applied evolution

10 Oct 2001 16:7 AR AR142-07.tex AR142-07.SGM ARv2(2001/05/10)P1: GJC

Annu. Rev. Ecol. Syst. 2001. 32:183–217Copyright c© 2001 by Annual Reviews. All rights reserved

APPLIED EVOLUTION

J. J. Bull1 and H. A. Wichman21Section of Integrative Biology, Institute of Cellular and Molecular Biology, Universityof Texas, Austin, Texas 78712-1023; e-mail: [email protected] of Biological Sciences, University of Idaho, Moscow, Idaho 83844-3051;e-mail: [email protected]

Key Words artificial selection, directed evolution, phylogenetics, resistance,evolutionary computation

■ Abstract Evolutionary biology is widely perceived as a discipline with relevancethat lies purely in academia. Until recently, that perception was largely true, exceptfor the often neglected role of evolutionary biology in the improvement of agricul-tural crops and animals. In the past two decades, however, evolutionary biology hasassumed a broad relevance extending far outside its original bounds. Phylogenetics,the study of Darwin’s theory of “descent with modification,” is now the foundation ofdisease tracking and of the identification of species in medical, pharmacological, orconservation settings. It further underlies bioinformatics approaches to the analysis ofgenomes. Darwin’s “evolution by natural selection” is being used in many contexts,from the design of biotechnology protocols to create new drugs and industrial enzymes,to the avoidance of resistant pests and microbes, to the development of new computertechnologies. These examples present opportunities for education of the public and fornontraditional career paths in evolutionary biology. They also provide new researchmaterial for people trained in classical approaches.

OVERVIEW

Evolutionary biology has undergone an expansion and transformation in the pastfew decades. Despite occasional claims to the contrary, the big changes in evolu-tionary biology have come from improvements in understanding mechanisms thatare fully compatible with Darwinism; descent with modification and natural selec-tion are still the conceptual foundations of the discipline. For example, a veritableexplosion of studies estimating the relationships among different species has re-fined our understanding of evolutionary history, but the modern version of the treeof life has many similarities to old ones and certainly supports a Darwinian model.The sequencing of genes and genomes has yielded insights into genetic mech-anisms underlying evolution, and we are even progressing toward a genetic un-derstanding of the major developmental and morphological transitions—advancesthat augment earlier ideas about these transitions. Theories based on natural selec-tion have led to revolutions in understanding behavior, parasitism, and a wealth of

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genetic and physiological mechanisms that benefit neither the individual nor thepopulation.

Some of the revolution in evolution has occurred outside the traditional aca-demic boundaries of evolutionary biology. Those who started as evolutionary biolo-gists two or more decades ago are beholding a transformation of the field into one ofbroad social relevance. Much of the biotechnology industry is concerned with creat-ing biological molecules that have specific functions. This goal-oriented enterprisehas quickly embraced evolutionary principles to direct the evolution of moleculesin test tubes and, in so doing, has profoundly expanded the horizon and relevanceof evolutionary biology. Evolutionary principles are suddenly the material of mul-timillion dollar patents, leading industrial biochemists to new drugs and othercommercial molecules. On a different front, the medical establishment, after longignoring evolution, is faced with an onslaught of drug-resistant microbes, has seenmonkey viruses jump into humans and accelerate into epidemics, and must nowuse evolutionary principles to understand the worldwide dynamics of pathogens.

The theme that unites the examples in this paper is that evolution and evolu-tionary biology are socially relevant. There are two main reasons for writing sucha paper. First, public perception of evolutionary biology is not up to date with thediscipline. “Evolution” is still a bad word to many people, not only because it isperceived as conflicting with some religious views, but also because it is widelyviewed as an irrelevant science with no social value. Acceptance of evolution-ary biology is far more likely when the public realizes that it holds the key tomany social improvements [a view that motivated G.C. Williams in his work onapplications of evolutionary biology to medicine (Nesse & Williams, 1994; G.C.Williams, personal communication)]. As evolutionary biologists, we need to usethese examples of relevance when explaining evolution to our students and thepublic. A second reason for writing this paper is that historical inertia in the train-ing of evolutionary biologists has resulted in a lack of exposure to socially relevantapplications. Individuals trained as evolutionary biologists will have much to offerin solving these problems, but they need to be aware of these applications. Careeropportunities for evolutionary biologists may already be more plentiful outsideacademia than inside it.

This paper is an introduction to some examples of socially relevant evolution-ary biology. We have chosen topics with which we are familiar and in whichevolutionary biology has already been used to produce an outcome or to affect apolicy: phylogenetics, artificial selection (in biotechnology), resistance manage-ment, and computation. Other applications of evolutionary principles to sociallyrelevant problems include Darwinian medicine (Lapp´e 1994, Nesse & Williams1994, Trevathan et al. 1999), infectious diseases (Ewald 1994, Morse 1994), andhuman impact on evolution (Palumbi 2001). An excellent overview of the so-cial relevance of evolution has been assembled and endorsed by eight scientificsocieties (Futuyma 1999).

The topics in this review fit logically into the conceptual framework of evolu-tionary biology. The first two sections are based on Darwin’s theory of descent


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with modification: The first is about estimating evolutionary history for biomedicaland other applications, and the second discusses the role of evolutionary models ininterpreting the molecular variation seen in the burgeoning genome databases. Thelast three sections are based on the neo-Darwinian framework for natural selection.The first describes examples in which humans have modified the elements of nat-ural selection to produce various biological commodities. The next is somewhatthe reverse of that, in which an understanding of natural selection is recruited totry to block evolution. The last section is a brief introduction to uses of models ofnatural selection in designing computer programs.

PHYLOGENETICS: USING THE TREE OF LIFE

The premise that all life shares common ancestry has been a central tenet ofevolutionary biology since Darwin. If we go back far enough in time, the genealogyof every organism alive today can be traced to a point that unites it with thegenealogy of any other organism alive today. By definition, closely related specieshave recent common ancestors, whereas the common ancestors of distantly relatedspecies go far back in time. Furthermore, the process is continual. Today’s speciesare themselves comprised of lineages that continue to diverge.

Phylogenetics is the study of these evolutionary genealogies. Despite the antiq-uity of the common-ancestry principle, the field of phylogenetics has matured im-mensely in the past 10 to 15 years, owing to advances in computer technology andDNA sequencing as well as to the development of explicit theories and method-ologies for phylogenetic reconstruction. Most phylogenetic methods use DNAsequences, protein sequences, or RNA sequences, but some methods can use mor-phological data, which are vital to the analysis of fossils. Not only do methods differin the types of data that can be analyzed, methods using the same types of data mayalso differ in the assumptions used to convert the data into evolutionary history.

The output of a phylogenetic analysis is a branching tree that represents theevolutionary history of the lineages being studied. The tree provides not only anested hierarchy of common ancestors going back in time, but also quantitativeinformation on the amount of change between the different points in the tree. Thetree may incorporate taxa whose common ancestors reach back over a billion years,or the tree may be limited to a group of viruses whose common ancestor existedonly weeks or months ago. The applications of phylogenetic methods to sociallyrelevant problems likewise occur at several timescales.

Disease Tracking: Molecular Epidemiology

Phylogenetics has become indispensable in identifying disease reservoirs and intracking the step-by-step transmission of some viruses. The conceptual basis ofthis work is as follows. We want to know the source of a virus infecting person X.Suppose that four different possible sources have been identified: A, B, C, and D.


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These four sources could be different infected individuals who had contact with X,they could be different geographic locations that person X visited, or they couldbe four species of mammals living in the village of person X that are sometimesinfected with the type of virus in X. The problem in figuring out the source is thatnone of the viruses in A, B, C, and D will necessarily have the same sequence asthe virus in X. Phylogenetic analysis gets around that problem by establishing theevolutionary relatedness of viruses from each of the possible sources (Figure 1).The analysis not only indicates which of the viruses in A–D are most similar tothat in X (source D in Figure 1), it also indicates how closely related the virusesare and, hence, whether the source might be other than A–D.

Phylogenetic analysis is now a standard part of any disease epidemiology. Belowwe offer a few of the many applications.

Figure 1 The virus acquired by individual X is compared by phylogenetic methodswith viruses from four possible sources (A–D). The context for this could be any of thefollowing. (a) A–D are different individuals infected with HIV who had sex or sharedneedles with individual X, and we are trying to find out who transmitted the virusto X. (b) A–D are different mammal species with rabies viruses circulating in theirpopulations, and X is a human who died of rabies. Which mammal species transmittedthe infection? (c) A–D are mice with hantavirus from New Mexico, Texas, California,and Nevada. X is a Texas resident who recently traveled across the United States andbecame infected with hantavirus. Where did X contract the virus? In this hypotheticalexample, the virus in X is most closely related to the virus from source D, which givesinformation about the likely source (and place) of transmission.



