Tag Archives: fitness

Questions from Jeremy Fox about the LTEE, part 1

EDIT (23 June 2015): PLOS Biology has published a condensed version of this blog-conversation.

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Over at the Dynamic Ecology blog, Jeremy Fox asked me some interesting questions about the history, philosophy, and science of the E. coli long-term evolution experiment. Perhaps mistakenly—in terms of time management, not my interest!—I agreed to try to answer them … though over what time frame, I’m not sure. Anyhow, here is Jeremy’s first question followed by my (very) short and (too) long answers.

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  • When you first started the LTEE, did you consider it to be a low risk or high risk experiment? Because I could see arguing both ways. In some ways, it’s low risk, because one can imagine lots of different possible outcomes, all of which would be interesting if they occurred. But in other ways, it’s high risk–I imagine that many of the interesting outcomes (including those that actually occurred!) would’ve seemed unlikely, if indeed they’d even occurred to you at all. Or did you not worry much about the range of possible outcomes because the experiment was basically a lottery ticket? “This’ll be cheap and not much work, let’s just do it and see what happens. Something really cool might happen, but if it turns out boring that’s ok because it wasn’t a big investment.”

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The short answer: Life was good, and I wasn’t thinking about risk. Or as they say about investing: it’s better to be lucky than smart!

The long, non-linear* answer: I’d already had success with some shorter duration, more traditionally designed experiments (e.g., Lenski, 1988), and so it wasn’t a total shot in the dark—that is, I knew the LTEE would yield data. I also knew, though, that it was an unusually abstract, open-ended, and non-traditional experiment, and that it might not appeal to some people for those reasons. But I loved (and still do) the seemingly simple (but in reality complex) questions, issues, and hypotheses that motivated the LTEE.

I never thought of the LTEE project as a “lottery ticket”, but some follow-up work that grew out of it had that feel.** And, oddly enough, there was one lottery-ticket aspect of the research early on, although that reflected a lack of preparation rather than a well-conceived feature.***

Maybe I was overly confident, but I’d also say that I was pretty sure the outcomes—whatever they might be—would be “cool.” The questions were intriguing, and there hadn’t been many, if any, previous attempts to answer them quite so directly. Data would be forthcoming, and even if the results weren’t definitive, I felt there would be some interest in trying to interpret whatever data emerged.**** Plus, I knew enough about what would happen—based on the experiments I had already done—that I was confident that the data and analyses would be informative with respect to at least some of my questions. Also, the use of microbes to study evolution in action was still uncommon, so the novelty of the approach would ensure some interest among my colleagues—although let me emphasize that Lin Chao, Dan Dykhuizen, Barry Hall, and Bruce Levin, among others, had already demonstrated the power of using microbes for experimental studies of evolutionary questions.*****

I should also say, in case it’s not obvious, that I had no idea or intention that the experiment would continue for anywhere near as long as it has lasted—nor that it might, I now hope, be running long after I’m gone. I had previously performed some experiments that lasted several hundred generations, and as I saw the dynamics and thought about the math behind the dynamics, I realized that over those time scales I might be seeing the effects of only one or two beneficial mutations as they swept to fixation. That hardly seemed satisfactory for experiments to explore the structure of the fitness landscape. So I decided the experiment should run for 2,000 generations, over which time I expected there would be at least several fixations of beneficial mutations in each population (and I was right), and that would deserve calling it long-term. That would take a little less than a year, given the 100-fold dilution and 6.6 generations of re-growth each day.

