depend on the speed and nature of the changes in the fitness landscape. Nilsson and Snoad (2002a) studied a simple model of a viral quasispecies that has to track a moving fitness peak, and calculated the optimal mutation rate in this model. The model assumptions are the following: the fitness peak remains static for a time interval of length τ1 . Then it moves to a position nearby in the sequence space. The distance from the old position to the new position is k1 point mutations. The peak remains static at the new position for a time interval of length τ2 , until it moves to another nearby position, this time with distance k2 , and so on. The lengths of the intervals τi and the distances ki need not be identical from shift to shift. Nilsson and Snoad found that the optimal mutation rate could be expressed simply as µopt = k/(στ), where k is the average mutational distance that the peak moves, σ is the mean replication rate of the virus on the peak, and τ is the average time between peak shifts. We can rewrite Nilsson and Snoad’s result such that its interpretation becomes more intuitive. Note that 1/σ is the average time the virus needs to replicate, and that therefore στ is the average time between peak shifts measured in virus generations. Thus Nilsson and Snoad’s result states that the virus can optimally adapt to the changing environment if its genomic mutation rate per generation is the mean distance between peaks divided by the number of generations between successive peak shifts. This result makes intuitive sense: with this mutation rate, the majority of mutants in the virus population will, by the next peak shift, have accumulated exactly the right number of mutations to be located at the position of the new peak. It is interesting to note that for k = 1, we recover a result that Kimura had already derived in a much simpler model in 1967 (Kimura 1967). Note that Nilsson and Snoad’s result is very different from the optimal mutation rate of an ill-adapted virus in a static environment, that is, of a virus that

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can still discover many advantageous mutations even when the environment does not change. In this case, the optimal mutation rate is given by the mean effect of deleterious mutations (Orr 2000). A mutation rate of this magnitude optimally balances the beneficial effect of new advantageous mutations with the deleterious effect of too high a mutational load. It is not clear whether Orr’s theory or Nilsson and Snoad’s theory is more relevant to RNA viruses. The answer depends on the time scale on which changes happen, and on how easy it is for a virus to reach optimal adaptation after a change in the fitness landscape has taken place. If changes happen only occasionally, and a number of beneficial mutations need to be discovered to reach optimal adaptation to the new environment, then Orr’s theory is probably more relevant. If on the other hand, changes happen regularly, and only one or two mutations are needed to reach optimal adaptation to the new environment, then Nilsson and Snoad’s theory should apply.

5 Co-evolution In the preceding sections, we have discussed virus evolution in fitness landscapes that change because of external forces. By “external forces” we mean that the virus has to adapt to environmental changes, but the environment is not influenced by the adaptive moves of the virus. In many cases, however, the virus imposes a time-dependent fitness landscape on the environment just as much as the environment imposes a time-dependent fitness landscape on the virus. Typical examples are the interaction between virus and immune system, and co-evolution of virus and host in general. This section is not meant as an in-depth review of these areas, but rather as an entry point that should help the reader locate the relevant literature in this field. Kamp and co-workers (Kamp and Bornholdt 2002; Kamp et al. 2002) have extended earlier work on a virus population that is tracking a moving optimum (Nilsson and Snoad 2000) to the case where the moving optimum is defined by an adaptive immune system, which itself is tracking the virus population (see also Kamp 2003 for a review). Interestingly, in this case not only does the virus population form a quasispecies, but also the immune system can be interpreted as a quasispecies: under B cell proliferation and somatic hypermutation, a mutant cloud of B cells develops that behaves similarly to a viral quasispecies. Kamp and Bornholdt found that both the viral quasispecies and the B cell quasispecies suffer from the classical error threshold if their mutation rates are too high. In addition, the viral quasispecies also suffers from a dynamic threshold if its mutation rate is too low in comparison

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to that of the B cells. In this case, the virus cannot escape rapidly enough from the immune system, and is driven to extinction. Kamp and co-workers also calculated the optimal mutation rates for both the immune system and the virus and found that both results compare favorably with experimental observations (Kamp and Bornholdt 2002; Kamp et al. 2002). The optimal mutation rate for the virus has a particularly simple and intuitive interpretation: the mutation rate should be such that each viral epitope acquires on average exactly one mutation within the time-frame in which the immune system reacts to that epitope. (Note that this result is essentially identical to the one of Kimura 1967 discussed in the preceding section.) Brumer and Shaknovich (2004) have extended this work to study the effect of semiconservative replication (where both template and copied sequence acquire mutations, as is the case in replication of double-stranded DNA) vs conservative replication (where mutations arise only in the copied sequence, as is the case with most RNA-based viruses). They have found that the optimal viral mutation rate is largely unaffected by the mode of replication, but that the model dynamics away from this optimum differ substantially with the mode of replication. Brumer and Shaknovich suggest that their result might explain why DNA-based viruses tend to have much smaller mutation rates than RNA-based viruses. In general, a substantial amount of modeling work on the interaction between viruses and immune systems has been done by theoretical immunologists. While many of the basic immunological models do not explicitly consider evolution, they provide a natural starting point for more realistic models that do include evolutionary effects. Good introductions to theoretical immunology and its relation to virus dynamics and virus evolution are the book by Nowak and May (2000) and a recent review article by Perelson (2002). Co-evolution of viruses and host cells has been studied experimentally in several viral species, particularly during persistent infection. Infection of BHK-21 cells with foot-and-mouth-disease virus can select cells of increased resistance, which in turn selects viral populations of increased virulence for nonselected host cells (de la Torre et al. 1988; Hernández et al. 1994). Similar results have been reported for experiments with persistent infection of poliovirus (Borzakian 1992), reovirus (Ahmed et al. 1981; Dermody et al. 1993), and mouse hepatitis virus (Chen and Baric 1996). As a consequence, persistently infected cells are less susceptible to viral infection, and this susceptibility tends to correlate with the ability (or lack thereof) of the virus to enter the cells (Borzakian 1992; Dermody et al. 1993; Chen and Baric 1996). Another model of co-evolution that has been studied extensively both theoretically and experimentally is that of defective interfering particles (DIPs)

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and their full-length wild-type (wt) counterparts. DIPs are mutant viruses with large deletions in their genomes. Because of these deletions, DIPs cannot produce all the proteins needed for replication, but they can use these proteins from co-infecting full-length wt helper viruses. Several authors have studied the dynamics of DIPs and their associated helper viruses theoretically (Bangham and Kirkwood 1990; Szathmáry 1992; Kirkwood and Bangham 1994; Frank 2000). DIPs can accumulate when the multiplicity of infection (MOI) is high, for two reasons: First, at high MOI, DIPs frequently co-infect cells together with wt, and thus have access to the wt proteins that they need for replication. Second, DIPs have a smaller genome size than wt, and thus can replicate faster than wt and use up a larger share of wt protein than the wt does itself. The simplest models of this system predict that DIPs and wt will evolve toward a stable equilibrium, and that a DIP-resistant mutant can occur which will replace the DIP–wt system (Szathmáry 1992). More elaborate models that take into account the changing size of the virus population, for example under recurrent passaging, show that fluctuating levels of DIPs and wt are more realistic, and that the DIP–wt system can spontaneously go to extinction (Bangham and Kirkwood 1990; Kirkwood and Bangham 1994; Frank 2000). For example, extinction happens when a severe bottleneck removes all wt particles from the virus population. While DIPs have been identified in many viral species (reviewed in Holland 1991), persistent VSV infection of mammalian cells is the system of DIP-driven evolution that is characterized in most detail. DIPs accumulate during infection of mammalian cells at high MOI, but not at low MOI. Eventually, DIP-resistant full-length mutant genomes appear, and rise to fixation as they replace both wt and DIPs. These mutants are typically characterized by extensive rearrangement of the genomic termini (O’Hara et al. 1984). If viral replication continues at high MOI, then new DIPs arise that can use the full-length mutant genomes as helper viruses. Thus starts a second phase of interference. Such cycles of the emergence of interfering DIPs followed by the emergence of resistant full-length genomes can proceed for long periods of time (DePolo et al. 1987).

6 Discussion and Conclusions While the first 25 years of quasispecies theory were dominated by studies of static fitness landscapes, in recent years several groups have started to analyze quasispecies dynamic in time-dependent environments. It is comforting to see that by and large, the results derived for static fitness landscapes apply—

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with only minor modifications—to dynamic fitness landscapes as well. There is no need to replace quasispecies theory with some other theory just because viruses do not live in perfectly stable environments. On the contrary, the theory for periodically changing fitness landscapes, for example, is actually mathematically equivalent to that of a static fitness landscape, and can be analyzed with exactly the same tools. However, dynamic fitness landscapes do of course introduce new effects that cannot be observed in static fitness landscapes. The most dramatic such effect is probably the dynamic error threshold, discovered by Nilsson and Snoad (2000), which puts a lower bound on the minimum mutation rate at which a virus population is still capable of following the changes in the landscape. This dynamic error threshold, in combination with the classical error threshold known from static landscapes, restricts the useful range of mutation rates to a fairly narrow region, and it has been suggested that the mutation rates of several RNA viruses fall right into this region (Kamp and Bornholdt 2002; Kamp et al. 2002). A particularly useful way to describe the overall behavior of an evolving population on a dynamic fitness landscape is to say that the population acts like a low-pass filter. The slower the changes in the landscape, the more accurately the population will track them, and the smaller the time lag from the change in the landscape until the population reacts to it. The faster the changes, on the other hand, the less accurately the population will track them, and the larger the time lag until the population reacts. In the limit of very fast changes, the population will stop to track the changes completely, and simply adapt to the time-averaged fitness landscape. This review has shown that we have a fairly good understanding of quasispecies theory in time-dependent environments, at least on a broad, qualitative level. However, as always, the devil is in the detail, and there remain many open questions. Most importantly, the theoretical models tend to be based on simplified fitness landscapes, in which a single gene sequence provides full function, and any mutation to that sequence destroys or at least severely inhibits the function. However, we know that mutations can come with a broad spectrum of effects, from fully neutral to somewhat deleterious to lethal. In a changing environment, this spectrum may change over time as well, for example as a function of the virus host. One of the main goals of future work in this area must be to determine the detailed structure of realistic fitness landscapes and how exactly they change over time. To realize this goal, close collaboration between theoretical and experimental groups will be necessary. Acknowledgements AI45686.

Work at the Medical College of Ohio is funded by NIH grant

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Kamp C (2003) A quasispecies approach to viral evolution in the context of an adaptive immune system. Microbes Infect 5:1397–1405 Kamp C, Bornholdt S (2002) Co-evolution of quasispecies: B-cell mutation rates maximize viral error catastrophes. Phys Rev Lett 88:068104 Kamp C, Wilke CO, Adami C, Bornholdt S (2002) Viral evolution under the pressure of an adaptive immune system—optimal mutation rates for viral escape. Complexity 8:28–33 Kimura M (1967) On the evolutionary adjustment of spontaneous mutation rates. Genet Res 9:23–34 Kirkwood TB, Bangham CR (1994) Cycles, chaos, and evolution in virus cultures: a model of defective interfering particles. Proc Natl Acad Sci U S A 91:8685–8689 Li Y, Wilke CO (2004) Digital evolution in time-dependent fitness landscapes. Artificial Life 10:123–134 Martín Hernández MAM, Carrillo EC, Sevilla N, Domingo E (1994) Rapid cell variation can determine the establishment of a persistent viral infection. Proc Natl Acad Sci U S A 91:3705–3709 Nilsson M, Snoad N (2000) Error thresholds on dynamic fitness landscapes. Phys Rev Lett 84:191–194 Nilsson M, Snoad N (2002a) Optimal mutation rates in dynamic environments. Bull Math Biol 64:1033–1043 Nilsson M, Snoad N (2002b) Quasispecies evolution on a fitness landscape with a fluctuating peak. Phys Rev E 65:031901 Novella IS, Clarke DK, Quer J, Duarte EA, Lee CH, Weaver SC, Elena SF, Moya A, Domingo E, Holland JJ (1995) Extreme fitness differences in mammalian and insect hosts after continuous replication of vesicular stomatitis virus in sandfly cells. J Virol 69:6805–6809 Novella IS, Hershey CL, Escarmís C, Domingo E, Holland J (1999) Lack of evolutionary stasis during alternating replication of an arbovirus in insect and mammalian cells. J Mol Biol 287:459–465 Nowak M, Schuster P (1989) Error thresholds of replication in finite populations— mutation frequencies and the onset of Muller’s ratchet. J Theor Biol 137:375–395 Nowak MA, May RM (2000) Virus dynamics. Oxford University Press, Oxford O’Hara PJ, Nichol ST, Horodyski FM, Holland JJ (1984) Vesicular stomatitis virus defective interfering particles can contain extensive genomic sequence rearrangements and base substitutions. Cell 36:915–924 Orr HA (2000) The rate of adaptation in asexuals. Genetics 155:961–968 Perelson AS (2002) Modelling viral and immune system dynamics. Nature Rev Immunol 2:28–36 Perelson AS, Essunger P, Ho DD (1997) Dynamics of HIV-1 CD4+ lymphocytes in vivo. AIDS 11:S17–S24 Ronnewinkel C, Wilke CO, Martinetz T (2001) Genetic algorithms in time-dependent environments. In Kallel L, Naudts B, Rogers A (eds) Theoretical aspects of evolutionary computing. Springer, Berlin Heidelberg New York, pp 261–285 Szathmáry E (1992) Natural selection and dynamical coexistence of defective and complementing virus segments. J Theor Biol 157:383–406 Turner PE, Elena SF (2000) Cost of host radiation in an RNA virus. Genetics 156:1465– 1470

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Viruses as Quasispecies: Biological Implications E. Domingo (u) · V. Martín · C. Perales · A. Grande-Pérez · J. García-Arriaza · A. Arias Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain [email protected]

1

Introduction: How a Theory Met Reality and Vice Versa . . . . . . . . . . . . . 52

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Molecular Characterization of RNA Virus Populations . . . . . . . . . . . . . 55

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The Importance of Being Mutant Spectra . . . . . . . . . . . . . . . . . . . . . . . 58

4 4.1

Suppressive Effects of Mutant Spectra . . . . . . . . . . . . . . . . . . . . . . . . . 61 Consequences for Fitness Determinations . . . . . . . . . . . . . . . . . . . . . . . 64

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Interactions Within Mutant Spectra: A Proposal . . . . . . . . . . . . . . . . . . 65

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Additional Roles for Minorities: Memory in Viral Quasispecies . . . . . . . 69 Memory and Long-Term Evolutionary History: A Distinction . . . . . . . . . 71

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Virus Entry into Error Catastrophe: An Antiviral Strategy Derived from Quasispecies Dynamics . . . . . . . . . . 72

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Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Abstract During viral infections, the complex and dynamic distributions of variants, termed viral quasispecies, play a key role in the adaptability of viruses to changing environments and the fate of the population as a whole. Mutant spectra are continuously and avoidably generated during RNA genome replication, and they are not just a by-product of error-prone replication, devoid of biological relevance. On the contrary, current evidence indicates that mutant spectra contribute to viral pathogenesis, can modulate the expression of phenotypic traits by subpopulations of viruses, can include memory genomes that reflect the past evolutionary history of the viral lineage, and, furthermore, can participate in viral extinction through lethal mutagenesis. Also, mutant spectra are the target on which selection and random drift act to shape the long-term evolution of viruses. The biological relevance of mutant spectra is the central topic of this chapter.

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1 Introduction: How a Theory Met Reality and Vice Versa The historical overview of evolutionary virology by John Holland in the closing chapter of this volume documents the many indications of genetic and phenotypic instability of RNA viruses, and the growing suspicion over the second half of the twentieth century that something was fundamentally different between RNA genetics and DNA genetics. The first evidence that an RNA virus consisted of distributions of mutant genomes, and that the wild type existed only as a weighted average of many different sequences, was obtained by sampling genomic sequences of biological clones of bacteriophage Qβ by Charles Weissmann and colleagues in Zurich (review in Holland, this volume). Weissmann presented these experimental results to Manfred Eigen and his colleagues at a Max Planck Institute meeting in Klosters (Switzerland) in 1978. Eigen had developed the first theoretical treatment of a system that replicated with limited fidelity, so that the replication process regularly generated mutant copies of the templates (Eigen 1971). This theory, later to become known as quasispecies theory (Eigen and Schuster 1979), was developed to understand self-organization and adaptability of early life forms on earth. In seeing the experimental results on phage Qβ, Eigen exclaimed “Quasispecies in reality!” and this represented the beginning of a productive interaction between theoretical biophysics and experimental virology (further information on these early encounters is given in Domingo et al. 1995). Quasispecies theory is covered in the chapters by Biebricher and Eigen and by Wilke et al. in this volume, and here we review biological implications of quasispecies dynamics for RNA viruses. The extended definition of quasispecies currently used by virologists is the following: “Viral quasispecies are dynamic distributions of non-identical but closely related mutant and recombinant viral genomes subjected to a continuous process of genetic variation, competition and selection, and which act as a unit of selection” (Domingo 1999). This definition incorporates general principles of Darwinian evolution, whose effects, in the case of viruses, can be observed within short time periods (usually days and weeks) both in natural hosts, and in model systems such as alternative hosts in vivo or in cell cultures. Short-term evolution of viruses underlies virus adaptation to compartments within infected organisms, that may contribute to time-dependent changes of disease symptoms and to disease progression. This is particularly evident with RNA viruses, and several examples are discussed in this volume, and others are regularly reported in the current literature on virology. Some terms (such as “fitness”, “environment”, etc.) used in this and other chapters, and that have been approached mainly with viral infections in cell culture, may seem abstract and distant

