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Evolution Is an Experiment: Assessing Parallelism in Crop Domestication and Experimental Evolution (Nei Lecture, SMBE 2014, Puerto Rico) Brandon S. Gaut*,1 1 Department of Ecology and Evolutionary Biology, University of California, Irvine *Corresponding author: [email protected]. Associate editor: Sudhir Kumar Abstract In this commentary, I make inferences about the level of repeatability and constraint in the evolutionary process, based on two sets of replicated experiments. The first experiment is crop domestication, which has been replicated across many different species. I focus on results of whole-genome scans for genes selected during domestication and ask whether genes are, in fact, selected in parallel across different domestication events. If genes are selected in parallel, it implies that the number of genetic solutions to the challenge of domestication is constrained. However, I find no evidence for parallel selection events either between species (maize vs. rice) or within species (two domestication events within beans). These results suggest that there are few constraints on genetic adaptation, but conclusions must be tempered by several complicating factors, particularly the lack of explicit design standards for selection screens. The second experiment involves the evolution of Escherichia coli to thermal stress. Unlike domestication, this highly replicated experiment detected a limited set of genes that appear prone to modification during adaptation to thermal stress. However, the number of potentially beneficial mutations within these genes is large, such that adaptation is constrained at the genic level but much less so at the nucleotide level. Based on these two experiments, I make the general conclusion that evolution is remarkably flexible, despite the presence of epistatic interactions that constrain evolutionary trajectories. I also posit that evolution is so rapid that we should establish a Speciation Prize, to be awarded to the first researcher who demonstrates speciation with a sexual organism in the laboratory. Key words: convergence, adaptive mutation, domestication, experimental evolution. Introduction Crop Domestication The Domestication Syndrome Most of our modern crops were domesticated within the last 12,000 years (Meyer and Purugganan 2013), but domestication has occurred across varied cultures and geography. Rice, oranges, and soybean were domesticated in China; potatoes, peanuts, and sweet potatoes were domesticated in South America; wheat and barley were likely domesticated in Turkey and Syria; whereas maize, squash, and beans were domesticated in Mexico (Doebley et al. 2006; Purugganan and Fuller 2009). Although the species and locations of domestication vary, domestication events are typically associated with the series of morphological changes that are known collectively as the ß The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: [email protected] Mol. Biol. Evol. 32(7):1661–1671 doi:10.1093/molbev/msv105 Advance Access publication May 26, 2015 1661 Perspective The Modern Synthesis outlines a simple process: Evolution is driven by mutations whose fates are dictated by either genetic drift or natural selection. Within this process, mutations are produced randomly, with fitness effects that are deleterious, neutral or beneficial. We know surprisingly little about the distribution of these effects, except that beneficial mutations are relatively rare (Orr 2005). Nonetheless, it is these beneficial mutations that are the building blocks of adaptive change. Just as we know little about the fitness effects of new mutations, we also know very little about the number of potentially beneficial mutations. For any specific environmental challenge for any organism, there may be only a small and finite number of potentially beneficial genetic mutations. If this is true—that is, the genetic path to adaptation is highly constrained—then one would also expect evolution to be highly repeatable (or convergent) at the genetic level. In other words, if it were possible to follow the process of adaptation across a highly replicated experiment, we would expect the replicates to harness the same limited set of beneficial mutations over and over again. In contrast, if the number of potentially adaptive mutations is large, such that there are few genetic constraints, we expect low repeatability. Generally speaking, little is known about repeatability and constraints in the adaptive process, in part because there are few well-documented examples of adaptive alleles in natural populations (but see Peichel et al. 2001; Hoekstra et al. 2006; Reed et al. 2011; Huerta-Sanchez et al. 2014). In this commentary, I will discuss two replicated experiments that differ tremendously with respect to their breadth, timeframe, and even the organisms. The two experiments are crop domestication, which has been taking place across the globe for the last approximately 12,000 years, and experimental evolution of Escherichia coli, which has taken place in a lab at UC Irvine over the course of a year. I will discuss the results from, and the limits of, both experimental systems, but the overall intent is to glean insights into the constraints and flexibility of the adaptive process. Gaut . doi:10.1093/molbev/msv105 “domestication syndrome” (Hammer 1984). This syndrome includes transitions to 1) fewer but larger fruits or grains, 2) more robust plants, 3) fewer branches, 4) the loss of both seed dispersal and seed dormancy, and 5) other important characteristics, such as shifts in photoperiod sensitivity. Viewed broadly, domestication can be seen as a replicated experiment, because domesticated crops tend to experience selective pressures that drive them toward these shared phenotypic shifts (Fuller et al. 2014). Maize and Nucleotide Polymorphism Most of the morphological changes associated with domestication traits occur under strong artificial or unconscious selection and thus are mediated by beneficial mutations (Ross-Ibarra et al. 2007). Much of my early career was dedicated to identifying genetic regions that contain these beneficial variants, using population genetic approaches and focusing particularly on maize. Among all crops, maize (Zea mays ssp. mays) is perhaps the most fascinating example of morphological change associated with domestication. When maize was domesticated in Mexico approximately 9,000 years ago (Matsuoka et al. 2002), the domesticators produced a radically modified plant that differed in several morphological features compared its wild progenitor, which is commonly called “teosinte” (for a review, see Doebley 2004). There is no such thing