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What We’ve Learned about Evolution from the LTEE: Number 5

This is the fifth in a series of ~10 posts in which I summarize what I think are the most important findings and interesting discoveries from the LTEE.  The previous entry, number 4, also has links to the earlier entries.

Number 5.  We have seen large changes in the spontaneous mutation rate in some of the LTEE populations.  These changes reflect an interesting tradeoff between short-term fitness and long-term evolvability.

Proximate causes.  Six of the 12 LTEE populations evolved to be so-called “hypermutators” by 50,000 generations.  The proximate (i.e., biochemical) causes of these changes are mutations in genes whose products are involved in DNA repair or the degradation of molecules that cause damage to DNA.

These mutations typically cause the rate of point mutations throughout the genome to increase by roughly 100-fold (Sniegowski et al., 1997, Wielgoss et al., 2013), so their effects are not at all subtle.  They also change the spectrum of mutations:  mutations in the mutS gene, which encodes a protein involved in mismatch repair, cause increased A·T–>G·C and G·C–>A·T transitions (Lenski et al., 2003); while mutations in mutT, which encodes an enzyme that degrades an oxidized nucleotide, cause A·T–>C·G transversions (Barrick et al., 2009).

Evolutionary effects.  The evolutionary effects of these hypermutators are subtle and interesting.  In essence, one can think of mutations that produce hypermutators as affecting the tradeoff between short-term fitness and long-term evolvability.

Short-term cost.  Of all the possible mutations that might occur, many more are deleterious than are beneficial. Therefore, hypermutators produce more maladapted progeny than otherwise identical cells with a lower mutation rate.  Hence, hypermutators suffer a fitness cost caused by the increased production of progeny with deleterious mutations.

However, the E. coli strain that was the ancestor to the LTEE has a low point mutation rate, which we’ve estimated as ~10-10 per base-pair per generation (Wielgoss et al., 2011).  Given the genome contains ~5 x 106 base-pairs, this rate translates to only ~0.0005 point mutations per genome per generation.  Therefore, even a 100-fold increase means that most hypermutator progeny are mutation-free.  Considering that only a fraction of genomic sites are subject to mutations that would be deleterious in the LTEE environment, we infer that the short-term cost to a 100-fold hypermutator is ~1% (Wielgoss et al., 2013).

Evolvability benefit.  Even a 1% cost is not trivial, so how can a hypermutator survive and spread through a population?  In fact, most hypermutators do not survive; the vast majority of mutations that cause hypermutators will die out as a consequence of that short-term cost.  However, hypermutators result from loss-of-function mutations, and a dozen or so large genes are targets for these mutations.  Hence, new hypermutators will continually be regenerated in large populations.  Absent other forces, an equilibrium frequency of hypermutators would be reached that reflects the balance between the rate of appearance of hypermutators by new mutations in the relevant genes and the rate at which they are removed by selection against the deleterious mutations they cause—in other words, the familiar mutation-selection balance of population-genetics theory.

But another force is at play: the populations in the LTEE are not sitting on a fitness peak, so there are on-going opportunities for beneficial mutations to appear.  And a hypermutator cell has a much higher probability of generating a beneficial mutation than does a “normal” cell.  In essence, there’s a race to produce the next winner.  If a hypermutable cell generates the next beneficial mutation that sweeps through the population, then the hypermutator will “hitchhike” along with it because, without sex, the two mutations are linked.

Combining forces.  So how do the short-term cost and the evolvability benefit play out together?  Mutations that knock out any one of the genes involved in DNA repair probably occur at a rate between 10-5 and 10-6 per generation, and the resulting hypermutable cells have a fitness disadvantage of ~1% owing to the production of deleterious mutations.  At mutation-selection balance, the frequency of hypermutators is between 0.01% (10-4) and 0.1% (10-3).  Let’s use 0.05% to illustrate.

Although the hypermutators are a small minority, on a per capita basis each of them has a 100-times higher probability than a normal cell of generating the next winner.  So 5% of the time, a hypermutator will be swept to fixation, but most of the time the winner will be produced by a normal cell.  Now consider the fact that each of the LTEE populations has had many beneficial mutations go to fixation over its history.  After 14 selective sweeps, the odds are better than 50:50 that at least one of those beneficial mutations was generated by a hypermutator.