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from the real world of viral diseases. Yet they are not. Fitness values express a replication capacity, and competitive growth occurs in infected hosts when variant forms of the same virus meet to penetrate a compartment of a differentiated organism, or to replicate in the same cell subset. Environment may mean specific cell types or cell assemblies in tissues and organs where virus replication takes place, or a set of physiological conditions that may affect virion stability or susceptibility of cells to viral variants. The main steps involved in middle-term and long-term RNA virus evolution have some resemblance with some of the steps that determine shortterm evolution in quasispecies dynamics (Fig. 1), despite their occurring with very different space-time scales. The triggering event in these evolutionary episodes is reproduction with genetic variation. Then positive and negative selection, together with random sampling (drift) that take place within a host, or between host individuals, shape the genetic composition of the virus. Reproductive success can be quantified as relative fitness values (QuiñonesMateu and Arts, this volume) or with epidemiological parameters such as the basic reproductive rate (or ratio) (Ro), defined as the average number of secondary cases resulting from the introduction of a single infected case into a susceptible population. Ro is a general parameter (which can be applied to demography of any type of biological entity) that can predict the long-term epidemic spread of a pathogenic agent (reviews concerning application to viral infections in Nowak and May 2000 and Woolhouse 2004). Both fitness and Ro values capture average values of fundamentally heterogeneous entities, and such averages may vary as an infection progresses or an epidemic spreads, thus adding an additional level of complexity to the interpretation of the reproductive success of a virus. While the reproductive ratio is generally used in epidemiological investigations, fitness finds its application in the comparison of the relative replication capacity of variant viruses within a single host or in cell culture. As indicated by Biebricher (1999), evolutionary success depends on two components of the phenotype: those that determine survival and those that determine the rate of production of viable progeny, and the combination of the two is what we call fitness. Virologists have adapted growth-competition experiments to measure the relative capacity of two viruses to produce infectious progeny, thus providing estimates of relative fitness values under a given set of environmental conditions (Holland et al. 1991). Fitness can be measured in other ways (DeFilippis and Villarreal 2000), and it is virtually impossible to design a measurement that captures in full the potential evolutionary success of viruses replicating in their host organisms (Biebricher 1999; DeFilippis and Villarreal 2000). Despite limitations in the measurements and significance of fitness values for RNA viruses, variations in relative fitness, based on

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a

b

Fig. 1a,b Schematic representation of viral quasispecies in an infected host. Viral genomes are represented as horizontal lines, and mutations as symbols on the lines. a Upon infection with an RNA virus (even with a single particle, as depicted here, enlarged about 106 times), viral replication leads to a mutant spectrum of related genomes, termed viral quasispecies. Nucleotide sequencing of the ensemble produces a consensus sequence which includes in each of its positions (nucleotide or amino acid) the residue found most frequently in the corresponding position of the distribution (mutant spectrum). b Different mutant distributions are found in different infected organs or at different sites of the same organ. As further discussed in the text, in real infections multiple mutant spectra that can amount to a large number of replicating (or potentially replicating) genomes (up to 109 or even 1012 per infected individual) provide highly dynamic mutant repertoire

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viral yields in cell culture, have been immensely powerful in characterizing the population dynamics of RNA viruses (see references in the reviews by Domingo and Holland 1997; Quiñones-Mateu and Arts 2002; Novella 2003; and the chapters by Quiñones-Mateu and Arts and Escarmís et al., this volume). As usually performed, fitness values incorporate differences in replication capacity (both of viruses in isolation and when co-replicating in the same cells) at all stages of the viral life cycle. Restricting fitness measurements to individual events of the replication cycle (RNA synthesis, viral-specific protein synthesis, virion assembly, etc.) would entail additional ambiguities (i.e. RNA replication may be coupled to translation, translation to assembly, etc.). Quiñones-Mateu and Arts in the next chapter of this volume discuss several important implications of fitness measurements based on viral yields. Figure 2 shows a schematic view of quasispecies dynamics in relation to population size and fitness variations. But there are additional implications of the links between quasispecies theory and virus population dynamics.

2 Molecular Characterization of RNA Virus Populations Since RNA viruses replicate as complex mutant distributions (Figs. 1 and 2), determination of the consensus nucleotide sequence (or the consensus amino acid sequence of encoded proteins) provides very fragmentary information of the genetic composition and of the evolutionary potential of a virus population. Analyses of individual genomic sequences of mutant spectra can be achieved by two alternative procedures. One is to isolate virus either from individual plaques developed on cell monolayers or from an infection following end-point dilution. Viral RNA is then subjected to reverse transcriptionpolymerase chain reaction (RT-PCR) amplification and nucleotide sequencing. This procedure leads a sequence that cannot be influenced by possible misincorporations introduced during the RT-PCR amplification (that can arise due to the limited copying fidelity of the enzymes used in the amplification). In sequence screening of biological clones, a bias may occur that favours representation of genomes that are more infectious (produce early cytopathology or larger plaques) in the particular cell line or primary culture chosen. A second means to characterize a virus population is to subject total RNA extracted from the biological specimen of interest to RT-PCR, followed by molecular cloning and sequencing of DNA of individual molecular clones. This procedure does not depend on infectivity of the viral RNA in a cell culture system. Here a bias may arise from the low fidelity of the enzymes used in the RT-PCR, which may result in an overestimate of the nucleotide sequence

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Fig. 2 Scheme of quasispecies dynamics and fitness variations. Top: Viral genomes are depicted as horizontal lines and mutations as symbols on the lines (see also Fig. 1). Small arrows indicate genetic bottlenecks experimentally realized as plaque-to-plaque transfers. Serial bottleneck events lead to accumulation of mutations in the consensus sequence. The large arrow represents replication of the quasispecies without population size limitations. Bottom: Fitness (F) variation associated with passage regime. Bottleneck events lead to a decreases in fitness; however, at low fitness values a fluctuating pattern with a constant average fitness is observed (Escarmís et al., this volume). Large population passages result in a fitness increase that may or may not result in a modification of the consensus sequence. Again, at high fitness, a fluctuating pattern of fitness values is observed, presumably reflecting a limitation exerted by the population size on the capacity for fitness increase (Novella et al. 1995, 1999). (Figure adapted from Domingo 1999, with permission)

heterogeneity. This can be solved by using high-fidelity polymerases, correct amplification reaction conditions (adequate pH and ionic composition, unbiased concentrations of chemically intact nucleoside triphosphates, etc.), and control experiments to determine a basal error rate for the system (Arias et al. 2001). Another bias may come from insufficient initial template RNA so that several sequences that originate from the same viral template RNA may be represented among the clones analyzed. This will generally result in an underestimate of the nucleotide sequence heterogeneity. Some simple calculations have been applied to derive the expected number of independent sequences (those that will represent different templates in the viral population) from the

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last dilution of the template RNA that yields positive RT-PCR amplification (Airaksinen et al. 2003). Our rule of thumb is that when a 1:100 dilution of the template yields positive RT-PCR amplification, we proceed with the cloning and sequencing of the amplification product of the undiluted template. When the amount of template is limiting, alternative procedures (such as multiple parallel amplifications prior to cloning and sequencing) must be sought (Airaksinen et al. 2003). It is a common practice to align at least 20 genomic (or subgenomic) sequences and to determine the mutation frequency and Shannon entropy (Table 1). In most situations, 20–100 sequences represent a tiny minority of Table 1 The characterization of mutant spectra of viral quasispecies 1. Mutation frequency Definition: The proportion of mutant nucleotides in a genome population. Mutant frequency may refer to the proportion of genomes harbouring a specific mutation. Calculation: Determine the total number of mutations (counting repeated mutations only once) relative to the consensus (defined by the same set of sequences) and divide by the total number of nucleotides that have been sequenced (i.e. 10,000 when the same 500 nucleotides of 20 individual genomes have been determined). In some cases, it may be interesting for statistical reasons to count all mutations found (counting repeated mutations as many times as they occur), yielding a maximum mutation frequency. 2. Shannon entropy Definition: The proportion of different genomes in a mutant distribution
Calculation: Normalized Shannon entropy, Sn = − [ i (pi × lnpi )] | ln N, in which pi is the proportion of each different sequence of the mutant spectrum, and N the total number of sequences compared. 3. Genetic distance and Hamming distance Definitions: Genetic distance is the number of mutations that distinguish any two sequences from the population. The average for all possible pairs reflects the genetic complexity. Hamming distance is the number of mutated positions in a genome with respect to the best adapted sequence (the most abundant) in a genome distribution. For quasispecies analysis, a list of Hamming distances (or the average for the population) can characterize the complexity of the ensemble. Calculation: Align sequences and divide the number of mismatched positions between any two sequences by the number of identical positions. Matrices of pair-wise genetic distances are used to determine phylogenetic trees. For distantly related sequences (rarely occurring within a quasispecies), multiple sequence alignments using CLUSTAL W and adequate scoring of gaps may be needed. Based on Eigen 1992; Volkenstein 1994; Domingo 1996; Mount 2004; Domingo et al. 2005.

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the total number of viral genomes in a biological specimen, and therefore this procedure must be regarded as a very crude sampling of a viral population. In some cases, a selective agent may permit the screening of minority components of mutant spectra. For example, in studies on quasispecies memory (described in Sect. 6) analyses of mutant spectra were based on the repertoire of mutants resistant to a monoclonal antibody that neutralized the dominant FMDV population, but did not neutralize the portion of the mutant spectra of interest (Ruiz-Jarabo et al. 2002, 2003b). Despite these limitations, determination of nucleotide sequence heterogeneities in virus populations using correct reagents and adequate controls has consistently documented that most RNA viruses (and also some DNA viruses) consist of complex mutant spectra, with an average number of 1–100 mutations per genome (Sect. 3). The degree of heterogeneity within a viral population will be influenced by the fidelity of the replication machinery, the distance (in rounds of replication) from a clonal origin, constraints for variation (both at the RNA and protein levels), and intervening bottlenecks in the evolutionary history, among other influences. Scientists should be cautious in attributing to high-fidelity RNA replication what may be a standard error level together with negative selection, which maintains an invariant consensus nucleotide sequence. The literature contains several cases of such likely misinterpretation of data. It is not surprising that comparisons of different virus–host systems have led to exceedingly broad ranges of genetic diversity within mutant spectra. This should not blur the common underlying influences and the general biological relevance of population complexity even if seemingly modest in terms of mutations per individual genome. This is justified in the next section.

3 The Importance of Being Mutant Spectra The relevance of viruses replicating as mutant spectra is intuitively obvious since any individual mutant genome of the ensemble can potentially differ in behaviour from other individuals or from the ensemble of genomes. The importance of such a population structure is strengthened by considering five parameters that characterize a mutant distribution: average number of mutations per genome, virus population size, genome length, mutations needed for a phenotypic change, and virus fecundity (Table 2). Despite all cellular organisms being highly polymorphic genetically (in that distinct alleles from a gene are represented among individuals of one biological species), the level of heterogeneity of RNA virus populations confers a much greater adaptability

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than the levels of polymorphism estimated for cells. This is a consequence of parameters 2 and 3 (listed in Table 2): as an example, a viral genome of 10,000 nucleotides has a total of 3 × 104 possible single mutants (disregarding fitness effects of mutations), which is a figure well below the population size of many natural virus populations. In contrast, the total number of possible single mutants for a mammalian genome is about 1010 , well above the population size of mammalian species. That is, the capacity to explore sequence space (a concept discussed in the chapter by Biebricher and Eigen in this volume, which refers to the total number of possible sequences available to a genetic system, reviewed by Eigen and Biebricher 1988) is far greater for viruses than for cellular organisms. This confers adaptability to viruses (with amply recognized biological implications) and renders viruses suitable experimental systems for probing evolutionary concepts (see Domingo et al. 2001; Flint et al. 2004, and other chapters of this volume). One of the critical parameters in viral quasispecies is the number of mutations in an RNA virus that is needed for a phenotypic change in the virus (Table 2). Indeed, if a relevant phenotypic change (for example, a modification in host cell tropism, resistance to neutralizing antibodies or to an antiviral agent, etc.) depended on the occurrence of 50–100 mutations in a viral genome (to invent a simple example), then the quasispecies nature of RNA viruses (with the characteristic parameters we measure today; Table 2) would

Table 2 Some important parameters that influence the adaptability of viral quasispecies 1. Average number of mutations per genome in a mutant spectrum Generally it amounts to an average of 1–100 mutations per genome. (See text for reasons for broad range). 2. Virus population size Variable, but very high upper limits. An acutely infected organism may include 109 –1012 viral particles at any given time. Even a single viral plaque on a cell’s monolayer can yield 103 –1010 particles. 3. Genome length 3 kb–32 kb 4. Mutations needed for a phenotypic change Many recorded adaptive changes depend on one or a few mutations (see text). 5. Fecundity Variable. Average of < 1–106 particles per cell have been measured. High fecundity promotes quasispecies dynamics, as discussed in several chapters of this volume. Based on Domingo et al. 2001 and references therein.

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be largely irrelevant for short-term adaptation. This is because in the exploration of sequence space the probability of arriving at a point corresponding to a mutant with the required 50–100 mutations is exceedingly low (10–200 to 10–400 !). Even if we relax the requirement to multiple combinations of mutations, it can be estimated from the Poisson distribution that the probability of genomes with any 50 mutations in a population of 1011 individuals is about 10–44 genomes (on a purely statistical basis, ignoring fitness effects; additional representative figures can be found in Domingo 1997). As evidenced by many experimental results with RNA viruses of virtually all families that have been examined, one or a few amino acid replacements are sufficient to modify a biologically relevant feature of a virus, as amply recorded in the literature (review in Domingo et al. 2001; examples of single mutations associated with epidemiologically relevant adaptations of arboviruses are given in the chapter by Weaver in this volume). Because phenotypically relevant mutations can be frequently represented in mutant spectra, they have the potential to become dominant (command the development of new mutant distributions) in response to environmental demands, despite the modulating effects of mutant spectra described in Sect. 4. Multiple constraints for variability have been evidenced in viral genomes (Simmonds and Smith 1999; Simmonds et al. 2004). Therefore, many variants with one or few mutations that may confer biologically relevant, adaptive traits may not be represented in a mutant spectrum (under a set of environmental conditions) due to fitness costs. It is impressive that despite such constraints (for example, genome-scale, ordered RNA structures in hepaciviruses; Simmonds et al. 2004), the level of heterogeneity and adaptive capacity of viral populations are the ones we record with unfailing continuity with any virus we examine in some detail. A highly significant example is the occurrence of mutants with one or a few amino acid substitutions that confer decreased sensitivity to antiviral inhibitors. This is a general phenomenon – documented with many viruses since the 1960s both in vivo and in cell culture – which complicates enormously the treatment of viral disease (a recent overview can be found in Domingo 2003; see also previous versions published in Progress in Drug Research for a historical account). It is not the case that “suddenly” a virus strain “appears” that is resistant to an inhibitor. Resistant mutants are generated as components of mutant spectra and then may become dominant when virus replication occurs in the presence of the inhibitor. Perhaps the most dramatic example has been the selection over the years of mutants of human immunodeficiency virus type 1 (HIV-1) resistant (or with decreased sensitivity) to the antiretroviral inhibitors (targeted mainly to reverse transcriptase, protease or surface structures involved in virus fusion or cell recognition) used in clinical practice (see the chapter by Mullins and Jensen, this volume). Both experi-

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mental analyses of HIV-1 populations (Coffin 1995; Nájera et al. 1995) and theoretical predictions (Ribeiro et al. 1998; Gerrish and Garcia-Lerma 2003) suggest that such mutants may preexist in HIV-1 populations, even when they have not been exposed to the inhibitors. In addition, resistant mutants that may occupy very low frequency levels in HIV-1 mutant spectra in the absence of the inhibitor may be raised to higher frequency levels in the presence of the inhibitor. The recently described phenomenon of memory in viral quasispecies, including memory subpopulations of HIV-1 in vivo (Sect. 6), supports even further a role of genome subpopulations in the response of viruses to inhibitors (Domingo et al. 2003). The fitness costs of inhibitor-resistance mutations, as well as relative fitness values of wild-type and resistant mutants in the absence and the presence of the inhibitor will influence the kinetics and degree of dominance of inhibitor-resistant mutants (treated in the chapter by Quiñones-Mateu and Arts, this volume). Paradoxically, the presence of a mutant with a specific phenotypic trait may not be sufficient to guarantee its dominance even when a selective constraint favours that phenotypic trait. The reason for this important phenomenon again has to do with the presence of a mutant spectrum surrounding the relevant mutant, and this is discussed next.