as an ear of corn in the wild; instead, teosinte plants have hard, triangular seeds that are organized within a single row. Maize typically produces a single ear per plant, but teosinte produces several inflorescences per plant. Maize typically lacks lateral branches, but branching can be extensive in teosintes. In fact, teosinte is so morphologically distinct from maize that the identification of its wild ancestor was uncertain until the use of isozyme markers in the 1980s (Doebley 1990). My interest in maize began when I was a graduate student in Michael Clegg’s lab at UC Riverside in the late 1980s and early 1990s. The polymerase chain reaction (PCR) was a relatively new technique, but it was clear would enable the study of new evolutionary questions. I was particularly interested in population genetics at the DNA sequence level, but at the time only one paper had described nucleotide polymorphism from a population sample in any species. This paper, the landmark 1983 paper by Martin Kreitman, detailed sequence variation within the Adh locus among ten Drosophila melanogaster individuals. The paper remains a classic in the field of population genetics, and it is particularly impressive when one considers that PCR was not available when Kreitman did his work. Given the Adh paper from Drosophila, my goal as a graduate student was very simple: To measure the level and pattern of nucleotide sequence variation for a plant gene. I chose to study maize and its wild relatives both because it is an engaging example of morphological change and because of wide economic interest. I chose to study the Adh gene because it had been cloned and sequenced in maize (Dennis et al. 1984), providing an important foundation for my work. Ultimately I produced a sample of eight sequences of the Adh 1662 MBE gene (fewer than Kreitman, who did not have PCR!), representing both domesticated maize and wild teosinte individuals. The sequences provided evidence of recombination among alleles, showed that nucleotide changes coincided with isozyme variation, and reported the first estimate of nucleotide polymorphism within a plant gene (Gaut and Clegg 1993). In retrospect, the maize Adh paper was an unremarkable mimic of Kreitman’s paper, except for two observations. The first was that nucleotide variation was high among the domesticated sample; eventually we would learn that domesticated maize has about an order of magnitude more sequence polymorphism than humans (Tenaillon et al. 2001). The second observation was that lineage sorting was extensive, which was clear because the eight sequences did not cluster by their taxon of origin. Taken together, both of these observations implied that maize has retained high nucleotide polymorphism. Demography and the Identification of Beneficial Alleles The maize results were surprising because we did not expect a domesticated crop to retain substantial genetic diversity. This expectation was based both on previous isozyme data (Doebley 1990) and on first principles: When a crop is domesticated, the domesticated germplasm necessarily represents a bottlenecked subset of the wild species. The size of that subset depends on many factors, including the geographic range of the ancestor, the degree of population subdivision in the wild ancestor, and the number of domestication events. Hence, our maize observations raised a number of questions about the dynamics of a genetic bottleneck. Can a bottlenecked population be small and yet still lead to the retention of substantial genetic diversity? If so, how large was the domestication bottleneck that led to maize? To address these questions, we investigated the size of the domestication bottleneck in maize, based on a much larger DNA sequence data set from the Adh region (Eyre-Walker et al. 1998). Our approach was to contrast DNA sequence variability between maize and its wild ancestor and then to fit coalescent models to summaries of the sequence data, assuming the presence of a demographic effect (i.e., the domestication bottleneck). We found that the duration of the bottleneck scaled linearly with the bottleneck population size. For example, if we took the maximum duration of domestication to be 2,800 years, as supported by archeological evidence, we estimated the population size during domestication to be several thousand individuals. Shorter durations suggested much smaller populations. Importantly, this study also verified that a great deal of polymorphism may be retained through a strong bottleneck, so long as the duration of the bottleneck is short. While based on a simple demographic model and a small amount of data, to my knowledge this work was the first empirical attempt to estimate demographic history from sequence polymorphism data from any species. Evolution Is an Experiment . doi:10.1093/molbev/msv105 The estimation of demographic history remains an interesting intellectual pursuit, but it was also a means to an end. In the study of crops, the ultimate goal is to identify genes that have been under positive selection during domestication—that is, the beneficial alleles that helped mediate the profound morphological and physiological changes that accompany domestication. The identification of these beneficial alleles is helpful for two reasons. The first is that these genes are likely to be agronomically important; the simple fact that they have been under selection in the past suggests that they have contributed to valuable traits. This approach—that is, the use of population genetics to find agronomically important genes independently of phenotype—is what has become known as the “bottoms-up” approach (Ross-Ibarra et al. 2007) or alternatively as “sweep-mapping.” The second reason, which is the focus for this paper, is to search for positively selected genes and alleles, because they may provide insights into the evolutionary process. In theory, the set of genes and alleles that contribute to domestication can be compared across species, potentially providing insight into convergence in adaptation or, alternatively, genetic flexibility and novelty. Is it also worth noting parenthetically that maize is a powerful system for identifying selected genes, for three reasons. First, the high level of polymorphism in maize and particularly its wild ancestor provides reasonable statistical power to identify the substantial declines in diversity that may indicate a selection event. Second, although there are exceptions (Tian et al. 2009), linkage disequilibrium (LD) in maize decays over relatively short 1–2 kb intervals (Remington et al. 2001; Tenaillon et al. 2001). This is important because it implies that a window of low diversity, which is a potential signal