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We interrupt this program

We interrupt our irregularly scheduled blogging to wish the long-term evolution experiment a very fit 31st birthday!

Here are two pictures of graduate student Kyle Card doing today’s transfers, and thereby starting off the next year of their evolutionary journey.

Kyle Card setting up LTEE transfers on 31st birthday

Kyle Card transfers LTEE on 31st birthday

Today’s entries in the LTEE notebook are shown below.

LTEE notebook on 31st birthday

We also had a visitor who picked up some strains from the freezer over the weekend, and who left us a note on the lab’s whiteboard.

Zack left note 23-Feb-2019

We ate a Galapagos-themed cake, shown below, a couple of weekends ago when we celebrated the February birthdays of Charles Darwin, Abe Lincoln, and the LTEE.

Darwin cake 2019

Thank you Kyle, and thanks to everyone who has ever performed transfers and/or done research on the LTEE lines.

Last but not least, here’s a lovely post by Roberto Kolter at Small Things Considered wishing the LTEE a happy birthday!

 

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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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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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Some Wrinkles in Time

Today is another milestone for the E. coli long-term evolution experiment—the LTEE, for short. I did the 10,000th daily transfer today at about noon.

REL doing LTEE transfer 10,000 with Neerja keeping a close eye on me

[Yours truly, doing the 10,000th LTEE transfers. Technician Neerja Hajela is keeping a close eye on me, and with good reason. Photo by Thomas LaBar.]

Some of you will remember we just celebrated the LTEE’s 29th birthday a few weeks ago, on February 24th. And if you’re quick with math, you might be thinking: “Wait a second: 29 years times 365 days per year is a lot more than 10,000 days. Have Lenski and his team screwed up?”

The answer is both yes and no. Let me explain.

The LTEE began on February 24, 1988 [1, 2].

From February 24, 1988, to March 13, 2017, equals 10,609 days on which we could have done transfers. But we’ve only had 10,000 transfers. What happened to those other days?

In short, the bacteria spent the 609 “lost” days in a freezer at –80°C or in a refrigerator at 4°C.

One chunk of days was lost when the LTEE was moved from my lab at UC-Irvine, where I started the experiment, to MSU, where it is today. Moving a lab is difficult: it requires moving people, moving equipment and materials, often renovating space, obtaining new supplies and equipment, hiring new people, and trouble-shooting and otherwise getting everything organized to resume work [3].

We lost 191 days from April 8, 1992, when the 10,000-generation samples went into the freezer at UCI, to October 16, 1992, when the LTEE restarted from the frozen samples at MSU.

Most of the other days have been lost as a result of various accidents. I’m often asked, when I give talks on the LTEE, how we’ve kept the experiment going so long without contamination, broken flasks, equipment failure, etc.

The short answer is that we haven’t. Many accidents have happened along the way.

There are 3 main types of accidents, each of which involves a different sort of interruption and recovery.

Little mistakes: Sometimes a flask has a hairline crack; when you take it out of the incubator the next day, there’s just a puddle of salt on the bottom. Or maybe someone knocked over a flask while doing the daily transfers. In cases like these where a mistake occurs that is immediately recognized, we go back in time (and lose) one day.

How do we do that? Each day, after the transfers have been made, we don’t immediately discard the previous day’s cultures. Instead, we put them in a refrigerator, where we can use them to restart the experiment after these little mistakes. The bacteria have finished growing long before each day’s transfer, so they are in stationary phase, and their metabolic activity is even lower sitting there at 4°C. Restarting the populations from the refrigerated cultures is a perturbation, of course, but a tiny one in the scheme of things.

When these little mistakes happen to one population, we go back a day for all the populations. We do that so that the rhythm of the experiment, which involves quality-control checks and freezing samples at regular intervals, is the same for all of the populations.