4 Suppressive Effects of Mutant Spectra A mutant virus potentially capable of becoming dominant in an evolving viral quasispecies (either because of its high fitness or because it harbours a selectable trait) may remain as a minority in the population, depending on the nature of the mutant spectrum in which it is immersed. This concept was first documented with a numerical example of two master sequences of small size differing in fitness by 10%, replicating near the error threshold (see the chapters by Biebricher and Eigen and by Wilke et al., this volume, and Sect. 7 in this chapter). Interestingly, the inferior mutant outgrew the fitter one when the inferior mutant was surrounded by 50 closely related mutants that were somewhat better adapted than the mutants that surrounded the fitter master. This and other numerical simulations (Swetina and Schuster 1982; review in Eigen and Biebricher 1988) suggest a strong influence of the mutant spectrum on the behaviour of any particular variant and supports the view that the target of selection is not a single species, but rather the distribution of the quasispecies as a whole (Eigen and Biebricher 1988; Perales et al. 2005). The prediction that the mutant spectrum can affect the dominance of an individual mutant has found experimental confirmations both in vivo and in

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cell culture (Table 3). The first evidence was obtained using vesicular stomatitis virus (VSV) (de la Torre and Holland 1990). A mutant spectrum of VSV suppressed replication of a mutant of superior fitness than the ensemble, unless the mutant was present above a certain frequency in the population. In another instance of remarkable practical relevance, it was found that in anti-poliomyelitis vaccine preparations, the dominant attenuated poliovirus (PV) suppressed neurological disease associated with minority virulent PV, unless the latter was present above a minimal concentration in the vaccine (Chumakov et al. 1991). Some strains of the arenavirus lymphocytic choriomeningitis virus (LCMV) (see the chapter by Sevilla and de la Torre, this volume) induce a hormone-deficiency syndrome in mice (reviewed by Oldstone 2002). Remarkably, some co-infecting nonpathogenic strains of LCMV suppressed expression of pathogenic strains in such a way that no disease was manifested (Teng et al. 1996). Studies with foot-and-mouth disease virus (FMDV) have provided two additional examples (recent reviews on FMDV in Rowlands 2003; Sobrino and Domingo 2004; Mahy 2005). FMDV serially passaged in BHK-21 cells in the presence of polyclonal antibodies directed to a specific antigenic site of the virus, generated a complex mutant spectrum of antigenic variants of low relative fitness. Such mutant spectra included biological clones that manifested high fitness when separated from the mutant cloud (Borrego et al. 1993). In the transition to error catastrophe of FMDV (Sect. 7), pre-extinction viral RNA (which is defined as viral RNA extracted from the population that, in the serial passages in the presence of mutagens, precedes the one in which virus extinction occurs) interferes with infectious RNA (González-López et al. 2004). Interference was documented by co-electroporation of cells with two RNAs and it was exerted by pre-extinction RNA but was not exerted by a defective FMDV RNA (containing an in-frame deletion), unrelated viral and nonviral RNAs, or the same pre-extinction RNA reduced in size by chemical treatment. It was proposed that due to mutations in pre-extinction viral RNA, abnormal expression patterns and expression of abnormal viral proteins may jeopardize the replication capacity of coexisting infectious RNA (González-López et al. 2004). In support of this proposal, FMDV 3Ds (polymerases) harbouring deleterious mutations have been identified in mutagenized populations (Sierra et al. 2000; Arias et al. 2005; see also Sect. 5 for additional information on possible mechanisms of suppressive effects of mutant spectra). An alternative model, termed positive clonal interference, taken from bacterial population genetics, was proposed to explain dominance of one VSV clone over another (Miralles et al. 1999). In this model, the basis for the interference is competition among genomes carrying advantageous mutations during replication as large populations. In addition to a technical problem

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derived from the likely presence of defective interfering (DI) particles in the co-infections carried out to test the model (see Domingo 2003 for a more detailed discussion of this problem), recent results with FMDV show that suppression does not require comparable fitness of the two competing populations (González-López et al. 2005), in agreement with a suppressive effect exerted by a low fitness mutant distribution on genomes displaying higher fitness (Sect. 5). The suppression of individual genomes by mutant spectra (Table 3) confers additional biological relevance to population bottleneck events that may intervene during virus replication and evolution. In addition to effects on virus population dynamics (see the chapter by Escarmís et al., this volume), and in the relationship between virulence and transmission mode (Bergstrom et al. 1999), bottlenecks may release a portion of mutant spectrum from the complete cloud that may modulate the behaviour of minority genomes (Fig. 3). Also, some contradictory results seen in the literature on the frequency of deleterious mutations or epistatic effects of mutations in RNA viruses may in part derive from the fact that it is not possible to “freeze” a mutant to remain as the “same” individual in the course of replication: a cloud forms immediately and unavoidably. Thus, what initially was “a” genome with one or two mutations of interest will soon become a “distribution” of genomes

Table 3 Modulating effects of mutant spectra on individual virus variants Cell culture Mutant spectrum of vesicular stomatitis virus (VSV) suppresses a VSV variant of superior fitness (de la Torre and Holland 1990). Low fitness antibody-escape population of foot-and-mouth disease virus (FMDV) suppresses individual antigenic variants displaying high fitness (Borrego et al. 1993). Pre-extinction FMDV RNA interferes with infectious FMDV RNA (González-López et al. 2004). Defective lymphocytic choriomeningitis virus (LCMV) contribute to suppression of infectivity and extinction of the virus (Grande-Pérez et al. 2005). In vivo Attenuated poliovirus (PV) can suppress neurological disease in monkeys, associated with virulent PV (Chumakov et al. 1991). Nonpathogenic LCMV can suppress manifestation of the growth hormone-deficiency syndrome in mice, associated with pathogenic LCMV strains (Teng et al. 1996).

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Fig. 3 Scheme of the effect of a population bottleneck on the replication of an individual viral genome. Lines represent a complex mutant spectrum in which individual mutations have not been indicated. An individual genome characterized by two specific mutations (dot and rectangle) is surrounded by a mutant spectrum, which suppresses its replication by the biochemical mechanisms discussed in the text (see also Fig. 4). A population bottleneck represented by the shaded rectangle relieves the individual genome of part of the suppressing ensemble, thereby facilitating expression of the individual genome, which may increase its frequency (right). Experimental evidence and references are given in the text

with additional mutations that may affect the behaviour of the genomes harbouring the initial mutations. Careful monitoring of progeny sequences and mutant spectrum complexities is needed. It is tempting to suggest that some of the uncertainties that appear to characterize virus evolution in vivo (for example, that an antibody-, CTL- or inhibitor-resistant variants may raise to dominance or not) may relate to suppressive effects of mutant spectra perturbed by intra-host bottleneck events of different intensities, prompted by the replicative characteristics of the virus in its permanent interaction with of the host. As further penetration into the fine population structure of viruses in vivo becomes technically feasible, it may be possible to elucidate some of these issues. 4.1 Consequences for Fitness Determinations In the course of fitness determinations [usually by growth competition between the virus to be tested and a reference virus in a defined biological

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environment (see the chapter by Quiñones-Mateu and Arts, this volume)] suppressive effects of mutant spectra may also influence the outcome of the competition. Indeed, as discussed in Sect. 1, the mutation rates during RNA genome replication and retrotranscription render extremely unlikely the sustained synthesis of genomes without mutations (Drake and Holland 1999) (although, interestingly, evolution appears to have adjusted mutation rates to permit the presence and survival of the nonmutated class when the latter are fitter than mutant progeny). Thus, despite the fact that during fitness determination a competition is established that should result in dominance of the most fit mutant distributions, modulating effects of mutant spectra of the type discussed in the previous section cannot be excluded, and they have been recently evidenced in the extreme case of fitness determinations of pre-extinction FMDV populations (Gonzalez-Lopez et al. 2005). The converse effect, that is, complementation among mutants (Moreno et al. 1997; Agol 2002), can also occur at the stage of fitness determination in which the two competing ensembles co-infect the same cells, and this effect has been modeled (Wilke et al. 2004) (see the chapter by Wilke et al., this volume). Taking relative virus yield in a given environment as a measure of fitness is justified because first, no virus genome ever exists as a “single nucleotide sequence”; and second, when it exists (as in the very beginning of a formation of a plaque on a cell monolayer) it rapidly becomes a mutant spectrum in the first infected cell. Thus, initial RNA replication rates are fictitious for these reasons and others summarized in Sect. 1, supporting the adequacy of fitness values given as relative viral yields (see Sect. 1 and the chapter by Quiñones-Mateu and Arts, this volume).

5 Interactions Within Mutant Spectra: A Proposal It is a rooted tradition, not only among virologists but also in some schools of population genetics, to view the individual as the standard unit of selection, although this is a debated subject (as a general discussion of this topic see Lewontin 1970; Williams 1992). In this section, we argue that since RNA viruses consist of mutant spectra, and components of the spectra may (and often do) deviate genotypically and phenotypically from other components of the same spectrum, the behaviour of an individual may (and often will) be influenced by surrounding individuals that we call “the ensemble” (extreme cases of suppression of individual variants are discussed in Sect. 4). We further suggest that this feature of viral quasispecies is consistent with many observations made in classical viral genetics.

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Viral interference provides a useful introduction to our argument. Interference referred initially to the inhibition of multiplication exerted by one virus on an entirely different virus entering the same cell (see reviews in White and Fenner 1986; Youngner and Whitaker-Dowling 1999; Condit 2001). This led to the distinction between homologous and heterologous interference, depending on the relatedness of the two viruses under analysis. Interference acquired its major impetus with the discovery of defective, noninfectious genomes (generally deletion mutants) that interfered with the corresponding standard virus and mediated the establishment of persistent infections of some RNA viruses (see reviews in Huang and Baltimore 1970; Perrault 1981; Holland 1990). The interfering genome, whether infectious or noninfectious, often has a higher affinity than the standard (interfered) virus for a viral or host protein (or other host component). Here a conceptual overlap with the concept of fitness exists (García-Arriaza et al. 2005). Most fitness measurements involve a phase of intracellular competition between the two viruses to be tested. This is because even in competitions started at low MOI there will be a second round of cell infection that, depending on the initial yield, may involve high MOI. This point has been discussed by (Novella 2004) regarding complementation, and it applies to fitness assays as well. When intracellular competition occurs, the difference in progeny production may have the same molecular basis as interference. This has been manifested in recent studies that have shown a fitness-dependent interference by a bipartite version of the FMDV genome (García-Arriaza et al. 2004, 2005). We arrive at the dilemma that either fitness values should be determined avoiding any type of intracellular competition, or it must be accepted that fitness differences (generally measured for mutants of the same virus) are influenced by intracellular competition events akin to those underlying viral interference. Intracellular competition could be avoided either by each virus infecting cells on separate culture dishes or animal hosts, or by restricting replication to the initial round of progeny production following infection at low MOI; both requirements pose technical problems. Interference may be mediated by interferon, which evokes a general antiviral state in a cell, as well as by a number of interactions between the interfering and interfered viruses (White and Fenner 1986; Youngner and Whitaker-Dowling 1999; Condit 2001; Agol 2002). Concerning possible interference between mutants of the same virus (the form that may be most relevant to quasispecies behaviour), interference may be associated with multiple, different mutants which, even if present individually at low frequency, may have collectively the effect of a dominant-negative mutant. The latter may act through different mechanisms: for example, in the “rotten apple” hypoth-

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esis, a defective polypeptide produced by the mutant may enter a multimeric complex, inhibiting its activity. In the “road-block” hypothesis, a defective function (protein or regulatory region) may sequester a factor (or a site) required for replication of both viruses. In the “all things in moderation” hypothesis, imbalances in gene dosage may lead to decreases in efficiency of steps in the life cycle of both viruses. In the “direct competition” hypothesis, both viruses compete for some viral or host factor. According to the “attractive genome” hypothesis, the dominant-negative virus may possess sites that bind required factors, and such sites may be more abundant or show higher affinity than the sites in the wild-type virus. These different hypotheses have been taken from the summary by Youngner and Whitaker-Dowling (1999), and we propose that at least some of these mechanisms may underlie modulating effects observed within mutant spectra of viral quasispecies (Fig. 4). This was proposed to explain the interfering activity of pre-extinction FMDV RNA (González-López et al. 2004) and it emphasizes increasing evidence of the critical role that defective genomes may have in dictating virus behaviour (Grande-Pérez, et al., 2005; see also the chapter by Escarmís et al., this volume). Within mutant spectra, multiple potentially dominant-negative mutants (when present at high frequency) may have a synergistic effect in preventing standard virus replication by one or several of the proposed mechanisms (Youngner and Whitaker-Dowling 1999). These negative effects on replication of individual mutants may be favoured by the multifunctional nature of viral proteins (both in mature and precursor forms), increasingly recognized for many viruses (Cornell et al. 2004; Flint et al. 2004). Parallel concepts can be applied both to modulating effects of mutant spectra (Table 4) and to the transition to error catastrophe (González-López et al. 2004; Grande-Pérez et al., in press; see also Sect. 7). Mutants with complementing or interfering capacity have been referred to as cooperators or defectors, respectively, during processes of competition inside a host (Turner and Chao 1998; Novella 2004; Novella et al. 2004). What we know of quasispecies’ capacity to attain different fitness levels suggests that the same altered viral protein may be advantageous or disadvantageous depending on the dominant genomes in the mutant distribution (Fig. 4). Thus, our proposal is that a mutant spectrum of a replicating viral quasispecies constitutes a “genetic microcosmos” in which interactions among components of a mutant spectrum may include effects similar to those previously characterized between well defined mutants of a virus or between two different viruses. What is observed to occur “between populations” is now extended to interactions among “components of a population”. Experiments are now in progress to further test the validity of this proposal.

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Fig. 4a–c Scheme of the effect of a trans-acting viral gene product on the replicative efficacy of a viral quasispecies. A Three forms of the same viral gene (1, 2, 3; shaded box in genome; symbols represent mutations) encode a protein with decreasing biological activity (white to dark grey) that acts as a homopolymeric hexamer (needed to produce viral progeny). B In a mutant spectrum in which 1 and 2 are expressed, increasing proportions of gene 2 will result in a decrease in activity of the hexameric protein, and, consequently of viral yield. C In a mutant spectrum in which 2 and 3 are expressed, increasing proportions of gene 3 will result in a decrease in activity of the hexameric protein, and, consequently of viral yield. Note that the same viral subunit can act as a negative modulator (B) or a positive modulator (C) depending on the activity of the accompanying subunit. In real viral quasispecies, modulating effects are far more complex than illustrated here because a range of different activities in trans-acting products may be present, several viral and host gene products may participate in heteropolymeric complexes, viral proteins are often multifunctional, and regulatory signals on the viral RNA may be involved

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6 Additional Roles for Minorities: Memory in Viral Quasispecies Viral quasispecies may possess in their mutant spectra minority genomes that reflect those that were dominant at an earlier phase of quasispecies evolution. This has been shown experimentally with FMDV in cell culture (Ruiz-Jarabo et al. 2000, 2002; Arias et al. 2001, 2004; Baranowski et al. 2003) and HIV-1 in vivo (Briones et al. 2003; Briones et al., personal communication). The experiments with FMDV took advantage of two well-studied classes of mutants: an antigenic variant selected with a neutralizing mAb and a very unusual variant that contained in its genome an internal polyadenylate tract, generated upon repeated plaque-to-plaque passage of FMDV clones (Escarmís et al. 1996; see also the chapter by Escarmís et al., this volume). When populations dominated by either one of these mutants were serially passaged in cell culture, revertant viruses with the wild-type sequence (that is, without the amino acid substitution that led to antigenic variation and absence of the internal polyadenylate tract, respectively) became dominant. However, genomes with the initially dominant markers did remain in the mutant spectrum not at a basal level accounted solely by mutational pressure, but at levels 10- to 100fold higher. A scheme of implementation of memory is shown in Fig. 5 and the main features of quasispecies memory are summarized in Table 4. Two types of memory were distinguished during HIV-1 infections in vivo. One was the “replicative” memory as defined with FMDV, and the other was a “cellular” or “anatomical” memory derived from the retroviral life cycle with Table 4 Main features of quasispecies memory Replicative memory Memory genomes derive from genomes that were dominant at an early phase in the evolution of the same viral population. Memory genomes are present at higher frequencies (in the cases studied 10 to 100 times) than expected from mere mutational pressure exerted on parental genomes. Memory genomes are lost upon subjecting virus to a population bottleneck. Memory genomes increase in fitness in parallel with the dominant genomes, as replication proceeds (Red Queen hypothesis). Memory genomes may decrease in frequency as virus replicates. Cellular or anatomical memory Found when viral reservoirs (displaying no replication or limited replication) are activated and contribute to the composition of an evolving quasispecies. Based on Ruiz-Jarabo et al. 2000, 2002, 2003b; Briones et al. 2003; Arias et al. 2004.

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Fig. 5 Schematic representation of implementation, maintenance and loss of memory genomes in a viral quasispecies. Genomes of the mutant spectra are depicted as horizontal lines, and mutations as symbols on the lines. The mutant spectra have been divided in three levels: level 1 includes those genomes found most frequently in the mutant distribution; level 2 includes a frequency class potentially occupied by memory genomes; and level 3 gathers the less abundant genomes that occur as a result of mutational pressure and the competitive rating of all components of the mutant spectrum. Initially genomes with the genetic marker A belong to level 3. When genomes with marker A are selected, they occupy the entire quasispecies. Upon further replication, when genomes with A display a selective disadvantage with respect to genomes lacking A, genomes with the A marker will eventually become undetectable in the consensus sequence of the quasispecies. However, genomes with marker A may remain as memory genomes in level 2 because in the course of selection their fitness increased (upper right mutant distribution). When bottleneck passages (of sufficient intensity to exclude level 2 genomes) intervene, memory is lost and genomes with A occupy again level 3 as in the initial mutant distribution (bottom right mutant distribution). In real viral quasispecies, mutants are ranked to form many frequency levels (perhaps a continuum of frequency levels) rather than 3, as assumed here for simplification. Experimental evidence and references on implementation, maintenance and decay of quasispecies memory in cell culture and in vivo are given in the text and in Table 4. (Based on Domingo 2000, with permission)

integration of proviral DNA in cellular DNA, which results in reservoirs that may re-emerge at a later stage of infection (Briones et al. 2003; Briones et al., personal communication). Reemergence (or raise to dominance) of ancestral

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(minority) genomes in vivo has been documented with several viral infections (Borrow et al. 1997; Wyatt et al. 1998; Karlsson et al. 1999; Briones et al. 2000; Lau et al. 2000; García-Lerma et al. 2001; Imamichi et al. 2001; Kijak et al. 2002; Charpentier et al. 2004), providing additional support for the concept of memory in viruses both in cell culture and in vivo. The experiments to investigate the possible presence of memory in viral quasispecies (Ruiz-Jarabo et al. 2000) were motivated by features of virus evolution that fit those of complex adaptive systems (see reviews in Gell-Mann 1994; Frank 1996; Solé and Goodwin 2000). A characteristic of such systems is that they vary, yet they have continuity in the form of some built-in (heritable) component, and are endowed with mechanisms to respond to external stimuli. There is a continuous interplay between the components of the system and the interacting environment. Examples of complex adaptive systems are neurological activity in memory and learning, and the immune system of vertebrates in which long-lived memory T cells may be expanded when the organism is again exposed to an antigen identical (or related) to the one that triggered the initial response. Such potential to act quickly in response to an environmental demand has a functional parallel in the presence of memory genomes in viral quasispecies. Indeed, memory in viral quasispecies may serve the adaptive scheme of viruses by providing a molecular reservoir capable of reacting swiftly to a selective constraint which has been previously experienced by the same population, provided no bottlenecks intervened (Table 4). All evidence suggests that immune or other internal physiological responses in hosts are neither uniform nor continuous, and that memory may help confronting discontinuous or fluctuating challenges that require dominance of related viral genome subsets. Minority genomes present at higher than basal levels, associated with mutational pressure, need not be related only to memory. They may occur as a consequence of the fitness values of randomly generated variants that, although not sufficient to drive the genome subset to dominance, may allow the subset to remain at levels similar to those associated with memory. All these possibilities provide an incentive to develop methods to diagnose the presence of minority genomes in viral populations in vivo, for example for a more personalized and adequate antiviral treatment plan (i.e. to avoid antiretroviral inhibitors for which mutations that contribute to resistance are present at memory levels) (reviewed in Domingo et al. 2003). 6.1 Memory and Long-Term Evolutionary History: A Distinction There are two concepts related to memory in viruses that should be distinguished. One is a form of long-term memory, not erased by population

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bottlenecks, that has gradually shaped the genetic structure and replication strategy of viruses. A present-day virus can be viewed as a “reservoir of memories” of all influences that have conformed its present organization and biological potential, in constant co-evolution with hosts that themselves are an “outcome of history”. In contrast, the quasispecies memory discussed in previous paragraphs is an immediate, short-term consequence of the recent evolutionary history of a virus, independently of the historical events that gradually shaped its current configuration.