ERADICATION OF WILD-TYPE POLIOVIRUS FROM THE AMERICAS Poliomyelitis is aparalyzing, occasionally fatal, disease caused by an RNA virus. On recovery frominfection, a person carries lifetime immunity, but there are three forms of the virus,and immunity to one form does not confer immunity to the others. Less than 1%of those infected by the virus actually develop the disease, but the incidence ofdisease per infection is thought to increase with age. It has thus been speculatedthat the widespread outbreaks of polio disease that occurred in the first part of thetwentieth century were a consequence of social hygiene, because improvements insocial hygiene delayed the average age of infection (Nathanson et al. 1995, Garrett2000).

Over 38,000 cases of poliomyelitis were reported in the United States in 1954.The first vaccine was approved a year later, and within 2 decades, the native poliovirus was thought to have been eradicated from the Americas. The only knownhost for poliovirus was human beings, making eradication seem a feasible goal.However, isolated cases of polio continue to occur in the Americas. With theexception of an outbreak in a religious community that avoided vaccines and arecent outbreak in the Dominican Republic (Greensfelder 2000), these isolatedcases failed to materialize into epidemics because the population maintained highlevels of vaccination (which continues today), but they raised the possibility thatnative polio strains might still be present. The alternative explanation for thesesporadic cases was either nonnative poliovirus introduced from parts of the worldwhere it was still endemic or a vaccine strain. The vaccine in use after 1961 was alive, attenuated virus that was capable not only of transmission, but also of evolvinginto a more virulent form. Phylogenetic analysis showed that the viruses that gaverise to isolated cases and epidemics were invariably either vaccine derived orwild strains originating from outside the Americas; no native, American virus hasbeen found, and it is presumed to have been eradicated (Rico-Hesse et al. 1987).An assault to eradicate poliovirus worldwide continues to this day, aided by theknowledge that the virus does not lay hidden in nonhuman reservoirs.

ORIGIN OF HIV The most notorious human infectious disease to have arisen inthe 1900s is AIDS (acquired immune deficiency syndrome). Unknown until about1981, 20 years later it was estimated that more than 30 million people were infectedworldwide and more than 16 million had already died. This disease is caused by theretrovirus HIV (human immunodeficiency virus), which exists in two basic forms,HIV-1 and HIV-2. Although the HIV epidemic is now driven by human-humantransmission, phylogenetic evidence indicates that HIV-1 originally came fromchimpanzees and HIV-2 (which is less abundant and less often fatal than HIV-1)came from a type of monkey known as the sooty mangabey (Gao et al. 1999, Hahnet al. 2000). Phylogenetic evidence supports multiple introductions of HIV-1 andHIV-2 into humans this century (Korber et al. 2000).

HIV TRANSMISSION BETWEEN PEOPLE One of HIV’s unusual properties is that itevolves rapidly, even for a virus. That property is an unfortunate one from the


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perspective of curing an infection or creating a vaccine, because the virus evolvesquickly in response to drug treatment and immune attack (Coffin 1996, Colgrove &Japour 1999, Mosier 2000). However, its rapid evolution enables fine-scale analysiswith molecular epidemiology that is not possible with most other viruses. Startingfrom a single virus in a person, the infection will blossom into a miniature tree oflife, the viral lines ever expanding as the infection continues. Consequently, twoviruses from one person usually have different genome sequences, but sequences ofvirus from one person will be more similar to each other than they are to sequencesof viruses from other people. This property potentially allows determination of theindividual who transmitted the virus.

One of the early analyses of HIV transmission revolved around a Florida dentistwith AIDS whose patients exhibited an unusually high incidence of HIV infection(10 patients were eventually discovered to be HIV+). By itself, the clustering ofHIV+patients associated with this dentist might have been regarded as evidence ofdentist-to-patient transmission, but this cluster was discovered in the earliest daysof understanding HIV, and the possibility of unknown routes of infection had to beconsidered. If the dentist was not the source of the infection, it was important todiscover the source to halt further transmissions. On the other hand, if the dentistwas the source, the implications for health care practices were enormous. Thestakes were very high either way. Phylogenetic analysis suggested that the patientviruses were close relatives of the dentist’s virus in all but two cases, and thosetwo patients had other known risk factors (Ou et al. 1992, Hillis & Huelsenbeck1994, Hillis et al. 1994). Thus the dentist was likely the source of infection for theeight patients with no known risk factors.

Perhaps the most sensational case of HIV molecular epidemiology was a crim-inal case in Lafayette, LA. A physician was accused of injecting his formermistress with blood containing HIV. He had been giving her vitamin B injec-tions, and it was supposedly the final injection in August 1994 that containedthe blood with HIV. When the woman was diagnosed with HIV and hepatitisC in December 1994, she suspected the physician’s injection as the source; atthe time of a blood donation in April 1994, she had been negative for bothviruses. This case was unusual in that the person infecting the woman (the physi-cian) was not himself infected, so it was necessary both to locate the patientwhose HIV infected the woman and to demonstrate that the physician had ac-cess to this patient’s blood. Records were discovered in the physician’s officeindicating that blood had been drawn from two patients during the week in ques-tion; one patient was previously known to be HIV+ and the other positive forhepatitis-C. Phylogenetic analyses showed that the HIV sequences of the ex-mistress clustered within those of the patient with HIV, supporting her story (Stateof Louisiana Criminal Dockett #96CR73313; D. Hillis, personal communication).The physician was convicted of attempted second-degree murder and sentencedto a 50-year term in prison in this first use of phylogenetics in a U.S. criminalcourt.



Predicting Evolution

A novel step in phylogenetic analysis—predicting evolution—was introduced inwork on influenza A, the respiratory virus that causes flu epidemics every year.At any one time, several influenza types are circulating in the human population.A viral type is defined by how the virus reacts with a set of antibodies (Webster1993). Two viral proteins, hemagglutinin and neuraminidase, determine the viraltype.

For some decades, influenza isolates have been stored by the Centers for DiseaseControl; these isolates include lineages that are now extinct in human populations.When techniques were developed to obtain viral gene sequences, it was possiblenot only to estimate the evolutionary history from currently circulating lineages,but also to use the stored isolates to sequence extinct types to obtain detailed insightinto ancestral states. An unusual property of influenza discovered from this work isthat the level of sequence diversity has not progressively expanded over time (Fitchet al. 1997, Bush et al. 1999). At any point over the past 15 years, variation existedaround a recent common ancestor that was never more than a few years old. Thus,viral extinctions occurred as fast as new types evolved, with only one ultimatewinner. Each winner would continue to generate new variants that competed inthe population, and only one of them would win, and so on. The phylogeny of thisprocess was a tree with a long trunk and lots of short (dead-end) side branches.

The study that predicted influenza used the hemagglutinin gene for a singleviral type, HA3. The fact that only one lineage of HA3 survives means that it maybe possible to predict which of the variants circulating in a population will giverise to the viruses of the future. Fitch et al. (1997) observed rapid evolution at a fewresidues in the hemagglutinin gene. This finding was facilitated by the analysis ofhistorical samples, because the repeated evolution at common sites had otherwiseerased earlier evolution at those sites. Those rapidly evolving residues provided anobvious yardstick by which to compare different viruses: Of the viruses present atany one time, the ones with the most evolution at those sites were the candidatesas progenitors of future lineages. Bush et al. (1999) developed this predictivestatistic and applied it retrospectively to the existing data. The prediction workedexceedingly well, although it awaits a truly a priori test (which will certainly becarried out in the coming years).

Will this method lead to better predictions for flu vaccines? Not necessarily.The difficulty in deciding each year which flu vaccine to produce lies in predictingwhich of the circulating viral types will infect the most people. The phylogeneticprediction is, instead, of which viral type will prevail in the long term and, thus,covers a longer timescale and a smaller magnitude of viral variation than is neces-sary for vaccine design. Furthermore, the worldwide pandemics of influenza (thatinfect and kill more people than average) are caused by a fundamentally differenttype of sequence change than was studied in the HA3 predictions (Webster 1993).However, the phylogenetic prediction method points toward a new level of utilityfor this kind of evolutionary biology.


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Species Identification on the Tree of Life:Ribotyping and Other Methods

The cells of all organisms from bacteria to humans (but not viruses) carry genesthat build ribosomes, the scaffolding to assemble proteins. Parts of the ribosomeare proteins, and other parts (rRNA) are RNA molecules whose ancestry may betraced all the way back to the hypothetical “RNA world,” before DNA was thegenetic material. Portions of these rRNA genes evolve slowly, so their sequencesare very similar even between distantly related organisms; other portions are morevariable. The essential nature of rRNA genes combined with the wide range ofvariation in rates of evolution across different stretches of the molecules makesthem ideally suited for estimating the tree of life. This approach to assessing thediversity of and relationships among all life on the same scale was pioneered byWoese (2000). This tree of life shows three main domains: Bacteria, Archaea, andEukarya. The impressive diversity of plants and animals is confined to a modestbranch in one of these three domains, the Eukarya (Figure 2).

