Tag Archives: natural selection

Half Life

Today marks a unique day for the LTEE and me.

The LTEE started on February 17, 1988.  That was 11,517 days ago.

I was born on August 13, 1956.  That was 23,034 days ago.

That means that the LTEE is now half as old as I am.

To put it another way, I’ve spent half a lifetime on the LTEE.

Well, that’s not quite the right way to put it, since I’ve done a few other things during that time. Like raising a family—with a lot of help.  And a lot of other science, also with a lot of help, not to mention all the work of so many students and collaborators on the LTEE itself.

And unlike a radioactive isotope, the bacteria haven’t been decaying—they’ve been getting better and better at living in their flask-worlds.

My hope is that this long-term evolution experiment will continue for a long time. A very long time. For a lot longer than my own lifetime.

Here are a couple of photos from around the time the LTEE started. The first one shows Madeleine and me camping near Joshua Tree National Park in the summer of 1987, at the annual retreat of the UC-Irvine EEB department, and only a couple months before the birth of our youngest. The next one shows me snuggling with my three kids in early 1989.

june-1987-desert-x-with-mjan-1989-with-3-kiddos

How time flies. Luckily, though, I get to snuggle with my three grandkids now.

Bacterial generations. Human generations. Growing, evolving, and learning.

 

 

 

 

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On damaged genes and polar bears

Michael Behe has a new book called Darwin Devolves, published by HarperOne. Nathan Lents, Joshua Swamidass, and I wrote a review of that book for the journal Science. (You can find an open-access version of our review here.) As our review says (in agreement with Behe), there are many examples of evolution in which genes and their functions have been degraded, sometimes yielding an advantage to the organism. Unfortunately, though, Behe largely ignores the ways that evolution generates new functions and thereby produces complexity. That’s a severe problem because Behe uses the evidence for the ease of gene degradation to support his overarching implication that our current understanding of the mechanisms of evolution is inadequate and, consequently, the field of evolutionary biology has a “big problem” and is therefore in scientific trouble.

I hope to accomplish several things in a series of posts. (I initially planned to write three posts, but it will now be more than that, as I delve deeper into several issues.) In my first post, I explained why Behe’s so-called “first rule of adaptive evolution” does not imply what he says it does about evolution writ large. In summarizing, I wrote that Behe is right that mutations that break or blunt a gene can be adaptive. And he’s right that, when such mutations are adaptive, they are easy to come by. But Behe is wrong when he implies these facts present a problem, because his thesis confuses frequencies over the short run with lasting impacts over the long haul of evolution.

In this post, I take a closer look at Behe’s “rule” and how one might decide whether or not a particular mutation is damaging to a particular gene in a particular context. I’ll then describe and discuss the example that Behe chose to illustrate his argument at the outset of his book, calling attention to the fact that his inferences were indirect, and as a result a key conclusion was quite possibly wrong. [These issues came to my attention based on work by Nathan Lents, Art Hunt and Joshua Swamidass. They voiced concerns about this example on their own blogs, here and here. I’ve now done my own reading, and in this post I attempt to provide just a tiny bit of important technical background before addressing the main concern, as I see it.]

II-A. How does one know if a mutation has damaged a gene?

Behe’s first rule of adaptive evolution says this: “Break or blunt any functional gene whose loss would increase the number of a species’ offspring.” Every biologist knows that many mutations break or reduce the functionality of genes and the products they encode. Every biologist also realizes that this can sometime increase an organism’s fitness (i.e., its survival and reproductive success), in particular when two conditions are met. First, the function has to be one that is not—or rather, no longer—useful to the organism. For example, eyes are no longer useful to an organism whose ancestors lived above ground, but which itself now lives in perpetual darkness in a cave. Second, there must be a meaningful cost to the organism (again, in the currency of fitness) of having the functional form of the gene, and that cost must be reduced or eliminated for the mutated version of the gene. This second point means that mutations that break or blunt a particular gene—even one that is useless—are not necessarily advantageous; they might instead be selectively neutral, such as when an encoded protein is still expressed but, for example, has diminished activity on a substrate that isn’t even present. Therefore, compelling evidence for a broken or blunted gene in a particular lineage suggests that the gene’s function is under what evolutionary biologists call “relaxed” selection—relaxed because some capability that was useful during the history of a lineage is no longer important under the organisms’ present circumstances. However, that does not mean that the loss or diminution of the capability necessarily provided any advantage; instead, the gene could have decayed by the random fixation of mutations that were entirely inconsequential for fitness.