7 Virus Entry into Error Catastrophe: An Antiviral Strategy Derived from Quasispecies Dynamics Theoretical studies summarized in the chapters by Biebricher and Eigen and by Wilke et al., this volume, led to the very significant prediction that there must be a limit to the error rate that a replicating system can tolerate if the genetic information is to be maintained (Swetina and Schuster 1982; Eigen and Biebricher 1988; Nowak and Schuster 1989; Eigen 2002). This limit is termed the error threshold and its value depends on the complexity of the genetic information conveyed by the system. Here the term “complexity” means the information encoded by a genome (regulatory regions and number of different open-reading frames), which must be distinguished from the number of different sequences for any regulatory region or open-reading frame represented in a population of genomes; the latter is often referred to as complexity of the mutant spectrum or quasispecies complexity. For RNA viruses, which generally encode little or no redundant information, complexity (in its first meaning) can be equated with genome length. It may be expected that the most complex RNA viruses (such as the coronaviruses, with 27–32 kb genomes) may display higher average copying fidelities than the least complex RNA genomes. In this respect, it will be interesting to establish whether a domain found in the SARS and other coronavirus genomes, which corresponds to a nuclease activity, can provide a proofreading–repair function during coronavirus replication (Snijder et al. 2003). The interactions among components of mutant spectra (Sect. 5) also provide a biochemical basis for impairing viral replication to the point of leading to the information meltdown typical of error catastrophe. Interestingly, the proposed negative interactions (for example, among trans-acting products as discussed in Sect. 5 and depicted schematically in Fig. 4) also favour a more likely replicative collapse when the number of open-reading frames is high (higher complexity in the sense of number of genes encoding proteins). Un-

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der mutational pressure, more gene products can be deficient and contribute to replicative incompetence. This is a biochemical counterpart of the genetic basis of error catastrophe. Measurements of LCMV RNA and infectivity levels during enhanced mutagenesis of steady-state persistent infections of the virus in cell culture have defined two viral extinction pathways. One of them occurs at low mutagenic activities, and it is mediated by defective genomes which drive the infective genomes towards extinction; both experimental results and a computational model support the lethal defection model of virus extinction, with a prominent role of the mutant spectrum (Grande-Pérez et al. 2005). There is considerable experimental evidence that RNA viruses replicate with an error rate which is close to the error threshold for maintenance of genetic information. In several viral systems, it has been documented that an increase in mutation rate by added chemical mutagens results in decreases in infectivity, and in some cases it may lead to virus extinction (Holland et al. 1990; Lee et al. 1997; Loeb et al. 1999; Crotty et al. 2000, 2001; Loeb and Mullins 2000; Sierra et al. 2000; Lanford et al. 2001; Pariente et al. 2001; Contreras et al. 2002; Grande-Pérez et al. 2002; Airaksinen et al. 2003; Ruiz-Jarabo et al. 2003a; Severson et al. 2003; reviews in Eigen 2002; Anderson et al. 2004; Domingo 2005b). Two lines of study are particularly promising regarding a possible clinical application of error catastrophe. One concerns the mechanism of antiviral activity of the nucleoside analogue ribavirin (1-β-D-ribofuranosyl-1, 2, 3-triazole-3-carboxamide; Rib), a licensed drug extensively used in clinical practice, which shows antiviral activity against a number of DNA and RNA viruses (Snell 2001; see also Airaksinen et al. 2003 and references therein). Rib is phosphorylated by cellular enzymes to its mono-, di- and triphosphate forms and it exerts its antiviral activity through different mechanisms. One of the mechanisms unveiled by Crotty and colleagues, working with poliovirus, is enhanced mutagenesis as a result of incorporation of ribavirin monophosphate (Rib MP) into poliovirus RNA by the viral polymerase (Crotty et al. 2000, 2001; Graci and Cameron 2002). It has also been shown that Rib MP can be incorporated by the hepatitis C virus (HCV) and coxsackievirus B3 polymerase (Maag et al. 2001; Vo et al. 2003; Freistadt et al. 2004). This raises the intriguing possibility that benefits of Rib (alone but mainly in combination with interferon α or its derivatives) for treatment of chronic HCV infections (McHutchison et al. 1998, among many other studies) may be partly due to its mutagenic activity (see also the chapter by Pawtlosky, this volume, for alternative mechanisms of Rib action on HCV infection). If this were established (by many ongoing studies), it would mean that the principle of virus entry into error catastrophe as an antiviral activity would already have been successfully documented without experts being aware of the underlying mechanisms.

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A second line of promising research was launched by de la Torre and his collaborators by showing that the base analogue 5-fluorouracil (FU) prevented the establishment of a persistent lymphocytic choriomeningitis infection in mice (Ruiz-Jarabo et al. 2003a). FU is mutagenic for a number of RNA viruses (Pringle 1970; Eastman and Blair 1985; Sierra et al. 2000 and references therein) and is used to treat some types of human cancer (Parker and Cheng 1990). The demonstration that it can be a potent antiviral agent in vivo constitutes a proof-of-principle that is extremely encouraging for the prospect of a clinical application of virus entry into error catastrophe (for recent reviews of error catastrophe as an antiviral strategy, see Anderson et al. 2004 and Domingo 2005b). This recent field of research illustrates how decisive is to cultivate basic research in its many facets to arrive at potential practical applications, often in unpredictable ways.

8 Concluding Remarks The studies summarized in this and other chapters of this book emphasize two growing concepts in the studies of infectious diseases that apply not only to viral infections, but also to bacterial, fungal and parasitic infections, as well as to cancer cells. One is the increasing experimental evidence in support of the remarkable statement by Theodosius Dobzhansky, written three decades ago, that “Nothing in biology makes sense except in the light of evolution” (Dobzhansky 1973). Dealing with evolutionary concepts is not rooted in the tradition of many schools of biochemistry and molecular biology. However, recent developments point to a decisive contribution of evolutionary events as part of the interactions between hosts and infectious agents. Justification of this statement includes the recognition of pathogen variation and adaptability in disease emergence and reemergence (Smolinski et al. 2003), as well as in pathogenesis (this volume and many references included in it). Evolutionary events underlie selection of viruses resistant to antiviral agents, and of bacteria resistant to antibiotics (for example, Welsh 2003). The list could go on for any pathogen whose replication cycles last orders of magnitude less than those of the hosts they infect (see the overview of molecular mechanisms of pathogen adaptation in Domingo 2005a). A concept that originated in physics and that is gradually penetrating many fields of science (including biology and infectious diseases) is complexity and emergence (Cowan et al. 1994; Solé and Goodwin 2000). What this concept entails is that certain phenomena whose occurrence depends on interactions among many individual influences cannot be accounted for by the sum of

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the individual contributions. Disease emergence offers an example: although we know (or strongly suspect with a reasonable basis) that human mobility, demographic changes and urbanization favour human-to-human pathogen transmission, that many agents are potentially zoonotic and that in many areas of the world there is close contact between humans and animals, it was not possible to predict the AIDS epidemic. At most, it can be predicted that because of (at least) thirteen different factors (Smolinski et al. 2003) (each one including, in turn, multiple sub-factors!) it is very likely that in the coming years new infectious diseases will emerge. Can studies on complexity be of use to infectious diseases? Probably, but the way such help can be translated into practical results is hardly graspable at the moment. This is not so, however, with regard to considering evolutionary concepts in designing antiviral strategies, since important successes (for example, highly active antiretroviral treatments to control HIV infections, or the prospects of lethal mutagenesis as an antiviral strategy) have been a consequence of understanding and trying to counter the evolutionary potential of pathogens. The need of a holistic view of biology with a focus on evolution, emergence and complexity has been emphasized by biologists working in areas other than infectious diseases, such as general evolution and developmental biology (Woese 2004). A point has been reached that encourages a transdisciplinary approach to problems of biological complexity, including viral quasispecies. The challenge is to obtain detailed molecular information provided by current biochemistry and molecular biology and to integrate it with the concepts of complexity and evolution, with the aim of providing a more complete picture of the problems we face. Acknowledgements Work was supported by grants BMC 2001–1823-C02–01, CAM (08.2/0046/2000, 08.2/0015/2001.1) GR/SAL/0172/2004 to Genetrix, S.L., EU, and Fundación Ramón Areces. AG-P was supported by a postdoctoral contract from CAM and a contract Ramón y Cajal from MCyT, JG-A was supported by a predoctoral fellowship from MEyC, and AA by a predoctoral fellowship from CAM and an I3P contract from CSIC.

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CTMI (2006) 299:83–140 c Springer-Verlag Berlin Heidelberg 2006


Virus Fitness: Concept, Quantification, and Application to HIV Population Dynamics M. E. Quiñones-Mateu1 (u) · E. J. Arts2 1 Department of Molecular Genetics, Section Virology, Lerner Research Institute,

Cleveland Clinic Foundation, 9500 Euclid Avenue/NN10, Cleveland, OH 44195, USA [email protected] 2 Division of Infectious Diseases, Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA

1 1.1 1.2

Concepts in Viral Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Lessons from Other RNA Viruses and Application to the HIV Model . . . . 85

2 2.1 2.1.1 2.1.2 2.1.3

Quantification of Viral Fitness . . . . . . . Methods Used to Quantify HIV Fitness . Viral Growth Kinetics . . . . . . . . . . . . . Single-Cycle Infections . . . . . . . . . . . . Growth Competition Experiments . . . .

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3 3.1 3.1.1 3.1.2 3.1.3 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9

Application to HIV Population Dynamics . . . . . . . . . . . Replicative Fitness of Drug-Resistant Viruses . . . . . . . . Viruses Resistant to Protease Inhibitors . . . . . . . . . . . . Viruses Resistant to Reverse Transcriptase Inhibitors . . . Viruses Resistant to Other Antiretroviral Inhibitors . . . . Clinical Implications of HIV Fitness . . . . . . . . . . . . . . . Fitness of the Transmitted HIV Isolate . . . . . . . . . . . . . HIV Fitness During Disease Progression . . . . . . . . . . . . HIV Fitness and Immune Response . . . . . . . . . . . . . . . HIV Diversity and Fitness . . . . . . . . . . . . . . . . . . . . . . A Model for HIV Fitness During Disease . . . . . . . . . . . . Are There Intersubtype HIV Variations in Viral Fitness? . Is the Fitness and Pathogenicity of HIV Decreasing? . . .

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Abstract Viral fitness has been broadly studied during the past three decades, mainly to test evolutionary models and population theories difficult to analyze and interpret with more complex organisms. More recent studies, however, are focused in the role of fitness on viral transmission, pathogenesis, and drug resistance. Here, we used human immunodeficiency virus (HIV) as one of the most relevant models to evaluate the importance of viral quasispecies and fitness in HIV evolution, population dynamics, disease progression, and potential clinical implications.

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1 Concepts in Viral Fitness Fitness of RNA viruses has been a topic of study for several decades. As outlined below, sentinel studies on the fitness of foot-and-mouth disease virus (FMDV), vesicular stomatitis virus (VSV), and bacteriophages have addressed and tested basic evolutionary and fitness theories such as Red Queen Dynamics (Clarke et al. 1994; Novella et al. 1995a; Domingo et al. 1996; Sole et al. 1999), Muller’s ratchet (Chao 1990; Duarte et al. 1994; Escarmis et al. 1998; Domingo et al. 1996; Bergstrom et al. 1999), the competitive exclusion principle (Clarke et al. 1994; Domingo et al. 1996; Sole et al. 1999; Anderson and May 1996), Fisher’s geometric model (Burch and Chao 1999), Wright’s fitness landscape (Burch and Chao 1999), and even evolutionary game theory (Turner and Chao 1999). The preference in utilizing RNA viruses, obligate, nonliving parasites as compare to complex or even other simple organisms is the short generation time and high mutation frequencies, which can be controlled by defining the host environment. In contrast to these detailed fitness studies testing evolutionary models, research on the impact of viral fitness on the etiological disease and pathogenesis is limited to a handful of studies. Even these virus studies can be segregated into those focused on fitness during vertical transmission between individual multicellular hosts (e.g., in the human population) and fitness within a host spreading through susceptible cells, which may be analogous to horizontal transmission (as defined by evolutionary theory) and spread. Here, we will use one of the most studied RNA viruses in the last 20 years, human immunodeficiency virus (HIV), as a model to illustrate the complex relationship between viral quasispecies and fitness. 1.1 Definition Fitness is a parameter defining the replicative adaptation of an organism to its environment (reviewed in Domingo and Holland 1997; Domingo et al. 1999). Survival of the fittest is the concept that drives evolution in a complex population. Within a given viral quasispecies, each clone has a fitness value, representative of those viral properties (e.g., activity and stability) undergoing selection in that particular environment. During viral replication within a defined microenvironment, different genomes encode virus that replicate at high rates, continually mutate, but generally remain under the same selective pressures (Domingo et al. 1999). Positive (Darwinian) selection implies that one or more members of the quasispecies are better suited to a given environment, whereas negative selection eliminates unfit variants (Domingo et al.

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1996, 1999; Domingo and Holland 1997). In the case of HIV, each individual member of the quasispecies has an intrinsic growth rate, known as replicative fitness. This concept can be extended to the swarm of variants that constitute HIV isolates. For example, a broad range of replication capacities within a population of viruses with no phenotypic or genotypic evidence of drug resistance (wild-type viruses) have been reported (Wrin et al. 2001; Barbour et al. 2004). Therefore, the term “viral fitness” has emerged to describe the fact that, under defined environmental conditions, different HIV isolates may have different replicative fitness. Selective pressure in the form of drug therapy often leads to dramatic shifts in the quasispecies distribution, as a virus that was poorly fit in the presence of drug can rapidly emerge as the most fit in the presence of drug (Coffin 1995; Domingo and Holland 1997; Domingo et al. 1997, 1999). During this in vivo selection, several drug-resistant variants may emerge and compete for dominance. These resistant isolates will pass through the drug-induced bottleneck and initiate a new quasispecies distribution that will again be governed by replication efficiency, now in the presence of drugs (Coffin 1995; Loveday and Hill 1995). The factors determining which virus population eventually wins are not well defined. However, both host factors and chance likely contribute to the tremendous variability in evolutionary pathways that HIV may take when confronted with drug pressure. 1.2 Lessons from Other RNA Viruses and Application to the HIV Model Much can be learned from the evolution of other RNA viruses and applied to HIV. In terms of basic evolutionary theory derived from and tested with RNA viruses, HIV follows many of the basic models, as described below and in other chapters of this book. Unfortunately, much of the ground-breaking theoretical work on transmission mechanisms/rates and subsequent disease progression caused by pathogens (Cooper et al. 2002; Anderson and May 1986; Lenski and May 1994; Boots and Sasaki 1999) have been supported by few empirical studies (Weaver et al. 1999; Novella et al. 1995c; Messenger et al. 1999; Elena 2001; Bergstrom et al. 1999). In terms of the pathogenesis as influenced by viral fitness, HIV is quite different than most RNA viruses in that acute infection leads to chronic disease and in all cases, eventual mortality. Although mortality is common during acute disease of many RNA viruses (Knipe and Howley 2001), the only other RNA viruses that cause significant mortality due to prolonged chronic disease may be hepatitis B and C viruses (Knipe and Howley 2001). Even in the case of hepatitis B and C infection, progressive disease is generally associated with cirrhosis or hepatocellular carcinoma, which are only indirectly affected by virus destruction and loads (Knipe

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and Howley 2001). Even simian immunodeficiency virus (SIV) infection of various primates (Hirsch 2004), infections by genetically engineered SIV–HIV hybrids (Etemad-Moghadam et al. 2002; Joag 2000), or lentiviral infections of non-primates (Leroux et al. 2004) are relatively poor animal models for HIV pathogenesis in humans, let alone HIV evolution and fitness during disease. As discussed below, the models of HIV evolution and fitness were derived in part from experimental models and evolutionary theories derived from various parasites. As summarized previously (Elena 2001), these models and theories include the study of genetic drift on the accumulation of deleterious mutations (Chao 1990; Duarte et al. 1992), the fixation of beneficial mutations (Burch and Chao 1999; Miralles et al. 1999a), the extent of competition among coexisting genotypes (Clarke et al. 1994), the role of population patchiness and gene flux (Miralles et al. 1999b), the units of selection (Miralles et al. 1997), the existence of frequency-dependent selection mechanisms (Miralles et al. 2000), the different effect of vertical and horizontal transmissions (Messenger et al. 1999; Elena et al. 2001; Bergstrom et al. 1999), the cooperation between viruses (Turner and Chao 1999), or the cost of host radiation (Turner and Elena 2000). Virulence is typically defined as the rate in host mortality as a consequence of infection (Bull 1994), which can be further refined to reproduction rate and pathogenic potential of the parasite (Bremermann and Pickering 1983). In contrast, a parasite’s fitness is dependent on its survival and adaptability in a given environment. Hence, there is often confusion between the principles of virulence and fitness when applied to the interaction and survival of both parasite and host. Ewald, Anderson, May and others suggest that a pathogen will reduce its reproductive success/rates and virulence to ensure survival when host population size, density, and reproduction rates are limited (Ewald 1994; Anderson and May 1992). In terms of a pathogen spreading through a host population, higher fitness of a parasite is often defined by a reduction in reproduction and virulence. In contrast, the fitness of virus within a host is not always analogous to other pathogens. Viruses are obligate parasites that require a living cell for reproduction and survival. Thus, higher fitness within a host is dependent on mechanisms that enhance spread between susceptible host cells such as improved replication efficiency and increased transmission efficiency. For many viral infections, there are large susceptible cell populations, which are at high densities and quickly replenished. However, fitness within a host in contrast to fitness in the host population is subjected to different bottlenecks in the form of innate antiviral and acquired host immune responses. Survival and increasing fitness is dependent on immune avoidance (Goulder and Watkins 2004). There is often a balance between the selection of viral genotypes “resistant” with typically lower replication efficiencies and simply increased replication rates that can offset the viral clearance by host

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responses (Troyer and Arts, in press). However, this delicate balance between a host and viral pathogen resulting in prolonged but pathogenic infection is almost a unique attribute of HIV. Most viruses rapidly kill their hosts (e.g., Ebola and Hanta viruses; Mahanty and Bray 2004), are eliminated by an effective immune responses (e.g., influenza; Knipe and Howley 2001), lie dormant or hide in compartments relatively inaccessible to host antiviral responses (e.g., various Herpesviruses; Knipe and Howley 2001), or even establish an innocuous, sometimes symbiotic relationship with its host (e.g., SIV and GBV; Hirsch 2004; Stapleton 2003). Thus, no virus–host system appears to be a good model for HIV disease, but different stages in the HIV disease may parallel that of other host pathogens. Even in HIV research, a topic that is often overlooked is the impact of fitness on HIV transmission, disease progression, evolution, and prevalence in the human population. This review will outline the effect of drug resistance on HIV fitness, which has been the focus of several hundred studies. However, the vast majority of HIV infections worldwide are not being treated with conventional antiretroviral therapies. The rate of new infections continues to increase, people progress to AIDS and die, and yet little is still known about the phenotypic differences between the heterogeneous etiological agent. Recent studies suggest that the nature of the virus itself, and not solely manifestations of host factors and the immune response is contributing to HIV disease progression (Quiñones-Mateu et al. 2000). Numerous studies have also shown that there are possible differences in interpatient and intersubtype HIV fitness (Quiñones-Mateu et al. 2000; Kanki et al. 1999; Soto-Ramirez et al. 1996; Ball et al. 2003). However, these fitness studies are plagued by the same difficulties and shortcomings found in research on the fitness of drug-resistant HIV strains. What is an appropriate assay to measure ex vivo fitness? Are primary isolates or pseudotyped HIV clones most suitable for accessing differences in fitness? How do we compare ex vivo HIV replicative capacity to viral fitness within a patient or even within the human population? Although this field remains controversial, recent studies aided by new technologies are now making progress in answering some of the questions related to HIV fitness and AIDS (Ball et al. 2003; Quiñones-Mateu et al. 2000; Kanki et al. 1999; Soto-Ramirez et al. 1996).