Over the years, the rRNA tree of life has been filled in densely, covering thou-sands of taxa. Of course, the relationships of many of the taxa in this tree of lifeare consistent with earlier theories, but many taxa have been added whose rela-tionships were previously obscure. Equally important is the fact that this tree pro-vides a universal standard for comparing any life form to any other (which wasnot possible when using morphology, for example). The utility of the rRNA treeof life has been facilitated by the development of different sets of polymerasechain reaction (PCR) primers that can be used to amplify parts of rRNA genesfrom any organism or amplify them from only specific taxa. It is now routine toacquire DNA or RNA from an unknown taxon and to use its rRNA sequences toidentify it, or at least identify its closest known relatives. This technique (knownas ribotyping) can be extremely useful because it allows identification of the dif-ferent species of organisms present in a community, even though it may be im-possible to culture the organisms or recognize them by any other method (Pace1997).

Ribotyping is not the only molecular method used for species identification. Per-haps the main advantage of ribotyping is that it can be used across the full spectrumof life (excepting viruses) without knowledge of the organisms being typed. Butother methods may be preferred when working within narrow taxonomic groups(e.g., mammals, or at a finer level, whales). Thus, noncoding regions of mito-chondrial DNA are often more sensitive than rRNA sequences in distinguishingclosely related species and subspecies, because these mitochondrial sequencesevolve faster. These sensitive methods are not only useful in identifying a species,they may also provide information about the geographic location of the taxon aswell (the DNA “zip codes” of Baker 1994).

USING THE TREE OF LIFE: PATHOGEN IDENTIFICATION AND DISEASE ETIOLOGY Sev-eral diseases have no known infectious causes. Ribotyping and other genotyping



Figure 2 A tree of life, based on 18S rRNA sequences. The tree shows the three domains(Bacteria, Archaea, and Eukarya) as well as many representatives in each domain. Note whatlittle divergence is represented by the animals and plants relative to the entire tree. The 18Ssequences from hundreds of life forms (but not viruses) have been obtained and can be re-solved on this tree, with closest relatives sharing the most recent common ancestors (thefigure is simply too crowded to portray the many species that have been analyzed in thisfashion). The 18S sequences from samples of unknown identity can thus be mapped ontothis tree to discover what types of life forms were present in the sample. (Figure courtesy ofMitch Sogin.)

methods offer a way of identifying whether diseased tissues are associated withany microbe (Relman & Falkow 1992, Relman 1998). If the causative agent isa previously unknown species, evolutionary biology plays a role in identifyingwhat type of organism it is, i.e., in identifying its closest known relatives. Once asuspect microbe has been identified, steps can be taken to treat with appropriatedrugs. Had this approach been available at the time, it might have greatly facilitatedthe discovery that ulcers were associated with a bacterium. Relman (1999) listedfive infectious diseases with causative agents recently discovered by genotypingmethods. An extension of this approach is to use ribotyping to assess the microbialcomposition of a community of organisms, as in animal guts and other passages.Changes in the composition of a community may foreshadow a predisposition todisease or the onset of disease (Kroes et al. 1999).


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USING THE TREE OF LIFE: CONSERVATION AND FORENSICS Concern for endan-gered species has led to widespread adoption of laws limiting the harvesting,marketing, and even possession of tissues and other products of those organisms.Although some derivatives of a species are unmistakable (e.g., a sea turtle shell,an elephant tusk), others are not so obvious. In many cases, an endangered specieshas close relatives that are not endangered and are legitimately marketed. If theharvesting occurs at a remote site, far from the eyes of concerned observers, by thetime the meat of an endangered species reaches market, it may be indistinguish-able from the meat of a legal species. Using genotypic methods, Baker & Palumbi(1996) and Baker et al. (2000) reported the sale of meat from endangered species ofwhales in Asian markets. Ribotyping and similar molecular methods allow simpleand portable means of identifying endangered species in the marketplace.

GENOMICS AND BIOINFORMATICS

From the perspective of human health, a major goal of genomics work is to under-stand not only the function of human genes, but also the impact of mutations inthose genes, and how drugs can be designed to modify or repair those functions.Yet we humans are neither sufficiently genetically variable nor amenable to ex-perimentation that the function of most genes could be ascertained from just ourspecies. An immeasurable benefit to understanding human genetics comes fromwork on other species—model organisms. As we now know, work on the geneticsof other eukaryotes can often be extrapolated to humans. One recent example isthe identification of a human gene responsible for a sleep disorder based on theDrosophila circadian clock geneper (Toh et al. 2001).

The extrapolation of information between species will accelerate now thatcomplete genome sequences are available for many species. Rapid advances inbiotechnology have taken us from the first complete sequence of a small DNAviral genome (Sanger et al. 1977) to the complete sequence of the human genomein less than 25 years (Venter et al. 2001). During the same period, the numberof transistors per computer processor has increased 6000-fold. The marriage ofincreased computing power and large biological data sets has spawned the fieldof bioinformatics, which is dominated by the analysis of nucleic acid and proteindata. Bioinformatics is firmly rooted in evolutionary biology. From the initial stepof carrying out a BLAST search (Altschul et al. 1990) of a database to identifyrelated sequences, to multiple sequence alignment (Higgins & Sharp 1988), tothe identification of orthologous genes in other species (DeBry & Seldin 1996,Tatusov et al. 1997, Chambers et al. 2000), bioinformatics is a comparative ex-ercise, and descent with modification is one of its inherent and most essentialassumptions.

Molecular evolution, specifically sequence divergence, is both a plus and aminus in bioinformatics. A gene in humans and its counterpart in yeast will nothave the same DNA sequence even if the function of both has remained the same



since their common ancestor. Divergence can be so great that it is difficult toalign or even recognize homologous genes. However, once alignments of homol-ogous genes are achieved, evolutionary divergence can be very informative. Thedivergence of genes with the same function is the evolutionary equivalent of an ex-periment in which gene positions are mutated to identify the functionally importantones—neutral or nearly neutral changes accumulate over evolutionary time, butchanges that disrupt gene function are weeded out by the filter of natural selection.The concept that the most highly conserved amino acid residues are important forgene function is firmly entrenched in molecular biology (Benner 1995, Golding &Dean 1998). But not all residues that are important for function are highlyconserved—adaptive evolution is achieved by genetic change. Statistical signaturesof past adaptive evolution can be recognized as high rates of nonsynonymous tosynonymous substitutions among homologous sequences (McDonald & Kreitman1991, Boyd & Hartl 1998, Crill et al. 2000) or as correlated changes betweendifferent residues (Gutell et al. 2000).

ARTIFICIAL SELECTION

Artificial Selection in the Past

Nearly all the common animals and plants we use today were domesticated thou-sands of years ago, some (sheep, goats, dogs, wheat, and rice) at least 9000 yearsago. Domestication probably started as a process of taming, then of captive breed-ing, and finally of selecting for specific traits. These early domestications may wellhave been the first experiments in applied evolution. Their impact was so profoundas to make civilization possible by enabling societies to switch from hunting andgathering to agriculture (Ucko & Dimbleby 1969, Clutton-Brock 1999). Althoughfew new species have been domesticated in the past millennium, we have conti-nued to refine the old ones. The success of artificial selection is evident in itsultimate creation of selected phenotypes well outside the extremes of the originalspecies. For example, Chihuahuas, Saint Bernards, pit bulls, and golden retrieversdiffer in both appearance and behavior, and nothing like any of these breeds wouldhave been found in a population of wild dogs. Fruits of modern strains of domesticplants are much larger than those of their ancestors; corn (maize) is profoundlydifferent from its ancestor, teosinte.

A model of evolution by natural or artificial selection has three components:(a) variation, (b) inheritance, and (c) differential reproductive success (Lewontin1970). Darwin’s theory was formulated largely on an understanding of variationand differential reproductive success but a relatively poor understanding of inher-itance. The inheritance void began to be filled early in the twentieth century withthe rediscovery of Mendel’s work, enhanced (in the West) by a close associationof evolutionary biology with agriculture to improve methods of artificial selec-tion. This productive marriage continues today with the use quantitative trait locus(QTL) mapping in both fields.


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Another cultural relationship between evolutionary theory, genetics and agri-culture, was not so productive, however. In the 1930s, Trofim Lysenko rejectedDarwin’s theory of natural selection in favor Lamarck’s theory of the inheritanceof acquired traits. Lysenko’s subsequent rise in power as the head of the SovietMinistry of Agriculture directly destroyed genetic research and decimated agricul-ture in the Soviet Union (Soyfer 1994, Garrett 2000). The history of Lysenkoismhighlights the danger of letting political and other nonscientific ideology dictatescientific practice.

Historically, artificial selection was a manipulation of differential reproduc-tive success. Parents closest to the desired phenotype were chosen to produce thenext generation, and individuals that fell short of the ideal were omitted from thebreeding population. The components of variation and inheritance were presentbut were often not manipulated from their natural states, except for the occasionalintroduction of novel strains or wild relatives into the breeding stock. Mutationrates were seldom, if ever, purposely manipulated, but it is possible that some ofthe “wide crosses” used to introduce variation into the breeding population couldhave increased mutation rate by inducing transposition or other mutagenic mech-anisms. Although the mechanisms of inheritance in artificial selection were notfundamentally different from inheritance during natural selection, the manipula-tion of crosses frequently increased the level of inbreeding and thus the ability toselect for the expression of recessive traits.

