Tag Archives: Rob Pennock

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



Filed under Education, Science

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


Comments Off on Evolution Education in Action

Filed under Education

Favorite Examples of Evolution

When the cold bites, When the review stings, When the news is sad, I simply remember these evolving things, And then I don’t feel so bad! — with apologies to Rodgers and Hammerstein

Over on Twitter, the biology students from George Jenkins High School in Lakeland, Florida, asked me and many others: “What’s your favorite example of evolution?”  There are so many fascinating examples that it’s hard for me to pick just one. So, here are half a dozen examples that are among my favorites.

  • The discovery by Neil Shubin and colleagues of Tiktaalik, an extinct fish (pictured below) from the Devonian that was poised to give rise to terrestrial vertebrates. You can read about this work in Shubin’s award-winning book, Your Inner Fish, which was also made into a PBS show.
  • The discovery by Svante Pääbo and colleagues of the Denisovans, an extinct lineage of humans, based on sequencing a complete genome from the finger bone of a girl who lived tens of thousands of years ago.
  • The analysis by Tami Lieberman, Roy Kishony, and colleagues of the genetic adaptation of an opportunistic species of bacteria to the lungs of patients with cystic fibrosis. I’ve blogged about that paper here.
  • Here’s one from the long-term experiment in my own lab — the evolution of the ability to use citrate that arose in just one of the 12 populations and after more than 30,000 generations. There are nice summaries of this work in Carl Zimmer’s blog here and here.
  • A study by Hod Lipson and Jordan Pollack on the evolution of robots. I remember hearing about this paper and being shocked: “Wait a second. Robots are expensive, and most things go extinct during evolution. How could they even afford do this?” I had to read the paper to realize they were evolving virtual robots in a physical simulation of the real world. They then built and tested the winners in the physical world. And indeed, the robots worked as they had evolved to do.
  • Applying the mechanisms of evolution to artificial systems is a fascinating approach useful for both biology and engineering. One of my favorite basic-science uses of this approach was a paper where we used digital organisms – computer programs that self-replicate, mutate, and compete for resources – to show how very complex functions could evolve if simpler functions were favored along the way. These simpler functions provided building blocks for the more complex functions, illustrating how evolution works by tinkering and borrowing already existing structures and functions and using them in new ways. Incidentally, this work involved collaboration between a computer scientist (Charles Ofria), a philosopher (Rob Pennock), a physicist (Chris Adami), and a biologist (me).

Readers: Please feel free to add your own favorite examples of evolution in the comments section below.

[The picture below shows the Tiktaalik fossil discovered by Neil Shubin and colleagues.  It was posted on Wikipedia by Eduard Solà, and it is shown here under the indicated Creative Commons license.] Tiktaalik


Filed under Education, Science