Of course, propagating the lines for 2,000 generations was one thing—running the competitions to measure fitness, analyzing the data, writing the paper, responding to reviews, all that took longer. So while the experiment began in February 1988, the first paper (Lenski et al., 1991) was not submitted until August 1989, resubmitted September 1990, accepted that November, and finally published in December 1991. Meanwhile, the LTEE itself continued and the generations ticked by. The baseline work of keeping the populations going is not that onerous—yes, somebody has to attend to the transfers every day, but once a lab team reaches a moderate size, it’s not too hard to arrange. And I lived next to the campus in Irvine, so it wasn’t hard for me to come in on the weekends and holidays … and my wife still loves me, and my kids recognized my face ;>)

You also wondered whether some of the interesting possible and actual outcomes had occurred to me when I started. Definitely not! I had made a strategic decision to make the environment of the LTEE very simple in order to eliminate, or at least reduce, certain complications (especially frequency-dependent interactions and clonal interference). And while I think my planning kept these complications from getting out of hand, the tension between the simplicity of the experimental design and all the complications has definitely been part of its interest. That tension, along with time, the evolutionary potential of the bacteria, and the smart, talented, creative** and hard-working students and colleagues have made the LTEE what I call “the experiment that keeps on giving.”

Footnotes

*Hey, that’s what footnotes are for, right?

**I’ve thought that way about some follow-on work that uses the LTEE lines, but not about the project as a whole. Here are a couple of examples of “lottery tickets” that people suggested to me, and that won big. A former postdoc Paul Sniegowski, now on the faculty at Penn, wanted to know whether the actual mutation rate itself might be evolving in the LTEE populations. Bingo! Several lines evolved hypermutability and so, curiously enough, the first mutations we ever mapped affected the mutation rate itself (Sniegowski et al., 1997). Another example: Dominique Schneider is a molecular microbiologist in Grenoble, and we’ve collaborated for over 15 years. He thought we should look at whether DNA topology—the physical supercoiling inside the cell—might have changed in the LTEE lines. Well, I thought to myself, why would it change? But Dom’s lab will do all the work, so sure, why not look? And it turns out, sure enough, that DNA supercoiling changed repeatedly in the LTEE lines (Crozat et al., 2005), and it even led us to discover a gene not previously known to affect supercoiling (Crozat et al., 2010). There’s a lesson here, by the way—work with people who are smarter, who have different interests, and who have different skills than oneself.

***I actually started two versions of the LTEE—not one experiment with two proper treatments, but two separate experiments that differed in terms of both the starting strain and the environment. Unlike the successful LTEE, I hadn’t done any previous evolution experiments with the other ancestral strain and environment. Anyhow, I soon stopped the other version when the populations evolved a phenotype that made it very difficult to work with them. In brief, the populations evolved to make pinprick-sized colonies that were next-to-impossible to count in the assays we use to measure fitness. Who needed that headache! So, in a way, I guess I had two lottery tickets: I hadn’t done the relevant prior work for one of them, whereas the one that paid off was actually a pretty strategic gamble.

****I was at UC Irvine when I started the LTEE, and Michael Rose was one of my colleagues there. His work on the evolution of aging—postponed senescence—in fruit flies (e.g., Rose 1984) was an inspiration in terms of the importance and power of long experiments. We also spent a lot of time discussing fitness landscapes, the alternative perspectives of Sewall Wight and R. A. Fisher about the dynamics on those landscapes, and what experiments might tell us. Michael didn’t design, direct, or do the lab work for the first LTEE paper, but he helped me clarify my thinking and write the first paper on the LTEE (Lenski et al., 1991). Perhaps more importantly, his interest in the questions and issues made me realize that other smart people would also be interested.

*****I used to complain, mostly in jest, that “Evolutionary biologists say I’m asking the right questions, but studying the wrong organism, and microbiologists tell me I’m studying the right organism but asking the wrong questions.” I got that sort of response occasionally, but many people from both fields were very interested and encouraging. For example, I remember David Wake telling me, after one of my first talks about the LTEE, how much he liked both the questions and the approach.

References

Lenski, R. E. 1988. Experimental studies of pleiotropy and epistasis in Escherichia coli. II. Compensation for maladaptive pleiotropic effects associated with resistance to virus T4. Evolution 42: 433-440.

Lenski, R. E., M. R. Rose, S. C. Simpson, and S. C. Tadler. 1991. Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. American Naturalist 138:  1315-1341.