Artificial selection moved into a new realm with biotechnology (Kauffman1993). In contrast to agriculture, biotechnology specializes in evolving smallthings, i.e., molecules and microbes. Evolutionary methods in biotechnology havemuch in common with classical artificial selection. The same three factors—variation, inheritance, and differential reproductive success—are manipulated. Butit is no longer the artificial selection of old. The methods used in biotechnology,including DNA sequencing of the entire evolved genome, determination of molec-ular structures, and monitoring gene expression levels, provide unparalleled levelsof analysis of evolution. The combination of experiments, replication, product-oriented research, and analysis of results has allowed rapid attainment of a newlevel of scientific accomplishment in evolutionary biology.

Directed Evolution

Various protocols based on evolution are currently used to fashion nucleic acids(ribozymes and aptamers) or proteins with specific functions. The directed evolu-tion of novel biological pathways and evolutionary engineering of whole genomesmay not be far off. These accomplishments were made possible by changingthe components of variation, inheritance, and differential reproductive success.Nature comes close to some of these methodologies, and indeed, several areborrowed from nature, using microbes and various types of parastic genetic ele-ments. Biotechnology has, nonetheless, created unnatural means of evolvingmolecules.



Perhaps the most extreme form of directed evolution is the in vitro replica-tion of nucleic acids outside of life-forms. The earliest system of this sort wasSpiegelman’s replication of the Qβ genome with the Qβ RNA-dependent RNApolymerase (Spiegelman et al. 1968). Qβ is a phage whose genome encodes fourgenes, one being the polymerase enzyme that makes RNA copies of the phage’sRNA genome. By isolating the phage’s replication enzyme and then placing theRNA genome in a cocktail of nucleotides plus the enzyme, the genome was repli-cated on its own. In that environment none of its genes were being expressed—thegenome was just a molecule that was evolving to copy itself in an environmentwhere its genes were irrelevant. It rapidly evolved to a small size.

The Spiegelman in vitro system did not allow evolution to be directed toward anygoal other than fast self-replication. Perhaps the first purely in vitro system that didallow directed evolution was the Systematic Evolution of Ligands by EXponentialenrichment (SELEX) system that was simultaneously developed by two differentlabs (Ellington & Szostak 1990, Tuerk & Gold 1990). This elegant system allowedone to start with a synthesized pool of nucleic acids (Figure 3). The ends of thosenucleic acids were “constant” regions, the sequences of which matched sequencesof primers for PCR amplification. The middle regions were randomized. This poolof molecules was then washed across many copies of a particular target molecule(e.g., a protein). The molecules that bound the target stayed behind, and the restwere washed off. The bound molecules were then eluted into a separate tube,amplified by PCR, and passed through the cycle again. In this way, a nucleic acid-binding species (“aptamer”) could be obtained for any particular target molecule(Tuerk & Gold 1990, Gold et al. 1995, Osborne & Ellington 1997, Famulok &Mayer 1999).

A variety of interesting variations on this theme has since been developed.Beaudry & Joyce (1992) used an in vitro selection and amplification scheme tomodify the function of a ribozyme, an RNA molecule with enzymatic activity.The starting ribozyme cleaved RNA molecules, but directed evolution produceda ribozyme that cleaved DNA molecules. Breaker & Joyce (1994) then evolvedDNA molecules that could cleave RNA. These methods were similar to the SELEXmethod in using PCR to amplify molecules that survived the selection, but themethods differed from SELEX in the selection itself. Table 1 lists the impressivevariety of unnatural ribozymes (not known from nature) that have been evolvedby these methods. Although these few examples highlight nucleic acids, directedevolution also manipulates proteins and entire microbes. In many ways, thesenew technologies are so different from natural evolution as to warrant a specialterm (e.g., techno-evolution or technovolution?). However, the term “ directedevolution,” which is now in wide use, does have the advantage of conveying themessage that these socially beneficial applications are based on the foundations ofstandard evolutionary biology.

DIFFERENTIAL REPRODUCTIVE SUCCESS: REPRODUCTION Reproduction of thethings being selected is essential to evolutionary progress. In the past, one had


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Figure 3 SELEX, a purely in vitro, nonliving scheme of evolution by natural selec-tion. (1) A heterogeneous pool of oligonucleotides (the squiggles at thetop) is passedthrough a column of anchored target molecules (dark ovals). All oligos have identicalsequences on their ends but vary in the sequences of their middle region. (2) Oligovariants whose sequences facilitate binding remain behind in the column, whereasvariants that do not bind pass through and are washed away. (3) Binding oligos areeluted, by changing the salt concentration or pH of the solution. (4) The oligos fromstep3 are amplified by PCR. (5) The cycle is repeated by passing the amplified pool ofoligos over the column. After a few cycles, the oligo sequences remaining are limitedto those capable of binding the target molecules.

to work with an organism capable of reproducing, but now we can literally createand then evolve molecules that reproduce themselves in the right cocktail of en-zymes and other nutrients. The most common method of reproducing nucleic acidmolecules is PCR. A similar but less popular method is self-sustained sequencereplication (3SR). In PCR, DNA molecules are the parents, and their progeny arecomplementary DNA molecules. In 3SR, RNA molecules are the parents, cDNAmolecules are the progeny, and transcription of these DNA molecules createsRNA copies as the grandchildren. PCR uses synchronized cycles of reproduction,in which all molecules in the tube reproduce once and then stop until the next cycle



TABLE 1 Unnaturalribozymes evolved by directedevolutiona

Ribosyl 2′-O–mediated cleavageMg2+-dependent cleavagePb2+-dependent cleavage

Ribosyl 2′,3′-cyclic P hydrolysisPb2+ dependent

RNA ligation3′,5′ Ligation (class 1)2′,5′ Ligation5′,5′ Ligation

RNA phosphorylationClass 1 (ATP-S)Class 1 (ATP)

Self-aminoacylation

Acyl transfer reactionAcyl transferAminoacyl transfer

Self-nitrogen alkylation

Suflur alkylation

Biphenyl isomerization

Prophyrin metalation

aRNA molecules with these activities areunknown from nature. (From Jaeger 1997.)

is initiated. 3SR is a method in which everything happens continuously (Fahy et al.1991).

For many purposes, the directed evolution of proteins and many other types ofmolecules still requires an organism. A gene is expressed in an organism (typicallya bacterium or yeast), but the gene may be removed from that organism andsubjected to various manipulations before being returned to the same or a differentorganism. Biotechnology thus allows a gene to hitchhike as part of an organism butthen be divorced from that organism at will. However, two new methods allow invitro translation to couple a protein to its mRNA, enabling proteins to be evolvedin a SELEX-like fashion (Wilson et al. 2001). Limited forms of peptide self-replication have also been produced, although the chemistry of the peptide copyingmechanisms is fundamentally different from that of nucleic acids (Lee et al. 1997).

DIFFERENTIAL REPRODUCTIVE SUCCESS: DIFFERENTIAL SUCCESS A challenge incarrying out directed evolution is to come up with a powerful method to ma-nipulate the differential reproductive success of the molecules under selection.The goal is to choose genes whose phenotypes offer the greatest improvement as


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parents for the next round of selection. This can be done directly through selectionor indirectly through screening. True selection occurs when molecules with thedesired phenotypes reproduce faster than molecules with less desired phenotypes;in the extreme case, molecules with the desired phenotypes are the only ones tosurvive. In screening, the good and bad survive; the phenotype of each moleculeis individually assessed—for example, through a colorimetric enzyme assay—andgenes for the most desired molecules are chosen as parents for the next generation.The distinction between selection and screening in directed evolution thus appearsto be functionally the same as between natural selection and artificial selection(D. Futuyma, personal communication). True selection is generally much morepowerful than screening because the number of variant molecules that can be sub-jected to selection is generally much larger than for screening (Arnold & Volkov1999). However, schemes for true selection can be difficult to design.

The form of selection used in much of biotechnology is what quantitative ge-neticists refer to as truncation selection—reproducing only those molecules thatmeet a certain standard, in the same way that a livestock breeder mates only an-imals that achieve a certain weight or fat content. In biotechnology, the selectionmight be determined by the ability of a bacterial cell to grow on a toxic or antibioticsubstrate. In other cases, as in aptamer selection, reproductive success is based onadherence to a target molecule. In some protocols for DNA enzyme evolution,reproductive success is determined in the reverse fashion—escaping from a boundstate is the gateway to future reproduction.

One of the advantages of evolution in biotechnology is that truncation selectioncan achieve astronomical levels. The limiting factors in any truncation selectionare (a) the range of variation, which is limited by the number of different genotypesthat exist, and (b) the fecundity of those organisms between bouts of selection. Ifan asexual organism produces only two offspring between bouts of selection, forexample, then a 50% culling is the long-term upper limit, whereas if the fecundityis 1000, then a 99.9% culling is possible. The latter allows much more rapid evo-lution, genetic variation permitting. Methods such as cloning genes into selectablebacterial plasmids and PCR allow amplifications (reproduction) of as high as 109

between bouts of selection. Such levels could be achieved only in special cases ofnatural selection (e.g., with microbes evolving in a new environment or invadinga new host species).

