Tag Archives: experimental evolution

Five More Years

The E. coli long-term evolution experiment (LTEE) began in 1988, and it has run for over 32 years with only occasional interruptions. The latest interruption, of course, reflects the temporary closure of my lab during the ongoing coronavirus pandemic. Fortunately, one of the advantages of working with bacteria is that we can freeze population samples and later revive them, which will allow us to resume their daily propagation when it is prudent to do so.  Indeed, we’ve frozen samples of all 12 populations throughout the LTEE’s history, allowing “time travel” to measure and analyze their fitness trajectories, genome evolution, historical contingencies, and more.

Even as the experiment is on ice, the lab team continues to analyze recently collected data, prepare papers that report their findings, and make plans for future work. Their analyses use data collected from the LTEE itself, as well as from various experiments spun off from the LTEE.  Nkrumah Grant is writing up analyses of genomic and phenotypic aspects of metabolic evolution in the LTEE populations.  Kyle Card is examining genome sequences for evidence of historical contingencies that influence the evolution of antibiotic resistance. Zachary Blount is comparing the evolution of new populations propagated in citrate-only versus citrate + glucose media. Minako Izutsu is examining the effects of population size on the genetic targets of selection, while Devin Lake is performing numerical simulations to understand the effects of population size on the dynamics of adaptive evolution.  So everyone remains busy and engaged in science, even with the lab temporarily closed.

Today, I’m excited to announce two new developments.  First, the National Science Foundation (NSF) has renewed the grant that supports the LTEE for the next 5 years. This grant enables the continued propagation of the LTEE lines, the storage of frozen samples, and some core analyses of the evolving populations. The grant is funded through the NSF’s Long Term Research in Environmental Biology (LTREB) Program, which “supports the generation of extended time series of data to address important questions in evolutionary biology, ecology, and ecosystem science.” Thank you to the reviewers and program officers for their endorsement of our research, and to the American public and policy-makers for supporting the NSF’s mission “to promote the progress of science.”

Second, Jeff Barrick joins me as co-PI on this grant for the next 5 years, and I expect he will be the lead PI after that period.  In fact, Jeff and his team will take over the daily propagation of the LTEE populations and storage of the sample collection even before then. I’m not planning to retire during the coming grant period. Instead, this transfer of responsibility is intended to ensure that the LTEE remains in good hands for decades to come. In the meantime, Jeff’s group will conduct some analyses of the LTEE lines even before they take over the daily responsibilities, while my team will continue working on the lines after the handoff occurs.

Several years ago I wrote about the qualifications of scientists who would lead the LTEE into the future: “My thinking is that each successive scientist responsible for the LTEE would, ideally, be young enough that he or she could direct the project for 25 years or so, but senior enough to have been promoted and tenured based on his or her independent achievements in a relevant field (evolutionary biology, genomics, microbiology, etc.). Thus, the LTEE would continue in parallel with that person’s other research, rather than requiring his or her full effort, just like my team has conducted other research in addition to the LTEE.”

Jeff is an outstanding young scientist with all of these attributes. Two years ago he was promoted to Associate Professor with tenure in the Department of Molecular Biosciences at the University of Texas at Austin.  He has expertise in multiple areas relevant to the LTEE including evolution, microbiology, genomics, bioinformatics, biochemistry, molecular biology, and synthetic biology. He directs a substantial team of technicians, postdocs, and graduate students, which will provide ample coverage for the daily LTEE transfers (including weekends and holidays). Last but not least, Jeff has participated in the LTEE and made many contributions to it including:

  • Participated in propagating the LTEE lines and related activities while he was a postdoc in my lab from 2006 to 2010.
  • Authored many papers using samples from the LTEE, including almost all of them that have analyzed genome sequences as well as several recent papers examining the genetic underpinnings of the ability to use citrate that evolved in one lineage.
  • Developed the open-source breseq computational pipeline for comprehensively identifying mutations that distinguish ancestral and evolved genomes.

