Tag Archives: experimental evolution

Optimizing the product of the wow factor and the beneficial mutation supply rate

This post follows up on my post from yesterday, which was about choosing a dilution factor in a microbial evolution experiment that avoids the loss of too many beneficial mutations during the transfer bottleneck.

If we only want to maximize the cumulative supply of beneficial mutations that survive dilution, then following the reasoning in yesterday’s post, we would chose the dilution factor (D) to maximize g Ne = (g2) Nmin = (g2) Nmax / (2g), where Nmax is a constant (the final population size) and D = 1 / (2g). Thus, we want to maximize (g2) / (2g) for g > 0, which gives g = ~2.885 and D = ~0.1354, which is in agreement with the result of Wahl et al. (2002, Genetics), as noted in a tweet by Danna Gifford.

The populations would therefore be diluted and regrow by ~7.4-fold each transfer cycle. But as discussed in my previous post, this approach does not account for the effects of clonal interference, diminishing-returns epistasis, and perhaps other important factors. And if I had maximized this quantity, the LTEE would only now be approaching a measly 29,000 generations!

So let’s not be purists about maximizing the supply of beneficial mutations that survive bottlenecks. There’s clearly also a “wow” factor associated with having lots and lots of generations.  This wow factor should naturally and powerfully reflect the increasing pleasure associated with more and more generations.  So let’s define wow = ge, which is both natural and powerful.  Therefore, we should maximize wow (g2) / (2g), which provides the perfect balance between the pleasure of having lots of generations and the pain of losing beneficial mutations during the transfer bottlenecks.

It turns out that the 100-fold dilution regime for the LTEE is almost perfect!  It gives a value for wow (g2) / (2g) of 75.93.  You can do a tiny bit better, though, with the optimal ~112-fold dilution regime, which gives a value of 76.03.

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If

Every day, we propagate the E. coli populations in the long-term evolution experiment (LTEE) by transferring 0.1 ml of the previous day’s culture into 9.9 ml of fresh medium. This 100-fold dilution and regrowth back to stationary phase—when the bacteria have exhausted the resources—allow log2 100 = 6.64 generations (doublings) per day. We round that to six and two-thirds generations, so every 15 days equals 100 generations and every 75 days is 500 generations.

A few weeks ago, I did the 10,000th daily transfer, which corresponds to 66,667 generations. Not bad! But as I was walking home today, I thought about one of the decisions I had to make when I was designing the LTEE. What dilution factor should I use?

If … if I had chosen to use a 1,000-fold dilution instead of a 100-fold dilution, the LTEE would be past 100,000 generations. That’s because log2 1,000 = ~10 generations per day. In that case, we’d have reached a new power of 10, which would be pretty neat. As it is, it will take us (or rather the next team to take over the LTEE) another 14 years or so to get there.

I’ll discuss my thinking as to why I chose a 100-fold dilution factor in a bit. But first, here’s a question for you, which you can vote on in the poll below.

Let’s say that we had done a 1,000-fold daily dilution all along. And let’s say we measured fitness (relative to the ancestral strain, as we usually do) after 10,000 days.  Do you think that the mean fitness of the evolved populations subjected to 1,000-fold dilutions after 100,000 generations (on day 10,000) would be higher or lower than that of the evolved populations subjected to 100-fold dilutions after 66,667 generations (also day 10,000)?

I’ll begin by mentioning a couple of practical issues, but then set them aside, as they aren’t so interesting. First, a 100-fold dilution is extremely simple to perform given the volumes involved (i.e., 0.1 and 9.9 ml). And the LTEE was designed to be simple, in order to increase its reliability. A 1,000-fold dilution isn’t quite as easy, as it involves either an intermediate dilution or the transfer of a smaller volume (0.01 ml), which in my experience tends to be a bit less accurate. Second, the relative importance of the various phases of growth—lag, exponential, transition, and stationary—for fitness would change a bit (Vasi et al., 1994).

Setting those issues aside, here was my thinking about the dilution factor when I planned the LTEE. In asexual populations that start without any standing genetic variation, the extent of adaptive evolution depends on both the number of generations and the supply rate of beneficial mutations. The supply rate of beneficial mutations, in turn, depends on the mutation rate (m) times the fraction of mutations that are beneficial (f) times the effective population size (Ne).