VARIATION AND INHERITANCE: ELEVATING LEVELS OF GENETIC VARIATION Perhapsthe greatest deviations from natural evolutionary processes used in biotechnologyhave come from manipulating genetic variation. These advances include elevatinggenetic variation and recombining molecules.

MUTATION The rate of evolution in many experiments is limited by genetic vari-ation, largely because the high levels of truncation selection that may be appliedcan take advantage of levels of variation that lie orders of magnitude beyond any-thing natural. To raise mutation rates, a variety of approaches have been developed.



The most extreme is simply to incorporate high levels of variation in synthesizedmolecules. Pools of oligonucleotides can be synthesized with arbitrary base com-positions at specific sites. These molecules can be used as the direct target ofselection (as in aptamer evolution studies) or as material cloned into vectors thatthen becomes translated into protein. At the extreme, the sequence of the initialselective population is completely randomized and all bases have equal frequen-cies. Current technology limits the pool size to about 1016, so an exhaustive searchof “deep random” is limited to a sequence of 27 bases (Landweber 1999). Whenlarger stretches of randomized sequences are used, this limitation means that in-dependent experiments using the same protocol may arrive at different solutionsbecause only a small fraction of the possible sequences are present in the initialpool of molecules. Evolution in these cases is dominated by the “tyranny of smallmotifs” pattern (Ellington 1994), such that any solutions to the selective challengethat are specified by a small number of residues will certainly be present in thepool of randomized molecules, whereas solutions specified by a large numberof residues may not. So the evolved solutions tend to be the simpler ones, notnecessarily the best ones.

Oligo synthesis is not the only means of elevating mutation rates. If moleculesare not synthesized, then they are copied from preexisting templates. Natural en-zymes and processes are invariably used for this replication, and their inherent errorrates are usually much lower than wanted. Other methods of generating variationrely on inflating the error rate during replication or introducing errors into the tem-plate itself. The standard method of elevating whole-organism mutation rates hasbeen to expose the organism to a chemical or physical mutagen. That method doesnot allow precise control over the genomic locations of the mutations, although itallows some control over the types of genetic changes, because different agentstend to cause different types of mutations. More recent methods involve inflatingthe error rates during replication: (a) PCR with unnatural bases, with asymmetricbase compositions, and/or with manganese; (b) amplification that involves alter-nately copying between DNA and RNA because the transcription step (DNA intoRNA) is highly error prone (e.g., Fromant et al. 1995).

RECOMBINATION One of the most powerful techniques used in directed evolu-tion is recombination among variants of the same or similar molecules. Severalmethods for performing in vitro recombination are now routine (known in thefield as gene shuffling or molecular breeding). Pim Stemmer of Maxygen devel-oped the first purely in vitro shuffling method (Stemmer 1994), which involvespooling DNA templates, nicking them to cut strands, denaturing them, and let-ting them come back together before patching them up. Molecules require onlyshort regions of identity to realign, so recombination (strand exchange) can occurbetween molecules that differ substantially. The variants to be shuffled can bethose already improved during earlier rounds of selection, or they can be natu-rally occurring homologous genes from a diversity of species—family shuffling(Crameri et al. 1998). Family shuffling has the same advantage as increasing genetic


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variation by introducing novel strains or wild relatives into the genetic stock: Natu-ral selection has already weeded out the deleterious mutations from these variants.

In at least some cases family shuffling radically accelerates the process of di-rected evolution. For example, when cephalosporinase genes from four microbialspecies were independently evolved through one round of DNA shuffling, theyshowed up to an eightfold improvement in their resistance to the antibiotic mox-alactam. A single cycle of family shuffling that combined all four genes yieldeda 540-fold improvement in resistance. The evolved gene showing the greatest in-crease in resistance was made of eight fragments from three of the four genes andhad an additional 33 amino acid substitutions (Crameri et al. 1998).

VARIATION AND INHERITANCE: VARIANTS WITH REDUCED CONSTRAINTS Repro-ductive success is an ultimate necessity in any form of evolution by natural orartificial selection. Yet in a narrow sense, reproduction may have nothing to dowith the phenotype sought by artificial selection. The reproductive requirementoften limits the progress that can be achieved in artificial selection. For example,most people prefer to eat seedless watermelons, but seeds are needed for the nextgeneration. (Seedless watermelons are created as triploid hybrids from crossesbetween different strains.) One of the big successes in biotechnology has been thereduction of such constraints, so that molecules with desired phenotypes can bepropagated without concern for their correlated negative effects on reproduction.Several tricks facilitate the divorce between reproductive constraints and selectionon the desired phenotype.

REPRODUCTION BY PCR When a nucleic acid (RNA or DNA) molecule itself isselected for the phenotype, as in aptamer or ribozyme evolution, amplificationby PCR is not only the easiest method of reproducing the molecule, it also en-tails minimal constraints. The ends of the molecule must match primer sequences,but intervening sequences are largely irrelevant. Earlier methods of amplification(cloning) incorporated the DNA molecule in a plasmid, and a plasmid’s repro-duction is tied to that of its host. Not only was cloning a cumbersome methodof amplification, if the cloned sequences were incompatible with cell growth, theamplification would not work. PCR is a nonselective amplification method, suchthat the entire pool of nucleic acids (with suitable ends) experiences minimal evo-lution during the amplification (Bull & Pease 1995). Thus with PCR, differentialreproductive success can be virtually removed from the amplification/reproductionstage.

LIMITING THE DELETERIOUS CONSEQUENCES OF PHENOTYPE EXPRESSION In manycases, the desired phenotype requires protein expression. The standard way tocreate proteins is to put the gene inside cells and let the cellular machinery buildthe protein. This approach can be a problem if the protein is incompatible withcellular function. A compatibility problem can be overcome by placing the gene in



vectors that offer control over gene expression. Gene expression can be suppresseduntil the host has reproduced, so that even if the cell is killed by expression, theDNA vector from selected colonies can be recovered and put into new cells. Thenew methods for in vitro translation and coupling of the peptide to its mRNAavoid the problem entirely, however (Wilson et al. 2001). Another mechanismfor limiting deleterious consequences applies at the protein level instead of thegenome level. When evolving peptide sequences, it is often desirable to couplethe evolvable peptide with a reporter peptide (for screening or perhaps as part ofthe selection). Activity of a reporter peptide will be incompatible with sequenceinsertions at many positions, but there are often regions that can tolerate smallor even large peptide additions without destroying activity, such as protein endsor loops. Thus a fusion protein is created that has both the reporter activity and avariable region that can be selected for a different function. This rationale underliesthe highly successful methods of the yeast two-hybrid assay (Chien et al. 1991),phage display (Smith 1985), and some other systems.

VARIATION AND INHERITANCE: UNNATURAL MOLECULES The synthesis of unnat-ural molecules, which is now routine chemistry, has affected artificial selection intwo ways: New types of molecules can be evolved, and old types of moleculescan be evolved toward new targets. Replication of new kinds of nucleic acidsis possible with modified bases, sometimes known as base analogs. The ribonu-cleotides in natural RNA contain the bases adenine, uracil, guanine, and cytosine.Several types of modified bases have been incorporated into ribonucleotides, anda few types have been incorporated into deoxyribonucleotides. These modifiedbases have novel groups attached that do not interfere with hydrogen bondingbut that can alter the characteristics of the nucleic acid in other ways. Providedthe polymerase will accept the base analog, it is straightforward to create a mixof bases that will result in a nucleic acid with one or more of the natural basesreplaced by the analog. These nucleic acids may then be subjected to the usualselections, in the hope that the product will be superior to the natural nucleic acids(Tarasow et al. 1997, Wiegand et al. 1997, Sakthivel & Barbas 1998, Battersby et al.1999).

Novel molecules may also serve as selective agents. This phenomenon has beenvisited many times in human history, as pesticides were applied to control pests orsynthetic drugs were used to treat microbes, and the offended organisms respondedwith their own evolution (see section below). One of the more interesting uses ofnovel molecules as a selective agent is based on the symmetry of mirror-imagemolecules. Life uses theL-forms of amino acids andD-forms of nucleic acids,but the mirror image forms of both can be synthesized chemically. The novelmethod uses a synthesizedD-peptide as a target against a pool ofD-nucleic acids aspotential aptamers; theD-peptide is the unnatural form, whereas theD-nucleic acidsare of the natural form so that they can be evolved through directed evolution. Whena successfulD-aptamer is evolved, itsL-form is synthesized (which is unnatural).


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Because of the mirror-image symmetry, the syntheticL-aptamer recognizes theL-peptide that is found in nature. This two-step technique was used to create anL-form DNA aptamer that inhibits vasopressin and, unlike theD-aptamer, wasresistant to nuclease degradation (Klussmann et al. 1996, Williams et al. 1997).