Bigger slipups: Another sort of problem can occur if the entire experiment is compromised in a way that is not immediately recognized. For example, the autoclave might not be working properly, and we realize that bottles of media that we’ve been using for a few days are contaminated. In that case, the cultures stored in the refrigerator won’t help us.

But we don’t have to start the LTEE all over at t = 0. (If we did, then the experiment wouldn’t be here today!) Instead, we go back to the last time that we froze samples, just like we did when we restarted the experiment after the move from UCI to MSU. Importantly, we restart the LTEE from whole-population samples, not individual clones, so that we do not lose the diversity that is present in an evolving population.

Of course, moving the bacteria into and out of the freezer is a perturbation, involving the addition of a cryoprotectant, freezing the cells, thawing them, and re-acclimating them to the conditions of the LTEE. Still, it happens only occasionally. Moreover, all of the samples used in competitions or other assays go into the freezer, come out, and are re-acclimated to the relevant conditions before measurements are made.

Dreaded cross-contamination: The third kind of accident is when bacteria from one LTEE population “migrate” into another population. That’s not supposed to happen, because it compromises the statistical independence of the populations, which are units of replication on which many analyses rest. I worried about this issue before I started the LTEE, because one of the central questions that motivated the experiment is the reproducibility of evolution. And I’m glad I worried about it. Fortunately, there was a pretty easy way of dealing with this concern from the outset.

Six of the 12 populations started from cells of an ancestral strain, REL606, that cannot grow on the sugar arabinose; they are phenotypically Ara. The others started from cells of a mutant, REL607, that can grow on arabinose; these populations are Ara+. There is no arabinose in the LTEE environment, and the mutation that allows growth on arabinose has no measurable affect on fitness in that environment. However, when Araand Ara+ cells grow on Tetrazolium Arabinose (TA) agar in a petri dish, they make red and white (or pink) colonies, respectively.

Ecoli-plate

[Mix of Araand Ara+ colonies on TA agar.]

The arabinose phenotype serves two important purposes in the LTEE. First, we use it to estimate the abundance of competitors in the assays we perform to measure relative fitness. To that end, we typically compete an evolved Ara population sample against the Ara+ ancestor, and vice versa. Second, with respect to the possibility of cross-contamination, we alternate Ara and Ara+ populations during the daily transfers. The idea is that, if an accidental cross-contamination does occur, it will likely involve adjacent populations and lead to cells that have the wrong phenotype (i.e., produce the wrong-colored cells on TA agar) in a population. So we check each population for that phenotype whenever we freeze samples.

When we find one or more cells that produce the wrong-colored colony, we have to figure out what to do. There are various additional checks that we can perform, especially nowadays when DNA sequencing has allowed us to discover many mutations—additional markers—that uniquely identify each population. In particular, these extra markers have, in recent years, let us distinguish between “false alarms” (new mutations that affect colony color on the TA agar) and actual cross-contamination events. In any case, when we’ve had suspected or confirmed cross-contamination events, we restart the invaded population from the previous sample [4]. We then typically monitor that population by plating samples periodically on TA agar, to make sure it didn’t have a low frequency of cross-contaminating invaders even before that earlier sample was frozen. As a consequence of restarting invaded populations, some of the LTEE populations are 500 generations (or multiples thereof) behind the leading edge.

So today’s 10,000th daily transfer applies to some, but not all, of the LTEE populations.

Despite these precautions and procedures, I worried that somehow we had slipped up and there were undetected cross-contamination events. Maybe there had been an especially fun party one Friday night … and on Saturday someone forgot the protocol and transferred all six red Ara populations in a row before moving on to the six white Ara+ populations. In that case, a cross-contamination might occur but not be detected. So I was thrilled when we sequenced hundreds of genomes from different generations of the LTEE populations and there was no evidence of any cross-contamination. Have I mentioned all the terrific people who have worked with me?

One of the unsung heroes of the LTEE is my technician and lab manager, Neerja Hajela. She has worked with me for over 20 years now, and she’s probably done more daily transfers than everyone else combined.