Someone might reasonably ask if the LTEE will work in the same way when it is moved to another site. The answer is yes: the environment is simple and defined, so it is readily reproduced. Indeed, I moved the LTEE from UC-Irvine to MSU many years ago, the lab has moved between buildings here at MSU, and we’ve shared strains with scientists at many other institutions, where measurements and inferences have been satisfactorily reproducible. As an additional check, Jeff’s team at UT-Austin ran a set of the competition assays that we use to measure the relative fitness of evolved and ancestral bacteria, and we compared the new data to data that we had previously obtained here at MSU. The two datasets agreed well, in line with the inherent measurement noise in assessing relative fitness. Fitness is the most integrative measure of performance of the LTEE populations, and it is potentially sensitive to subtle differences in conditions. These results provide further evidence that, when the time comes, the LTEE can continue its journey of adaptation and innovation in its new home.

Evolve, LTEE, evolve!

LTEE flasks repeating

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Do you teach a biology lab that has been disrupted by the coronavirus outbreak?

The following is a guest post written by my colleague, Rob Pennock.

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Do you teach a biology lab that has been disrupted by the coronavirus outbreak?  If so, you may want to consider using the Avida-ED experimental evolution platform as a virtual replacement.

Avida-ED logo

To limit the spread of the coronavirus, many colleages and universities have suspended in-person classes, and instructors have had to scramble to replace them with on-line instruction.  Biology faculty who teach laboratory-based courses find it especially difficult or impossible to do their planned lab exercises.  Avida-ED may provide a valuable substitute for some classes.

Avida-ED is an award-winning educational application developed at Michigan State University for undergraduate biology courses. It is aimed at helping students learn about evolution and the scientific method by allowing them to design and perform actual experiments to test hypotheses about evolutionary mechanisms using evolving digital organisms.  Funded by the NSF, Avida-ED is the educational version of a model system used by researchers to perform evolution experiments–including many that have been published in leading scientific journals (see some examples below).  Avida-ED is not a simulation, but an instantiation of the evolutionary mechanisms and process that allows for real experiments.  Avida-ED produces copious data that can be analyzed within the application or exported for statistical analysis.  Avida-ED has been used in classrooms across the country and around the world for over a decade.

Here are more reasons that Avida-ED may provide a useful, quick replacement for your lab:

  • Avida-ED is free.
  • Avida-ED requires no special registration or configuration.
  • Avida-ED is accessible on-line and runs locally in your web browser.
  • The user-friendly interface requires little technical training to use.
  • It includes ready-to-use exercises to teach a variety of evolutionary concepts.
  • It can also be used for open-ended labs where students design and perform their own experiments.
  • It can be used to teach principles of experimental design and scientific method.

See the Avida-ED web site for:

  • Link to the Avida-ED application launch page.
  • Model exercises (under the Curriculum link).
  • The Avida-ED lab book.
  • Quick start user manual.
  • Background information about digital evolution.
  • Articles about Avida-ED, including effectiveness studies.

The Avida-ED team is working to provide instructional videos for the core exercises from train-the-trainer workshops that we have offered in previous summers, where we teach faculty how to use the software in their own classes.  We can also provide instructor support materials for some exercises offline for certified instructors.  A mirror of the Avida-ED site is available in case the primary site goes down.

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Lenski, R. E., C. Ofria, T. C. Collier, and C. Adami.  1999.  Genome complexity, robustness and genetic interactions in digital organisms.  Nature 400: 661-664.

Wilke, C. O., J. Wang, C. Ofria, R. E. Lenski, and C. Adami.  2001.  Evolution of digital organisms at high mutation rates leads to survival of the flattest.  Nature 412: 331-333.

Lenski, R. E., C. Ofria, R. T. Pennock, and C. Adami.  2003.  The evolutionary origin of complex features.  Nature 423: 139-144.

Ofria, C., and C. O. Wilke.  2004.  Avida: A software platform for research in computational evolutionary biology.  Artificial Life 10: 191-229.

Chow, S. S., C. O. Wilke, C. Ofria, R. E. Lenski, and C. Adami.  2004.  Adaptive radiation from resource competition in digital organisms.  Science 305: 84-86.

Ostrowski, E. A., C. Ofria, and R. E. Lenski.  2007.  Ecological specialization and adaptive decay in digital organisms.  American Naturalist 169: E1-E20.

Clune, J., R. T. Pennock, C. Ofria, and R. E. Lenski.  2012.  Ontogeny tends to recapitulate phylogeny in digital organisms.  American Naturalist 180: E54-E63.