2 Quantification of Viral Fitness How do we measure, in vitro, a parameter that by definition is determined by the interaction between viral quasispecies and a particular environment that

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changes many times? This very same question has plagued many virologists for the last 30 years. Many different approaches have been used to address this problem, each one of them with their intrinsic advantages and limitations. Mono- or single-infection assays used to quantify viral growth in vitro are among the simplest methods to estimate viral replication capacity. However, growth competition experiments designed to measure the capacity of two viruses to produce infectious progeny are the current gold standard to estimate relative fitness values on a predetermined environment (Holland et al. 1991; Domingo et al. 1999). This method allows the analysis of the contribution of every aspect of the viral life cycle (from entry into the host cell to the release of a new virus particle) to overall viral fitness. Furthermore, as described below, viral competitions are able to discern and quantify small, but highly significant, differences on fitness values. Understandably, some studies have tried to simplify the evaluation of viral fitness, severely limiting the analysis to single events within the viral cycle. Although these studies have greatly contributed to our understanding of the role of individual events of the replication cycle to overall viral fitness (reviewed in Quiñones-Mateu and Arts 2001), the capacity of an RNA virus to successfully replicate on a given setting may well involve an entanglement of events which, as a whole, define the fitness of the virus in a particular set of environmental conditions. As described to this point, viral fitness determinations using in vitro growth competition experiments have been based on the quantification of virus production after multiple rounds of replication (reviewed in Domingo and Holland 1997; Domingo et al. 1999), which guarantee the inclusion of every single phase of the viral cycle on each fitness measurement (similar to what should be observed in vivo). Recently, this well-established methodology has been challenged using individual viral clones to estimate their fitness based on parameters such as intracellular replication rate, cellular viral yield, time spent by a viral particle on a single infection, and the rate at which viruses diffuse in the medium (Cuevas et al. 2005). This interesting exercise resembles many studies conducted using HIV as a model where viral entry, RNA reverse transcription into DNA, viral integration and maturation have all been individually analyzed and correlated to overall HIV fitness (reviewed in Quiñones-Mateu and Arts 2001). However, as in the case with HIV, fitness measurements of single-steps in the viral life cycle hardly mimic viral fitness as a whole. Cuevas et al. (2005) suggested that fitness correlates with the intrinsic growth rate but not with viral yield. To our knowledge, both parameters are unavoidably linked. Furthermore, although this method could be useful to analyze fitness values of individual members within a viral quasispecies, the correlation between viral fitness and viral yield in actual in vivo situations is evident; that is, viral quasispecies better adapted to growth in

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a given host (more fit) will produce a higher number of viruses, which at the end is one of the most important parameters associated with viral evolution and pathogenesis (Quiñones-Mateu et al. 2000). 2.1 Methods Used to Quantify HIV Fitness Viral fitness is closely linked to a particular environment (e.g., a human host offers a variety of cell types and microenvironments to the infecting HIV, resulting in various selective pressures). Thus, it is logical to think that the optimal method to assess viral fitness is in vivo, by examining HIV kinetics in plasma (Goudsmit et al. 1996). However, host-to-host comparisons of in vivo HIV fitness are very difficult to evaluate because of differences in host genetics and immune response. Therefore, in vivo HIV fitness studies are limited to the emergence of specific quasispecies or drug-resistant mutants, and cannot determine the impact of specific substitutions on replicative fitness. In contrast, ex vivo fitness assays of primary HIV isolates are independent of host-specific variables and focus on replication efficiency in standardized systems. Multiple methods have been employed to measure HIV replicative fitness in vitro, including 1. The catalytic activity of HIV enzymes 2. Viral production in monoinfected cultures 3. Infectious virions:virus particle ratios 4. Single-cycle infection assays 5. Growth competition experiments (reviewed in Clavel et al. 2000; Nijhuis et al. 2001; Quiñones-Mateu and Arts 2001, 2002) However, there is also little continuity between in vitro studies, creating specific ambiguities in our understanding of HIV fitness. Many different factors will affect (and thus must be controlled) HIV fitness measurements. Figure 1summarizes the steps and variables that need to be taken into consideration for the analyses of HIV fitness. Most in vitro fitness studies are performed with virus pseudotyped with HIV genes (e.g., PR, RT, ENV, GagPol protease cleavage sites), often PCR-amplified directly from patient plasma or cells (Kellam and Larder 1994; Shi and Mellors 1997; Hertogs et al. 1998; Martinez-Picado et al. 1999; Robinson et al. 2000; Bleiber et al. 2001). One of the main advantages of these recombinant viruses is the ease and flexibility of directly comparing replicative fitness and drug sensitivity of various target genes within a neutral HIV backbone (Mammano et al. 1998; Croteau et al.

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Fig. 1 Steps in designing an HIV fitness assay. A compilation of the most important factors to be considered in the development of methods to quantify HIV fitness. EIA, enzyme immunoassay; RT, reverse transcriptase; PCR, polymerase chain reaction; MOI, multiplicity of infection; TCID50, tissue culture infectious doses

1997; Sharma and Crumpacker 1997, 1999; Martinez-Picado et al. 1999, 2000; Imamichi et al. 2000). However, there is always the possibility that by looking at the “snapshot” (recombinant viruses) we may be missing the overall replicative fitness given by complete HIV isolates. Here, we briefly describe three of the most accepted methods to determine HIV replicative fitness in vitro. 2.1.1 Viral Growth Kinetics This approach utilizes biochemical markers, such as p24 antigen concentration or reverse transcriptase activity, to measure viral growth in monoinfections (Borman et al. 1996; Doyon et al. 1996; Sharma and Crumpacker 1997; Mammano et al. 1998; Deeks et al. 2001). Differences in the replication kinetics of HIV mutants can be compared in parallel infections. Such assays, however, can only discern gross changes in replicative fitness and cannot accurately define the impact of subtle genetic changes on the replication rates of HIV isolates. In general, direct competition between two different viruses is a more accurate and sensitive assay to detect minute fitness differences (Holland et al. 1991; Quiñones-Mateu et al. 2000; Weber et al. 2003b). 2.1.2 Single-Cycle Infections Recombinant viruses are generated using patient-derived HIV sequences (generally the protease, reverse transcriptase, and Gag-Pol cleavage sites),

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which are cloned into a replication-deficient HIV plasmid vector, usually HIVNL4–3 or HIVHXB2 . This technique is useful for assessing the effect of specific drug-resistance mutations on viral replicative fitness, since it eliminates the confounding effects of mutations or polymorphisms located in other genomic regions. These vectors are also engineered to contain an indicator gene such as luciferase or β-galactosidase to allow quantification of viral replication. Activity of the reporter gene is determined after a single round of viral replication, following co-transfection with the test or control vector and a murine leukemia virus envelope expression vector. This method of measuring viral replication has been mastered by ViroLogic, Inc. (Petropoulos et al. 2000; Wrin et al. 2001; Deeks et al. 2001) adapting the PhenoSense assay used in drug-susceptibility assays. Although results obtained with this system have been shown to correlate well with in vivo measures (Weber et al. 2003b; Prado et al. 2002), it is also limited by the use of recombinant viruses and assessment of only specific steps in the replication cycle. Furthermore, this single-cycle infection assay cannot be used effectively in growth competition experiments. 2.1.3 Growth Competition Experiments Head-on competitions in cell culture between two viral isolates of the same species provides the internal control lacking in monoinfections. Growth competition experiments involve co-infection of a cell culture by two different HIV isolates. Following exposure of cells to a viral mixture, the proportion of viruses after several viral passages is compared with that in the initial mixture (Holland et al. 1991). Although laborious assays, growth competitions provide a more accurate measurement of viral replicative fitness by detecting the effect of small differences in replication rates and are generally reproducible in the same cell culture environment. Many different approaches have been developed to measure dual virus production in growth competition experiments. Most methods rely on laborious point mutation assays or on the sequencing of a large number of clones (Croteau et al. 1997; Sharma and Crumpacker 1997; Harrigan et al. 1998; Martinez-Picado et al. 1999, 2000; Imamichi et al. 2000). However, new studies use more rapid techniques to estimate the frequency of the two viruses in the competition: heteroduplex tracking assay (Yuste et al. 1999; Quiñones-Mateu et al. 2000; Nelson et al. 2000; Resch et al. 2001), realtime PCR (De Ronde et al. 2001; Lu and Kuritzkes 2001; Weber et al. 2003b), or recombinant viruses with reporter genes (Lu and Kuritzkes 2001; Zhang et al. 2004; Neumann et al. 2005; Weber and Quiñones-Mateu, unpublished results).

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What is the best method to measure HIV replicative fitness? Most studies analyzing the effects of drug-resistance mutations on HIV replicative fitness have used recombinant viruses based on the same genetic background (Mammano et al. 1998; Croteau et al. 1997; Sharma and Crumpacker 1997, 1999; Martinez-Picado et al. 1999, 2000; Imamichi et al. 2000). Although useful in correlating amino acid substitutions with alterations in replicative fitness without the effects from mutations outside the targeted viral gene, a great diversity between these assays hampers even comparisons of fitness values obtained using the same drug-resistant mutants cloned into the same HIV backgrounds. In a recent review article, we outlined discrepancies in the replicative fitness values of various viruses analyzed in several independent studies (Quiñones-Mateu and Arts 2002). Contradictory results were obtained depending on the assay used in each study, which may be due to differences in methodology (e.g., mono-, single-cycle, and competitive infections) or the use of various genetic backgrounds (e.g., primary HIV isolates or laboratory strains such as HIVNL4–3 and HIVHXB2 ) (Fig. 2). It is clear that a consensus on the experimental approach used to measure fitness has to be established to allow a direct qualitative and quantitative comparison between different studies.

3 Application to HIV Population Dynamics It is difficult to durably control HIV replication with currently available therapies, in part because of the extraordinary capacity of the virus to develop resistance to antiretroviral drugs (the theoretical basis are discussed in the chapters by Biebricher and Eigen, Wilke et al., and Domingo et al., this volume). All currently available classes of antiretroviral therapy, i.e., protease, reverse transcriptase, and fusion inhibitors, can select for mutations within the target gene that confers high-level drug resistance. Indeed, the capacity of a novel compound to select for a mutation is now used as evidence that the experimental agent has anti-HIV activity. Although these mutations result in an increased ability of the variant to replicate in the presence of drug, they often reduce the in vitro replicative fitness of the virus compared with wild-type strains (in the absence of drug pressure). Thus, in the absence of antiretroviral therapy, strains containing drug-resistance mutations have a reduced replicative fitness compared to the wild-type (wt) quasispecies within the population (Coffin 1995). Selective pressure in the form of drug therapy leads to dramatic shifts in the quasispecies distribution and fitness of those mutants with decreased sensitivity to the respective antiretrovirals (Coffin

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Fig. 2 Effects on viral replicative fitness of single mutations in genes targeted by antiretroviral drugs. Summary of integrase, protease, reverse transcriptase, and envelope mutations associated with resistance to the corresponding antiretroviral inhibitor. Red and black arrows represent a decrease or increase in viral replicative fitness, respectively, compared with the wild-type virus in the absence of antiretroviral drugs. Amino acids substitutions in blue do not affect viral replicate fitness (i.e., replicative fitness of the mutant virus is comparable to the wild-type strain used in that particular experiment). (This is a modified and updated version of data summarized in Quiñones-Mateu and Arts 2001)

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1995; Domingo and Holland 1997; Domingo et al. 1997, 1999). During this in vivo selection, several drug-resistant variants will emerge and compete for dominance. These resistant isolates will pass through the drug-induced bottleneck and initiate a new quasispecies distribution that will be governed again by replication efficiency (Coffin 1995; Loveday and Hill 1995). Many studies have examined the potential relationship of HIV replicative fitness with plasma viral load, drug resistance, and disease progression (reviewed in Quiñones-Mateu and Arts 2001, 2002; Nijhuis et al. 2001; Buhler et al. 2001). Here, we will describe the implications of viral fitness on different aspects of HIV drug resistance, pathogenesis, transmission, and evolution. 3.1 Replicative Fitness of Drug-Resistant Viruses Resistance to antiretroviral drugs is widespread in the Western hemisphere (Hirsch et al. 1998) and transmission of drug-resistant viruses is an increasing problem (Little et al. 1999, 2002; Tang and Pillay 2004). Currently, the nineteen antiretroviral drugs licensed in the United States fall into four classes: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and fusion inhibitors (FIs). Resistance mutations have been observed for all four classes of antiretroviral drugs (Deeks 2003; Gallant et al. 2003), including mutations in the gp41 envelope gene associated with resistance to enfuvirtide (EFV), the first in the new class of viral FI (Derdeyn et al. 2000; Menendez-Arias and Este 2004; Wei et al. 2002; De Ronde et al. 2001). Drug-resistance-associated mutations can either directly decrease the capacity of antiviral drug to inhibit HIV replication or compensate for the reduced replicative fitness associated with drug-resistance mutations. Both therefore contribute to increasing the relative fitness of the virus to replicate in the presence of drug. Such mutations are referred to as primary and secondary mutations (Quiñones-Mateu and Arts 2001). In general, primary resistance mutations involve amino acids located at the active site of the enzyme to which a given drug is directed (e.g., protease or reverse transcriptase). These mutations confer direct resistance to the drug, and often come at the price of compromised viral replication in the absence of drug pressure (reviewed in Nijhuis et al. 2001; QuiñonesMateu and Arts 2001). Secondary mutations (i.e., compensatory mutations) are selected and emerge under continued drug pressure. These mutations may also be located at the enzyme active site or its substrate cleavage site. Accumulation of compensatory mutations is assumed to restore the enzymatic activity of the drug-targeted resistant enzyme, usually leading to a rebound in fitness (Hirsch et al. 1998; Nijhuis et al. 2001; Berkhout 1999; Clavel et al.