There are many different uses and meanings of effective population size in population genetics, depending on the problem at hand: the question is “effective” with respect to what process? Without going into the details, we would like to express Ne such that it takes into account the expected loss of beneficial mutations during the daily dilutions. To a first approximation, theory shows that the relevant Ne is equal to the product of the “bottleneck” population size right after the dilution (Nmin) and the number of generations (g) between Nmin and the final population size during each transfer cycle (Lenski et al., 1991).

The final population size in the LTEE is ~5 x 108 cells (10 ml x 5 x 107 cells per ml), and it is the same regardless of the dilution factor, provided that the bacteria have enough time to reach that density between transfers. The 1,000-fold dilution regime would reduce Nmin by 10-fold relative to the 100-fold regime, although the 50% increase in the number of generations per cycle would offset that reduction with respect to the effective population size. Nonetheless, Ne would be ~6.7-fold higher in the 100-fold regime than in the 1,000-fold regime.

The greater number of generations in 10,000 days under the 1,000-fold regime would also increase the cumulative supply of beneficial mutations by 50%. Nonetheless, the extent of adaptive evolution, which is (under this simple model) proportional to the product of the elapsed generations and Ne, would be ~44% greater under the 100-fold dilution regime than the 1,000-fold dilution regime. So that’s why I chose the 100-fold dilution regime … I was more interested in making sure we would see substantial adaptation than in getting to a large number of generations.

Now you know why the LTEE has only reached 67,000 or so generations.

Of course, I could also have chosen a 10-fold regime, and by this logic the populations might have achieved even higher fitness levels. I could also have chosen a much higher dilution factor; even with a 1,000,000-fold dilution the ancestral strain could double 20 times in 24 h, allowing them to persist. Or at least they could persist for a while. With severe bottlenecks, natural selection becomes unable to prevent the accumulation of deleterious mutations by random drift, so that fitness declines. And if fitness declines to the degree that the populations can no longer double 20 times in 24 h, then the bacteria would go extinct as the result of a mutational meltdown.

Returning to the cases where the bottlenecks are not so severe, the theory that led me to choose the 100-fold dilution regime ignores a number of complicating factors, such as clonal interference (Gerrish and Lenski, 1998; Lang et al., 2013; Maddamsetti et al., 2015) and diminishing-returns epistasis (Khan et al., 2011; Wiser et al., 2013; Kryazhimskiy et al., 2014). It’s predicated, I think, on the assumption that the supply rate of beneficial mutations limits the speed of adaptation.

When the LTEE started, I had no idea what fraction of mutations would be beneficial. I think it was generally understood that beneficial mutations were very rare. But the LTEE and other microbial evolution experiments have shown that beneficial mutations, while rare, are not so rare as we once thought, especially once an experiment has run long enough (Wiser et al., 2013) or otherwise been designed (Perfeito et al., 2007; Levy et al., 2015) to allow beneficial mutations with small effects to be observed and counted.

So I think it remains an open question whether my choice of the 100-fold dilution regime was the right one, in terms of maximizing fitness gains.

And that makes me think about redoing the LTEE. OK, maybe not starting all over, as we do have a fair bit invested in the last 29 years of work. But maybe expanding the LTEE on the fly, as it were. We could, for example, expand from 12 populations to 24 populations without too much trouble. We’d keep the 12 original populations going, of course, but we’d spin off 12 new ones in a paired design (i.e., one from each of the 12 originals) where we changed the dilution regime. What do you think? Is this a good idea for a grant proposal? And if so, what dilution factor would you suggest we add?

Feel free to expand on your thoughts in the comments section below!

Note: See my next post for a bit more of the mathematics, along with a tongue-in-cheek suggestion for combining the effects of the beneficial mutation supply rate and a “wow” factor associated with having lots of generations.

References

Gerrish, P. J., and R. E. Lenski. 1998. The fate of competing beneficial mutations in an asexual population. Genetica 102/103:127-144.

Khan, A. I., D. M. Dinh, D. Schneider, R. E. Lenski, and T. F. Cooper. 2011. Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332: 1193-1196.

Kryazhimskiy, S., D. P. Rice, E. R. Jerison, and M. M. Desai. 2014. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344: 1519-1522.