Goldsby, H. J., A. Dornhaus, B. Kerr, and C. Ofria.  Task-switching costs promote the evolution of division of labor and shifts in individuality.  Proceedings of the National Academy of Sciences, USA 109: 13686-13691.

Covert, A. W. III, R. E. Lenski, C. O. Wilke, and C. Ofria.  2013.  Experiments on the role of deleterious mutations as stepping stones in adaptive evolution.  Proceedings of the National Academy of Sciences, USA 110: E3171-E3178.

Goldsby, H. J., D. B. Knoester, C. Ofria, and B. Kerr.  2014.  The evolutionary origin of somatic cells under the dirty work hypothesis.  PLoS Biology 12: e1001858.

Fortuna, M. A., L. Zaman, C. Ofria, and A. Wagner.  2017.  The genotype-phenotype map of an evolving digital organism.  PLoS Computational Biology 13: e1005414.

Canino-Koning, R., M. J. Wiser, and C. Ofria.  2019.  Fluctuating environments select for short-term phenotypic variation leading to long-term exploration.  PLoS Computational Biology 15: e1006445.

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We Interrupt This Experiment

Today I made the decision to close the lab and temporarily suspend our experiments, including the LTEE, in light of the expanding SARS-CoV-2 outbreak.

I started to say it was a difficult decision, but really it was not all that difficult.  Several considerations led me to this decision.

1/ The SARS-CoV-2 outbreak appears to be taking off in many countries, including the USA, despite the substantial containment that has been orchestrated in Wuhan and elsewhere in China.

2/ The absence of evidence of any local cases is not as comforting as it might be, given the near-absence of testing here and in most of the USA.

3/ MSU students just returned from spring break today.  Some of the superb undergrads who work in my lab went to places that have confirmed cases. None of the places they went are among the locations with intense outbreaks, but the confirmed cases in at least one location have grown noticeably in the past week. They also flew on planes to and from their vacations.

4/ As a team, we’re connected not only to one another, but also to people who are health-care workers and others with increased vulnerabilities to infections. (Not to mention that I’m over 60 …) When you think about it, pretty much everyone has those connections.

5/ We’re very lucky because our work is easy to stop and re-start. Our study organisms can be frozen away and revived whenever we see fit.  In the meantime, everyone has classes to take, papers to read and write, data to analyze, etc.  And a little extra time, hopefully, to reflect on and maintain the health and well-being of our friends, families, and selves.  So, we will all be busy, but doing things a bit differently than we had planned.

6/ As we freeze away the long-term lines, the lab notebook will record:  “On this day, the LTEE was temporarily halted and frozen down for the coronavirus pandemic of 2020.”

Hopefully, some future historian of science will look back on today’s entry and say: “What the hell was that all about?”

Freezing LTEE for SARS

[Devin Lake putting the LTEE populations into the ultra-low freezer, where they will stay until they are called back into action … evolution in action.]

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We Interrupt this Nasty Virus with Some Good News about Bacteria

Today is the 32nd birthday of the E. coli long-term evolution experiment (LTEE).  I started it on February 24th, 1988, when I was at the University of Califonia, Irvine.

Notebook entry start of LTEEIt also happens to be daily transfer number 11,000 for the experiment.  But wait, you ask: Is 365 x 32 really equal to 11,000?  (Not to mention the complication of leap years.)

LTEE flasks repeating

No!  365 x 32 = 11,680.  We’re almost 2 years behind perfection!  Over the years, we missed daily transfers for various reasons including the fact the experiment was frozen for several months around the time of my move from Irvine to Michigan State University, as well as some missed transfers and various mishaps (including contamination) along the way that have led us to restart the experiment from frozen samples.

Luckily, we don’t have to go back to the beginning–the LTEE wouldn’t have survived if we did. We freeze whole-population samples every 75 days, and those provide the backups that keep us going when needed.

So the LTEE is 32 years old today.  The evolving bacteria lineages, though, are younger, at a little over 30 years (11,000 / 365).  I prefer to think of them as timeless, though … having survived in and adapted to their tiny flask worlds for more than 73,000 generations.

Here’s grad student and lab manager Devin Lake doing today’s transfer.

Devin LTEE 32 years

And here’s Devin & me with the lab notebook. Devin is pointing to today’s entries.