Examples of Directed Evolution

A DNA ENZYME TO LIMIT ARTERIAL DAMAGE FROM ANGIOPLASTY Santoro & Joyce(1997) evolved a short DNA molecule that cleaved a specific site in a target RNAmolecule. From a sequence comparison between this DNA enzyme and its target,the DNA enzyme appeared to contain a catalytic core flanked on both sides byregions that formed Watson-Crick base pairs with the target RNA. This discoverysuggested that a DNA enzyme could be synthesized against almost any RNAmerely by changing its flanking sequences to complement the RNA sequence atthe desired target. In one application, the DNA enzyme was designed to cleave themRNA of theEgr-1 gene, whose expression causes unwanted proliferation of thearterial wall in response to damage from angioplasty (balloon inflation). Arterialproliferation is counterproductive because the goal of angioplasty is to expand thearterial canal, and the proliferation narrows it. Tests in a rat model showed that theDNA enzyme had the desired effect of reducing arterial proliferation (Santiagoet al. 1999).

ENZYMES FOR HOUSEHOLD AND AGRICULTURAL USE The year 2000 marked the5th Annual World Congress on Enzyme Technologies, organized by the IBC (In-ternational Business Communications). The sponsors were the biotech companiesMaxygen, Genencor, Diversa, and Thermogen. This meeting was dominated bytalks on using directed evolution to improve enzyme performance in specific set-tings. One industrial goal is improved cellulase activity. Agricultural waste in theform of corn stalks and other plant material offers a potential windfall of ethanolproduction, if cellulose can be digested with cheap enzymes. Another goal isthe improvement of enzymes (proteases, lipases) to use in laundry detergents toremove stains and other dirt. Cellulases, proteases, and lipases have been iso-lated from numerous organisms, but their activity levels are too low under appliedconditions to justify their use. For example, naturally occurring proteases do notfunction in warm, soapy water. Directed evolution is being used to improve thatperformance [for an introduction to this large area of research, see Marrs et al.(1999), Schmidt-Dannert & Arnold (1999), Voigt et al. (2000)].

EVOLVING PEPTIDES THAT LIMIT CRYSTAL SIZE Phage display is the insertion ofpeptide sequences into coat proteins of a bacteriophage (bacterial virus) so that thepeptide insert is able to contact surfaces in the phage’s environment. The size andlocation of the peptide insert is chosen so that it permits phage reproduction. Withrandomized inserts, a phage display library may contain billions of different insertsequences that can be selected for binding to many types of substrates. Using a



commercial phage display library, Whaley et al. (2000) recovered peptide epitopesthat bound gallium arsenate crystals; some epitopes discriminated different formsof the crystal. The peptide insert was a mere 12 amino acids long, yet it seemedthat crystal recognition could be achieved with even fewer than 12 amino acids.This discovery may be an important step in miniaturizing the manufacture ofsemiconductors (nanotechnology) because by binding the crystal lattice, thesepeptides may offer a simple way of reproducibly controlling crystal growth.

BLOCKING HIV EXPRESSION Many drugs have been developed to suppress HIV,but evolution of HIV resistance to these drugs fuels a continual demand for newdrugs. Some recent approaches to inhibit HIV use technologies involving RNAmolecules. These new methods include antisense RNA (RNAs that are complemen-tary to the single-stranded viral mRNAs), ribozymes (RNA enzymes that cleavethe viral mRNAs), and aptamers that bind HIV proteins. The antisense RNAs andribozymes can be developed merely from an understanding of HIV genome se-quences, whereas the aptamers need to be evolved in a SELEX-like manner. Whendifferent agents created by these three methods were compared for their efficacyagainst HIV in cell culture, successful inhibition was obtained only with aptamers(Good et al. 1997). Assuming that an anti-HIV aptamer could become an effec-tive drug in vivo, the encouraging aspect of this result is that new aptamers couldpossibly be evolved each time the virus evolved resistance to the old aptamers.

Other Approaches

Evolution is just one of several technology-driven methods for modifying moleculesfor specific uses. Methods for protein improvement are generally divided into ap-proaches known as rational design and irrational design (Arnold 1997, Arnold &Volkov 1999). Rational design consists of protein engineering by modification ofspecific amino acid residues based on knowledge of protein structure and mech-anistic details. Although this approach is potentially powerful, we are currentlyfar from having enough information about most proteins to apply rational design,and in general we lack the deep understanding of protein structure and functionthat would allow us to predict the outcome of specific amino acid substitutions(Tobin et al. 2000). The “irrational” approaches of directed evolution do not re-quire such detailed knowledge because they rely on selection to reach the desiredoutcome.

These applications in biotechnology not only offer a new relevance for evolu-tionary biology, they also provide new opportunities for biologists with classicaltraining in evolution. The industrial approach of large-scale experiments with afocus on products and detailed molecular analysis is often done in ignorance ofthe underlying classical framework. A truly golden opportunity lies ahead for themarriage of classical theory with industrial interests. What level of recombinationand mutagenesis is optimal? How rugged is the fitness landscape? It should be pos-sible to build a new level of evolutionary theory and help industry reach its goals


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in this new area of directed evolution, allowing for appropriate accommodation ofthe sometimes conflicting goals between industry and academia.

RESISTANCE MANAGEMENT

Many organisms (including humans) have been creative in developing chemistry todeter or kill unwanted competitors. For humans, the pests may be cancer cells, in-sects, mites, worms, weeds, or microbes. As is known all too well, virtually all ourattempts to control pests with chemical agents have led to resistance to those agents(Garrett 1994). The evolution of resistance is so routine that it seems inevitable,notwithstanding a few noteworthy examples, such as the American chestnut, inwhich an entire species with billions of individuals apparently failed to evolve re-sistance to an invading pathogen (Newhouse 1990). The question is how an under-standing of evolution can help us retard or even prevent the evolution of resistance.

The evolution of resistance is not a new problem. It accompanied the intro-duction of antibiotics and pesticides 50 years ago. Attention to this problem fromevolutionary biologists has lagged, however. Part of the reason for this slow re-sponse may have been the seemingly limitless supply of new chemicals. When thefirst antibiotics were isolated from nature, a virtual windfall of different drugs wasfound. The evolution of resistance was inconsequential when new drugs becameavailable faster than old ones failed. Likewise, new pesticides may have seemedeasy to engineer in the days before government regulation. Now, however, manyof the old technologies have reached limits, and the cost of obtaining governmentapproval is enormous (e.g., on the order of half a billion dollars for a new drug).There are thus ample incentives to use chemicals wisely and prolong the lives ofwhat works now.

One role of evolutionary biologists is to educate the public. A widespreadmisconception concerning antibiotics is that an individual who abuses them willdevelop a tolerance, so the drugs will no longer work for them personally. Thatis, some people mistakenly assume that the person’s body changes in response toantibiotic misuse. The true problem is that misuse of antibiotics encourages evo-lution in the bacterium so that it is no longer affected by the drug. If the bacteriumspreads, then people contracting it are at risk of an untreatable infection, no mat-ter how conscientious they have been in their past use of antibiotics. Resistancequickly becomes a global problem caused by evolution.

The evolution of resistance is affected by the manner in which we apply thetoxins. These factors are under our control and allow us at least to impede theevolution of resistance.

The Right Dose

No doubt the most widely acknowledged factor influencing the evolution of re-sistance is the dose. Many Americans, at least, are aware of the admonition to



take the full course of prescribed antibiotics, lest the infection return in a moreresistant, less easily treated form (this is especially a problem with tuberculosisinfections, in which eradication of the bacterium from a person requires months oftreatment). Evolutionary biologists since Darwin have been aware that weak selec-tion can ultimately yield phenotypes that lie well outside the range of phenotypescurrently in the population, whereas immediate selection for those extremes wouldfail and thereby extinguish the population. A low dose of a drug—low enough toallow survival of some sensitive individuals—is a form of weak selection. Its maindetriment is that it favors individuals with partial resistance; as partial resistanceevolves, full resistance is more easily attained. A second complication with a lowdose is that it leaves sensitive survivors that may mutate to resistance before fur-ther doses are applied. The evolution of bacterial resistance is now a factor whenrecommending levels of some antibiotics (Blondeau et al. 2001), and variations indose, both temporally and within the patient, are used to help understand the evo-lution of resistance (Baquero & Negri 1997, Baquero et al. 1997, Blondeau et al.2001). “Dose” is likewise a consideration when planting insect-toxic, transgenicplants to avoid insect resistance (see below).

Selective Application

Larger populations are more likely than small ones to contain resistant genotypes,so limiting the size of the population treated reduces the chance that resistancewill evolve. A simple way to limit the treated population yet still be effectiveis to treat only those pests causing damage. With antimicrobial agents, selectiveapplication would consist of treating only those patients manifesting an infection;with pesticides, selective application would consist of spraying only those cropsexperiencing economic injury levels of pests. Although this model is simple inprinciple, for social and technical reasons it is often difficult to institute. With anti-biotic treatment of bacterial infections, a strict adherence to selective applicationwould mean that patients are not given a drug until their infecting microbe hasbeen diagnosed as a strain sensitive to the drug. This practice is neither patient-friendly nor safe in all cases, as an infection can worsen during the time required fordiagnosis. Even when it is clear that antibiotic treatment is unwarranted, a patient’sdemands for a drug may override any physician concern for the eventual evolutionof resistance. The problem is a classic example of a phenomenon described byHardin (1968), “Tragedy of the Commons,” in that the cost/benefit ratio from theindividual’s perspective favors antibiotic overuse, in opposition to the commongood (Palumbi 2001). Industrial concerns further contribute to antibiotic overusein our environment because antibiotic food supplements yield faster livestockgrowth. The agricultural interest of greater meat production has so far been thevictor over proposals to restrict antibiotics in livestock food, despite clear evidencethat antibiotic food supplements encourage the evolution of antibiotic resistancein human pathogens (Garrett 1994).