of positive selection, is likely to be near a causative adaptive sight. In contrast, a selective sweep in a species with extensive LD may encompass many genes and maybe even entire genome regions. Finally, and just as importantly, John Doebley and his colleagues at the University of Wisconsin have cloned several genes that were known to contribute to domestication phenotypes. This work included genes like tb1, which govern patterns of axillary branching (Doebley et al. 1995; Clark et al. 2004). Not only did Doebley clone tb1, but he also studied its population genetics, showing that it harbors low diversity of the sort expected after a selective sweep. Genes like tb1 can be used, in effect, as positive controls for additional population genetic studies. Given the benefits of maize, we turned our sights toward trying to tease apart the effects of positive selection on nucleotide polymorphism from those of demography, and we ultimately devised likelihood methods to test for selected genes (Tenaillon et al. 2004). Of course, it is now common in population genetics to try to tease apart the effects of demography from selection, and the current methods far outstrip our early attempts in both sophistication and statistical power (see Li et al. 2012 for a review). Yet, it remains a difficult problem to identify the signature of positive selection, because the patterns of nucleotide polymorphism caused by positive selection and demographic effects are often similar (Haddrill et al. 2005). MBE We eventually applied our methods to a nucleotide polymorphism data set of 700 genes and 16 individuals, representing both maize and its wild progenitor teosinte (Wright et al. 2005). The data were generated in the old-fashioned way, using Sanger sequencing; the scale of this project was only possible through collaborations with colleagues like Doebley and Mike McMullen (University of Missouri) under the auspices of a large collaborative grant. After fitting models to the data, we were able to produce two outcomes. The first was a list of the genes, among the 700, that were most likely to have been targets of selection during domestication. Importantly, the tb1 positive control landed atop this list, providing some measure of confidence to the results. The second outcome was an estimate of the percentage of genes that were subjected to selection during domestication. We estimated that 2–4% of the genes in the sample retained a signature of positive selection; extrapolating that value to the entire genome, we predicted that approximately 1,200 genes had been under selection during domestication (Wright et al. 2005). The Comparative Genetics of Domestication: Maize versus Rice While we were doing our maize work, it became clear that the population genetics of crop domestication would soon be based on full genome sequences, potentially leading to the identification of hundreds of selected genes. It is now quite common for research groups to generate whole-genome sequence data from samples of their favorite cultivar and then use that data to infer the presence of selected genes or regions. Although the details of the analyses and the data have changed dramatically (see Meyer and Purugganan 2013), the basic approaches are similar to those we pursued with maize. Now, almost a decade later, genome-wide selection screens have been applied to maize (Hufford et al. 2012), rice (Huang et al. 2012), soybean (Lam et al. 2010), common bean (Schmutz et al. 2014), cucumber (Qi et al. 2013), and other crops. To me, the most exciting aspect of these data is that we should soon be able to compare results among taxa. These comparisons will help us determine whether the phenotypic shifts associated with domestication are caused by novel genetic solutions or instead caused by parallel genetic change across species. Among domesticated crops, there is likely no better system for comparison than the grasses (Glemin and Bataillon 2009), which includes important cereals such as maize, barley, wheat, sorghum, pearl millet, and rice. Like other domestication events, cereal domestication is associated with morphological transformations that have commonalities across taxa. All of the domesticated cereals have reduced seed dispersal, reduced branching or tillering, decreased seed dormancy, more synchronized seed maturation, an increase in grain size, and larger inflorescences compared with their wild ancestors. The remarkable parallels in phenotype beg the question: If domestication has targeted similar 1663 MBE Gaut . doi:10.1093/molbev/msv105 traits, has selection acted on orthologous (or even homologous) genes? Historically, there has been some evidence that this might be the case. For example, Paterson et al. (1995) found that major quantitative trait loci for domestication traits (including seed shattering, seed mass, and flowering time) localized to syntenous regions among three grass genomes (sorghum, rice and maize). Perenniality also maps to syntenous regions between rice and sorghum (Hu et al. 2003). In addition, starch biosynthesis genes have a history of selection in both rice and maize (Whitt et al. 2002; Olsen et al. 2006). But do these patterns hold on a genome-wide scale? That is, do selection screens suggest that selection during domestication has acted on similar sets of genes and pathways across taxa? If so, what might this imply about constraint on, and the repeatability of, adaptation? To date, arguably the best genome-wide selection screens for domestication genes have focused on two grass species: maize and rice. For the former, Jeff Ross-Ibarra and colleagues at UC Davis examined nucleotide polymorphism by sequencing the complete genome of 58 maize and 16 teosinte individuals (Hufford et al. 2012). They detected 1,200 regions (or 2–4% of genes) with low diversity that could be indicative of positive selection, a result that corresponds closely with our earlier study (Wright et al. 2005). The 1,200 regions encompass 1,766 genes. The second study, from Bin Han’s group at the Chinese Academy of the Sciences, reported 1,083 and 446 whole genomes from cultivated and wild rice individuals, respectively (Huang et al. 2012). By contrasting wild samples to cultivated samples, they identified 55 regions that may have been subjected to positive selection during rice domestication. These 55 regions encompass 2,547 genes. The question motivating this paper is whether adaptive evolution is constrained and thus often repeated or instead highly variable at the genetic level. In theory, these studies of maize and rice provide a template to address this question. Have the same genes been selected in rice and maize, such that the domestication of two very