Lang, G. I., D. P. Rice, M. J. Hickman, E. Sodergren, G.M. Weinstock, D. Botstein, and M. M. Desai. 2013. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500: 571-574.

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.

Levy, S. F., J. R. Blundell, S. Venkataram, D. A. Petrov, D. S. Fisher, and G. Sherlock. 2015. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519: 181-186.

Maddamsetti, R., R.E. Lenski, and J. E. Barrick. 2015. Adaptation, clonal interference, and frequency-dependent interactions in a long-term evolution experiment with Escherichia coli. Genetics 200: 619-631.

Perfeito, L., L. Fernandes, C. Mota, and I. Gordo. 2007. Adaptive mutations in bacteria: high rate and small effects. Science 317: 813-815.

Vasi, F., M. Travisano, and R. E. Lenski. 1994. Long-term experimental evolution in Escherichia coli. II. Changes in life-history traits during adaptation to a seasonal environment. American Naturalist 144: 432-456.

Wiser, M. J., N. Ribeck, and R. E. Lenski. 2013. Long-term dynamics of adaptation in asexual populations. Science 342: 1364-1367.

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Asking for a Skeptic Friend

I sometimes get email from people asking, in one way or another, whether our long-term evolution experiment (LTEE) with E. coli provides evidence of evolution writ large – new species, new information, or something of that sort. I try to answer these questions by providing some examples of what we’ve seen change, and by putting the LTEE into context. Here’s one such email:

Hi Professor Lenski,

I have a quick question. I’m asking because I am having a discussion with someone who is skeptical of evolution. The question is: Over the 50,000 generations of e-coli has any of the e-coli evolved into something else or is it still e-coli?

I am a non-religious person who likes to think of myself as an adherent to science but I am not sure how to respond to my skeptic-friend.

Thank you!

And here’s my reply:

Hello —-,

50,000 generations, for these bacteria, took place in a matter of ~25 years. They have changed in many (mostly small) ways, and remained the same in many other respects, just as one expects from evolutionary theory. Although these are somewhat technical articles, I have attached 3 PDFs that describe some of the changes that we have seen.

Wiser et al. (2013) document the process of adaptation by natural selection, which has led to the improved competitive fitness of the bacteria relative to their ancestors.

Blount et al. (2012) describe the genetic changes that led one population (out of the 12 in the experiment) to evolve a new capacity to grow on an alternative source of carbon and energy.

Tenaillon et al. (2016) describe changes that have occurred across all 12 populations in their genomes (DNA sequences), which have caused all of them to become more and more dissimilar to their ancestor as time marches on.

Best wishes,

     Richard

Although these articles were written for other scientists, they make three big points that I hope almost anyone with an open mind can understand.

  • We see organisms adapting to their environment, as evidenced by increased competitiveness relative to their ancestors.
  • Against this backdrop of more or less gradual improvement, we occasionally see much bigger changes.
  • And at the level of their genomes, we see the bacteria becoming more and more different from their ancestors.

In these fundamental respects, evolution in these flasks works in much the same way that evolution works in nature. Of course, the scales of time and space are vastly greater in nature than they are in the lab, and natural environments are far more complex and variable than is the simple one in the LTEE. But the core processes of mutation, drift, and natural selection give rise to evolution in the LTEE, just as they do (along with sex and other forms of gene exchange) in nature.

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Evolving Fun and Games

Science isn’t always fun and games. But sometimes it is!

This guest post is by Terry Soule, a computer scientist, and Barrie Robison, a biologist, both on the faculty at the University of Idaho. The BEACON Center for the Study of Evolution in Action brings together biologists, computer scientists, and engineers to illuminate and harness the power of evolution as an on-going process.

With BEACON’s support, Terry and Barrie have developed a video game, called Darwin’s Demons, where you must fight off enemies that are evolving to defeat your best efforts!

Feel free to comment here.  However, please send any technical queries via email to Terry (tsoule@cs.uidaho.edu) and/or Barrie (brobison@uidaho.edu).

*****

Thanks to BEACON’s support, Polymorphic Games has created the evolutionary video game Darwin’s Demons, and placed it on the Steam website as part of the greenlight process.