Neerja Hajela 13-Mar-2017

[Neerja Hajela, technician and lab manager extraordinaire.]

By the way, there were not 12, but 15, flasks in the trays while I was doing the transfers. What’s going on with that?

Flasks LTEE day 10,000

[The 15 LTEE flasks in the incubator.]

One of the extras is a blank—a culture without bacteria. If the medium in that flask is turbid the next day, then “Houston, we have a problem.” Another of the extras is a population we’re calling Ara–7. It was spun off population Ara–3 after we discovered—many thousands of generations later—that one lineage in that population had gone extinct for some reason that we do not understand. You can read more about that here. Ara–7 doesn’t count as one of the “real” LTEE populations, but it might prove useful in comparison with Ara–3 at some point in the future.

And the third extra? Remember what I said about cross-contamination? Well, we recently discovered a cross-contamination event in which cells that made red colonies on TA agar were found among the white-colony-forming cells of the Ara+1 population. Postdoc Zachary Blount confirmed they weren’t new mutants that made the wrong-colored colonies in Ara+1; instead, those cells had specific mutations that showed they came from population Ara–1, meaning they were cross-contaminating invaders.

Zachary Blount 13-Mar-2017

[Zachary Blount, aka Dr. Citrate.]

So we restarted Ara+1 from its previous frozen sample, monitored it by plating cells on TA agar, and … alas, up came some more of those red invaders. It’s interesting, in a way, because Ara–1 is one of the most fit LTEE populations, while Ara+1 is the very least fit, which means Ara+1 is especially susceptible to invasion from its Ara–1 neighbor in the daily transfers. Anyhow, we then restarted Ara+1 going back in time 1000 and 1500 generations—hence, the extra flask—and we will monitor those for a while by plating samples on TA agar. If neither of them shows any sign of invaders for several weeks, then we will continue only the one with the fewer “lost” generations and drop the other.

There’s one other little issue related to keeping time in the LTEE. Every day, we remove 0.1 mL from each flask culture and transfer it to 9.9 mL of fresh medium. That 100-fold dilution allows the bacterial population to grow 100-fold before it depletes the available resources. And that 100-fold growth corresponds to log2 100 ≈ 6.64 generations. But we round it up a tad to 6.67 generations, so that every 15 transfers equals 100 generations [5].

In any case, our fielding percentage (baseball jargon for the ratio of plays without errors to total chances on defense) is 10,000 / 10,609 ≈ 0.943. If we exclude the lost days associated with the move from UCI to MSU, then the percentage rises to 0.960. Not bad, not bad at all. Did I mention the terrific people who have worked, and are working, on the LTEE?

This post’s title is a play on the novel A Wrinkle in Time by Madeleine L’Engle.

[1] I first started the LTEE on February 15, 1988, but I then restarted it on February 24, because I got worried that the first arabinose-utilization mutation I had selected, which serves as a neutral marker, wasn’t quite neutral.

[2] So the LTEE experienced a leap day in its very first week!

[3] I was fortunate that three experienced graduate students—Mike Travisano, Paul Turner, and Farida Vasi—moved to MSU even before I did to help set up the lab, and that our research was allowed to continue in my UCI lab—led by technician Sue Simpson and John Mittler, who was finishing his PhD—after I moved in late December, 1991.

[4] To keep all the populations in sync with respect to the freezing cycle, we restart the others at the same time, too. Of course, for the others, we don’t go back in time—we use the latest sample, where the cross-contaminated population was discovered during the quality-control checks associated with the freezing cycle.

[5] In fact, 6.67 generations per day might be a slight underestimate given the possibility of turnover during stationary phase. Moreover, every lineage with a beneficial mutation that sweeps to fixation goes through more than the average number of generations, since each mutant lineage starts as one cell among millions.