Devin and Rich with LTEE notebook for 32nd birthday

And here’s what we wrote:

LTEE notebook 32nd birthday

For those with pathogens on their mind (and that’s a lot of us, with the new coronavirus spreading), you might wonder: Aren’t E. coli dangerous?  The short answer is only rarely. All of us have harmless or even beneficial strains of E. coli and many other bacterial species in our GI tract. The LTEE uses one of these harmless strains, one that has been studied in many labs for close to a century without problems. There are some strains of E. coli, though, that are nasty, and which are usually acquired by eating contaminated foods.  So wash your raw fruits and vegetables, cook your meats, and don’t worry about the LTEE bacteria … Just wish them a happy birthday today, and many more years of scientific discovery.

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Evolution goes viral! (And how real science works)

This is the fourth in a series of posts about a new book by Michael Behe, Darwin Devolves. Behe is a leading proponent of intelligent-design creationism (IDC), which asserts that known processes cannot adequately account for evolution and, therefore, some intelligent agent must be involved in the process. Behe is a professor of biochemistry, which gives him knowledge and credentials that most IDC advocates do not have. However, my posts explain why I think his logic is unsound and his evidence weak and biased.

In brief, Behe argues that random mutation and natural selection are almost entirely degradative forces that break or blunt the various functions encoded by genes, producing short-term advantages that are so pervasive that they prevent constructive adaptations, which he claims are very unlikely to emerge in the way that evolutionary biologists have proposed. Unlike young-Earth creationists, Behe accepts the descent of living species from common ancestors over billions of years. To reconcile these seemingly conflicting views, Behe invokes that an intelligent agent (presumably God, though IDC proponents avoid that word so that their ideas might appear to be scientific) has purposefully guided evolution over its long history by somehow inserting new genetic information into chosen lineages along the way. To make his strange argument, Behe works very, very hard to convince readers that standard evolutionary processes are (i) really, really good at degrading functions, and (ii) really, really bad at producing anything new.

In my first post, I explained that Behe’s arguments confuse and conflate what is easy and commonplace over the short run (i.e., mutations that break or blunt functional genes) with the lasting impacts of less frequent but constructive adaptations (i.e., new functions and subsequent diversification) over the long haul of evolution. My second post examined a case involving polar bears, which Behe highlighted as a compelling example of degradative evolution, but where a careful review of the science suggests that gene function improved. Behe also highlighted results from my lab’s long-term evolution experiment with bacteria, but in my third post I explained that he overstates his case by downplaying or dismissing evidence that runs counter to his argument.

In this post, I’ll discuss an experiment that Behe ignores in Darwin Devolves. (Behe clearly knows the work, because he wrote about it on the Discovery Institute’s anti-evolution blog. But as usual, he spun the story to obscure the problems for his arguments, all the while accusing the scientists who collect data to test hypotheses of spinning the story.) In fact, as I’ll explain, the results also undermine the claims in Behe’s two previous books, Darwin’s Black Box and The Edge of Evolution, about the supposed shortcomings of evolution.

(Before presenting this experiment, I want to mention briefly two other papers that readers interested in what else Behe missed or downplayed might want to read. First, Rees Kassen posted a preprint of a paper on “Experimental evolution of innovation and novelty.” He reviews empirical evidence and discusses conceptual issues bearing on the origin of new functional abilities observed in many experiments with bacteria and other microbes. Second, Chris Adami, Charles Ofria, Rob Pennock, and I published a paper over 15 years ago that demonstrated the logical fallacy of Behe’s assertions about irreducible complexity. Behe mentions that paper derisively, without addressing its substance in Darwin Devolves, as follows: “A computer simulation of computer program development that ignores biology entirely.” A more accurate statement would have been: “Computer programs can evolve by random mutation and natural selection the ability to perform complex functions that show the concept of irreducible complexity is total nonsense.” The rest of this post is longer than I planned, because I want to provide background for readers who aren’t microbiologists, and because—like so much of science—it’s an interesting story with unexpected twists and turns along the way.)

IV. Phage lambda evolves a new capability without breaking anything

There are a lot of viruses in the world. Fortunately, most of them don’t infect humans. Many of them infect bacteria, as it so happens. In fact, before antibiotics were used as therapeutic agents, there was hope that bacteriophages (“bacteria eaters”), or phages for short, would be useful in treating diseases. And now, with the evolution of pathogenic bacteria that are resistant to many or all available drugs, researchers are reconsidering the possibility of using phages to treat some infections.