Darwin’s Demons adds an evolutionary component and modern flair to an arcade classic.  Darwin’s Demons models biological evolution using enemies with digital genomes. Enemies acquire fitness by being the most aggressive, accurate, and longest lived, and only the most fit enemies pass their genomes to the next generation. The result? The creatures you found hardest to kill have all the babies, making each generation more challenging than the last!

The game includes in-game graphs for tracking evolution, displays the most fit enemies from each wave, and has an experiment mode where you can set parameters like the mutation rate, fitness function, etc.  It also dumps all of the evolutionary data to a file.  So, there are opportunities for experiments on user driven evolution if anyone is interested.  (We are more than happy to share the code and/or make simple modifications for controlled experiments.)

If you get the opportunity please try out the demo (downloadable at either of the sites listed above, with Windows, MAC, and Linux versions), vote for us on Steam, and send us comments, suggestions, or ideas for future directions and collaborations.

Thanks,

— Terry Soule (tsoule@cs.uidaho.edu), Computer Science, UI

— Barrie Robison (brobison@uidaho.edu), Biological Sciences, UI

 

Darwin's Demons

[Darwin’s Demons: image from the Polymorphic Games website]

*****

 

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Evolution Education in Action

This entry is a guest post by my MSU colleague Jim Smith. Jim is one of the PIs on an NSF-supported project to develop Avida-ED as a tool for learning about evolution in action and the nature and practice of science. (Besides Jim’s work with Avida-ED, many readers will be interested in Evo-Ed, a project where he and colleagues have developed teaching and learning materials organized around six case studies of evolution that integrate knowledge of the genetic, biochemical, physiological, and ecological processes at work.) Here is Jim’s report on the Avida-ED professional-development workshop that was recently held here at MSU.

*****

This past week, we had the pleasure of working together in a 2.5 day workshop with a group of biology faculty from across the country who are interested in evolution education.  As a part of our work in the NSF-funded Active LENS project, and as members of the BEACON NSF Science and Technology Center at Michigan State, our focus in this workshop was finding ways to incorporate the digital evolution software program, Avida-ED, into Biology course offerings.  Avida-ED allows students to understand evolution as an empirical science, where things can be studied and discovered via manipulative experiments, rather than solely as an historical science consisting mainly of observation and deep inference.

This Active-LENS Workshop brought together 20 biology teaching faculty over the course of 2.5 days to build lessons for their courses that incorporate Avida-ED.  On Day 1, we heard presentations from: Rob Pennock, who outlined what Avida-ED is, how it came to be, and why it is important; Rich Lenski, who introduced the group to his 28-year 65,000 generation long-term experimental evolution project and also described how the research platform, Avida, was used to evolve organisms with complex features; and Charles Ofria, who gave us a tour under the hood of Avida-ED, showing us how the program works on a computational level.

Avidian replicating

An Avidian and its offspring (with mutations) in Avida-ED.

In between these presentations, workshop participants were introduced to a new browser-based version of Avida-ED that is in its final stages of development.  Software developer Diane Blackwood is now “squashing bugs” in this beta version of Avida-ED (3.0), which will be released later this month.  Jim Smith then led the workshop participants through three hands-on exercises that allowed them to see first-hand how Avida-ED could be used in an educational setting to address specific misconceptions that students have about evolutionary processes.  For example, some students think that selection causes the mutations that are advantageous, so one exercise explores whether mutations that confer a beneficial trait arise sooner when selection favors the mutation than when it does not. We also introduced the participants to some independent research projects that our Introductory Cell and Molecular Biology students carried out using Avida-ED.

On Day 2, participants started on their journeys to develop their own Avida-ED lessons and spent most of the day doing so.  This was perhaps the most interesting and challenging part of the workshop, given that the participants came to us from a wide range of institutions and instructional settings.  Thus, each participant had his/her own set of opportunities and challenges to consider during the lesson planning sessions.