different crops has relied on the same adaptive genetic solutions? Or, in fact, have the traits associated with domestication resulted from adaptive changes in very different sets of genes? To address these questions, I downloaded the list of rice and maize genes that are found within selected genomic regions. Of the 2,457 rice genes within selected regions, 1,526 had an ortholog defined in maize (fig. 1) (Schnable et al. 2012). Of the 1,764 maize genes, 969 had an ortholog to a rice gene. Given these genic sets, how many ortholog pairs were identified as selected in both species? If evolution is completely constrained, such that adaption to the domesticated phenotype is wholly repeatable at the genetic level, we expect the two species to have the same sets of genes under selection. Thus, under complete constraint, we expect that each of the 969 selected genes from maize will have an ortholog within the set of 1,526 rice genes. The answer is, however, very far from 969; only 65 ortholog pairs have an apparent history of selection in both species! In fact, 65 genes is no different from the number expected under the null hypothesis that there are no constraints—that is, that 1664 FIG. 1. An analysis of the number of genes that have been inferred to lie in regions under selection during domestication. The number and identity of genes were taken from Huang et al. (2012) and Hufford et al. (2012) for rice and maize, respectively. The list of ortholog pairs was from Schnable et al. (2012). genes are chosen completely at random. Given 1,526 genes and 969 genes from the two species, the random expectation for the number of genes to be selected in both species approximately 68 genes (=[1,526 969]/21,528). The upper and lower 95% confidence interval on this expectation, based on 1,000 permutated data sets, is 56 and 81 genes. Taken at face value, these results suggest that the adaptive process is remarkably flexible, because different genes have been utilized to adapt to similar domestication phenotypes in the two species. Of course, it is possible that these results may be telling us as much (or more) about the limitations of the approach (and my admittedly superficial analysis) than about biological reality. I have already discussed some of the limitations of sweep-mapping approaches, such as the fact that it can be difficult to tease apart the effects of selection from those of demography. The comparison between species is even more difficult because of differences between species. For example, I mentioned that maize has high nucleotide diversity and low LD, but rice has exactly the opposite: Low nucleotide diversity and high LD (Mather et al. 2007). As a result, the statistical power to detect selection may be substantially lower in rice, and when selection is detected it is more likely to encompass a larger genomic region. In other words, we cannot know if the 55 regions in rice represent 55 selected genes, or selection on several genes within each of the regions, or selection on all 2,547 genes. Two Domestications of Common Bean I can understand if my audience doubts that sweep-mapping can be a comparative exercise, particularly between rice and maize. After all, the two species may have experienced some similar selection pressures during domestication, but certainly Evolution Is an Experiment . doi:10.1093/molbev/msv105 not all of the pressures were similar. Moreover, the species diverged greater than 50 Ma and differ substantially in genome structure (Gaut 2002). A better test is to compare domestication events between two closely related taxa, or better yet within the same species. Remarkably this is now possible for the common bean (Phaeseolus vulgaris L.). Common bean was domesticated not once but twice, in Mesoamerica (probably Mexico) and the Andes (Bitocchi et al. 2012, 2013). These two independent domestication events provide a rare opportunity to assess the genetic effects of domestication in what is, in essence, a replicated evolutionary experiment. Schmutz et al. (2014) examined the population genetics of the two domestication events by resequencing the genomes of 160 wild and cultivated accessions of common bean. They then used the data to screen for genes that may have been selected during the two domestication events, identifying 1,835 and 748 candidate genes in Mesoamerican and Andean domesticates, respectively, out of a total of 27,197 genes. How many genes were common to both domestication events? The answer is 59 genes or 0.2% of the total number of genes. Once again, 59 genes does not exceed the random expectation (mean 50 genes; upper 95% confidence interval = 62 genes, based on 1,000 permuted samples). This result again suggests a very low level of evolutionary constraint during the domestication process or, conversely, substantial evolutionary flexibility. (A similar result—i.e., little evidence for parallel selection on sites or genes—has also been shown for two independent adaptations of maize to high altitude [Takuno et al. 2015].) In the end, it is still not clear whether these results tell us more about biology, about inherent weaknesses in population genetic approaches, or about inadequate statistical design to properly compare domestication events. I suspect all of three factors are pertinent. Do Individual Genes Tell Us about Repeatability? At this point, it is unclear whether evolution is so flexible that completely different genes have been selected during the domestication of different plants, or alternatively that selection screens are too limited (as currently employed) to provide insights in a comparative context. It may be more straightforward to examine evolutionary constraints and flexibility by focusing on a single gene with a known phenotypic effect. Does a single gene play a similar role in domestication across taxa? To answer this question, I turn to the nonshattering phenotype. In nature, seeds shatter from the plant when they are mature so that they can disperse. A common feature of domestication is the loss of seed shattering; in domesticated plants, mature seeds remain on the plant to facilitate harvest. This trait has been studied extensively by plant biologists, such that the genes that contribute to the loss of shattering are known for a handful of species. One particularly good example is the Shattering1 (Sh1) gene of sorghum, which encodes a transcription factor (Lin et al. 2012). In a wonderful study, Lin et al. (2012) found that domesticated sorghums harbor three different mutations at the Sh1 locus, and all of MBE the mutations either truncate the protein or reduce expression. Thus, shattering has evolved by three independent mutations within sorghum, and all three