Two very important issues center on (i) how an observer can tell whether a particular mutation breaks or blunts a gene; and (ii) how that observer can determine whether the resulting mutation is advantageous. In short, neither inference is ironclad without an in-depth case-by-case investigation, although there are shortcuts that biologists often take because they make sense and are often sound, provided one takes care to understand the potential limitations of the inference. To characterize the biochemical consequences of a mutation, for example, the gold standard would be to perform detailed analyses of the activities of proteins encoded by different forms (alleles) of the same gene. That’s difficult, technical work.

But as I said, there are shortcuts that allow scientists to draw reasonable inferences in some cases. For example, a mutation that generates a premature stop codon (a so-called “nonsense” mutation) usually eliminates the encoded protein’s function. However, there are exceptions, such as when the premature stop is very near the end of the gene. It’s also possible that a truncated protein might even have some new activity and function, or that it might accumulate additional mutations that produce a new activity. That’s unlikely in any one case, but a lot of unlikely things can happen over the vast scales of space and time over which evolution has operated. As the Nobel laureate François Jacob famously wrote years ago, “natural 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.”

At the other end of the spectrum with respect to inferred functionality, some mutations change the DNA sequence of a gene, but they have no affect on the resulting amino-acid sequence of a protein. That happens because the genetic code is redundant, with multiple codons for the same amino acid. Such mutations are called “synonymous” and they are generally presumed to be neutral precisely because they don’t change a protein. Once again, however, there are some exceptions to this usually reliable inference; a synonymous mutation could affect, for example, the rate at which the protein is produced and even its propensity to fold into a specific conformation.

In the middle ground between these (usually) clear-cut extremes are the cases where a mutation produces an amino-acid substitution in the encoded protein. Does that mutation change the protein’s activity? If it does, is it necessarily damaging to the protein and/or to the organism with that altered protein? Biochemical and structural studies of proteins have shed light on this issue by identifying so-called “active sites” of many proteins—positions in the structure of a protein molecule where it interacts with a substrate and facilitates a chemical reaction. Mutations in and around active sites are more likely to affect a protein’s activity than ones that are far away. Also, even at the same site in a protein, different mutations are likely to have more pronounced affects on the protein’s activity, depending on whether the substitution affects the charge and/or size of the amino acid at that site.

Computational biologists have developed tools that take into account these types of information, which can be used to draw tentative inferences or make predictions about the likely effect of a specific mutation. Not surprisingly, one application is for understanding possible health effects of genetic variation in humans. For example, are certain variants in some gene likely to affect an individual’s susceptibility to cardiovascular disease?

One such tool is called PolyPhen-2. The website says: “PolyPhen-2 (Polymorphism Phenotyping v2) is a software tool which predicts possible impact of amino acid substitutions on the structure and function of a human proteins using straightforward physical and comparative considerations.” In addition to using structural information described above, it also uses information on whether a given site is highly conserved (little or no variation) or quite variable across humans and related species for which we have information. Why does it use that information? In essence, the program assumes that evolution has optimized a given protein’s activity for whatever it does in humans, related species, and our common ancestors. If a particular site in a protein varies a lot, according to that implicit assumption, the variants probably aren’t harmful because, well, if they were, then those lineages would have died out. If a site is hardly variable at all, by contrast, it’s presumably because mutants at those sites damaged the protein’s important function and led to the demise of those unfortunate lineages.

All that makes a lot of good sense … provided the protein of interest is performing the same function, and with the same optimal activities, in everybody and every species used in the analysis. Let’s look now at a specific case that Behe chose to highlight in his book.

II-B. The APOB gene in polar bears

Behe sets the stage for his rule—“break or blunt any functional gene whose loss would increase the number of a species’ offspring”—by summarizing the results of a study by Shiping Liu and coauthors that compared the genomes of polar bears and brown bears. Their paper examined mutations that distinguish these two species. The authors identified a set of mutations that had accumulated along the branch leading to modern polar bears, and in a manner that was consistent with those changes having been beneficial to the polar bears. One of the mutated genes, which was discussed in some detail both by the paper’s authors and by Behe, is called APOB. As Liu et al. wrote (p. 789), the APOB gene encodes ApoB, “the primary lipid-binding protein of chylomicrons and low-density lipoproteins (LDL) … LDL cholesterol is a major risk factor for heart disease and is also known as ‘bad cholesterol.’ ApoB enables the transport of fat molecules in blood plasma and lymph and acts as a ligand for LDL receptors, facilitating the movement of molecules such as cholesterol into cells … The extreme signal of APOB selection implies an important role for this protein in the physiological adaptations of the polar bear.”