Sniegowski, P. D., P. J. Gerrish, and R. E. Lenski. 1997. Evolution of high mutation rates in experimental populations of Escherichia coli. Nature 387: 703-705.

Crozat, E., N. Philippe, R. E. Lenski, J. Geiselmann, and D. Schneider. 2005. Long-term experimental evolution in Escherichia coli. XII. DNA topology as a key target of selection. Genetics 169: 523-532.

Crozat, E., C. Winkworth, J. Gaffé, P. F. Hallin, M. A. Riley, R. E. Lenski, and D. Schneider. 2010. Parallel genetic and phenotypic evolution of DNA superhelicity in experimental populations of Escherichia coli. Molecular Biology and Evolution 27:2113-2128.

Rose, M. R. 1984. Laboratory evolution of postponed senescence in Drosophila melanogaster. Evolution 38: 1004-1010.

 

LTEE flasks repeating

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Putting GMOs on a Tight Leash

Two papers appeared in the latest issue of Natureone from Farren Isaacs’ group and the other from George Church and colleagues—that presented, developed, and demonstrated a strategy for limiting the spread of genetically modified organisms, or GMOs, in the event that they are accidentally released or deliberately applied to the environment.

My Involvement with GMO Discussions in the 1980s

I was actively involved in discussions about environmental applications and field testing of genetically engineered organisms back in the 1980s. As a postdoc in 1984, I had a short letter in Nature where I suggested a containment strategy for an early proposed application of genetically modified “ice-minus” bacteria. Later that year I attended a small meeting on environmental applications of GMOs at the Cold Spring Harbor Laboratory, and a short report was published in the Bulletin of the Ecological Society of America. As faculty member at UC Irvine in 1986, I served as a consultant on a report for the Office of Technology Assessment of the US Congress. I also co-organized and moderated a lively public debate on the benefits and risks of genetically engineered organisms between Jerry Caulder, who was CEO of a biotech company, and the distinguished ecologist Daniel Simberloff, an expert on invasive species.

At that time, one of the arguments—the “excess baggage hypothesis”—for the safety of GMOs was that genetically engineered functions would impose a metabolic burden and thereby reduce the fitness of the organisms, so that they wouldn’t be good competitors in nature. While that argument made some sense as a trend or tendency, it didn’t seem likely that it would apply in every possible case given the potential for new environments and/or compensatory adaptations to favor novel functions. In 1988, I wrote a review for Trends in Ecology & Evolution with a postdoc, Toai Nguyen, on the “Stability of recombinant DNA and its effects on fitness” that made these points.

As a result of my interest in and involvement with these issues, I was asked to serve on two expert panels—one convened by the Ecological Society of America (ESA), the other by the National Research Council (NRC) arm of the National Academy of Sciences—that wrote lengthy reports, both published in 1989. In both reports, the committees tried to emphasize that one needed to consider two different issues. (1) What, if any, were the potential problems that might be caused by the release of particular GMO? (2) In the event that some problem actually did arise, would the GMO (or its engineered genes) survive and possibly spread in the environment? Or would the problem be resolved by halting further applications of the GMO, because they would then simply die off?

(These panels were hard work, but through them I met some great scientists, including Jim Tiedje and Rita Colwell among many others.)

After that extensive involvement with this science-policy issue in the 1980s, my research tended toward more basic questions in the years that followed. Meanwhile, of course, there has remained substantial scientific, commercial, and public interest in the methods and applications of genetic engineering. The two recent papers in Nature reflect the latest efforts to ensure the safety of GMOs by putting them on a tight leash.