mutations lead to the loss or reduction of function of the same gene. This observation suggests that the route to reduced seed shattering in sorghum is most easily traversed through Sh1. All other cereal crops have also lost shattering. Is Sh1 implicated in other grass species, too? Two major genes for shattering have been found in rice, and these genes are not orthologous to Sh1. However, the rice region syntenic to Sh1 does map as a quantitative trait locus with a minor genetic effect on shattering. Moreover, the ortholog to Sh1 has reduced levels of transcription and is located within an inferred selective sweep (Lin et al. 2012). Thus, although it is not entirely clear, it appears that mutations within the rice Sh1 ortholog may have played a role in the loss of shattering in rice, but probably not the primary role, as it does in sorghum. In contrast, a major locus (Q) for shattering has been identified in wheat, but it is from a different gene family than Sh1; there is as yet no evidence that Sh1 homologs have played a role in shattering in wheat. Finally, maize is closely related to sorghum, and in this species the shattering phenotype maps to the Sh1 ortholog (Lin et al. 2012). Altogether, the message is varied: Sh1 is critical in sorghum and likely so in its close relative maize; it may have played a role in rice, but likely not a major one; and to date it appears to have played no role in wheat. At the very least, the take-home message is that there are multiple genetic routes to the nonshattering phenotype (i.e., “There’s more than one way to skin a cat”). Parallel Evolution in Crops: Challenges and Promise What can we conclude from these results? First, like many others (Doebley et al. 2006; Ross-Ibarra et al. 2007; Morrell et al. 2011; Meyer and Purugganan 2013; Olsen and Wendel 2013), I hold that the domestication of crops represents a wonderful opportunity to assess both the effects of selection on genomes and to infer the kinds of genetic changes that fuel adaptation. For example, the study of domestication genes has provided meaningful insights as to the relative role of regulatory versus structural genes in evolutionary change (Doebley et al. 2006; Olsen and Wendel 2013). A continued focus on domestication promises to yield additional insights into evolutionary processes. Second, researchers who apply the technique of selectivesweep mapping have been slow to recognize that comparative approaches may have merits (but see Glemin and Bataillon 2009; Morrell et al. 2011; Fuller et al. 2014). This lack of recognition is unfortunately reflected in the experimental design (or the lack thereof) of most selectionmapping studies. Ideally, one would like experiments to be somewhat standardized, so that that conclusions among taxa can be compared directly. For example, it would be marvelous if studies were designed so that they offered relatively similar statistical power to detect an allele with a selection coefficient of some assumed value. Surprisingly, however, most of the sweep-mapping studies on crops have not been based on well-substantiated experimental design. These days one can 1665 Gaut . doi:10.1093/molbev/msv105 perform power analyses to gain some insight into the number and type of individuals that need to be sampled to achieve a given level of power (Przeworski 2002; Kessner and Novembre 2015). Yet, few (if any) population genomic studies of crop domestication have utilized this basic step. (For that matter, it is a step rarely used in genome-wide association studies [GWAS] in plants, which also limits the comparison of GWAS results between species.) Until the adherents of these approaches reach for more explicit standards—and perhaps reviewers need to impose those standards—I fear that the opportunity for insightful comparisons may be missed. Third, even with more explicit standards, it is clear that the results of sweep-mapping (and also GWAS) will be difficult to compare between taxa because of the underlying biology. For example, the ability to detect selection depends on the history of the favored allele. Selection may be relatively easy to detect for a de novo mutation (i.e., a hard sweep) but more difficult to detect whether the beneficial variant preexisted as a common neutral polymorphism prior to domestication (i.e., a soft sweep) (Innan and Kim 2004; Przeworski et al. 2005) or whether adaptation is driven by multiple, functionally equivalent alleles (Ralph and Coop 2010). We also have a growing appreciation for the fact that domestication is not a simple process for some genes, because some alleles have a complex history of migration between cultivars and wild populations and may ultimately contribute to local adaptation (Hufford et al. 2013). Finally, it is important to recognize that selection may actually act on a pathway, not a single gene, so that systems-level interpretation is ultimately needed to make sense of comparative data. Escherichia coli Experimental Evolution Although I have not studied the domestication of maize for several years, I still marvel at domestication as a process, and I am sure we will continue to learn a great deal about evolution through the study of domestication. However, it also has become clear that the question of repeatability in evolution might best be pursued in a more simple system. To that end, my colleagues and I began our ongoing studies about the evolution of E. coli to thermal stress. These studies began with two exceptional colleagues at UC Irvine: Al Bennett and Tony Long. Bennett has a history of work in E. coli that traces back to a collaboration with Richard Lenski (Michigan State University). When Lenski established his long-term E. coli evolution experiment at UC Irvine in the 1980s, Bennett worked with him to simultaneously investigate the effect of thermal stress on E. coli. Bennett was interested in thermal stress because temperature governs the rate of biological reactions that contribute to respiration, growth, and reproduction; it therefore generates a complex physiological response. In 1990, Bennett and Lenski published a paper demonstrating the rapid fitness response of E. coli to thermal stress (Bennett et al. 1990) that preceded the first article about Lenski’s long-term experimental populations (Lenski et al. 1991). Later, Bennett teamed with Long to characterize some of the genetic changes that occurred during E. coli adaptation to thermal stress. Bennett, Long, and coauthors showed that 1666 MBE at least a handful of mutations—particularly deletions— occurred in parallel across replicated populations (Riehle et al. 2001). These studies suggested that the evolution of E. coli to thermal stress is repeatable at some level, such