My lab is best known for the long-term evolution experiment (LTEE) with E. coli bacteria. But over the years, my students have also performed other experiments with a variety of microbes, including some viruses that infect E. coli. One of those viruses is called lambda. For decades, lambda was probably the most intensively studied virus on the planet—just as E. coli was a model for understanding bacterial genetics and physiology, lambda became a model for understanding viral genetics and infection.

One reason lambda became a hit was because it has an interesting life cycle. After lambda enters a bacterial cell (and assuming the cell lacks some internal defenses), the virus can do one of two things. It can commandeer the host, hijacking the cellular machinery to produce a hundred or so progeny before bursting the host cell and releasing its “babies” to find new cells to infect. Alternatively, the virus’s DNA may be integrated into the host’s chromosome, hiding out and being replicated alongside the host’s genes—though the virus may later exit the chromosome and reactivate its lethal program. (Pretty neat, and a bit scary, right?)

Well, as cool as that is, it’s not what my student Justin Meyer (now on the faculty at UCSD) was studying. He was using a strain of lambda that can’t integrate into the bacterial cell’s chromosome—a successful infection takes only the first route, killing the cell in the course of making more viruses. Justin was studying this simpler virus because we were interested in whether the evolution of the bacterial hosts in response to the presence of lambda virus might depend on what food we gave the bacteria.

Let’s back up and explain why that might matter. Viruses like lambda don’t just glom onto any part of a bacterial cell; instead, they adsorb to specific receptors on the cell’s surface, with a successful attachment triggering the injection of their DNA into the cell. Lambda recognizes a particular cell-surface protein called LamB. (Despite decades of study of the interaction between lambda and E. coli, including experiments that specifically sought to see whether mutants could exploit other receptors, no one had ever seen lambda use any other receptor.) Of course, E. coli doesn’t make LamB for the sake of the virus. The LamB protein is one of several “porin” proteins that E. coli produces, and which serve as channels to allow molecules, like sugars, to cross the outer cell envelope. (Other proteins transport sugars across the inner cell membrane.) LamB, in particular, is a fairly large channel that allows the sugars maltose and maltotriose to enter the cell. Maltose and maltotriose are made of two and three linked glucose molecules, respectively. Glucose, being smaller, can readily enter a cell via smaller channels. When growing on glucose, E. coli cells don’t bother to produce much LamB protein. However, when cells sense that maltose or maltotriose, but not glucose, is present they activate the gene that encodes LamB. In doing so, however, the cells become more vulnerable to lambda, because that protein serves not only to transport these larger sugars but also as the receptor for the virus.

Coming back to Justin Meyer’s research, we wanted to see how different sugars affected the bacteria’s evolutionary response to lambda. (Justin and I have a paper in press comparing outcomes across the glucose, maltose, and maltotriose environments.) We reasoned that, if the bacteria were fed glucose, they could damage or delete the lamB gene that encodes the LamB protein. If the bacteria mutated the LamB protein, then the virus might counter with a mutation that restored their affinity for the mutated protein; but if the bacteria deleted or otherwise destroyed the LamB protein, we reasoned the virus would go extinct.

However, the first experiment using only the glucose treatment played out differently than what we expected—that’s science, and that’s why you do experiments—and it set Justin’s research off in a new direction. Instead of mutating the lamB gene, the bacteria evolved resistance to the virus by mutating another gene, called malT, that encodes a protein that activates the production of LamB. The viruses didn’t go extinct, however, because there was some residual, low-level expression of the LamB protein. That was enough to keep the viruses going, which also meant they could keep evolving.

To make a long story short, after just 8 days, one of six lambda populations evolved the ability to infect malT-mutated cells by attaching to a different surface protein, one called OmpF (short for outer membrane protein F). This evolved lambda virus could now infect E. coli cells through the original receptor, LamB, or this new one, OmpF. It had gained a new functional capability.

To understand this change, Justin sequenced the genome of this virus. He found a total of 5 mutations compared to the lambda virus with which he had begun. All 5 mutations were in the same gene, one that encodes the J protein in the “tail” of the virus that interacts with the cell surface. He also sequenced the J gene for some other viruses isolated from the same population. He found one virus that had 4 of these 5 mutations, but which could not infect cells via the OmpF receptor. Did that mean that only one of the 5 mutations was necessary to evolve this new function?