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Who Knows Where the Time Goes

Today is the 29th birthday of the long-term evolution experiment (LTEE). As I wrote on Twitter: “May the cells live long & prosper, both in & out of the -80C freezers.” I hope they—and the rest of the world—will be evolving and improving long after I’m gone.

Anyhow, after my tweet, Luis Zaman asked for a picture of me on my own 29th birthday. (I started the LTEE when I was 31.) Alas, I don’t have one. But I’ve found some pictures from around that time—including just before and after I moved to UC-Irvine to start my first faculty position, and over the next few years up to about the time I started the LTEE.

Summer, 1985: This photo is from Amherst, Massachusetts, where I did my postdoc with the amazing Bruce Levin, who hosted a goodbye party for us. From left to right: Ralph Evans, a brilliant graduate student and dear friend, who died tragically just a few years later of brain cancer. My beautiful wife, Madeleine. Our one-year-old daughter Shoshannah, being held by forever-young Bruce. Yours truly, holding our three-year-old son Daniel. And Miriam Levin, an art historian.

amherst-goodbye-party-summer-1985

October, 1985: Shoshannah on my shoulders at the San Diego Zoo, a few months after we moved to Irvine.

october-1985-san-diego-zoo-with-shosh

March, 1986: First-year faculty member burning the midnight oil in our Las Lomas apartment at UCI. Working on a paper? Or getting ready to teach 700 students the next day? (Two sections of Ecology, a required course for Bio Sci majors, with an hour to recuperate in between. It was well worth it, though, because one of the students in one of the many quarters I taught that course was the great Mike Travisano.)

march-1986-working-late

October, 1986: Moving up in the world, we bought a new house on Mendel Court in University Hills. My parents visited, and that’s my mother, Jean, a poet who loved science.

october-1986-mendel-court-with-mom

March, 1987: The great Lin Chao came for a visit. We grew pea plants on the trellis below the number 6—after all, it was 6 Mendel Court.

march-1987-with-lin-chao

June, 1987: One of the fun events at UCI was Desert X (for extravaganza), hosted by Dick MacMillan, the chair of Ecology and Evolutionary Biology, on his property near Joshua Tree National Park. With Madeleine, who is “holding” our Number 3.

june-1987-desert-x-with-m

June, 1987: Working Xtra hard at Desert X with close friend and colleague Al Bennett.

june-1987-desert-x-with-al

September, 1987: With an already smiling one-month-old Natalie.

sept-1987-with-natalie

January, 1989: Time for some snuggles. Meanwhile, the LTEE is not quite a year old.

jan-1989-with-3-kiddos

The title of this post is a song by Fairport Convention, with the hauntingly beautiful voice of the late, great Sandy Denny. You should listen to it.

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On Time and Space

The long-term evolution experiment (LTEE) began in 1988, and the E. coli populations are approaching 60,000 generations.  That’s a long time for an experiment, and I hope it continues for much, much longer.

But when I give talks about the LTEE, I also try to remind people that 26 years is only a drop in the proverbial bucket of evolutionary time.  If you were to add these experimental populations to the tree of life—or even to a tree showing only other E. coli strains—they would not be visible to the eye because the branches they represent—tiny twigs, really—would be so short (in time) and so close (in genetic distance) to their ancestors.

On Time and the LTEE

Life has existed on Earth for roughly 3.5 to 4 billion years.  That’s about 140 million times longer than the LTEE has existed.  Expressed the other way around, this experiment has been running for about 0.0000007% of the time that life has been evolving on our planet.

As I said, a mere drop in the bucket of time …

That’s a somewhat mixed metaphor, though, with “a drop in the bucket” being a statement about space and relative volumes, not about time.  And that got me wondering about the spatial scale of the LTEE relative to the spatial scale of the biosphere.

If the LTEE is just 0.0000007% as old as life on Earth, what fraction of the space—of the total biovolume—of life on our planet exists in the confines of the LTEE?