that the number of genetic solutions to thermal stress may be limited. However, the initial thermal stress experiments were based on only six replicate populations. To better measure the potential diversity and repeatability of adaptive genetic changes, Bennett and Long had the idea to replicate the experiment at much higher levels. Together, the three of us performed a thermal stress experiment on greater than 100 replicate populations. The experiment itself was relatively simple (fig. 2). We followed Lenski’s experimental design, in which a single ancestral E. coli was inoculated into replicate tubes, and the bacteria in each tube were allowed to grow under poor nutrient conditions. The population was then diluted daily into fresh media. The primary difference between our experiment and Lenski’s long-term experiment was the temperature: 42.2 C as opposed to the standard culture temperature of 37.0 C. In addition, we utilized an ancestral strain (REL1206) that was already well adapted to low glucose media, so the primary selection pressure was temperature. We ran the experiment for 2,000 generations, or roughly 1 year, which was ample time to record a fitness response (Bennett et al. 1990). Our goal was to measure fitness increases among populations and, more importantly, to identify the genetic variants that contribute to adaptation to thermal stress. We answered the first question by measuring the fitness of the evolved clones relative to their ancestor. On average, the 115 clones taken from the 115 replicate populations at the end of the experiment were approximately 40% more fit at 42.2 C than their ancestor (Tenaillon et al. 2012). We answered the second question by sequencing the complete genome from one clone of each of 115 replicated populations. After disregarding one clone that had evolved a high mutation rate, we ultimately identified 1,258 mutations from the sequences of 114 clones. We concluded that most of these mutations were adaptive, based on two lines of reasoning. First, population sizes were quite high, in the millions of bacteria, even during daily dilution (fig. 2); with large population sizes, natural selection is efficacious and genetic drift is expected to be weak. FIG. 2. A schematic of the experiment to adapt Escherichia coli to thermal stress. DM25 is a low glucose media; REL1206 is the ancestor which was already adapted to DM25. Each population experienced a 1:100 daily dilution, during which the population size (N) was expected to remain high. The sequenced clones were isolated from separate replicate populations at generation 2000. MBE Evolution Is an Experiment . doi:10.1093/molbev/msv105 Second, the resultant genome sequences—which were analyzed by another invaluable collaborator, Olivier Tenaillon (INSERM, France)—had ratios of nonsynonymous to synonymous mutations that were 1.0, consistent with extensive adaptive evolution (Tenaillon et al. 2012). Insights into Flexibility and Constraint By the end of experiment, we had a bounty of adaptive mutations—arguably more than had been identified in any previous experiment. What did these mutations tell us about adaptive convergence, constraint, and flexibility at the genetic level? To begin, we found that genetic parallelism depends on the scale of measurement. Focusing first on point mutations, relatively few specific point mutations were found to occur in parallel across clones. Of the 789 observed point mutations, the vast majority (~650) were unique to a single clone. As a result, the average fraction of shared point mutations between clones was very low, at 2.6% (fig. 3B). That said, a few mutations were reasonably common among the 114 genomes. For example, one mutation—an isoleucine to asparagine substitution in codon 15 of the transcription termination factor rho (I15N)—was found in 15 different clones. Another mutation— (I966S) in the RNA polymerase B subunit (rpoB)—was also found in 15 different clones. The fact that these mutations were found across many individually replicated populations indicates that they were not only convergent but also highly beneficial. Nonetheless, convergence (or repeatability) at the level of point mutations was the exception rather than the rule. The picture changed somewhat when we focused on the genes in which mutations occurred. We detected mutations in over 250 genes. However, some genes were targeted highly across replicates; for example, the ybaL gene was mutated in 65 of 114 independently evolved clones, a highly nonrandom result (P < 10 200). Most of the ybaL mutations differed at the molecular level, but at least 17 interrupted ybaL function by introducing frameshifts and two more introduced large deletions. This high level of convergence implies that the loss of ybaL, a potassium transporter, is adaptive under the conditions of our experiment, but we do not know the mechanism—that is, why a knock-out or knock-down of ybaL is A adaptive. Another case of convergence was found for a large, overlapping deletion event. The deletion was 71 kb in length, resulted in the deletion of 64 genes, and was found in 35 evolved clones. This particular deletion was flanked by insertion sequence (IS) elements, which likely mediated a high mutation rate through homologous recombination (Tenaillon et al. 2012), which helps explain the high number of observed large deletion events (fig. 3A). Overall, convergence was much higher among genes than among nucleotide sites: As I mentioned, two strains shared 2.6% of single base mutations on average but shared 20.2% of genes modified by mutation. Interestingly, the gene with the most mutations was rpoB, with a total of 87 mutations, and it was also the only gene that contained more than one mutation in a single clone. The gene with the next-highest number of mutations was ybaL, but the gene with the third most mutations was the transcription termination factor rho. Hence, somewhat remarkably, two of the three genes with the most mutations (rpoB and rho) function in transcription. Mutations in these two genes have the potential for substantial pleiotropic effects, which we are currently characterizing. Convergence, Constraint, and Epistasis Our observations led to three novel inferences about the process of adaptation in our experiment. First, the high level of convergence among genes likely reflects a limited number of functional targets (i.e., genes or interacting sets of genes) for beneficial mutation, such as the RNA polymerase complex. Indeed, using statistical and mathematical arguments, we showed that our experiment uncovered approximately 60 primary functional targets of high temperature adaptation (Tenaillon et al. 2012). Second, the relatively high level of convergence at the level of genes and functional complexes—but not at the level of individual mutations (fig. 3B)—suggested that there are many potential sites for beneficial mutations within a given gene or complex. To estimate the number of sites that can contribute to an adaptive response, Tenaillon applied a model of mutation sampling to our data. This model estimates that at least 850 possible sites of beneficial mutations are required to explain the 400 observed point mutations within the highly B FIG. 3. (A) The number of observed mutations within 114 Escherichia coli clones. “Indels” refers to short insertion and deletions events; “IS insertions” refers to movement of IS elements. (B) The average number of mutations found in common between two strains based on the level of study: Individual mutations, mutations within genes, and mutations within sets of interacting genes. Data are from Tenaillon et al. (2012). 