Download Perspective Evolution Is an Experiment

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
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Mol. Biol. Evol. 32(7):1661–1671 doi:10.1093/molbev/msv105 Advance Access publication May 26, 2015
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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.
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“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
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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.
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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).
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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
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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
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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
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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
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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
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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
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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.
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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).
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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
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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
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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). And
of course, it would be wonderful if the results were replicated
independently, in a second lab, prior to awarding the prize.
Now that we have the rules, all we need is the money. Does
anyone have $1 million to spare?
Acknowledgments
In its original form, this talk was designed to highlight the
contributions of my former postdocs and grad students; they
all deserve my thanks and gratitude. Special thanks go to A.
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Rodriguez-Verdugo for allowing the use of one of her figures.
S. Hug, A. Gonzalez-Gonzalez, and A. Rodriguez-Verdugo suggested changes to the manuscript. G. Gaut helped his old
man with R code. The work was supported by National
Science Foundation Grant DEB-0748903.
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