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Does limited use (selective application) reduce resistance levels? It is of courseclear that antibiotic use led directly to the widespread evolution of antibiotic re-sistance in many bacteria, as indicated by geographic and temporal correlationsbetween drug resistance and antibiotic use (Garrett 1994, 2000; Granizo et al.2000). Limiting antibiotic use during the past four decades would at least haveslowed the rate at which resistance evolved. But selective application also causescurrent resistance levels to drop (Cristino 1999).

Combination Therapy

Simultaneous application of multiple agents may extinguish a small population,even when high doses of single agents would not work, for the same reason thathigh doses of a single agent are better than low doses: The chance that resistantindividuals occur in the initial population decreases with the magnitude of theimposed mortality. If resistance to a single agent can be conferred by a singlemutation, then multiple agents may offer the advantage that no single mutationconfers complete resistance. Perhaps the original use of combination therapy wasin the treatment of tuberculosis with the first antibiotics (Ryan 1993). Treatmentwith a single antibiotic resulted in the evolution of drug resistance within thepatient. Simultaneous treatment with a combination of three drugs allowed theinfection to be cured and the within-patient evolution of resistance prevented.More recently, the simultaneous use of multiple drugs to treat HIV infectionshas resulted in prolonged avoidance of AIDS because the virus is so effectivelycurtailed with the harsh treatment (Coffin 1995, Matsushita 2000). The long-termsuccess of many antiviral vaccines may likewise stem from the immunity generatedagainst multiple targets through both the humoral and cell-mediated components.With other goals, however, simultaneous, multidrug treatment may not be the bestpractice (Bonhoeffer et al. 1997).

Charting the Course of Resistance

When more drugs are available than can be given to a single patient, the choice ofwhich drugs to take should be based on the evolution of resistance. Screening forpreexisting resistance to the drugs can ensure that the combination of drugs admin-istered is maximally effective. For example, anti-HIV drugs (of which there are nowmany) are so harsh on the patient that doses and numbers of drugs are often based onpatient tolerance levels. There are three major classes of anti-HIV drugs (proteaseinhibitors and two kinds of reverse-transcriptase inhibitors), but many drug optionsexist within each class. A patient on highly active antiretroviral treatment will takeone drug from each class to maximize the number of viral targets, but within eachclass, the drug that is taken is somewhat optional. Protocols are now being testedin which a patient’s viral population is assessed for resistance mutations, with thedrug choice based on that genetic information (O’Meara et al. 2001, MacArthur2000). It is obvious that this kind of approach requires understanding the molecularbasis of resistance. It also requires a technical means of assessing low levels of



resistance in the target population (e.g., on the order of 1% or even 0.1%), as suchlevels are not easily detectable yet can change rapidly in response to selection.

Refugia

One of the more exciting new developments in agriculture is the engineeringof transgenic crops expressing a gene from the bacteriumBacillus thuringiensis(Bt), the protein of which is highly toxic to butterflies and moths. This toxin (Bt)has long been a favorite of organic farmers, used as a spray prior to transgenictechnology, because it selectively kills only a small subset of insects and typicallydoes not harm the parasitic and predatory control agents. Corn and cotton are twomajor Bt transgenic crops in wide use now, and the potential for reducing pesticideapplications to the environment from them alone is impressive. Despite a publicbacklash against genetically modified foods and the consequent withdrawal of oneBt corn strain from the market, approximately one quarter of the U. S. corn crop isexpected to be Bt transgenic in 2001 (F. Gould, personal communication). Thereis thus a huge market for Bt strains.

Alleles for Bt resistance are already found at moderate frequencies in severalpest populations, in some cases at frequencies greater than 10% (Gould et al.1997, Tabashnik et al. 2000). These pests are thus poised for a rapid responseto Bt transgenic plants. Fortunately, levels of resistant insects are much lowerthan levels of resistant alleles because the resistance alleles are recessive, henceresistant insects occur at the square of the allele frequency. These surprisingly highfrequencies of resistance alleles are unexplained in at least some species, becausethere is no known history of exposure to agricultural Bt toxin (F. Gould, personalcommunication).

The commercial implications of Bt resistance are obvious and have inspiredthe U. S. Department of Agriculture and seed companies to anticipate and slow itsevolution (Gould 1998). An antiresistance evolution strategy is being employedthat depends on recessive resistance. Farmers growing Bt crops are mandated toplant a certain amount of non–Bt crops as well—until recently 4%; now it is higher.These non–Bt crops are planted in separate fields (refugia) close to the Bt crops,to provide a safe haven for pests that are not resistant to the toxin.

Refugia impact the evolution of resistance to Bt as follows. Pests in the refugiasurvive regardless of their resistance to the toxin. Pests in the Bt fields survive onlyif they are resistant, i.e., homozygous for the resistance allele. As long as resistancealleles are uncommon, most insects will be sensitive. Thus, refugia will producemany more insects than the Bt fields. With random mating between pests born inthe refugia and pests born in the Bt fields, most offspring will be sensitive to thetoxin (because they are either homozygous sensitive or heterozygous). Refugiawill maintain resistance alleles at low frequencies for long periods of time becausethey continually dilute the resistance genes and keep them from being selected.Resistance will eventually ascend, but the ascent is greatly delayed with refugia(Figure 4). This result is a special case of the population genetics principle that


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Figure 4 Effect of refugia on the delay ofBacillus thuringiensis(Bt) resistance evolution.Three curves of gene frequency evolution are shown: (a) no refugia, (b) 1% refugia, and (c)4% refugia. The evolutionary response to no refugia is immediate, because the only survivorsare recessive homozygotes. The ascent of the resistance allele is progressively delayed withlarger refugia because refugia are not selective for genotype and thus weaken selection forthe recessive, resistance allele. These simulations assumed complete mixing of individualsbetween refugia and Bt crops when mating and when ovipositing. They also assumed that theonly source of selection was mortality of all heterozygotes and of sensitive homozygotes inBt fields, that generations were discrete, and that the initial frequency of the resistance allelewas 0.001. The effect of refugia would be modified to the extent that these assumptions do nothold, especially the recessivity of the resistance phenotype and the mixing. Also, the curvesgive only allele frequencies; the actual numbers of surviving pests would be an importantfactor to consider in the economic injury to a crop, but that factor is not included here. Thesecurves were calculated by iteration of the equationp′ = (p2Waa+ pqWAa)/(p2Waa+ 2pqWAa

+ q2WAA), wherep is the frequency of the recessive resistance allele in generationt, andp′

is its frequency in generationt+ 1. The fitness values were determined asWaa = 1, WAa =WAA = R, whereR is the proportion of crops in the refugia.

weak selection on a recessive trait is ineffective when the recessive allele is rare(Crow & Kimura 1970); the strength of selection decreases with refugium size.One reason non–Bt crops are not mixed in fields with Bt crops is to prevent insectsfrom moving between the two types of plants and thus experiencing intermediatedoses of the toxin (which might reduce the recessivity of the resistance alleles).

Evolvable Drugs

Biotechnology offers new drugs from new classes of molecules but it also offersnew drugs from some old molecules—proteins and nucleic acids. (Some types



of RNA-based drugs were discussed above in a paragraph about blocking HIV.)Proteins and nucleic acids in essence have their own genome, so theoretically itshould be possible to evolve those drugs. Evolving a drug might provide a wayof producing new forms of the drug that overcome pests resistant to the originalform of that drug. At the moment, this approach is futuristic. It is analogous tothe use of combinatorial chemistry to produce new generations of antibiotics bymodifying subgroups of old drugs. Drug evolution was attempted in a model sys-tem of antisense RNA used to inhibit a virus (Bull et al. 1998). Antisense nucleicacids work on the simple principle of being complementary to a target sequence(in this case a regulatory sequence of the virus) and thus blocking gene expression.If the target sequence in the virus evolves to a resistant form, antisense moleculescomplementary to the new target are then easily created. This “arms race” canthus potentially be continued indefinitely because a new antisense RNA can becreated for every step the virus takes in evolving resistance. The empirical test ofthis theory supported some of the assumptions but not all: New antisense RNAscould be created that inhibited individual viruses resistant to the original antisenseRNA. However, the viral population exhibited a variety of escape mutants, andno single new antisense RNA could control the resistant viral population. Resis-tance polymorphism in the viral population thus thwarted control by the secondgeneration of drugs.