King of the mountain.  After a hypermutator becomes common, it becomes very hard to dislodge it from the population.  This difficulty follows from the same logic as above.  Once the hypermutator reaches 1% of the population, it has a 50% chance of generating the next winner; by the time it gets to a 50% frequency, the odds are 100:1 in its favor.  Thus, a hypermutator only needs to get lucky once, and then it becomes extremely difficult to displace it … at least so long as the population is far from the fitness peak.

Nothing lasts forever.  Even before a population reaches a fitness peak, its rate of fitness improvement typically decelerates, at least in a constant environment like that of the LTEE (Wiser et al., 2013).  At some point, the magnitude of the benefit that would result from reducing the mutation rate and its associated fitness cost may become commensurate with the fitness advantages that are available from other mutations.  When that happens, selection to reduce the mutation rate becomes effective, and the hypermutable “king of the mountain” can be displaced by a genotype with a lower mutation rate.

Indeed, we have observed this displacement occurring in one of the LTEE populations (Wielgoss et al., 2013).  In that population, not one but two lineages independently arose (see Figure below) that reduced the mutation rate by about half, while reducing the fitness cost from ~1% to ~0.5%.  The population thus remains hypermutable, but less so than before.

What the future may hold.  In that paper, we hinted that it is probably easier to reduce the mutation rate in stages rather than to revert to the ancestral rate in a single step.  That’s because the population is continuing to adapt, albeit at a slower rate.  A genotype with a 50% reduction in the mutation rate will save half of the fitness cost of the full-blown hypermutator, yet it will continue to produce 50 times as many other beneficial mutations as would a genotype that reverted to the ancestral mutation rate.  In essence, the fitness costs and the evolvability benefits are on very different scales.

The figure below shows the decelerating fitness trajectory (dark green curve, left axis) and the number of mutations (right axis) as the lineage with the ancestral mutation rate (blue) is replaced by a hypermutator lineage (red), which in turn is displaced by two independent lineages with somewhat lower mutation rates (light green and purple).  The figure comes from Wielgoss et al., 2013, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.

Mutation trajectory, PNAS 2013

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THE FIRST GIFT

—being comprised of DNA and shared in the

first cell division, 3 to 4 billion years ago.

 

 

Take this. It’s part of me

and everything I know

about this emergent art

of getting by.

 

Since what I am survived

this long, this place,

my information may enable you

to live a little.

 

 

 

******************************

By Jean Lenski (1928-1994)

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What We’ve Learned about Evolution from the LTEE: Number 3

This is the third in a series of posts where I summarize what I think are the most important findings and discoveries from the E. coli long-term evolution experiment (LTEE).  Links to the first two posts in this series are here and here.  Links to all of the references cited below are provided at the bottom of this post.

Number 3.  The LTEE has produced many interesting and striking examples of both parallel (repeatable) and divergent evolution across the 12 replicate populations, including at both the phenotypic and genetic levels.

These examples all bear on the reproducibility of evolution, which is one of the core questions that the LTEE seeks to address.  The answer is not a simple one with a dichotomous “yes/no” outcome, because evolution is an intriguing mix of random (mutation and drift) and directional (natural selection) processes.  But the LTEE offers the opportunity to examine this question more thoroughly than almost any other biological system studied to date.

Examples of Parallel Evolution in the LTEE

Fitness. The trajectories for fitness, as measured in the environment of the LTEE, have been very similar across the replicate populations, although they are certainly not identical (Lenski and Travisano, 1994; Travisano et al., 1995).  But perhaps that’s not too surprising because fitness integrates, rather than atomizes, the underlying changes.

Cell size. One of the most strikingly parallel trends has been in the size of the individual cells.  All 12 populations produce cells that are much larger than the ancestor (Lenski and Travisano, 1994).  If you had asked me, I would have thought the cells should become smaller based on surface-to-volume ratio considerations in a resource-limited environment.  But the bacteria obviously had a different “opinion” about this, so to say.

Genetics. And it’s not just phenotypic traits that show parallel evolution.  We’ve found three genes that have fixed mutations in all 12 populations (V. Cooper et al., 2001; Woods et al., 2005), although the exact mutations at the sequence level differ in almost every case.  By contrast, most of the 4,000+ genes retain the ancestral sequence in most or all of the lines because, while the LTEE is a long experiment, it’s still just a “drop in the bucket” of evolutionary time.