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2000). It is important to note that the rate of appearance of specific mutations is often highly dependent on the baseline sequence and the sequential selection of “de novo” compensatory mutations that contribute to viral fitness (Weber et al. 2003a 2003b; Rose et al. 1996; Precious et al. 2000). Here, we will briefly describe the replicative fitness of viruses resistant to PI, RTI, and other antiretroviral inhibitors. Figure 2 summarizes the effect on replicative fitness of single amino acid substitutions associated with resistance to the most common antiretroviral drugs. 3.1.1 Viruses Resistant to Protease Inhibitors HIV protease is the enzyme responsible for cleavage of the viral Gag and Gag-Pol polyprotein into mature structural proteins and enzymes found in the infectious virion (Park and Morrow 1993; Miller 2001). Seven HIV protease inhibitors have been approved to date in the United States, i.e., amprenavir (APV), indinavir (IDV), lopinavir (LPV), nelfinavir (NFV), ritonavir (RTV), saquinavir (SQV), and atazanavir (ATV). Numerous primary and/or secondary mutations have been associated with HIV resistance to PI (Miller 2001; Shafer et al. 2000). Primary drug-resistant substitutions rarely dominate the quasispecies in PI-naïve HIV-infected individuals (Lech et al. 1996), suggesting they confer a selective disadvantage to the virus (Kozal et al. 1996; Shafer et al. 2000). For most of the protease inhibitors, primary PI-resistant mutations cluster near the active site of the enzyme, reducing both catalytic activity and viral fitness (Borman et al. 1996; Croteau et al. 1997; Mammano et al. 1998; Miller 2001). Secondary mutations within the protease gene compensate for the impairment on HIV replication by helping the enzyme to adapt to the primary changes in the active site (Borman et al. 1996; Ho et al. 1994; Nijhuis et al. 1998, 1999; Eastman et al. 1998; Mammano et al. 1998; Rose et al. 1996). In addition, increased PI-resistance is often associated with substitutions in the protease cleavage sites (gag and pol genes) (Doyon et al. 1996; Zhang et al. 1997; Miller 2001; Clavel et al. 2000; Nijhuis et al. 2001), providing better peptide substrates for the mutated protease and compensating for the loss in viral fitness (Doyon et al. 1996; Mammano et al. 1998; Zennou et al. 1998; Clavel et al. 2000; Nijhuis et al. 2001; Tamiya et al. 2004; Myint et al. 2004). Perhaps the most dramatic example of the impact of PI resistance on viral replicative fitness in a single amino acid substitution at position 30 in the protease gene (D30N) (Martinez-Picado et al. 1999; Gonzalez et al. 2004), which confers resistance to nelfinavir. Interestingly, discordant responses have occurred in HIV-infected patients treated with PI (i.e., high viral loads and sustained CD4+ T cell counts) (Deeks et al. 2000), suggesting a pos-

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sible preservation of CD4+ T cell counts in patients failing PI-based therapy and a beneficial effect of HIV strains with impaired fitness (see Sect. 3.2). 3.1.2 Viruses Resistant to Reverse Transcriptase Inhibitors NRTIs compete for binding to RT with the native deoxynucleoside triphosphates (dNTPs), can be incorporated into elongating HIV DNA, and result in chain termination (Arts and Wainberg 1996). To date, six NRTIs have been approved for therapy in the United States, i.e., zidovudine (AZT), didanosine (ddI), zalcitabine (ddC), stavudine (d4T), lamivudine (3TC), and abacavir (ABC). Tenofovir (TDF), a nucleotide-RT inhibitor, has recently received approval from the FDA. NRTIs were the first class of antiretrovirals to be approved for anti-HIV therapy (Loveday 2001). Thus, it is not surprising that some of the first studies showing the effect of drug-resistance mutations on viral replication fitness were related to AZT (Goudsmit et al. 1997; GarciaLerma et al. 2000, 2004; Harrigan et al. 1998; Arts et al. 1998). Other studies followed, describing the effects of many NRTI-resistance mutations in viral replicative fitness (Quiñones-Mateu and Arts 2001). The M184V mutation, which confers resistance to 3TC, has a marked effect on reducing RT processivity and replicative fitness (Larder et al. 1995; Back et al. 1996; Keulen et al. 1997; Yoshimura et al. 1999; Sharma and Crumpacker 1999; Frost et al. 2000; Devereux et al. 2001; Picchio et al. 2000; Lu and Kuritzkes 2001; MartinezPicado et al. 2001). Similarly, the K65R mutation (associated with resistance to TDF, ddC, ddI, d4T, and ABC) (Gu et al. 1994; Zhang et al. 1994; Winston et al. 2002; Margot et al. 2002) reduces HIV replicative fitness (White et al. 2002; Deval et al. 2004; Weber et al. 2005). Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are noncompetitive or uncompetitive inhibitors that bind to a hydrophobic pocket adjacent to the polymerase active site of RT (Deeks 2001). This binding inhibits DNA polymerization by an allosteric change of the polymerase active site (Esnouf et al. 1995). Mutations in this hydrophobic binding pocket are rapidly selected during NNRTI therapy (Deeks 2001; Shafer et al. 2000). Three NNRTIs are currently approved for antiretroviral therapy in the United States: delavirdine (DLV), efavirenz (EFV) and nevirapine (NVP). Presence of NNRTI resistance mutations results in a slight decrease in RT polymerase activities relative to RNase H activity of the enzyme (Gerondelis et al. 1999; Archer et al. 2000). In contrast to NRTI, single-point NNRTI-resistant mutations such as 103N or 181C have limited effects on viral fitness but confer a high level resistance and persist in the absence of drug pressure (Imamichi et al. 2001; Archer et al. 2000; Iglesias-Ussel et al. 2002; Collins et al. 2004). Unlike other antiretroviral

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drugs, the genetic barrier to NNRTI resistance is minimal and replicative fit-resistant mutants are found at high frequencies in HIV quasispecies populations in the absence of therapy (Havlir et al. 1996; Demeter et al. 2000; Dueweke et al. 1993; Iglesias-Ussel et al. 2002). In addition to individuals, mutations conferring resistance to particular RT inhibitors, accumulation of multiple mutations associated with multidrugresistant (MDR) viruses may occur in heavily antiretroviral-experienced individuals. For example, the 151M mutation is thought to be the first MDR mutation selected in RT, followed by 75I, 77L, 116Y, and then 62V (also known as the MDR-151 complex) (Shirasaka et al. 1995; Shafer et al. 2000). Interestingly, specific combinations of these mutations actually appear more fit than wild-type viruses in the absence of drug (Garcia-Lerma et al. 2000; Kosalaraksa et al. 1999; Maeda et al. 1998). Another example is the MDR-69 complex, which confer high-level resistance to multiple RT inhibitors (Hirsch et al. 2000). Decreased viral replicative fitness in the absence of drugs is quite evident in HIV clones harboring these mutations in RT (Quiñones-Mateu et al. 2002; Prado et al. 2004). 3.1.3 Viruses Resistant to Other Antiretroviral Inhibitors Novel drugs in development target different steps in the virus life cycle, including host cell entry and viral integration. The most promising drugs are those that interfere with entry into host cells, either by inhibiting gp120 binding to the chemokine receptors CCR5 or CXCR4 (Cocchi et al. 1995; Simmons et al. 1997) or the fusion of the viral and cellular membranes (Kilby et al. 1998). We have shown that the rate of host cell entry appears to control fitness in wild-type viruses (Ball et al. 2003; Rangel et al. 2003). Thus, it is quite conceivable that decreased replicative fitness is also linked to increase resistance to these entry inhibitors. For example, we found that resistance to AMD3100, a bicyclam compound that binds to CXCR4, has a negative impact on HIV replication (Armand-Ugon et al. 2003). Another class of entry inhibitors was designed to block virus–cell fusion and target conserved fusion domains in gp41. Included in this class of inhibitors are T-20 (Enfuvirtide), T1249, C34, 5helix, and IQN-17 (Chan et al. 1997; Eckert et al. 1999; Greenberg et al. 2001; Kilby et al. 1998; Root et al. 2001). Enfurvirtide, the only fusion inhibitor approved to treat patients to date, is a synthetic peptide analog that binds to the alpha helix bundle region in the gp41 transmembrane domain, preventing formation of a hairpin structure necessary for membrane fusion. Although enfuvirtide-resistant recombinant viruses carrying mutations in the gp41 coding region (i.e., D36S, I37T, V38M/A, N42T/D, or N43K/S) have been

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shown to have diminished replicative fitness (Lu et al. 2004), recently data from our and other groups (using enfuvirtide-resistant HIV isolates) showed that pre-existent “compensatory” mutations within the env gene appear to guarantee a minimal effect of these mutations on HIV replicative fitness (Menzo et al. 2004; Weber and Quiñones-Mateu, unpublished results). Further studies using viruses resistant to this and other entry inhibitors are necessary to clarify the effect of drug-resistance mutations in the env gene over HIV fitness. Finally, integrase inhibitors (e.g., diketo acid analogs) are the most recent anti-HIV class of drugs (Fikkert et al. 2003; Hazuda et al. 2004a, 2004b; Svarovskaia et al. 2004). Resistance to these compounds is related to specific mutations in the integrase active site (i.e., 153Y, 66I, 154I, and 155S), which also impair enzymatic function in vitro (Hazuda et al. 2004a; Fikkert et al. 2003). As expected, increasing levels of resistance to integrase inhibitors is associated with significant loss of viral replicative fitness (Fikkert et al. 2003; Lee and Robinson 2004). 3.2 Clinical Implications of HIV Fitness A clinical paradox has emerged with the use of antiretroviral therapy: sustained CD4+ T cell counts despite the presence of relatively high viral loads. This “discordant” response has been attributed to a reduced viral replicative fitness following development of resistance to antiretroviral drugs (Deeks et al. 2001; Deeks 2003; Bates et al. 2003; Barbour et al. 2004). Although the immunologic benefits of suppressive antiretroviral therapies are greatest among persons in whom plasma HIV RNA levels are suppressed below the limits of detection, incomplete suppression of HIV replication with the emergence of viruses resistant to antiretroviral drugs seems to provide some degree of immunologic benefit. This would suggest that resistant viruses are less “pathogenic” in terms of inducing immune dysfunction than are wildtype viruses. It is not certain whether the diminished “pathogenicity” of drug-resistant viruses is related to diminished replicative fitness and a lower level of HIV replication or whether other viral pathogenesis factors determine the diminished ability of these strains to cause and maintain immune deficiency. Also, we still do not know if there is a correlation between replicative fitness and the ability of drug-resistant variants to be transmitted (i.e., the actual transmission event and not the ability of the newly transmitted virus to replicate in the new host). If so, it would mean that drug-resistant viruses have lower transmission fitness than wild-type strains, an issue still being debated (Leigh Brown et al. 2003; Simon et al. 2003; Gandhi et al. 2003). It is clear then that further fitness studies on HIV drug-resistant viruses could lead

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us to understand the potential of certain mutants to be inhibited by salvage therapy and should help to explain the apparent diminished pathogenicity and/or transmission of these isolates. 3.3 Fitness of the Transmitted HIV Isolate During primary HIV infection, individuals may be exposed to varying amounts of virus depending on the mode of transmission (Zhu et al. 1993, 1996 Richardson et al. 2003; Derdeyn et al. 2004). Sexual and vertical transmission generally involves small amounts of virus and infected cells, whereas a bolus dose of virus may be transmitted during direct blood-to-blood contact, e.g., contaminated needles or blood transfusions (Wolinsky et al. 1992; Zhu et al. 1993; Overbaugh et al. 1999). Regardless of the vertical transmission route, a significant genetic bottleneck reduces the HIV population size from donor to recipient (Zhu et al. 1993, 1996). The virus population recovered from primary HIV infections is more homogeneous with a narrow genetic distribution of quasispecies as compared to the donor HIV quasispecies. How this reduction in genetic diversity affects virus replication efficiency (or fitness) is not known, but fitness is dramatically decreased in other RNA virus systems when a stringent bottleneck is applied (Domingo and Holland 1997). As described below, quasispecies theory and Fisher’s geometric model suggest that larger, more diverse virus populations can evolve to higher fitness and that progressive, adaptive mutations must accumulate to override the deleterious changes. However, severe bottlenecks during each transmission event may have an effect of “turning back” the evolutionary clock by reducing genetic diversity, population size, and relative replicative fitness. As will be discussed, gains in viral fitness during HIV disease progression may be lost with each transmission event. Transmission models have been tested experimentally using FMDV, VSV, bacteriophage, and even HIV (Lazaro et al. 2003; Escarmis et al. 1996; Duarte et al. 1992; Yuste et al. 1999, 2000; Novella and Ebendick-Corp 2004; Elena et al. 1996; Burch and Chao 2004; Ruiz-Jarabo et al. 2003) (see chapter by Escarmís et al., this volume). Genetic bottlenecks can be created in vitro by reducing or maintaining small viral population sizes for each serial passage in tissue culture (Lazaro et al. 2003; Escarmis et al. 1996; Novella and Ebendick-Corp 2004; Elena et al. 1996). Alternatively, other studies have alternated different target cells for infection, resulting in stringent bottlenecks and immediate selection for tropic clones (Ruiz-Jarabo et al. 2003), which may be analogous to the tight genetic constraints observed in arthropod-borne viruses (Holmes 2003). The selective pressures applied in these experimental systems

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have reduced quasispecies diversity and significantly decreased fitness of the transmitted virus. Finally the most stringent bottleneck is the direct serial plaque-to-plaque transfer of virus (Lazaro et al. 2003). Although these experiments are not analogous to an in vivo transmission event, a plaque-to-plaque transfer involves a strong bottleneck, prevents the accumulation of most adaptive mutations, and leads to dramatic decreases in fitness. Interestingly, even in these extremes the virus is not brought to extinction but retains low-level fitness (Lazaro et al. 2003). Bergstrom et al. (1999) have described the consequences of vertical transmission bottlenecks on the evolution of pathogens by using both analytical and simulation methods. They demonstrated that severe bottlenecks reduce the virulence of a pathogen due to stochastic loss of fitness, a process analogous to Muller’s ratchet. They also contrast the differences between horizontal and vertical transmission, which vary dramatically in terms of more severe bottlenecks in the latter. When a pathogen is spread by vertical transmission, as is the case with HIV, the pathogen strives to reduce virulence in the interest of ensuring host survival and reproductive success. As described below, there may be evidence that HIV is actually evolving to reduce its virulence/fitness during the current pandemic (Ball et al. 2003). When the inoculum size is large, changes in the virus population due to sampling effects become insignificant and the genetic distribution of clones in the donor and recipient quasispecies will be similar during transmission. When inoculum size is small due to severe bottlenecks, reduction in fitness may be simply be due to sampling error from the population of clones in the donor. As described in Fig. 1, the transmission bottleneck with HIV may be most severe in cases of vertical transmission (Zhu et al. 1993, 1996). Most infections through heterosexual vertical transmission reduce the diversity in the donor quasispecies (population as many as 1012 clones = 10,000 nt genome × 0.001 substitutions/nt × 1010 virus particles/day) (Ho et al. 1995; Perelson et al. 1996; Wei et al. 1995; Preston et al. 1988; Mansky and Temin 1995) to only a few clones infecting the recipient. The selective factors creating the bottleneck in HIV transmission are diverse and likely include: 1. Host factors such as innate immune response 2. Density of target cells at the site of infection 3. Number of transmitted virions 4. The structure of transmitted viral quasispecies. Quasispecies distribution and size of the transmitted pool may have a significant effect on fitness of the infecting isolate and subsequent disease progres-

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sion (Domingo and Holland 1997; Novella et al. 1995b). The viral properties selected during initial infection may not be the same attributes necessary for efficient dissemination and rapid turnover during acute disease. A combination of these viral characteristics may only be found in exposures with a significant load of diverse HIV quasispecies. Finally, environmental differences (pH, target cells, mucosal composition) at the site of exposure may not only affect the efficiency of transmission but also fitness of the infecting isolates (Overbaugh et al. 1999; Blauvelt et al. 2000). For example, there appear to be phenotypic and genotypic differences between HIV variants infecting men and women following heterosexual contact (Overbaugh et al. 1999). Recently, Richardson et al. (2003) have shown that the diversity of the HIV population responsible for primary infection of women is related to the severity of subsequent disease, i.e., higher HIV-1 genetic diversity at transmission resulted in faster disease progression. Based on models by Bergstrom, Domingo, Chao, Holland and others, the greater sampling of clones from the donor HIV quasispecies would result in a greater chance of infection by high-fitness clones, which could expand more rapidly in the recipient HIV quasispecies (see, however, alternative possibilities in the chapters by Domingo et al. and Escarmís et al., this volume). Although there may be an element of chance in the expansion of a particular HIV clone, phenotypic selection does occur in nearly every HIV infection. There are two basic phenotypes of HIV that have different host cell tropisms. The SI HIV isolates, which can form syncytia in infected T cell cultures, utilize the CXCR4 chemokine receptor in addition to CD4 for host cell entry. The nonsyncytium-inducing (NSI) isolates utilize CCR5 as a co-receptor for entry. SI/X4 HIV isolates can dominate the quasispecies but only in late HIV disease. Regardless of HIV-1 phenotype in the donor, only the NSI/R5 variant is vertically transmitted to a recipient (reviewed in Fenyo et al. 2000). Preferential transmission of NSI/R5 over SI/X4 HIV isolates is contradictory to increased turnover of SI/X4 HIV over NSI/R5 isolates in culture (see Sect. 3.4) (Tersmette et al. 1988; Bjorndal et al. 1997). Thus, efficiency or fitness of transmission appears not to be related to the HIV replicative capacity measured in normal cell culture. As described below, this fitness dichotomy is not restricted to co-receptor usage but is also observed in transmission of different NSI/R5 HIV isolates in the human population (Blackard et al. 2001). Although in vivo findings suggest that NSI/R5 HIV isolates may out-compete the SI/X4 variants at the site of primary infection, one report suggests that the NSI/R5 isolates only predominate after a temporary expansion of SI/X4 HIV isolates is quenched by an activated immune response (Cornelissen et al. 1995). However, this observation is difficult to reconcile with the finding that humans who are homozygous for a deletion in the CCR5 gene (i.e., lack CCR5 on

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any cell surface) are typically resistant to HIV infection (O’Brien and Moore 2000; Dean et al. 1996b). To date, the universal factors (i.e., found in almost every human host) involved in the selection of NSI/R5 HIV isolates during transmission and asymptomatic disease are not well defined. Langerhans cells (LCs) are found embedded in mucosa (e.g., vaginal mucosa) and may be the first cell targets for primary heterosexual transmission (Blauvelt et al. 2000; Braathen et al. 1987; Soto-Ramirez et al. 1996). LCs may play a role in NSI/R5 HIV selection since CCR5 and not CXCR4 is preferentially expressed in situ and in the absence of external stimuli (Blauvelt et al. 2000). A recent report describes increased replication of an NSI/R5 (HIVBal ) over an SI/X4 (HIVIII-B ) isolate in LCs embedded in skin-derived explants even though the opposite is true in PBMC cultures or other permissive cell lines (Blauvelt et al. 2000; Soto-Ramirez et al. 1996). Aside from the preferential infection by NSI/R5 isolates, there is little evidence supporting the notion that specific clones from the donor quasispecies are selected during transmission. It is conceivable that one NSI/R5 clone may be more adept than others at infecting LCs or other primary target cells at the site of transmission. However, more recent results suggest the contrary. In competition experiments with a subtype B and C primary HIV isolate, the subtype B isolate dominated over the C strain in PBMC cultures regardless of the genetic background of the human donor (Ball et al. 2003). In contrast, these two viruses competed equally in skin explant cultures where LCs were the only target cell (Ball et al. 2003). Fitness differences derived from these two primary human cultures

Fig. 3 A model for increasing HIV-1 replicative fitness and genetic diversity during disease progression