In conjunction with, and in between, bouts of lesson planning, Jim Smith introduced participants to and/or reminded them about how to use backward design to plan instruction.  In addition, Mike Wiser presented data showing how he has been using Avida to study fundamental research questions in evolutionary biology, and also presented results of research he has been doing as a member of our team to study impacts of the use of Avida-ED in educational settings.  Moshe Khurgel, who participated in last year’s Active-LENS workshop, described his Avida-ED implementation at Bridgewater College (VA) this past year, and provided the participants with a great set of tips and things to consider as they developed their own curricular pieces.  Louise Mead rounded out the set of presentations on Day 2 by providing participants with some basics on how to assess student learning, and how the work done by the participants would fit into the overall Discipline Based Education Research (DBER) goals of the Avida-ED team.

The big payoff came on Day 3, when each participant team presented their ideas for implementation of Avida-ED into their courses.  These were great! Projects that were presented ranged from the use of Avida-ED in a case-based framework utilizing oil spill remediation to explore how (and when) genetic variation arises in populations (Introductory Cell and Molecular Biology, Kristin Parent and Michaela TerAvest, Michigan State), to using Avida-ED to explore concepts in phylogenetics and compete organisms directly against each other in a March Madness framework (300-level Microbiology Lab, Greg Lang and Sean Buskirk, Lehigh University), to using Avida-ED to explore environmental effects on species diversity (300-level Ecology course, Kellie Kuhn and David Westmoreland, Air Force Academy). Many other creative and innovative ideas were presented by the other participants.

Events such as this 2.5 day workshop are true highlights of an academic life. Working with dedicated faculty who are motivated and energized by the prospect of creating excellent learning experiences for their students is a real pleasure.  It also gives one hope for the future of American science.

The best news is that we will be doing this 2.5 day workshop again next year. Sound like fun? If so, give one of us a shout (I’m at jimsmith@msu.edu), and we’ll see what we can do to have you join the group in the summer of 2017!

— Jim Smith

*****

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A Blast from the Past

Sometimes you need a thick skin to be a scientist or scholar. Almost everyone, it seems, has encountered a reviewer who didn’t bother to read what you wrote or badly misunderstood what you said.

In other cases, you realize on reflection that a reviewer’s criticisms, although annoying and even painful at first, are justified in whole or in part. Addressing the reviewer’s criticisms helps you improve your paper or grant. That’s been my experience in most cases.

Sometimes, though, a reviewer just doesn’t like your work. And occasionally they can be pretty nasty about it. Here’s a case that I experienced on submission of the first paper about the Long-Term Evolution Experiment.

{You can click on the image of the review to enlarge it.}

Rev 1 of 1991 LTEE

A few choice lines:

“This paper has merit and no errors, but I do not like it …”

“I feel like a professor giving a poor grade to a good student …”

“The experiment is incomplete and the paper seriously premature …”

“I am upset because continued reliance on statistics and untested models by population geneticists will only hasten the demise of the field.”

“Since the Deans of Science at most universities can only count and not read, I can fully appreciate the reasons for trying to publish this part of the work alone.”

“Molecular biology … should be used whenever possible because molecular biologists control the funding and most of the faculty appointments.”

I’ve occasionally shared this with members of my lab when they get difficult reviews to remind them that it’s not the end of the world or their career, or even the paper that has been scorched.

PS The revised paper was accepted by The American Naturalist. In fact, it won the best-paper award there for the year in which it was published. It has also been cited hundreds of times.

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On the Evolution of Citrate Use

Those who follow the long-term evolution experiment (LTEE) with E. coli know that the most dramatic change we have observed to date is the origin of the new ability to grow on citrate. It’s dramatic for several reasons including the fact (external to the LTEE) that E. coli has been historically defined as a species based in part on its inability to grow on citrate in oxic environments and the fact (internal to the LTEE) that it was so difficult for the bacteria to evolve this ability that only one of the populations did so, and that it took over 30,000 generations even though an abundance of citrate has been present in the medium throughout the LTEE. Even after 64,000 generations, only the Ara–3 population has evolved that new ability.

Zachary Blount, formerly a graduate student and now a postdoc in my lab, has spent the last decade studying the evolution of this population and its new ability. His two first-authored papers in PNAS (2008) and Nature (2012) demonstrated, respectively, that (i) the origin of the ability to grow on citrate in the LTEE was contingent on one or more “potentiating” mutations that happened before the “actualizing” mutation that conferred the new function first appeared, and (ii) the actualizing mutation was a physical rearrangement of the DNA that brought together a structural gene, citT, that encodes a transporter and a previously unconnected regulatory region to generate a new module that caused the phenotypic transition to Cit+. These papers presented and discussed much more than these two points, of course, but they are the key findings. More recently, Zack was a coauthor on a paper in eLife (2015) by Erik Quandt, Jeff Barrick, and others that identified two mutations in the gene for citrate synthase—one that potentiated the evolution of citrate utilization, and another that subsequently refined that new function.