On Space and the LTEE

That’s a harder a question to answer.  We know the volume of the LTEE:  there are 12 flasks, one for each of the evolving populations, and each flask contains 10 milliliters (mL) of liquid medium.  (In medicine, by the way, a drop has been defined as 1/20th of a mL, so each flask in the LTEE contains 200 drops.)  If we sum across the populations, then the LTEE occupies 120 mL.

Before you read further:  What’s your quick intuition?  Is the LTEE larger on this spatial scale than on the temporal scale?  Or is the LTEE smaller?

Volumes and Numbers

How should we estimate the volume of Earth’s biosphere?  Here are three back-of-the-envelope approaches to get a rough sense of the scale.

1)  Most of the Earth is covered by its oceans, which are full of life.  While life is not equally abundant throughout the oceans, none of that space is entirely devoid of life.  The total volume of Earth’s oceans is about 1.3 billion cubic km.  That’s a lot of mL!  A mL is a cubic centimeter, or cc, and that’s 1/(100^3) = 1 millionth of a cubic meter.  A cubic meter is 1/(1000^3) = 1 billionth of a cubic kilometer, and the oceans contain over a billion of those cubic kilometers.

So the 120 mL in the LTEE correspond to 120 / (1.3 x 10^9 x 10^9 x 10^6), or about 9 x 10^-22 of what  the oceans contain.  That’s just 0.000000000000000000009% of the volume of the oceans.

By this calculation, then, the temporal scale of the LTEE is ~75 trillion times greater than its spatial scale, when both are expressed relative to nature.  If the LTEE is “a drop in the bucket” with respect to time, then that drop has to be diluted by a factor of 75 trillion with respect to the oceans.

2)  Let’s try another quick-and-dirty calculation.  Most life, in the oceans and on land, is near the Earth’s surface.  The surface area of our planet is about 510 million square kilometers.  If we take just the top meter, that’s equivalent to 510/1000  = 0.51 million cubic kilometers.  That’s about 1/2600 of the volume of the ocean.  But even this conservative estimate of the volume of the biosphere makes the relative scaling of the LTEE with respect to time and space differ by a factor of 30 billion.

3)  Here’s one more approach—it’s based not on the volume of the physical environment but, instead, on the number of organisms in the LTEE and in the biosphere.  When grown to stationary-phase density in the LTEE environment (i.e., when the limiting resource, glucose, is depleted), the ancestral bacteria could achieve a maximum density of ~5 x 10^7 cells per mL.  Most populations have evolved so that they now produce slightly fewer, but larger, cells; and one population has evolved the ability to use the citrate that is also in the medium, and it now reaches a density that is several times greater than the other populations.  In any case, given 10 mL of medium for each population, and 12 populations, the total population size across the LTEE is on the order of 10^10 cells.

And how many cells exist in the Earth’s biosphere?  Whitman et al. (1998, PNAS) estimated that there are more than 10^30 prokaryotes—bacteria and archaea combined—in the biosphere, and they make up the great majority of all living things.

So by this approach, using the number of cells as a proxy for the spatial scale, the size of the biosphere is over 10^20 (a hundred-million-trillion) times larger than the LTEE.  We’re back into the trillions in terms of the relative scaling of the temporal and spatial scales of the LTEE.

On Time, Space, and the LTEE

By all three approaches, then, the LTEE is vastly older with respect to the history of life on Earth than it is large with respect to the size of Earth’s biosphere.

The LTEE really is a long-running experiment, as experiments go.

But the LTEE is a “drop in the bucket” with respect to how long life has been evolving on Earth.  And it is a vastly more miniscule “drop in the bucket” when compared to the spatial extent and number of living organisms on our planet.

Maybe I should give the LTEE a new name—the “incredibly tiny but relatively long-term evolution experiment.”

[Photo of a water drop on a leaf taken by tanakawho and shared on Wikipedia (en.wikipedia.org/wiki/File:Water_drop_on_a_leaf.jpg).]

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