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may be attributable to how these viruses compete. Relative HIV replicative capacity appears to be directly related to the efficiency of host cell entry. Cells such as LCs or other members of the dendritic cell lineage have the ability to trap virus on the cell surface through various adhesion molecules, including DC sign (Geijtenbeek et al. 2000; McDonald et al. 2003). Trapping of virus on the cell surface may negate the rate-limiting step of host cell entry (Ball et al. 2003; Rangel et al. 2003; Marozsan et al. 2005) and thus, dampen differences in fitness (Ball et al. 2003). The net result of these findings would be that all infectious NSI/R5 HIV clones had an equal opportunity to establish infection in the recipient. Of course, as indicated in Fig. 3, the extreme bottleneck and random sampling of the quasispecies would favor infection by clones falling under the peak of normal fitness distribution. How fitness increases and how this fitness distribution expands during disease will be discussed in the following sections. 3.4 HIV Fitness During Disease Progression Although HIV is the etiological agent of AIDS, it is often assumed that phenotypic characteristics and replication efficiency (ex vivo fitness) of the infecting, wild-type HIV isolates has little impact on the rate of disease progression. In general, acute HIV infection is followed by a chronic and progressing disease resulting in immunodeficiency and acquisition of various life-threatening opportunistic infections. Multiple studies have tried to compare both host and viral factors, with HIV pathogenesis and progression to AIDS. The strongest correlates of HIV disease progression include various host immunological and genetic factors: 1. A strong HIV-specific CD8+ cytotoxic lymphocyte throughout disease 2. Retention and proliferation of HIV-specific CD4+ lymphocyte response following acute infection (Pontesilli et al. 1998; Dyer et al. 1999; Cao et al. 1995; Pantaleo et al. 1995; Montefiori et al. 1996; Carotenuto et al. 1998; Rosenberg et al. 1997) 3. Some polymorphisms in the CCR5, RANTES, and SDF-1 promoter regions or altered expression of the chemokines (Dean et al. 1996a; Berger et al. 1999) 4. Specific HLA class I genes (De Maria and Moretta 2000) However, there are several general observations suggesting that HIV fitness may play an important role in disease progression, i.e., (a) HIV load is the best marker to date for disease progression (Mellors et al. 1996), (b) treatment

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with highly active antiretroviral therapy (HAART) may reduce viral loads to undetectable levels and delays progression to AIDS indefinitely, and (c) emergence of drug-resistant isolates and rebound in viral load results in resumption of normal disease progression. In addition to these general observations, there are also three clear examples that HIV phenotype can affect disease progression. First, the faster replicating SI/X4 HIV isolates are generally isolated during AIDS or in late HIV disease, whereas the slower replicating, NSI/R5 strains generally predominate during asymptomatic stages (Asjö et al. 1986; Tersmette et al. 1988; Bjorndal et al. 1997). Several studies have now shown that appearance of SI/X4 isolates does coincide with rapid decline in CD4 cells, a burst in viral load, and the onset of AIDS. However, SI/X4 isolates are inconsistently isolated in late stages of disease and are not a prerequisite for progression or AIDS (Cheng-Mayer et al. 1988; Schuitemaker et al. 1992; Fenyo et al. 2000). Using a mathematical model, Wodarz and Nowak (Wodarz and Nowak 1998) suggested that the evolution of SI/X4 strains depends on the fitness landscape of the HIV quasispecies. Variation in intrapatient HIV evolution and the fitness landscape would explain why SI/X4 viruses only appear in half of HIV-infected individuals. Recently, the dogma that all SI/X4 isolates are more fit in cell culture than NSI/R5 isolates has been challenged by competing several NSI/R5–SI/X4 pairs in PBMC cultures (Quiñones-Mateu et al. 2000). Although most SI/X4 were more fit in cell culture, NSI/R5 isolates from rapid progressors could outcompete SI/X4 isolates from long-term survivors or even SI/X4 isolates from patients displaying typical HIV progression (Quiñones-Mateu et al. 2000). Aside from these changes in co-receptor usage/cell tropism, evidence that HIV genetic alterations could affect disease progression was clearly demonstrated in a few long-term nonprogressor patients (LTNPs) shown to harbor HIV strains with nef deletions (Deacon et al. 1995; Kirchhoff et al. 1995). Independently of these studies, Daniel et al. (1992) had generated several HIV clones with similar nef deletions, all of which were replication defective in PBMC cultures. Several of the LTNP infected with nef -deleted viruses eventually progressed to AIDS (after >10 years of asymptomatic disease) (Brambilla et al. 1999). Changes in HIV fitness during this time period have not been published but would provide valuable information on the possible accumulation of compensatory mutations. It is important to note that a similar rebound in HIV fitness does occur during the emergence of drug-resistant mutations, albeit on a shorter time scale. In these LTNPs now progressing to AIDS, the Red Queen Hypothesis would support the notion that slow but continual replication of HIV would expand the quasispecies diversity and lead to increased fitness. Only genetic bottlenecks due to changes in selective pressure could reverse this trend (i.e., Muller’s ratchet). Although the impact of HIV fitness

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on disease progression is still not well understood, studies on SIV pathogenesis have provided valuable information about the disease process that leads to simian AIDS (reviewed in Whetter et al. 1999). Using the SIV model, Kimata et al. (1999) showed that antigenic and cytopathic properties of the SIV strain and not phenotype (e.g., SI vs NSI) predict fitness in the host. Emerging SIV variants have increased replicative capacity over the SIV strain used in the initial infection. Furthermore, infectious dose and virulence (viral fitness) of the initial inoculum did influence viral load during disease progression in SIV-infected macaques (Holterman et al. 2000). As described above, several viral parameters such as infectious dose, route of infection, and viral fitness may be contributing to the clinical course of HIV infection (Tersmette and Miedema 1990; Coffin 1986; Zhu et al. 1993; Lukashov and Goudsmit 1997). Subtle differences in viral fitness may also contribute to HIV pathogenesis and even overwhelm an HIV-specific immune response. In an attempt to correlate ex vivo HIV replicative capacity and disease progression, several studies have compared the replication differences of primary HIV isolates using monoinfections of PBMCs or a CD4+ tumor cell line (Blaak et al. 1998; Tersmette et al. 1988; Schuitemaker et al. 1992; Fenyo et al. 1989). Variations in the microenvironment of tissue culture systems, the lack of an internal control, and the possibility of a high virus titer saturating the target cells, all lead to inconsistent results with monoinfections. Moreover, a moderate fitness cannot be accurately measured in monoinfections because of the inherent variability between cultures. This may explain why this assay has only characterized drastic defects in the replication kinetics of HIV isolates from long-term nonprogressors (LTNPs). Several studies have examined the replication kinetics of HIV isolated from patients with atypical progression (Blaak et al. 1998; Fenyo et al. 1989; Schuitemaker et al. 1992). Using a monoinfection assay with primary HIV isolates, Blaak et al. (1998) showed that some LTNPs harbored NSI isolates with slow replication kinetics. This observation has been supported by other studies by this group (Kimata et al. 1999; Kwa et al. 2003). Until recently, there have been few studies that have compared the replication kinetics of primary HIV isolates using competition assays, which have become standard with most other RNA virus systems. A study published in 2000 described competition of HIV isolates from long-term survivors (LTSs) and typical progressors (PRO) with four reference HIV strains (i.e., primary isolates) to determine a relative replicative fitness in primary human CD4+ PBMCs (Quiñones-Mateu et al. 2000). PRO HIV isolates out-competed the reference strains in growth competition experiments, while the opposite was observed for HIV isolated from longterm survivors. These results suggest that regardless of the viral phenotype (NSI or SI), HIV isolates from long-term survivors were less fit than strains

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obtained from progressors (Quiñones-Mateu et al. 2000). The relative fitness values of each LTS and PRO isolate also showed strong correlations with viral RNA load (Quiñones-Mateu et al. 2000). This initial study on ex vivo HIV fitness in LTS or PRO patients has now been expanded to include at least four consecutive HIV isolates from each of ten patients studied over a 2- to 5-year period (Troyer et al. 2005). In each case, the relative fitness of the primary HIV isolates increased during disease and as a direct correlate of increasing viral load and decreasing CD4 cell counts (Troyer et al. 2005). These data support earlier findings that the fitness of the infecting HIV isolate may predict the clinical course of disease (Lukashov and Goudsmit 1997). As described above, the diversity of the infecting HIV isolate at acute infection did appear to correlate with subsequent disease progression (Richardson et al. 2003; Derdeyn et al. 2004), suggesting that a greater sampling of the donor quasispecies may increase the chances of transmitting higher-fitness clones. In a current model (Fig. 3), decreased genetic diversity in donor quasispecies and low infectious dose results in transmission of an HIV clone of reduced fitness. 3.5 HIV Fitness and Immune Response Rosenberg et al. (1997) have suggested that rapid depletion of CD4+ cells during acute/early infection results in an irreplaceable loss of HIV-specific T helper cells. In contrast, proliferation and retention of HIV-specific T helper cells during early disease is associated with slower disease progression (Rosenberg et al. 1997). Thus, it is quite conceivable that infection with an isolate of poor replicative capacity may lead to limited depletion of CD4+ cells and retention of HIV-specific T helper cells. Slower disease progression may then be due to a combination of an enhanced HIV-specific immune response and reduced HIV fitness. It is important to note that infection with an HIV clone outside of the normal distribution of quasispecies would be rare, which could explain the low frequency of atypical progression (e.g., LTS or rapid progressors). Considering most individuals would be infected with a virus of “modest” replicative fitness, HIV-specific CD4+ T cell help would persist, albeit at lower levels than in response to other pathogens that do not directly target CD4+ cells. This HIV-specific CD4+ T cell help could then stimulate class I and II restricted responses, increase anti-HIV antibody production, some of which may be neutralizing, and activate of HIV-specific CD8+ (or CD4+ ) cytotoxic T lymphocytes. This hypothesis brings up the “chicken or egg” scenario since strong CD4+ T cell responses can also stimulate HIVspecific CTL activity, which in turn can reduce HIV fitness by creating strong genetic bottlenecks and selecting for “less” fit CTL escape clones.

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HIV-specific CTLs are thought to be at least partly responsible for initial immunological control of HIV viremia following acute infection. Continual viral replication following the establishment of viral load set point may be related to virus “escape” from this CTL pressure. To understand the direct relationship between anti-HIV CTL activity and immune avoidance by virus, it is important to first define the immune response to HIV and second, provide an analogy for immune escape by comparing genetic/fitness changes associated with the emergence of HIV drug resistance. There are three class I genes: HLA-A, B, and C. Polymorphic alleles for each gene expand the breath of antigen presentation by simply providing the possibility of six different coding genes for MHC class I. Early studies suggest that homozygosity in these loci will result in a more limited CTL response to fewer epitopes, which may in turn lead to faster disease progression. In contrast, heterozygosity in combination with specific alleles at each loci (e.g., HLA-B57 or HLA-B27) can result in apparent control of viremia and even long-term nonprogression to AIDS (Carrington et al. 1999; Kaslow et al. 1996; Migueles et al. 2000; Gao et al. 2001). The presentation of specific HIV peptides in association with MHC class I varies and appears to be related to protein/peptide processing efficiency as well as the avidity/affinity of MHC I for the peptide. Dominant vs weaker epitope responses are likely due to multiple aspects of antigen presentation, including abundance of specific viral proteins in infected cells, peptide processing, MHC I-peptide interaction, and subsequent binding to specific T cell receptors. Regardless, dominant epitope responses during infection appear most responsible in controlling viremia (Santra et al. 2002; Goulder et al. 2001). Furthermore, several studies have now shown that the breadth and specificities of HIV-specific CD8+ T cells identified during acute infection may be distinct from those during chronic infection (Altfeld et al. 2001; Dalod et al. 1999; Cao et al. 2003). Thus, it is not surprising that HIV isolates that predominate at early infection often develop mutations in dominant CTL epitopes. Goulder and Watkins (2004) provide a thorough review on the emergence of CTL escape mutations in HIV as well as describe the escape mechanisms encoded by these mutations. HIV escape to a specific CTL response was first described by Phillips et al. in 1991 (Phillips et al. 1991) but was previously well documented in other virus infections (Aebischer et al. 1991; White et al. 1990). CTL epitope mapping of the HIV proteins for specific HLA genetic backgrounds has helped to identify various CTL escape mutations during the course of disease (Korber et al. 1997b). New technologies such as ELISPOT can then assess if a specific mutation within an epitope actually confers “resistance” or escape to the specific CTL response (Goulder and Watkins 2004). By employing these techniques, a large volume of literature has identified various

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escape mutations. Escape mutations that may have the greatest impact on disease progression typically emerge during early infection rather than chronic disease (Altfeld et al. 2001; Dalod et al. 1999; Cao et al. 2003). It is hypothesized that the emergence and possibly the timing of early escape mutations may be linked to (a) the strength of the CTL responses for specific epitopes and/or (b) with the cost of escape mutations on replicative fitness. Certainly, many putative epitopes and escape mutations are actually missed due to limitations of epitope mapping and the use of consensus peptides to generate this map. It is unreasonable to map the epitopes in all HIV-infected patients using the homologous HIV peptide libraries. The few studies that have used this approach have confirmed studies in SIV/macaque models (Evans et al. 1999; Allen et al. 2000; O’Connor et al. 2002; Barouch et al. 2002) and have described early escape in CTL epitopes found in accessory proteins (e.g., Tat, Vpr, and Nef) whereas escape in later time points were observed in structural genes (e.g., Gag) (Cao et al. 2003; Addo et al. 2001). As outlined by Goulder and Watkins (2004), it is tempting to speculate that epitopes encoded by the most conserved and functionally constrained HIV sequences may take longer to escape and may require the accumulation of compensatory mutations. For example, the emergence of an escape mutation in the conserved KK10 epitope (R264K) in gag appear in HLA-B27 restricted humans with the emergence of compensatory mutations (L268 M) (Kelleher et al. 2001). It is assumed that these mutations come at great fitness cost to the virus, but unfortunately, these hypotheses have not been substantiated with empirical data that actually test the replicative fitness of the virus prior to and after the appearance of escape mutations. HIV-specific CTL responses are likely responsible for a genetic bottleneck, which ultimately results in selection of CTL escape variants. However, the strength of this bottleneck is not well understood in terms of reducing quasispecies diversity or replicative fitness. In some ways, introduction of combination antiretroviral treatment may be analogous to an HIV-specific CTL response. Both are targeting multiple epitopes or gene functions. Both reduce viral load and may slow disease progression. However, only combination antiretroviral treatment reduces viral load to undetectable levels. In fact, many antiretroviral drugs such as non-nucleoside RT inhibitors and protease inhibitors can effectively lower viral loads to a greater extent than a CTL response directed at multiple epitopes (Chouquet et al. 2002; Goulder et al. 2001). Thus, it is likely that antiretroviral drugs induce a greater genetic bottleneck than CTL pressure. In addition, there is discrepancy in the nature of the target HIV gene or sequence. CTL epitopes are selected by protease processing of the viral proteins and by virtue of affinity/avidity of the various HIV peptides for MHC class II molecules. In contrast, antiretroviral drugs were selected to target specific enzymatic steps in viral replication. Resistance

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to ARVs is typically conferred by mutations that reduce drug binding and are in relatively conserved regions of the protein (e.g., RT or PR) that contribute to enzymatic function (Larder 2001; Arts and Wainberg 1996). Thus, it is not surprising that resistant mutations have a significant fitness cost (QuiñonesMateu and Arts 2002). Mutations that emerge in CTL epitopes during early HIV infection may or may not fall in conserved sites or regions important for function. It is possible that a high proportion of these mutations may not have a cost to overall fitness that is comparable to drug-resistant mutations. As described earlier, this topic has not been explored in experimental studies. Many studies on fitness theory using other RNA viruses indicate that horizontal transmission in the absence of strong bottlenecks will permit the accumulation of adaptive mutations (Burch and Chao 1999, 2000; Burch et al. 2003). Again, experimental approaches have not been employed to address this topic. Do escape mutations in CTL epitopes revert back to the wild-type sequence? Are compensatory mutations adequate to regain fitness and stabilize this genotype? When applying Wright’s fitness landscape to this scenario, it is possible that the valley of fitness loss due to a CTL escape mutation may be sufficiently shallow to permit a rapid climb to the adjacent fitness ridge with only a few compensatory mutations. Thus, these original CTL escape mutations would likely not revert later in disease progression. In more conserved and functionally constrained sites such as (a) in HLA-B27-restricted CTL KK10 epitope in gag (R264K) (Goulder et al. 1997; Kelleher et al. 2001) or (b) in the RT of 3TC-resistant virus (M184V) (Frost et al. 2000; Newstein and Desrosiers 2001), escape or resistance may result in a deeper valley of fitness loss, which would require a longer time for the accumulation of compensatory mutations to stabilize the escape genotype, i.e., analogous to a slow climb up to the fitness ridge. In these cases, reduced CTL pressure in late disease stages or removal of antiretroviral drug pressure would result in a reversion of the escape/resistance mutation to a wild-type sequence and possibly a jump back to original fitness ridge. 3.6 HIV Diversity and Fitness Genetic diversity in HIV is often overlooked as a parameter of disease progression due to complications in interpretation. However, the great propensity of HIV to mutate and recombine undoubtedly shapes the fitness of virus in the context of replication kinetics, host tropism, and immune evasion. Most RNA viruses have a polymerase capable of high mutation rates and yet diversity remains relatively low within an individual host infection (Moya et al. 2004; Simmonds 2004). This low genetic diversity may be one reason for resolution

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of these acute RNA virus infections by host immune response. As described earlier, not all mutations result in adaptation. In the presence of constant selective pressure, deleterious mutations may accumulate to aid in viral clearance. How these deleterious mutations interact and affect fitness is the basis of many studies in evolutionary biology and population genetics (Kondrashov and Crow 1991; Partridge and Barton 1993; Barton and Charlesworth 1998). Epistasis is defined by the association between mutations on fitness and may be dependent on the total abundance of deleterious and adaptive mutations in a genome. Independent relationships between mutation would result in a linear, additive/subtractive interaction between mutations (multiplicative; Fig. 4; Burch et al. 2003). Synergistic or “negative” epistasis is somewhat analogous to Muller’s ratchet and suggests that the accumulation of deleterious mutations relative to total mutations during selective pressure would lead to continual log decreases in viral fitness (Burch et al. 2003). However, “negative” epistasis also applies to the theoretical case of two independent, beneficial mutations resulting in less than additive gains in fitness when linked through mechanisms such as recombination. This theory of synergistic or negative epistasis was recently challenged by experimental data showing that continual plaque-to-plaque transfers of FMDV does not result in steady but rather fluctuating decline in fitness (Lazaro et al. 2003). In these cases, compensatory adaptation (restoration of fitness) may occur faster under synergistic epistasis because it allows for increased impact of each compensatory mutation

Fig. 4 Hypothetical fitness effects of increasing deleterious mutation number. (Adapted from Burch et al. 2003).