As it turns out, the answer is no. To better understand what had happened, Justin scaled up his experiments and ran an additional 96 replicates with lambda, E. coli, and glucose. In 24 cases, the viruses evolved the new mode of infection within three weeks. Justin sequenced the J gene from the viruses able to target OmpF in those 24 cases, and in 24 other cases where the virus could still use only the LamB receptor. He found that all 24 with the new capability had at least 4 mutations; these included 2 changes that were identical in all 24 lines, a third that further mutated one of the same codons (sets of 3 DNA bases that specify a particular amino acid to be incorporated into a protein), and another mutation that was always within a span of 11 codons. All of these mutations cause amino-acid substitutions near the end of the J protein, which is known to interact with the LamB receptor. The J protein is over 1100 amino acids in length, and so this concentration and parallelism (repeatability across lineages) is striking and strongly implies that natural selection favored these mutations.

Remember, too, that nothing is broken. These viruses can now use both the original LamB receptor and the alternative OmpF receptor. (This fact was demonstrated by showing that the viruses can grow on two different constructed hosts genotypes, one completing lacking LamB and the other completely lacking OmpF.)

None of the 24 viruses that had not evolved the ability to use the OmpF receptor had all 4 of these mutations. However, three of them shared 3 of the 4 mutations with viruses that had acquired that new ability. And yet, none of those had any capacity to grow on cells that lacked the LamB receptor. In other words, the set of all 4 of these mutations was needed to produce this new ability—no subset could do the job. (We initially lacked one of the four possible viral genotypes having each subset of 3 mutations. Later work confirmed that all four mutations are required.)

At first glance, it seems like none of the viral lineages should have been able to acquire all 4 mutations, at least if you accept the flawed reasoning from Behe’s previous book on The Edge of Evolution. If you need all 4 mutations for the new function, so the thinking goes, and if none of them provide any degree of that function, then you would need all 4 mutations to occur in one lineage by chance, which is extremely unlikely. (How unlikely is difficult to calculate precisely. To get some inkling, none of the 48 sequenced J genes—including both those that did and did not evolve the new capability—had even one synonymous mutation. Synonymous mutations don’t change the amino-acid sequence of an encoded protein, and so they provide a benchmark for the accumulation of selectively neutral mutations.)

And yet, 24 of the 96 lineages did just that—they evolved the new ability, and in just a few weeks time. If you’re into intelligent design, then I guess you’d have to conclude that some purposeful agent was pretty darn interested in helping the viruses vanquish the bacteria. If you’re a scientist, though, you’re trained to think more carefully and look for natural explanations—ones that you can actually test.

So how could 4 mutations arise so quickly in the same lineage? Natural selection. But wait, didn’t Justin find that all 4 of those mutations were required for the virus to exploit the new OmpF receptor? Yes, he did.

Our hypothesis was that the mutations that set the stage for the virus to evolve the ability to target OmpF were beneficial because they improved lambda’s ability to use its original LamB receptor. But wait, that’s the receptor they’ve always used. Shouldn’t they already be perfectly adapted to using that receptor? How can there be room for improvement?

If you’ve read my posts on polar bears and bacteria, you’ve probably got the idea. When the environment changes, all bets are off as to whether a function is optimally tuned to the new conditions. Lambda did not evolve in the same medium where Justin ran his experiments; and while lambda certainly encountered E. coli and the LamB receptor in its history, the cell surfaces the virus had to navigate in nature were more heterogeneous than what they encountered in the lab. In other words, there might well be scope for the viral J protein to become better at targeting the LamB receptor under the new conditions.

To an evolutionary biologist, this hypothesis is so obvious, and the data on the evolution of the J protein sequence so compelling, that it scarcely needs testing. Nonetheless, it’s always good to check one’s reasoning by collecting new data, and another talented student joined the project who did just that. Alita Burmeister (now a postdoc at Yale) competed lambda strains with some (but not all) of the mutations needed to use OmpF against a lambda strain that had none of those mutations. She studied six “intermediate” viruses, each of them isolated from an independent population that later evolved the ability to use OmpF.