1667 Gaut . doi:10.1093/molbev/msv105 targeted genes. With less conservative (and more realistic) assumptions, the model suggests thousands of potential sites for beneficial mutations within these functional groups. The apparent flexibility of adaptive genetic solutions is very surprising to me, because I naively assumed that any gene would have at most a handful of sites that could lead to an adaptive change. Our data suggest the opposite—that is, that a limited number of approximately 60 genes each have large numbers (perhaps hundreds!) of sites with the potential to harbor an adaptive mutation. Finally, one of the advantages of a large number of replicates was that there were enough mutations to perform meaningful statistical analyses. One intriguing question was whether mutations occurred randomly with respect to one another or whether there were statistical biases such that some mutations were found in positive or negative association with other mutations. We termed these positive or negative associations “statistical epistasis,” because they imply underlying epistatic interactions. Overall, we found pervasive associations among mutations. For example, mutations in both rpoB and rho within a single clone were observed far less often than expected at random (P < 10 5), suggesting negative epistasis between mutations in these two genes. Moreover, mutations in rho and rpoB were positively associated with different sets of genes. Mutations in rpoB tended to be positively associated with mutations in six rod genes that affect the cellular membrane, but the opposite was true of mutations in rho and rod genes. Altogether, our work uncovered two statistically separate genetic pathways, typified by mutations in rho and rpoB along with mutations in their associated genes. Thus, epistatic interactions appear to have constrained adaptive trajectories into at least two distinct adaptive pathways that included either rpoB (60 clones) or rho (31 clones), but rarely both. The prevalence of statistical epistasis was somewhat puzzling to us. However, some key ideas have recently been published by the Desai group at Harvard, based on their experiments with clonal yeast (Kryazhimskiy et al. 2014). They compared variation among lines descended from the same founding ancestor with variation among lines descended from different ancestors. Through this scheme they were able to assess the extent to which the genetic background influences evolutionary change. They found that the background of the founder had a large effect on the rapidity of adaptation, because ancestral backgrounds with low initial fitness prompted higher initial rates of adaptation. They concluded that this pattern is governed by a type of “global epistasis,” such that “. . . each individual beneficial mutation provides a smaller advantage in a fitter genetic background” (Kryazhimskiy et al. 2014). They also concluded that there is a large universe of potentially adaptive mutations, and each adaptation contributes to global epistasis irrespective of interactions between individual mutations. If I understand their idea correctly, it implies that epistasis can diminish the magnitude of the effect of an adaptive mutation (i.e., diminishing returns epistasis) but does not change the sign of a mutation from adaptive to deleterious. This contradicts my intuition for the E. coli system, because (e.g.) we predict that rho mutations 1668 MBE are not beneficial within the background of an rpoB mutant but are beneficial in other backgrounds. To sum, our E. coli experiment has shown that there is a large universe of potentially adaptive mutations at level of individual nucleotides. However, individual mutations are constrained by, or contingent upon, other mutations due to epistatic interactions. We are currently trying to understand these epistatic interactions more fully; the field still has much to learn about epistasis more generally. Do the Two Pathways Lead to Identical Phenotypes? The rpoB and rho adaptive pathways converge on one important phenotype: The ability to grow and thrive under thermal stress. And they appear to do so equally well, because there is no significant difference in relative fitness at 42.2 C between the sets of clones bearing rpoB and rho mutations (Rodriguez-Verdugo et al. 2014). Nonetheless, we are now actively trying to determine whether the two pathways differ in other respects by measuring the growth, gene expression, and metabolic properties of the 114 evolved clones. We have found that the two adaptive pathways do differ in one interesting aspect: Growth characteristics at low temperatures (Rodriguez-Verdugo et al. 2014). An outstanding student of mine, Alejandra Rodriguez-Verdugo, was intrigued by the idea of temperature trade-offs. She was thus interested in seeing whether our evolved clones “paid” for their adaptation to high temperature with poor growth characteristics at low temperatures. Of course, trade-offs are hypothesized to be common in evolution, and so we suspected that most or all of our clones would exhibit poorer growth characteristics at low temperatures relative to the REL1206 ancestor. In order to do this work, Rodriguez-Verdugo determined the lower boundary of the thermal niche of the REL1206 ancestor and our evolved clones. In other words, she found the lowest temperature at which each clone could persist and grow indefinitely under a regime of 100-fold daily dilution. For the REL1206 ancestor, this temperature was 18 C; at 17 C, the population crashed within a few days. Although we expected our evolved clones to exhibit a fitness trade-off at low temperatures, many did not! We found that 52% of the clones persisted 18 C, just like the ancestor, whereas the remaining 48% clones could maintain populations