Gene expression profiles. Perhaps my favorite example of parallel evolution is at the level of changes in gene expression across the entire “transcriptome” (T. Cooper et al., 2003).  We examined only two of the LTEE lines (because of costs) and we used the old approach of microarrays (as opposed to new RNAseq methods).  The changes in the global expression profiles were strikingly parallel, so that after 20,000 generations (when this analysis was done) these two independently evolved lines were more alike than either was to its ancestor (see figure below).  The identity of the genes whose expression changed in parallel suggested a shared underlying cause—a change in a “global” regulon, a high-level pathway that coordinately regulates the expression of many genes. From there, we tracked down a mutation in a gene called spoT, a key gene in that regulon.

And back to genetics. When the evolved version (allele) of spoT was moved to the ancestral genome, it conferred a significant competitive advantage, demonstrating that it was indeed a beneficial mutation.  Moreover, the ancestor with the evolved spoT allele recapitulated many of the changes in gene expression that we saw in the evolved lines and that led to its discovery, which provided satisfying closure to our inferences.  And when we sequenced spoT in all 12 of the LTEE lines, we found that 8 of them had substitutions in that gene.  Nonetheless, a mystery remained: one of the two populations with the expression profile that evolved in parallel, and which led to the discovery of the many parallel mutations in spoT, did not itself have a mutation in spoT.  A mutation in some other gene (not one of the other candidate genes we had sequenced) must “mimic” the effects of the evolved spoT mutation in the other line whose gene-expression profile we had studied.  The LTEE is not only a valuable resource for studying evolution, it also generates many mutations worthy of study from molecular, genetic, biochemical, physiological, and other perspectives.

Examples of Divergent Outcomes in the LTEE

Citrate utilization. The most striking case of divergence we’ve seen is that one of the populations evolved the ability to consume the citrate that has been present throughout the LTEE (Blount et al., 2008; Blount et al., 2012).  It took more than 30,000 generations for this innovation to arise in that population, and none of the other populations have figured it out even after almost 60,000 generations.

Growth on maltose and resistance to phage Lambda. There are many other, more subtle examples of phenotypic divergence. One that I find very interesting concerns the differences in adaptation to glucose and maltose (Travisano et al., 1995).  Maltose is simply a dimer of glucose. Glucose is the limiting resource in the LTEE (leaving aside the one line that evolved the ability to use citrate).  One might expect, therefore, that the bulk of fitness gains measured in the LTEE environment would carry over if maltose were substituted for glucose in the medium.  In fact, however, that is not the case.  After 2,000 generations, the variation among the replicate lines in their performance on maltose was at least an order of magnitude greater than their variation in glucose.  Now some of the lines cannot grow on maltose at all.  And the same mutations responsible for that complete loss of growth on maltose caused those lines to become resistant to infection by a virus, phage Lambda, even though the LTEE lines were never exposed to the virus (Meyer et al., 2010).

Inferences on Parallel and Divergent Evolution in Nature and in the Laboratory

Challenges in interpreting nature. Parallel and divergent outcomes are, of course, also seen in nature, but it is often difficult to interpret these cases.  If two or more lineages underwent parallel phenotypic changes, was it because they shared genetic variation that was present before the lineages split or by later gene flow?  If so, the parallel changes may not be truly independent evolutionary outcomes.  And even if shared variation can be excluded (e.g., the parallel phenotypic changes have different genetic bases), what’s the relevant denominator?  That is, how often did parallel evolution occur relative to how often it could have occurred? Also, if two or more lineages diverged phenotypically, does that reflect the random effects of mutation and drift?  Or might it reflect instead subtle differences in the environment (ones that may be imperceptible to us, but important to the organisms) or the ancestral genotypes (i.e., divergence that occurred prior to the lineages encountering similar environments)?

Easier inferences in the LTEE. By contrast, the 12 populations in the LTEE all started from the same ancestral strain of E. coli.  Although they share the same ancestor, the populations do not share genetic variation; in fact, there was no variation at the outset because each population was started from a single haploid cell.  In other words, all of the variation that underlies changes we observe in the LTEE arose by new mutations that occurred during the experiment itself.  And of course, the 12 populations have evolved under essentially identical conditions (or about as close as humanly possible), with a simple, defined, reproducible environment.

Parallel evolution

Divergent evolution

The figure below shows the comparisons in global gene-expression profiles between: (a) the ancestor and itself, as a control; (b) one evolved line and the ancestor; (c) another evolved line and the ancestor; and (d) the two independently evolved lines relative to one another.  The figure comes from T. Cooper et al., 2003, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.

Image from Cooper et al Gene expression

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Genome Dynamics During Experimental Evolution

Jeff Barrick and I have written a review article for Nature Reviews Genetics on “Genome dynamics during experimental evolution.” Our paper recently appeared as an advanced online publication; the print version should be out in a week or so.