My Thoughts on the Recent Papers

I was asked to comment on the Nature papers by Malcolm Ritter, a science reporter for the AP, and he briefly (and accurately) quoted me in a short news piece that appeared yesterday. In light of a question about my thoughts on Twitter, I thought I’d share my full remarks here:

Using genetically modified organisms in the environment raises a couple of intersecting issues. One concerns the effects those organisms have. Of course, GMOs are intended to provide some benefit—say, for bioenergy or agriculture—but in some cases the GMOs might have secondary or unanticipated harmful effects. If these harmful effects occur, and if they outweigh the benefits, then one would like to be able to recall the GMOs from the environment—sort of like recalling cars when some problem is discovered after they’ve been sold. The challenge is that GMOs are organisms, they are alive and can reproduce, and so they won’t necessarily just go away if one stops using them. Over the years, different strategies have been proposed to ensure that GMOs will, in fact, just die off after they’ve done their job, but these strategies have had holes, such as the possibility that evolution might break whatever leash the scientists put on the GMOs so that they could be recalled.

These two papers, though, point the way towards putting GMOs on a very tight leash, one that is meant to be unbreakable, by changing the genetic code of an organism so that its replication becomes dependent on certain synthetic building blocks—amino acids—that aren’t found in nature. So by applying these molecules along with the GMO in some environment, the GMOs can replicate and do their job. But if the synthetic amino acids aren’t supplied, then the GMOs won’t be able to replicate further after they’ve run out, and so that provides a leash that should rein the GMOs in if there is some problem. Of course, there are a lot of technical challenges to pulling this off, because you can’t make the organisms so weak that they can’t do their intended functions.

And of course, extending this approach from microorganisms—the subject of these papers—to crop plants would raise all sorts of additional questions about nutritional value and safety. Those are different issues and not what these papers are about.

Coda: Does this approach ensure containment of a GMO? Probably not. There aren’t many guarantees in life, and evolution has a history (billions of years, in fact) of finding clever solutions that might not occur to engineers and scientists. Does that mean that we should not use GMOs in nature? Not at all. As our ESA and NRC reports of a quarter-century ago stressed, one should consider both the benefits of a particular environmental application of a GMO and its potential harm if something goes wrong. In those cases where the benefits are great, and the potential for harm is very small (both in likelihood and magnitude), then the issues of containment and recall after a release are less critical. But in those instances where the potential risks of some GMO are substantial—either in terms of the likelihood or the magnitude of adverse effects—then every effort must be made to put the GMOs on a tight leash or, absent that, do not proceed with the proposed application.

[The image below is one part of Figure 1 from the Nature paper, titled “Recoded organisms engineered to depend on synthetic amino acids” and authored by Alexis J. Rovner, Adrian D. Haimovich, Spencer R. Katz, Zhe Li, Michael W. Grome, Brandon M. Gassaway, Miriam Amiram, Jaymin R. Patel, Ryan R. Gallagher, Jesse Rinehart and Farren J. Isaacs.  This image is shown here under the doctrine of fair use.]

Portion Fig 1 from Rovner et al, Nature, 2015

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Infectiously Fun Science

Science is sometimes frustrating. The work is often repetitive and even tedious. It can be hard to explain to our friends and families—and sometimes even to peers—what we’re doing and why we think it’s important and interesting. The current state of the academic job market is terrible.

But science is also often fun. There’s the joy of discovery, which grows out of the quieter excitement of seeing data come together to support or refute an existing idea and, perhaps, to generate a brand-new idea. If we’re lucky, we enjoy the recognition of our peers that comes when a paper is accepted, a grant funded, or a talk well received.

For those of us who study evolution, the frustrations can be magnified by critics and trolls who aren’t interested in evidence or reason, having already closed their minds to even the idea of evolution based on their narrow, literal reading—or, more often, someone else’s reading—of texts written in other languages long before science provided an evidence-based way to understand the world in which we live.

At the same time—and perhaps driven in part by the controversy surrounding evolution and religion—the field of evolution has long been blessed with great writers and speakers who are willing and able to engage the public. Twenty years before he published On the Origin of Species, Charles Darwin had already cemented his place in the public eye with his travelogue The Voyage of the Beagle. As a result, the Origin was an instant best seller on both sides of the Atlantic. And while Darwin shied away from speaking in public about his discoveries, Thomas Henry Huxley was a gifted orator who became “Darwin’s Bulldog” in public lectures and debates.