So we were keenly interested when we saw a new paper titled “Rapid evolution of citrate utilization by Escherichia coli by direct selection requires citT and dctA” by Dustin Van Hofwegen, Carolyn Hovde, and Scott Minnich. The paper is posted online as an accepted manuscript by the Journal of Bacteriology. What follows here are some overall impressions of their paper that Zack and I put together. We may follow these impressions later with some further analysis and comments.

* * * * *

Let’s begin by saying that it’s great to see other groups working on interesting systems and problems like the evolution of citrate utilization in E. coli.

Moreover, the actual science that was done and reported looks fine and interesting, though we have a few quibbles with some details that we will overlook for now. By and large, the work confirms many of the findings that were reported in our papers cited above:

(i) the ability to grow on citrate in the presence of oxygen can and does evolve in E. coli (Blount et al., 2008);

(ii) when aerobic growth on citrate evolves, it does not do so quickly and easily (Blount et al., 2008) but instead takes weeks or longer—more on that below;

(iii) all strains that have evolved this new ability have physical rearrangements that involve the citT gene and appear also to involve a so-called “promoter capture” whereby a copy of this transporter-encoding gene acquires a new upstream regulatory region (Blount et al., 2012); and

(iv) genetic context matters—the strain one uses affects the likelihood of evolving the Cit+ function (Blount et al., 2008) and the resulting ability to grow on citrate (Blount et al., 2012; Quandt et al., 2015).

The problem, then, is not with the experiments and data. Rather, the problem is that the results are wrapped in interpretations that are, in our view, flawed and fallacious.

“No new genetic information”

The authors assert repeatedly (last sentence of their Importance statement, and first and last paragraphs of their Discussion) that “no new genetic information evolved.” However, that statement flatly contradicts the fact that in their experiments, and ours, E. coli gained the new ability to grow on citrate in the presence of oxygen. We would further add (which we have not emphasized before) that these Cit+ strains can grow on citrate as a sole carbon source—when E. coli grows anaerobically on citrate, it requires a second substrate for growth in order to use the citrate (a phenomenon called “co-metabolism”).

The claim that “no new genetic information evolved” is based on the fact that the bacteria gained this new ability by rearranging existing structural and regulatory genetic elements. But that’s like saying a new book—say, Darwin’s Origin of Species when it first appeared in 1859—contains no new information, because the text has the same old letters and words that are found in other books.

In an evolutionary context, a genome encodes not just proteins and patterns of expression, but information about the environments where an organism’s ancestors have lived and how to survive and reproduce in those environments by having useful proteins, expressing them under appropriate conditions (but not others), and so on. So when natural selection—that is, differential survival and reproduction—favors bacteria whose genomes have mutations that enable them to grow on citrate, those mutations most certainly provide new and useful information to the bacteria.

That’s how evolution works—it’s not as though new genes and functions somehow appear out of thin air. As the bacterial geneticist and Nobel laureate François Jacob wrote (Science, 1977): “[N]atural selection does not work as an engineer works. It works like a tinkerer—a tinkerer who does not know exactly what he is going to produce but uses whatever he finds around him, whether it be pieces of string, fragments of wood, or old cardboards; in short, it works like a tinkerer who uses everything at his disposal to produce some kind of workable object.”

To say there’s no new genetic information when a new function has evolved (or even when an existing function has improved) is a red herring that is promulgated by the opponents of evolutionary science. In this regard, it seems relevant to point out that the corresponding author, Scott Minnich, is a fellow of the Discovery Institute and was an expert witness for the losing side that wanted to allow the teaching of “intelligent design” as an alternative to evolution in public schools in the landmark Kitzmiller v. Dover case.