Jeff did a superb job illustrating the population-genetics concepts that underpin this field, as well as organizing and synthesizing the literature.  It has been such a fast-moving field that we’re both a bit embarrassed to say when the article was first proposed.  However, we think it was worth the wait for our readers, given all the exciting work that has appeared since then.

The first bacterial genome to be fully sequenced, Haemophilus influenzae, was published in 1995.  That species was likely chosen not only because it is an important pathogen, but also because it has a fairly small genome with fewer than 2-million base pairs.  I don’t know the cost of that project, but I recently heard someone say that it cost roughly a million dollars to fully sequence another important bacterial pathogen around 2002 or so.  Fast-forward just a few years and I remember being shocked – along with other members of the audience at a conference in 2005 – when Greg Velicer reported that he and colleagues had sequenced the genome of a Myxococcus xanthus strain that was part of an evolution experiment, in order to identify the mutations responsible for changes in its social behavior.  Greg did that work while he was at the Max Planck Institute, so his funding was more generous than what most of us doing experimental evolution could afford.  But it meant that genomics was becoming affordable for basic research.  In 2009, Jeff Barrick and I published two papers that analyzed the genomes, not of single clones or pairs of samples, but from multi-generational series of 7 clones and 7 whole-population samples from one of the E. coli populations in my long-term evolution experiment.  That seemed like a lot then but now, just a few years later, Lang et al. deeply sequenced 40 experimentally evolving Saccharomyces cerevisiae populations at 12 time points each!  [Note added after comment: By “deeply sequenced” I mean the authors sequenced heterogeneous population samples, and they could thus follow the trajectories of specific mutational  variants and genetic diversity over time.]  The combination of experimental evolution and genomics is no longer a novelty – it has become a powerful and affordable tool that can be used as part of almost any project on experimental evolution.

Our review paper begins by contrasting the design of two types of evolution experiments: mutation-accumulation and adaptive evolution.  In the former type, the experimenter seeks to eliminate the effects of natural selection by forcing the populations through extreme demographic bottlenecks that purge genetic variation.  (Remember: natural selection does not work in the absence of genetic variation.)  In that way, one can estimate a mutation rate directly, by minimizing the otherwise confounding effect of selection on the observable rate of genetic change.  (Of course, lethal mutations will not accumulate, although these typically represent only a small fraction of all mutations.)  Whole-genome sequencing provides dramatically increased power and precision for estimating mutation rates because one can combine and integrate data across millions of base pairs and hundreds or thousands of generations.  By contrast, older studies could detect mutations in just one or a few genes – chosen because they were known to cause specific changes in traits that could be readily scored – and they typically involved only a few generations of growth.

Adaptive evolution experiments are designed to shed light on various aspects of the process of adaptation by natural selection.  Genomic analyses of adaptive evolution experiments have allowed investigators to identify mutations responsible for interesting phenotypic changes, examine the extent of parallel evolution at the genetic level, quantify the dynamics of genetic diversity within populations, compare rates of phenotypic and genomic evolution, and address many other old and new questions.  And these adaptive evolution experiments are becoming increasingly complex as many investigators are now studying systems with multiple species, temporally or spatially varying environments, sexual reproduction or horizontal gene exchange, and so on.  Genomics will play a critical role in understanding these increasingly complex systems and scenarios.

Our review closes by drawing attention to areas of research that aren’t quite experimental evolution, as the field is usually meant, but for which similar combinations of evolutionary and genomic analyses and interpretations will become increasingly important in the years ahead.  For example, we think it won’t be too long before it becomes routine practice in genetics to sequence the entire genomes of any mutant or recombinant strain of interest and its parent, in order to be sure that the procedures employed did not inadvertently lead to other changes besides those intended.  And we point out that the combination of genomic and evolutionary analyses is extremely powerful and interesting in the context of evolution in action in microbial pathogens (see this previous post for a compelling example).

Jeff and I hope that many readers will find our Nature Reviews Genetics article a useful summary of a fast-moving field, a helpful primer at the intersection of experimental evolution and population genetics (especially for microbial populations), and a valuable lead to fascinating papers for further reading.

[The figure below is one panel from one of the figures in Barrick and Lenski, 2013, Nature Reviews Genetics; it is shown here under the doctrine of fair use.]

Image from Barrick & Lenski 2013

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Luria and Delbrück, 1943, Genetics

Here’s the first of my blog posts on “must-read” papers.  I hope others will find these papers interesting and useful.