Other variations of this approach have also been developed, again withoutsuccess. Djordjevic & Klaenhammer (1997) and Bull et al. (2001) each developeda suicide plasmid cassette against a bacterial virus. Each plasmid contained a toxicgene downstream of a promoter expressed by the virus. Infection expressed thetoxic gene, which, in turn, killed the infection. A priori, it seemed likely that anarms race could be waged against the virus by inserting a new toxic gene into thecassette each time the virus evolved resistance to the old toxic gene. However, inboth systems the virus evolved resistance by a change in its transcriptional activity,selectively reducing expression from the plasmid regardless of which toxic genewas carried by the plasmid. In a further attempt to keep pace with the resistantvirus, Djordjevic & Klaenhammer (1997) created a new plasmid that containedmultiple copies of the viral promoter; this plasmid partially restored inhibitionagainst the virus, although at some cost to the uninfected cell (perhaps throughlow levels of constitutive expression).

It is not clear how widely the principle of drug “evolution” can be applied toblock resistance. Drugs that share a common molecular ancestor will likely usethe same molecular target. If evolution in the pest can protect the target and renderit inaccessible, then drug evolution will fail. The case of nonnucleoside reverse-transcriptase inhibitors (NNRTI) in HIV provides an analogy to drug evolution.Various NNRTI drugs have been developed, but they all function by similar mech-anisms, binding to the same pocket in the viral reverse transcriptase (Emini 1996).Resistance to one of these drugs often confers resistance to several of the others. Ifone regards the different NNRTI drugs as the equivalents of evolved drugs, then anevolved drug in this case affords much less protection against a resistant virus than


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does a drug using a different mechanism of inhibition. All the different evolvedforms of a drug may use the same basic mechanism of inhibition, just as differentNNRTI drugs use similar mechanisms, in which case viral resistance to one drugwill at least partly overlap with resistance to the others.

EVOLUTIONARY COMPUTATION

Introduction

Evolution is not restricted to biological systems, nor has the benefit of using evolu-tion to solve problems been limited to biology. Although application of evolution-ary principles to computer programming preceded the work of Fogel et al. (1966),their book brought the idea to the forefront. The goal of this work was lofty—“thatthrough a replication of specific aspects of evolution, means will be found forthe generation of an artificially intelligent automata. . . capable of solving prob-lems in previously undiscovered ways.” The field gained sophistication almost asquickly as its biological counterpart, and by the mid-1970s, Holland (1975) wasdiscussing coadapted gene sets and epistatic interactions in the context of compu-tation. Many concepts and much of the language of evolutionary computation (EC)are borrowed from the biological world. Bits are “loci,” potential solutions to aproblem, are termed individuals, chromosomes, or genomes, and they are changedby “mutation” and “recombination.” A collection of variant solutions, termed apopulation, is subjected to “selection” by imposing some measure of “fitness.” Thebest solutions are selected as “parents” for the next generation, they “reproduce”with mutation and recombination to produce a new population that is subjected toselection, and so on.

EC, a subfield of artificial intelligence, exists in several forms that differ in therepresentation of the problem that is evolved and the mechanisms of evolution em-ployed (Foster 2001). For example, in genetic programming, executable programsthemselves are evolved, whereas in genetic algorithms, the parameters that describea potential solution to a problem are evolved (Koza 1992). Although recombina-tion is commonly employed in genetic algorithms and genetic programming, it isgenerally not employed in the other two EC varieties, evolutionary programmingand evolutionary strategies, which instead rely on large-scale mutations.

Like studies of the evolution of biological systems, research in the area of ECmakes use of model systems. In this case the models are problems to be solved.These include the “traveling salesman problem,” an NP-complete problem (i.e.,a class of computationally difficult problems) in which one tries to determine theshortest route for a salesman traveling between a large number of cities. Anotherinteresting model problem is the “iterated Prisoner’s Dilemma,” in which onedecides when it is best to cooperate and when it is best to defect in a series ofconfrontations. This problem has been explored from the perspective of the evolu-tion of cooperation (Axelrod & Hamilton 1981) and is now a classroom standardin EC (Vogel 1995). Another common model problem in genetic programming



is symbolic regression, where one must evolve a program to reproduce the input-output behavior of an unknown function given only randomly selected inputs andfunction values.

Evolved programs have interesting properties that in some cases are shared withbiological systems. For example, evolution of a genetic program via recombinationis accompanied by “code bloat” (Langdon et al. 1999). Like biological genomes,such computer genomes almost always accumulate “junk,” and it is not easy toseparate the functional components of the genome from the nonfunctional ones. Infact, a defense that removes junk from the genome will lead to the evolution of codethat evades the editing algorithm (Soule & Foster 1998). However, genome sizecan be constrained directly by charging a fitness penalty for larger genomes. This issimilar to biological systems in which microbial genomes maintain more stream-lined genomes than those of most multicellular organisms, presumably because ofselection for rapid replication.

What Kinds of Problems are Best Explored with EC?

Although in principle evolutionary algorithms can be applied to any computationalproblem, they are best suited to very difficult problems—problems to which thereare no other known efficient solutions. As with biological evolution, EC may notfind the optimal solution because it is a stochastic approximation algorithm. Forproblems with a very large potential solution space, such as the traveling salesmanproblem in which the number of possible routes throughN cities isN!, EC willnot explore all possible solutions. But if the topology of the fitness landscape isrough, evolution is an efficient way to find and explore fitness peaks.

Biological problems are naturals for EC. On one hand, this approach can beused to build tools to solve difficult problems, such as predicting protein or RNAfolding, inferring phylogenies (Lewis 1998), or aligning DNA or protein sequences(Notredame & Higgins 1996). On the other hand, it can be used to model complexbiological systems, such as the immune system, ecosystems, or cells (Adami 1998).

Genetic algorithms have found applications for some very practical problems,such as scheduling and constructing complex timetables. In such cases, there maybe other methods that find equally good or even better solutions, but perhaps notwith the same flexibility and speed. Genetic algorithms have also been appliedto nonlinear filtering problems, such as processing signals from radar, sonar, andGPS satellites (Whitely et al. 2000).

Evolved programs tend to be more robust than user-created (written) programs.They can withstand more damage without total failure, a property that is analogousto biological homeostasis. The basis for robustness is not always clear, but at least insome cases, it is not merely redundancy in evolved programs (Masner et al. 2000).Different user-created programs may tend to use the same approach to a particularproblem, but evolved solutions will tend to be unique. An important potentialapplication of the robustness and the unique nature of evolved programs is faulttolerance. Where computer failure can have catastrophic consequences (i.e., on a


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space shuttle or the navigation system of an airplane), agreement between multipleindependent programs is used to reduce the probability of failure of the system.But if all the programs have the same underlying “fault” because they were writtenaccording to the same programming principles, agreement may not assure againstfailure. Evolved programs may provide greater fault tolerance than do writtenprograms. And it is not out of the question to imagine programs that evolve torepair themselves when they detect a fault (J.A. Foster, personal communication).

CONCLUSION

The examples presented here are but a small fraction of the applications of evo-lutionary biology to socially relevant issues. We have limited this review to ex-amples in which evolutionary principles and methods have actually been used tosolve problems. An even richer variety of problems is being approached with evo-lutionary perspectives, and some of these efforts are likely to yield fruit in the nearfuture.

A research focus on “applied evolution” will perhaps prove to be an ephemeralone. It does not offer a conceptual organization for a discipline as broad as evolu-tionary biology. New applications will, of course, continue to arise and prosper, andapplications may be useful in garnering funding and influencing students’ careerchoices. But at the moment, the defining basis for applied evolution is ignorance.Many people, both scientists and the public, are unaware that evolutionary biologyhas become very relevant, yet attacks to suppress the teaching of evolution receivewidespread support at the local level. We hope that ignorance of applications willbe short-lived, as texts and the news media begin to disseminate the examples, andevolutionary biology should emerge in the future with widespread public accep-tance. This review has focused on positive applications of evolution, but it is alsoimportant to understand these potential applications to guard against other usesof this technology, such as production of biological weapons of mass destructionor the evolution of damaging computer viruses. The time has arrived for widepublic understanding of the importance and relevance of evolutionary biology ineveryday lives.

ACKNOWLEDGMENTS

Many of the examples in this paper were identified through discussions with col-leagues. We thank D. Hillis, F. Gould, A. Ellington, M. Robertson, B. Levin, L.Ancel, L. Vawter, W. Maddison, I. Matsumura, S. Palumbi, D. Futuyma, R. Bush,M. Courtney, I. Eckstranol, and J. Foster for discussions, references, or, in somecases, personal accounts. Mitch Sogin provided Figure 2. Editorial comments ofD. Futuyma and D. Fautin helped improve the prose. The topics in this paper alsooverlap with a symposium we organized on applied evolution at the 2000 meet-ings of the Society for the Study of Evolution (Bloomington). We also are very



grateful for the support of the NIH (GM38737 and GM 57756) and the NSF (DEB9726902) during the time we prepared this review.

Visit the Annual Reviews home page at www.AnnualReviews.org

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