That tradition continues to this day. Some of my favorites include The Selfish Gene by Richard Dawkins, Wonderful Life by the late Stephen Jay Gould, Darwin’s Dangerous Idea by Daniel Dennett, and Your Inner Fish by Neil Shubin. Experts argue about scientific issues, minor and even major, contained in these books. But it’s hard for me to imagine an open-minded reader, someone interested in science and evolution, who would not find these books highly stimulating—even infectious in the sense of wanting to share them and the ideas they contain with others.

And speaking of infectious, new ways of communicating science have burst onto the scene since the printing press. For example …

Baba Brinkman is a rapper who raps about science, literature, public policy, and more. For your scientific enjoyment, here are three of my favorites from The Rap Guide to Evolution:

Performance, Feedback, Revision

Creationist Cousins

I’m A African

Here’s another from The Rap Guide to Human Nature:

Short Term Mating Dance

And here’s a brand-new one—on microbiology and disease—with a cameo appearance by yours truly and three students who work in my lab:

So Infectious

Whether you’re a scientist or not, I hope you’ll agree that these are worth sharing with your students, friends, and families!

[Image source: music.bababrinkman.com/album/the-rap-guide-to-evolution]

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

This is the fifth in a series of  posts isummarizing 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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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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What We’ve Learned about Evolution from the LTEE: Number 2

This is the second in a series of posts where I summarize what I think are the most important findings and discoveries from the LTEE.

Number 2.  An exciting new twist on the dynamics of adaptation by natural selection is the discovery that fitness can increase “forever” – or at least for a very long time – even in a constant environment.

A power-law model, which has no upper bound, gives a significantly better fit to the mean-fitness trajectories measured in the LTEE populations than does a model with an asymptote.

Moreover, the power law predicts the trajectory of fitness evolution with much greater accuracy.  That is, if we reduce the data so that it includes only the first 20,000 generations, the power law trajectory that fits this truncated dataset accurately predicts fitness out to 50,000 generations (blue trajectory in the figure below).  By contrast, the same procedure with the asymptotic model consistently underestimates the future fitness gains (red trajectory in the figure below).

Also, a dynamical model that incorporates clonal interference (competition between different beneficial mutations) and diminishing-returns epistasis (where the marginal effect of a beneficial mutation declines with increasing fitness) produces trajectories that have the same power-law form.  That, in turn, facilitates estimation of important population-genetic parameters including the rate of beneficial mutations and the average strength of the diminishing-returns epistasis.

The figure below shows the grand-mean fitness data (symbols with error bars) over 50,000 generations of the LTEE.  It also shows the trajectories predicted by the power law (blue curve) and by a model with an asymptote (red curve) using only the first 20,000 generations of data.  The figure comes from Wiser et al., 2013, Science; it is shown here under the doctrine of fair use.

Power law prediction, 2013

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What We’ve Learned about Evolution from the LTEE: Number 1

I’m sometimes asked to explain, in a general way, the main findings and discoveries from the long-term evolution experiment (LTEE) with E. coli.  So in a series of posts, I will briefly summarize a few of the most important findings and discoveries, as I see them, in very broad, conceptual terms.

I won’t provide the detailed results, but I will list some of the key papers that present them.  Interested readers can read those papers to see the methods, evidence, and data that support the findings and discoveries.

For anyone seeking a general introduction to the LTEE and the advantages that this experimental system offers for studying evolution, I recommend reading the 1994 paper by Lenski and Travisano (cited below) before all others.

Number 1.  Although no one (excepting cranks, zealots, and the uninformed) doubts the fact that adaptation by natural selection occurs, the LTEE provides a simple and compelling demonstration of its power and efficacy.  I’ve heard this point stated repeatedly after talks for the general public and even after seminars for other scientists.

The figure below shows the step-like fitness trajectory measured for one of the LTEE populations over the course of the first 2,000 generations of the experiment.  The figure comes from Lenski and Travisano, 1994, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.

Fitness trajectory, PNAS 1994

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