only at 19 C or higher. The most surprising aspect of her work is that the ability to persist at 18 C is associated with genetic mutations, particularly mutations in rpoB and rho. For example, the I966S mutation in rpoB is associated with an inability to persist at 18 C, whereas the rho I15N mutation associates with persistence at 18 C. The associations were confirmed by two methods. First, we engineered these single rho and rpoB mutations into the REL1206 ancestral background; the individual mutations resulted in the predicted growth dynamics. Second, and more generally, we found that there were significant differences in fitness at low temperature for all of the 60 clones that included an rpoB mutation versus the 31 clones with a mutation in the rho gene. Thus it is clear that the two adaptive pathways, which both convey adaptation to thermal stress at MBE Evolution Is an Experiment . doi:10.1093/molbev/msv105 42.2 C, are not identical with respect to additional phenotypes. Conclusions I have covered a lot of territory in this talk—from genes to genomes; from crops to bacteria—but there is an overarching theme: Genetic novelty, genetic constraints, and their roles in adaption. Unfortunately, both the crop and E. coli systems have substantial limitations that affect the ability to make robust and general conclusions. On the one hand, comparisons among crops are complicated by both biology (e.g., soft sweeps, mating systems, selection for similar but not identical phenotypes) and by the lack of explicit standards in the field. On the other hand, the E. coli work is limited by clonality and by the simplicity of the experimental model; the work ignores complicating factors such as recombination and complex environments. To my mind, many of the results based on the experimental evolution of clonal organisms such as yeast and E. coli are oversold as generally important when reality must be far more nuanced. Recognizing these limitations, I would like to offer three conclusions: 1. Evolution is remarkably flexible. Two features of the E. coli work really stand out to me. The first, stated above, is the extent of variation in point mutations. Few of the mutations are shared among clones, and yet the experimental system is such that the vast majority of these mutations likely leads to some adaptive benefit. The other is that many of the mutations—such as mutations in rpoB and rho—must be highly pleiotropic. Yet, somehow evolution proceeds out of the chaos of pleiotropy and mutational variation. Studies on comparative crop domestication suggest similar flexibility in the evolutionary process, because adaptive variants can be found in regulatory genes, structural genes, as copy number variants and as a consequence of a transposable element insertion (Olsen and Wendel 2013). In short, these two systems highlight the inherent flexibility of genetic mutations that can lead to adaptation. 2. Epistasis is pervasive. In our E. coli experiment (Tenaillon et al. 2012), we inferred that epistasis constrains adaption to one of two pathways. Subsequent studies in yeast have confirmed the prevalence of epistatic interactions and their effect on the fitness response (Kryazhimskiy et al. 2014). Of course, this is one force (epistasis) that is very easy to overstate based on the study of clonal organisms! Nonetheless, a growing body of literature suggests that epistatic interactions are also common in sexual species, both within and between genes (Gaertner et al. 2012; Tufts et al. 2015; Taylor and Ehrenreich 2015). For example, Corbett-Detig et al. (2013) not only found evidence for epistatic interactions between drosophila mutations but also argue that such interactions may fuel speciation events. MacKay (2014) has similarly argued that epistasis is a pervasive contributor to quantitative trait variation that often goes unnoticed, both because experiments lack the power to detect it and because researchers may not look for it. The paradox is that epistatic interactions constrain adaptation, because contingency matters with epistasis. One could remark that evolution is remarkably flexible despite the pervasiveness of epistasis. We still have much to learn about the interplay between the universe of potentially adaptive mutations versus the extent to which that universe is limited by epistatic (and other) constraints. 3. Evolution is fast. Strict adherents to the Modern Synthesis may disagree with this conclusion, but I am colored by my work in crops and bacteria. In both of these experimental systems, substantial evolutionary change can happen rapidly. For example, others and we have found that bacterial populations under strong thermal stress can recover rapidly (Mongold et al. 1999), even though they appear to be on the road to extinction. For crop plants, the data on the speed of domestication is not as clear. Modeling studies suggest that the nonshattering phenotypes may occur in less than 200 years and perhaps as rapidly as 20–30 years (Hillman and Davies 1990a, 1990b), but fossil data suggest that important domestication traits evolve over thousands of generations (Purugganan and Fuller 2009, 2011; Fuller et al. 2014). Personally, I am not in a position to strongly adopt one view or another, but my firm hunch is that some of the incipient morphological changes associated with plant domestication can occur rapidly—perhaps even within the span of a human lifetime—if both mutation rates and population sizes are large and if the trait is governed by loci of large effect. This brings me to a broader issue: To me, it seems that our field constantly apologizes to the lay public. “Never mind,” we say to the general public, “that you cannot witness speciation yourself, because it is a long and slow process.” To that I say: Bunk. Yes, some systems do evolve slowly; elephants, with their long generation times, come to mind. But I am fully convinced that evolution is so rapid for some organisms that we should be able to demonstrate speciation in the lab. And in fact, I think we should make it an explicit goal of our field. Mathematics has the Millennium Prizes, which are a series of seven problems associated with a $1 million prize for each solution. Evolutionary Biology should have a Speciation Prize for the first researcher who demonstrates speciation in the lab. The competition needs some rules, of course. As a starting point, I suggest that speciation be defined as reproductive isolation between two populations, as per Mayr; that it must occur in allopatric lab populations (no sympatric hocus-pocus!); and that the organisms must be sexual, diploid species (sorry, no polyploid speciation). 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