Luria, S. E., and Delbrück, M.  1943.  Mutations of bacteria from virus sensitivity to virus resistance.  Genetics 28, 491–511.

[I’ve cobbled this post together by borrowing from a couple of previous writings where I explained why the Luria and Delbrück’s experiment is my all-time favorite.  One of these earlier pieces was a Q & A in Current Biology (2003); the other was an essay that appeared in Microbe (2011) and then in the book Microbes and Evolution: The World That Darwin Never Saw (2012).  I’ve also tweaked the text and added some bits to make things flow.]

Short summary:  An elegant experiment — sometimes called the “fluctuation test” — by Salvador Luria and Max Delbrück showed that new mutations that made bacteria resistant to phage (viruses that infect bacteria) arose before the bacteria were exposed to the phage.  This paper removed the specter of Lamarckian inheritance from microbiology, and it set the stage for the tremendous advances in microbial genetics and molecular biology that took place over the next several decades.  Theirs was a beautifully simple, yet subtle, experiment on a fundamental concept.

Personal influence:  I first read about Luria and Delbrück’s paper as an undergraduate at Oberlin College, in a course taught by Richard Levin using Gunther Stent’s 1971 textbook, Molecular Genetics: An Introductory Narrative.  Stent took a historical approach to microbial and molecular genetics by emphasizing the ideas, questions, and experiments that led to the growth and success of those fields.  I remember the challenge of reading about Luria and Delbrück’s experiment, trying to wrap my head around how it worked and what it meant, and then the wonderful “Aha!” moment when I got it.

But I did not go directly on to work with microbes.  Instead, I did my Ph.D. in zoology, with my dissertation research on ground beetles in the mountains of western North Carolina.  Despite the pleasures of working outdoors, the research was slow, heavy rains often drowned the beetles in my pitfall traps, and it was difficult to imagine feasible experiments that would really test the scientific ideas that most excited me.  So as I pondered future directions, I recalled the Luria and Delbrück experiment that I had encountered as an undergraduate.  I remembered not only its elegance, but also the profound insight it gave into the tension between randomness and direction in evolution — a tension that continues to fascinate me and lies at the heart of the long-term evolution experiment in my lab.

It wasn’t until I was a postdoc, learning how to work with bacteria, that I actually read the Luria and Delbrück paper.  It’s not an easy paper to read.  If the experiment is unfamiliar to you, then you might want to read about it before reading the original paper.  Pages 556-558 in Sniegowski and Lenski (1995, Ann. Rev. Ecol. Syst.) briefly explain the experiment.

Historical perspective:  The science of genetics took hold with the rediscovery of Gregor Mendel’s experiments on pea plants in the early 1900s.  However, microbiologists remained baffled by the question of heredity in bacteria for several more decades.  They saw that bacteria could “adapt” to various challenges, but they couldn’t tell whether spontaneous mutants had appeared and been selected or, alternatively, whether the challenge had induced the cells to change themselves.  In 1934, a microbiologist, I. M. Lewis, wrote that “The subject of bacterial variation and heredity has reached an almost hopeless state of confusion . . . There are many advocates of the Lamarckian mode of bacterial inheritance, while others hold to the view that it is essentially Darwinian.”  And in 1942, Julian Huxley wrote Evolution: The Modern Synthesis and explicitly excluded bacteria from the then-modern synthesis on the grounds that “They have no genes in the sense of accurately quantized portions of hereditary substance …”

This confusion cleared the very next year with the publication of what is, to me, the single greatest experiment in the history of biology.  Working together, Luria, a biologist, and Delbrück, a physicist-turned-biologist, employed subtle reasoning and an elegant design to demonstrate that some mutations in E. coli occurred before the selective challenge was imposed, and therefore the mutations could not have been caused by the challenge.  In other words, mutations are random changes that occur whether or not they prove useful, while selection provides the direction in evolution by disproportionately retaining those mutations that are advantageous to their carriers and discarding others that are harmful.

Luria and Delbrück’s paper launched a tidal wave of research that led to the discovery of DNA as the hereditary material and to cracking the genetic code.  But it had little immediate impact on evolutionary research.  The new molecular biologists pursued their reductionist methods, while evolutionary biologists, grounded in natural history, didn’t want to study things they couldn’t even see.  These naturalists preferred beautiful butterflies and even homely fruit flies to E. coli that, after all, come from a rather uninviting habitat.

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