As part of their study, Liu et al. analyzed the polar-bear version of the APOB gene using the PolyPhen-2 computational tool described above. Roughly half the mutations in APOB were categorized by that program as “possibly damaging” or “probably damaging,” and the rest were called “benign.” Behe than concluded that some of the mutations had damaged the protein’s function, and that these mutations were beneficial in the environment where the polar bear now lives. In other words, Behe took this output as strong support for his rule.

So what’s the problem? The PolyPhen-2 program, as I explained, is designed to identify mutations that are likely to affect a protein’s structure and therefore its function. It assumes such mutations damage (rather than improve) a protein’s function because structurally similar mutations are rare in humans and other species used for comparison. It does so because it presumes that natural selection has optimized the protein to perform a specific function that is the same in all cases, so that changes must be either benign or damaging to the protein’s function. In fact, the only possible categorical outputs of the program are benign, possibly damaging, and probably damaging. The program simply cannot detect or suggest that a protein might have some improved activity or altered function.

The authors of the paper recognized these limiting assumptions and their implications for the evolution of polar bears. In fact, they specifically interpreted the APOB mutations as follows (p. 789): “… we find nine fixed missense mutations in the polar bear … Five of the nine cluster within the N-terminal βα1 domain of the APOB gene, although the region comprises only 22% of the protein … This domain encodes the surface region and contains the majority of functional domains for lipid transport. We suggest that the shift to a diet consisting predominantly of fatty acids in polar bears induced adaptive changes in APOB, which enabled the species to cope with high fatty acid intake by contributing to the effective clearance of cholesterol from the blood.” In a news piece about this research, one of the paper’s authors, Rasmus Nielsen, said: “The APOB variant in polar bears must be to do with the transport and storage of cholesterol … Perhaps it makes the process more efficient.” In other words, these mutations may not have damaged the protein at all, but quite possibly improved one of its activities, namely the clearance of cholesterol from the blood of a species that subsists on an extremely high-fat diet.

It appears Behe either overlooked or ignored the authors’ interpretation. Determining whether those authors or Behe are right would require in-depth studies of the biochemical properties of the protein variants, their activities in the polar bear circulatory stream, and their consequences for survival and reproductive success on the bear’s natural diet. That’s a tall order, and we’re unlikely to see such studies because of the technical and logistical challenges. The point is that many proteins, including ApoB, are complex entities that have multiple biochemical activities (ApoB binds multiple lipids), the level and importance of which may depend on both intrinsic (different tissues) and environmental (dietary) contexts. In this example, Behe seems to have been too eager and even determined to describe mutations as damaging a gene, even when the evidence suggests an alternative explanation.

[The picture below shows a polar bear feeding on a seal.  It was posted on Wikipedia by AWeith, and it is shown here under the indicated Creative Commons license.]

File:Polar bear (Ursus maritimus) with its prey.jpg

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Does Behe’s “First Rule” Really Show that Evolutionary Biology Has a Big Problem?

Michael Behe has a new book coming out this month called Darwin Devolves. Nathan Lents, Joshua Swamidass, and I wrote a review of that book for the journal Science. (You can also find an open-access copy of our review here.) It provides an overview of the problems we see with his thesis and interpretations. As our review states, Behe points to many examples of evolution in which genes and their functions have been degraded, but he largely ignores the ways that evolution generates new functions and thereby produces complexity. That’s a severe problem because Behe uses the evidence for the ease of gene degradation to support his overarching implication that the current scientific understanding of the mechanisms of evolution is inadequate and, consequently, the field of evolutionary biology has a “big problem.”

I won’t attempt to summarize Behe’s entire book nor our short review, as people can read those for themselves if they want. Instead, I hope to accomplish three things in this post and two more that will follow. In this first post, I explain why Behe’s so-called “first rule of adaptive evolution” does not imply what he says it does about evolution writ large. In the second post, I’ll discuss whether my long-term evolution experiment (the LTEE for short) does or doesn’t provide strong support for Behe’s position in that regard. In my third post, I’ll explain why I think that Behe’s positions, taken as a whole, are scientifically untenable.