“Rapid evolution of citrate utilization”

In the title of their paper and throughout, Van Hofwegen et al. emphasize that, in their experiments, E. coli evolved the ability to grow aerobically on citrate much faster than the 30,000 generations and ~15 years that it took in the LTEE. That’s true, but it also obscures three points. First, we already demonstrated in replay experiments that, in the right genetic background and by plating on minimal-citrate agar, Cit+ mutants sometimes arose in a matter of weeks (Blount et al. 2008). Second, rapid evolution of citrate utilization—or any evolution of that function—was not a goal of the LTEE. So while it is interesting that Van Hofwegen et al. have identified genetic contexts and ecological conditions that accelerate the emergence of citrate utilization (as did Blount et al., 2008), that in no way undermines the slowness and rarity of the evolution of this function in the context of the LTEE (or, for that matter, the rarity of Cit+ E. coli in nature and in the lab prior to our work). Third, the fastest time that Van Hofwegen et al. saw for the Cit+ function to emerge was 19 days (from their Table 1), and in most cases it took a month or two. While that’s a lot faster than 15 years, it’s still much longer than typical “direct selections” used by microbiologists where a readily accessible mutation might confer, for example, resistance to an antibiotic after a day or two.

So while we commend the authors’ patience, we do not think the fact that their experiments produced Cit+ bacteria faster than did the LTEE is particularly important, especially since that was not a goal of the LTEE (and since we also produced them much faster in replay experiments). However, in a manner that again suggests an ulterior nonscientific motive, they try to undermine the LTEE as an exemplar of evolution. The final sentence of their paper reads: “A more accurate, albeit controversial, interpretation of the LTEE is that E. coli’s capacity to evolve is more limited than currently assumed.” Alas, their conclusion makes no logical sense. If under the right circumstances the evolution of citrate utilization is more rapid than it is in the LTEE, then that means that E. coli’s capacity to evolve is more powerful—not more limited—than assumed.

“Speciation Event”

To us, one of the most interesting facets of the evolution of the citrate-using E. coli in the LTEE is its implications for our understanding of the evolutionary processes by which new species arise. Part of the reason for this interest—and the one that’s most easily stated in a popular context—is that the inability to grow on citrate is part of the historical definition for E. coli as a species, going back almost a century. But the deeper interest to us lies not in labeling a new species or debating where to draw the line between species—various criteria are used by different scientists, and inevitably there are many cases that lie in grey areas. Rather, as evolutionary biologists, we are most interested in the process of speciation—the ecological and genetic dynamics that lead to changing biological forms that, over time, are more and more like a new species until, eventually, perhaps far in the future, there is no doubt that a new species has evolved.

In short, speciation is not an event. As Ptacek and Hankison (2009, in Evolution: The First Four Billion Years) put it, “[S]peciation is a series of processes, with a beginning stage of initial divergence, a middle stage wherein species-specific characteristics are refined by various forces of evolution, and an end point at which a new species becomes a completely separate evolutionary lineage on its own trajectory of evolutionary change with the potential for extinction or further diversification into new lineages.” We realize that scientists (ourselves included) often use shorthand and jargon instead of writing more carefully and precisely. We have no doubt that one can find solid scientific papers that talk about speciation events; but except for cases that involve hybridization leading to polyploids that are reproductively isolated in a single generation (as sometimes occurs in plants), this is simply an imprecise shorthand.

In our first paper on the citrate-using E. coli that arose in the LTEE, we clearly emphasized that becoming Cit+ was only a first step on the road to possible speciation (Blount et al., 2008). One criterion that many biologists would apply to investigate speciation is whether a later form merely replaced an earlier form (evolution without speciation) or, alternatively, one lineage split into two lineages that then coexisted (incipient speciation). In fact, we showed that, after the new function evolved, the Cit+ and Cit lineages coexisted (and their coexistence was confirmed using genomic data in Blount et al., 2012). We concluded the 2008 paper by asking explicitly: “Will the Cit+ and Cit– lineages eventually become distinct species?” (emphasis added) and discussing how we might assess their ongoing divergence.