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(Moore et al. 2000). In conservation biology, negative or synergistic epistasis plays a role in determining the rate of fitness loss via genetic drift in small populations (Kondrashov 1994), but recent studies suggest that these general theories on epistasis do not always apply in pathogen evolution. In studies by Elena and Lenski (1997) and (de Visser et al. 1997) where Escherichia coli or Aspergillus niger were mutagenized, some mutation combinations showed negative epistasis, whereas others displayed positive epistasis. On average, no epistasis was evident but it is important to note that the worst mutational combinations could have been missed due to rapid extinction of the low fit variants. Epistatic theory was also examined in RNA viruses through the use attenuated viral vaccine (Burch et al. 2003), FMDV (Elena 1999), and RNA bacteriophage s6 (Burch and Chao 2004) in stringent bottlenecks, and ribavirin as a mutagen (Crotty et al. 2001). In two of these studies (Burch et al. 2003; Elena 1999), there was a log-linear relationship between mutation number and fitness, suggesting the lack of epistasis. The trend toward positive epistasis using various RNA viruses treated with ribavirin was not significant (Crotty et al. 2001). Only a recent study using s6 showed positive epistasis as inferred by the observation that low fitness genotypes were less sensitive to deleterious mutations (Burch and Chao 2004). In retroviruses, recombination due the diploid nature of the virion can reshuffle genetic material and can be viewed as analogous to sexual reproduction in higher organisms. It is generally understood that sex creates mutational combinations of very low fitness that are more quickly eliminated by selection (Eshel and Feldman 1970; Kondrashov 1988; Agrawal 2001; Siller 2001), but this theory may not be applicable for RNA virus systems (Novella et al. 2004; Froissart et al. 2004). The genetic complexity of the quasispecies and the high frequency of sex could contribute to rapid adaptation of viral clones due to recombination of two or more adaptive/compensatory mutations to overcome any deleterious changes. Recent studies by Bonhoeffer et al. (2004) and Sanjuan et al. (2004) have tested the epistasis theories by utilizing HIV1, capable of recombination and vesicular stomatitis virus, which can only evolve through point mutations and not recombination (respectively). The epistasis study on HIV-1 involved fitness and sequence data on 9466 HIV-1 isolates clearly described a slightly positive epistasis. Although these results are strongly supported by an extensive database, the population of sequences are skewed to one HIV-1 coding region (protease and reverse transcriptase) and derived specifically from patients under drug selection targeting these gene products (Bonhoeffer et al. 2004). Drug-resistant mutations that would lead to increased fitness in the presence of drug typically result in reduced fitness in its absence. Nonetheless, it is also likely that recombination could promote the inclusion of compensatory mutations and that any antagonism

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between negative mutations and recombination (evidence of “negative” epistasis) might simply be lost due to competitive elimination of these less fit clones in the viral population. Positive epistasis may imply that HIV-1 is gaining replicative fitness during the epidemic. However, this observation is controversial based on the tendency of pathogens to attenuate over time. Of course, the majority of studies described above test the impact of mutations on fitness in very controlled in vitro systems. The direct effect of neutral or positive epistasis in pathogen evolution and fitness within the complex host infection is poorly understood. In the face of strong selection pressure, virus must rapidly adapt to survive. Fitness of an obligate parasite, such as HIV, is defined by how these properties affect fecundity (or replication) and survival within a particular host environment. In general, fitness gains are dependent on increasing population size and genotypic complexity, commonly termed Red Queen dynamics (RQD). RQD for RNA viruses was best demonstrated by a study by Novella et al. (1995b) where they showed that repeated transfers of large VSV populations in cell culture resulted in exponential fitness gains. When it was later shown that HIV populations also increase in diversity and size during disease progression, it was natural to assume that this may be associated with concomitant increases in fitness. However, the forces shaping the HIV genetic variation during the course of chronic disease are complex. In the case of the envelope gene, genetic variation is likely the result of random genetic drift as well as selection by the immune response (both humoral and CTL) and by the infection of various host cell types with different co-receptors for entry. Following escape or adaptation to a new environment due to CTL pressure or host cell differences, the virus can accumulate mutations that enhance replicative fitness and possibly become fixed in the population. Four studies have now tested whether random drift or natural selection is shaping the HIV population during disease. Using coalescent theory-based methods and assuming neutrality, low population size, no recombination, and no population growth, Leigh Brown (1997) suggested that random genetic drift may be more important than natural selection in shaping the HIV populations. This hypothesis was supported by Seo et al. (2002), who used a maximum-likelihood approach but also assumed no population growth or recombination. In contrast, natural selection during intrapatient HIV evolution was supported by the linkage disequilibrium test for large population sizes applied by Rouzine and Coffin (1999). Unfortunately, these studies were limited by the number of HIV env sequences from sequential sampling of many patients. The empirical data from Shankarappa et al. (1999), which clearly showed increasing HIV diversity during asymptomatic disease, was employed by this same group to test natural selection vs random drift during disease (Shriner et al. 2004). By employing Tajima’s D test, Fu and Li’s D test,

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and the test of recurrent mutations, they discovered HIV genetic variation during disease is due mostly to random genetic drift with brief episodes of natural selection. These analyses did not support the possibility of recurring selective sweeps affecting env during chronic infection and that natural selection only played a minor role in driving diversity (Shriner et al. 2004). This result was somewhat surprising considering the HIV quasispecies continues to escape anti-HIV humoral/antibody response throughout chronic disease (Richman et al. 2003; Wei et al. 2003). If humoral responses did invoke strong selective pressure, there should have been continual or more frequent episodes of purifying selection in env due to escape. If fitness increases are due to distinct mutations that can be fixed in the population, there is also a dichotomy in the lack of natural selection and the observation that HIV replicative fitness and diversity both increase during disease (Troyer and Arts, in press). However, aside from the distinct mutations responsible switch in co-receptor usage, it appears that multiple combinations of different linked mutations may be responsible for increases in fitness during disease. Because of high recombination frequencies, these combinations that enhance fitness may never be fixed in the population. These new revelations in HIV genetic diversity during disease progression hark back to earlier studies that proposed the model of antigen diversity threshold in HIV disease (Nowak et al. 1991). In this model, the dynamic interaction between viral diversity and the human immune system suggests a threshold for HIV diversity, below which the immune system is able to regulate viral population growth but above which the virus population induces the collapse of the CD4+ lymphocyte population. As described below, this model could be modified to suggest that increases in replicative fitness as a function of increasing diversity (Troyer and Arts, in press) may be the key in the destruction of CD4+ T cell subset and escalating progression to AIDS. 3.7 A Model for HIV Fitness During Disease Based on the findings and assumptions in the above sections, a model of fitness during HIV disease progression can be formulated. This theoretical model was established on several fundamental observations, namely 1. HIV transmission is accompanied by a stringent bottleneck that only permits infection by few clones 2. Initial CTL pressure creates another genetic bottleneck for the selection of CTL escape mutants that have lower replicative fitness 3. Eventual escape from dominant CTL pressure promotes a slow expansion of the HIV population size and diversity

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4. HIV fitness steadily increases during disease 5. Exhaustion of susceptible cells/host destruction leads to decreases in diversity and possible attenuation. Figure 5 represents the distribution of HIV clones in the donor quasispecies. Following prolonged infection, the donor has accumulated a population of clones, each with a defined frequency and fitness. The normal distribution is likely to continually shift to the left (increasing fitness) as new high-fitness clones emerge and then dominate the population. Low-fitness clones may not be completely eliminated from the population as described in the quasispecies theory and the total fitness of the infecting HIV isolate is equal to the area under this curve. During transmission to the recipient (Fig. 5), the transmitted virus undergoes a stringent bottleneck, which would sample the normal distribution of clones in the donor. Based on the frequency of clones, most patients would be infected with clones of “moderate” fitness.

Fig. 5 A model for changes in HIV-1 population diversity and fitness during transmission

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However, increased sampling due to a weak bottleneck at transmission may result in higher genetic diversity in the recipient and more frequent sampling of the clones of higher and lower fitness. During the initial infection, the higher-fitness clones may then out-compete the less fit HIV clones, ravage the CD4 T cell population, and even decrease T cell-mediated immunity. This hypothetical scenario, albeit rare, could lead to rapid progression to AIDS. Aside from innate immunity, HIV populations rapidly expand in the absence of direct selection and loads can reach greater than 108 copies/ml of plasma following acute infection. Introduction of T cell-mediated immunity directed toward HIV will create another bottleneck, limiting quasispecies expansion and selecting for escape mutants with lower fitness (Fig. 5). The expanded clones at acute infection will be forced through repeated bottlenecks due to sequential escape from CTL pressure. Coupled with this escape, clones can select for compensatory mutations that may partly restore fitness (Fig. 5). However, Muller’s ratchet suggests that the accumulation of these deleterious mutations (CTL escape mutations) will override most compensatory mutations. This oscillation between fitness loss and slight fitness gain likely continues for at least 1 year and may subside with the advent of viral load set point. In other words, HIV may reach trough levels due to continual depletion by immune responses and the accumulation of deleterious mutations to escape the overall CTL response directed at multiple CTL epitopes. Following CTL escape, the greatest selective pressure on the virus population may be the HIV-specific humoral response. In most studies, it is clear that the humoral/antibody response is less efficient in controlling viremia than CTL response. Escape from humoral response is almost immediate and continuous throughout disease progression and, based on several models, is not sufficient to induce prolonged selection on the population. The major dominant epitope, the hypervariable V3 loop of the HIV envelope glycoprotein, is extremely accommodating to genetic drift, suggesting that humoral escape may come at minimal fitness cost. During this stage of disease progression, it is possible that increases in HIV diversity (due to random drift) could exceed the accumulation of deleterious mutations to escape CTL pressure (to possibly weaker CTL pressure), humoral responses, or adapt to changing host cell environments. Thus, minimal bottlenecks could allow the HIV quasispecies to expand in total number and diversity. Thus, concomitant fitness increases may suggest that Red Queen dynamics is in play. In panel C-III, the fitness and diversity would continue to increase until the susceptible cell population and density (CCR5+ /CD4+ cells) was exhausted. A new genetic bottleneck may coincide with the selection of clones in the quasispecies that can infect new susceptible host cell populations, i.e., CXCR/CD4-expressing cells. This bottleneck would reduce diversity and possibly decrease fitness. One discrepancy

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to this theory is the actual increased replicative capacity of CXCR4-tropic HIV isolates as compared to CCR5-tropic strains. However, it is still not clear as to when this co-receptor switch may occur during disease. Experimental studies are underway to test if the transition between CCR5 and CXCR4 co-receptor usage is accompanied by a decrease in fitness, which is obscured by the mixed HIV population until late in disease. 3.8 Are There Intersubtype HIV Variations in Viral Fitness? Divergent interpatient HIV evolution coupled with new introductions into susceptible human populations has led to the current HIV diversification from original zoonotic jumps in central Africa (Hahn et al. 2000; Korber et al. 2000). Although a founder effect results in further HIV spread and divergent evolution in different regions in the world, the phylogenies of most non-African HIV strains can be traced to central African isolates (Hahn et al. 2000; Korber et al. 2000). At least two to three separate zoonotic jumps from chimpanzees into humans led to the disproportionate spread of HIV groups M (main), O (outlier), and N (non-M/non-O) (Hahn et al. 2000; Gao et al. 1999; Kuiken et al. 1999). Increased fitness has likely played a key role in the predominance and extreme variation of HIV group M over group N or O isolates. Phylogenetic and recombination analyses further subdivided the HIV group M into nine subtypes (A, B, C, D, F, G, H, J, and K) and fourteen circulating recombinant forms (CRF) (reviewed in Quiñones-Mateu and Arts 1999; Peeters 2000). These subtypes and CRFs are unequally distributed across the globe, e.g., subtype B in the Americas and Europe whereas A, C, and CRF02-AG are the most prevalent clades in Africa (Quiñones-Mateu and Arts 1999; Peeters 2000). To date, CRF have been identified in nearly every region of the world where two or more subtypes co-circulate and may account for over 10% of new HIV infections (Peeters 2000; Korber et al. 1997a; Quiñones-Mateu and Arts 1999). The proportions of subtypes in defined populations are not stable but are in constant flux due to new introductions of HIV subtypes, changes in human behavior, therapeutic intervention, mode of transmission, and possibly subtype fitness. Over the past decade, there has been a considerable shift in the epicenter of the HIV epidemic from sub-Saharan Africa to Southern Africa, India, and Southeast Asia (Essex 1999; Kalish et al. 1995; Santiago et al. 1998; Lole et al. 1999). Subtype C has now emerged as the predominant clade in the world due to these regional pandemics and accounts for at least half of all infections worldwide (Hu et al. 1996; Rodenburg et al. 2001). Although subtype B likely preceded subtype C as a founder clade in India and China,

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most of the new infections in these countries are attributable to subtype C isolates or intersubtype B/C recombinants (Rodenburg et al. 2001; Piyasirisilp et al. 2000). Similar trends have been observed in Kenya, Tanzania, and South America (e.g., Brazil and Argentina) (Quinn 1996; Essex 1999; Lole et al. 1999; Hu et al. 1996; Van Harmelen et al. 1999). This rapid insurgence of subtype C may be due to a founder effect or to differences in intersubtype fitness. Any difference in ex vivo fitness likely reflects genotypic or phenotypic variations between subtypes. For example, most subtype C isolates appear to have an extra or third NFkB element in the LTR as compared to the two sites found in most subtype, which augment transcription in the presence or absence of HIV Tat protein (van Harmelen et al. 2001; Rodenburg et al. 2001; Hunt and Tiemessen 2000). A subsequent study on HIV protease activity showed increased cleavage of peptide substrates by HIV subtype C vs subtype B protease (Velazquez-Campoy et al. 2001). These phenotypic data suggest that dominance of subtype C in the HIV epidemic could be due to increased replicative capacity of subtype C isolates over other HIV subtype isolates. In addition to these phenotypic differences, subtype C isolates rarely switch from a NSI/R5 phenotype to an SI/CXCR4 phenotype during late disease (Cecilia et al. 2000). However, a recent study has compared the fitness of nine subtype B and six subtype C HIV isolates in a pair-wise competition experiment in PBMC culture (Ball et al. 2003). The relative fitness values were not statistically different in pair-wise competitions involving isolates of the same subtype (intrasubtype B or C competitions). In contrast, almost all of the subtype C isolates were out-competed by the subtype B isolates in the pair-wise competitions. In contrast to previous reports with recombinant HIV clones containing fragments of the subtype C genome, these data suggest that subtype C isolates are likely less fit than subtype B isolates (Ball et al. 2003). The poor relative fitness of subtype C isolates can also fit into the model for subtype C dominance in the epidemic. Decreased fitness is linked to slower disease progression, which could result in increased transmission time (Quiñones-Mateu et al. 2000). A recent study has now ranked the fitness order of most human lentiviruses by performing pair-wise fitness comparisons with isolates of different types (HIV-1 vs HIV-2), HIV-1 groups (M vs O), and subtypes (A, B, C, and D) (Arien et al., personal communication). Regardless of the donor human PBMCs used for these competitions, a definitive fitness rank order was established: HIV-1 group M (subtypes A, B, and D) > group M-subtype C > HIV-2 >> HIV-1 group O. This order in replicative fitness was also observed when virus pairs were added to human dendritic cells then co-cultured with primary, quiescent T cells, which is model for HIV-1 transmission. These results suggest that reduced replicative and transmission fitness may be contributing to the low

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prevalence and limited geographical spread of HIV-2 and group O HIV-1 in the human population. 3.9 Is the Fitness and Pathogenicity of HIV Decreasing? It is apparent that some simian immunodeficiency viruses (SIV) have become highly adapted to their host species over an extended period of time, resulting in asymptomatic, nonpathogenic infections (Whetter et al. 1999). Based on the subtype C fitness results, is it possible that HIV is evolving to an attenuated state? This hypothesis was proposed by Temin more than 10 years ago (Temin 1989). Rapid attenuation of virus was first observed in the myxoma virus infection of Australian rabbits (Ewald 1993). Several introductions of this highly lethal virus resulted in a decrease of nearly half of the rabbit population followed by rapid evolution of a less virulent (fit) virus (Fenner and Kessler 1994). When a virus “jumps” and infects a new species, an increase in

Fig. 6 Schema of the relationship between transmission and pathogenesis fitness and its effect in HIV-1 evolution. More virulent (red) viruses would rapidly kill their hosts, reducing the time for transmission and permitting the expansion of less virulent (blue) viruses that are still capable of establishing new infections. These less pathogenic and more transmissible viruses will then out-compete the more virulent viruses, at the population level

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the initial virulence can only be supported in minor pandemics (e.g., Ebola pandemics). It is evident that to survive, viruses must continue to propagate in a living host and that a low/attenuated level of pathogenesis represents a trade-off between virulence and transmissibility. Ewald (1993) suggested that more virulent viruses would rapidly kill their hosts, reducing the time for transmission and permitting the expansion of less virulent viruses that were still capable of establishing new infections. In the case of HIV, the continual spread of this lethal virus is a consequence of long asymptomatic but transmissible periods following initial infection. Individuals infected with a more pathogenic (high-replication) strain will progress faster to HIV disease, decreasing the probability of viral transmission. In contrast, attenuated HIV strains (i.e., lower replicative capacity) would in theory delay disease progression and increase the likelihood of transmission (Fig. 6). Based on this theory, changes in HIV pathogenesis in the population may not be easily identified by common correlates of disease progression (e.g., viral load and CD4 cell counts). For example, the nonpathogenic infection of Sooty mangabey monkeys with SIV (Rey-Cuille et al. 1998) results in extremely high viral load, while maintaining transmissibility in the absence of symptomatic disease. Thus, the higher viral loads observed in patients infected with subtypes B or A as compared to subtypes C or D, respectively may be unrelated to the effect of subtype on disease progression. Acknowledgements M.E.Q-M is supported by research grants NIH-HL-67610, NIHDE-015510, NIH-AI-36219 (Center for AIDS Research at Case Western Reserve University), and unrestricted research grants from Agouron Pharmaceuticals, Inc. E.J.A. is supported by research grants NIH-HD-0-3310-502-02 and NIH-AI-AI49170.

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