I. Behe’s “First Rule of Adaptive Evolution” Confounds Frequency and Importance

Behe’s latest book is centered around what he calls “The First Rule of Adaptive Evolution: Break or blunt any gene whose loss would increase the number of offspring.” As he wrote in an immediate, dismissive response to our review: “The rule summarizes the fact that the overwhelming tendency of random mutation is to degrade genes, and that very often is helpful. Thus natural selection itself acts as a powerful de-volutionary force, increasing helpful broken and degraded genes in the population.”

Let’s work through these two sentences, because they concisely express the thrust of Behe’s book. The first sentence regarding “the tendency of random mutation” is not too bad, though it is overly strong. I would tone it down as follows: “The tendency of random mutation is to degrade genes, and that is sometimes helpful.” My reasons for these subtle changes are that: (i) many mutations are selectively neutral or so weakly deleterious as to be effectively invisible to natural selection; (ii) while loss-of-function mutations are sometimes helpful to the organism, I wouldn’t say that’s “very often” the case (though it may be in some systems, as I’ll discuss in part II); and (iii) even those degradative mutations that are not helpful on their own sometimes persist and occasionally serve as “stepping stones” on the path toward new functionality. This last scenario is unlikely in any particular instance, but given the prevalence of degrading mutations it may nonetheless be important in evolution. (This scenario does not fit neatly within the old-fashioned caricature of Darwinian evolution as only proceeding by strictly adaptive mutations, but it is certainly part of modern evolutionary theory.)

Behe’s next sentence then asserts the power of the “de-evolutionary” process of gene degradation. This is an unjustifiable extrapolation, yet it is central to Behe’s latest book. (It’s not the sort of error I would expect from anyone who is deeply engaged in an earnest effort to understand evolutionary science and present it to the public.) Yes, natural selection sometimes increases the frequency of broken and degraded genes in populations. But when it comes to the power of natural selection, what is most frequent versus most important can be very different things. What is most important in evolution, and in many other contexts, depends on timescales and the cumulative magnitude of effects. As a familiar example, some rhinoviruses are the most frequent source of viral infections in our lives (hence the expression “common cold”), but infections by HIV or Ebola, while less common, are far more consequential.

Or consider an investor who bought stocks in 100 different companies 25 years ago, of which 80 have been losers. Ouch? Maybe not! A stock can’t lose more than the price that was paid for it, and so 20 winners can overcome 80 losers. Imagine if that investor had picked Apple, for example. That single stock has increased in value by well over 100-fold in that time, more than offsetting even 80 total wipeouts all by itself. (In fact, research on the stock market has shown the vast majority of long-term gains result from a small minority of companies that, like Apple, eventually become big winners.)

In the same vein, even if many more mutations destroy functions than produce new functions, the latter category has been far more consequential in the history of life. That is because a new function may enable a lineage to colonize a new habitat or realm, setting off what evolutionary biologists call an “adaptive radiation” that massively increases not only the numbers of organisms but, over time, the diversity of species and even higher taxa. As one example, consider Tiktaalik or some relative thereof, in any case a transitional kind of fish whose descendants colonized land and eventually gave rise to all of the terrestrial vertebrates—amphibian, reptiles, birds, and mammals. That lineage left far more eventual descendants (including ourselves), and was far more consequential for the history of life on Earth, than 100 other lineages that might have gained a transient advantage by degrading some gene and its function before eventually petering out.

Asteroid impacts aren’t common either, but the dinosaurs (among other groups) sure felt the impact of one at the end of the Cretaceous. (There remains some debate about the cause of that mass extinction event, but whatever the cause its consequences were huge.) Luckily for us, though, some early mammals survived. Evolution often leads to dead ends, sometimes as a consequence of exogenous events like asteroids, and other times because adaptations that are useful under a narrow set of conditions (such as those caused by mutations that break or degrade genes) prove vulnerable over time to even subtle changes in the environment. It has been estimated that more than 99% of all species that have ever existed are now extinct. Yet here we are, on a planet that is home to millions of diverse species whose genomes record the history of life.

Summing up, Behe is right that mutations that break or blunt a gene can be adaptive. And he’s right that, when such mutations are adaptive, they are easy to come by. But Behe is wrong when he implies these facts present a problem for evolutionary biology, because his thesis confuses frequencies over the short run with lasting impacts over the long haul of evolution.

[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

 

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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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