Alita ran two sets of competitions between the evolved and ancestral viruses. In one set, the viruses fought over the ancestral bacterial strain; in the other set, they competed for a bacterial strain that had previously coevolved with lambda and become more resistant to infection. Four of the six evolved intermediate viruses outcompeted their ancestor for the naïve bacteria, and all six prevailed when competing for the tough-to-infect coevolved host cells. Alita ran additional experiments showing that the intermediates were better than the ancestral virus at adsorbing to bacterial cells—the precise molecular function that the J protein serves. These results clearly support the hypothesis that the first few mutations in the evolving virus populations improved their ability to infect cells via the LamB receptor.

Natural selection did its thing, in other words, discovering mutations that provided an advantage to the viruses. Some of the resulting viruses—those with certain combinations of three mutations—just happened to be poised in the space of possible genotypes such that a fourth mutation gave them the new capacity to use OmpF.

Now let’s step back and think about what this case says about the validity of the arguments that Behe has made in his three books.

Anybody remember Behe’s first book, Darwin’s Black Box, published in 1996? There, Behe claimed evolution doesn’t work because biological systems exhibit so-called “irreducible complexity,” which he defined as “… a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning.” Evolution can’t explain these functions, according to Behe, because you need everything in place for the system to work. Strike one! Lambda’s J protein required several well-matched, interacting amino acids to enable infection via the host’s OmpF receptor. Removing any one of them leaves the virus unable to perform that function. (Alas, Behe’s argument wasn’t merely mistaken, it also wasn’t new—since Darwin, and as explained in increasing detail by later biologists, we’ve known that new functions evolve by coopting and modifying genes, proteins, and other structures that previously served one function to perform a new function.)

The Edge of Evolution, Behe’s second book, claimed that evolution has a hard time making multiple constructive changes, implying the odds are heavily stacked against this occurring. Strike two!! Lambda required four constructive changes to gain the ability to use OmpF, yet dozens of populations in tiny flasks managed to do this in just a few weeks. That’s because the intermediate steps were strongly beneficial to the virus, so that each step along the way proceeded far faster than by random mutation alone.

Darwin Devolves says that adaptive evolution can occur, but that it does so overwhelmingly by breaking things. Strike three!!! The viruses that can enter the bacterial cells via the OmpF receptor are not broken. They are still able to infect via the LamB receptor and, in fact, they’re better at doing so then their ancestors in the new environment. (In his blog post after our paper was published in Science, Behe used the same sleight of hand he used to downplay the evolution of the new ability to use citrate in one LTEE population. That is, Behe called lambda’s new ability to infect via the OmpF receptor a modification of function, instead of a gain of function, based on his peculiar definition, whereby a gain of function is claimed to occur only if an entirely new gene “poofs” into existence. However, that’s not the definition of gain-of-function that biologists use, which (as the term implies) means that a new function has arisen. That standard definition aligns with how evolution coopts existing genes, proteins, and other structures to perform new functions. Behe’s peculiar definition is a blatant example of “moving the goalposts” to claim victory.)

As Nathan Lents, Joshua Swamidass, and I wrote in our book review, “Ultimately, Darwin Devolves fails to challenge modern evolutionary science because, once again, Behe does not fully engage with it. He misrepresents theory and avoids evidence that challenges him.”

If you’ve followed the logic and evidence in the three systems I’ve written about—polar bears adapting to a new diet, bacteria fine-tuning and even evolving new functions as they adapt to laboratory conditions, and viruses evolving a new port of entry into their hosts—you’ll understand why Behe’s arguments against evolution aren’t taken seriously by the vast majority of biologists. As for Behe’s arguments for intelligent design, they rest on his incredulity about what evolution is able to achieve, and they make no testable predictions about how the designer intervenes in the evolutionary process.

[The images below show infection assays for 4 lambda genotypes on 2 E. coli strains. The dark circles are “plaques”—areas in a dense lawn of bacteria where the cells have been killed by the virus. The viruses (labeled at bottom) include the ancestral lambda virus and 3 evolved genotypes. One bacterial strain expresses the LamB receptor (top row), while the other lacks the gene that encodes LamB (bottom row). All 4 viruses can infect the cells that produce LamB, but only the “EvoC” virus is able to infect the cells without that receptor. Images from Meyer et al., 2012, Science paper.]

Lambda plaque assays

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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 choose 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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