By contrast, Van Hofwegen et al. dismiss the idea of speciation out of hand, not only by calling it an event but by treating the issue as though it hinges, literally, on the individual mutations that produced a Cit+ cell. For example, they write: “[B]ecause this adaptation did not generate any new genetic information … generation of E. coli Cit+ phenotypes in our estimation do not warrant consideration as a speciation event.” And in the penultimate sentence of their paper, they say: “[W]e argue that this is not speciation any more than any other regulatory mutant of E. coli.” (We also note that this is a rather bizarre generalization, as though the gain of function that gave access to a new resource is equal in regards to its speciation potential to, say, the loss of regulation of a function that is no longer used by a lineage in its current environment. Both might well be adaptations, but one seems much more likely to begin the process of speciation.)

In conclusion, Van Hofwegen, Hovde, and Minnich have done some interesting experiments that shed further light on the nature of the mutations and ecological conditions that allow E. coli cells to evolve the ability to grow aerobically on citrate, a function that this species cannot ordinarily perform. However, they misunderstand and/or misrepresent the relevance of this system for evolutionary biology in several important respects. 

And the meaning of historical contingency

The paper by Hofwegen et al. is accompanied by a commentary by John Roth and Sophie Maisnier-Patin. Their abstract begins: “Van Hofwegen et al. demonstrate that E. coli rapidly evolves ability to use citrate when long selective periods are provided. This contrasts with the extreme delay (15 years of daily transfers) seen in the long-term evolution experiments of Lenski and coworkers. Their idea of ‘historical contingency’ may require reinterpretation.”

Historical contingency is a complicated notion, but it essentially means that history matters. In Blount et al. (2008), we made it clear what we mean by historical contingency in the context of the evolution of the Cit+ lineage in one of the LTEE populations. Was this an extremely rare event that could have happened at any time? Or did it instead depend on the occurrence of a sequence of events, a particular history, whereby an altered genetic context evolved—a potentiated background—in which this new function could now evolve?

Roth and Maisnier-Patin’s suggestion that our idea of “historical contingency” may require reinterpretation reflects a false dichotomy between historical contingency, on the one hand, and the effects of different selection schemes, on the other. The fact that evolution might be fast and not contingent on genetic background (though the evidence of Van Hofwegen et al. is, at best, ambiguous in this regard) in one set of circumstances has no bearing on whether it is contingent in another set of circumstances. The historical contingency of Cit+ evolution is not mere conjecture. We showed that the evolution of this new function in the LTEE was contingent. In replay experiments, Blount et al. (2008) showed that that the Cit+ trait arises more often in later-generation genetic backgrounds than in the ancestor or early-generation backgrounds. Moreover, Blount et al. (2012) performed genetic manipulations and showed that a high-copy-number plasmid carrying the evolved module that confers the Cit+ function had very different phenotypic effects when put in a Cit clone from the lineage within which Cit+ evolved than when placed in the ancestor or even other late-generation lineages not on the line of descent leading to the emergence of the Cit+ bacteria. In the clone on the line of descent, this module conferred strong, immediate, and consistent growth on citrate. In the other genetic backgrounds, growth on citrate was weak, delayed, and/or inconsistent.

The hypothesis of historical contingency is not mutually exclusive with respect to causal factors of an ecological or genetic nature—it simply says that factors that changed over time were important for the eventual emergence of Cit+. Moreover, historical contingency was invoked and demonstrated in a specific context, namely that of the emergence of Cit+ in the LTEE—it does not mean that the emergence of Cit+ is historically contingent in other experimental contexts, nor for that matter that other changes in the LTEE are historically contingent—in fact, some other evolved changes in the LTEE have been highly predictable and not (or at least not obviously) contingent on prior mutations in the populations (e.g., Woods et al., PNAS, 2006). [For more on historical contingency and the LTEE, you can download a preprint of Zack’s latest paper from his website: Blount, Z. D. A Case Study in Evolutionary Contingency. Studies in the History and Philosophy of Biology and Biomedical Sciences.]

Erik Quandt offers this analogy to illustrate our point that contingency depends on context: “It’s kind of like the difference between being an average person attempting to dunk a basketball when all by yourself, with unlimited time, and maybe even with a trampoline versus having to get to the rim in a game with LeBron James and the Cavs playing defense. Just because you can do it by yourself under optimal conditions, does this negate the difficulty of doing it in an NBA game or say anything about the kind of history (training and/or genetics) that you would need for that situation?”

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LTEE lines centered on citrate #11

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