I take all my hats off to Richard Lenski and his team. If you’ve never heard of them, they are the group that has been running an evolution experiment with E. coli bacteria non-stop for the last 25 years. That’s over 50 000 generations of the little creatures; in human generations, that translates to ~1.5 million years. This experiment has to be one of the most amazing things that ever happened in evolutionary biology.
(Below: photograph of flasks containing the twelve experimental populations on 25 June 2008. The flask labelled A-3 is cloudier than the others: this is a very special population. Photo by Brian Baer and Neerja Hajela, via Wikimedia Commons.)
It doesn’t necessarily take many generations to see some mind-blowing things in evolution. An irreducibly complex new protein interaction (Meyer et al., 2012), the beginnings of new species and a simple form of multicellularity (Boraas et al., 1998) are only a few examples. However, a few generations only show tiny snapshots of the evolutionary process. Letting a population evolve for thousands of generations allows you to directly witness processes that you’d normally have to glean from the fossil record or from studies of their end products.
Fifty thousand generations, for example, can tell you that they aren’t nearly enough time to reach the limit of adaptation. The newest fruit of the Long-Term Evolution Experiment is a short paper examining the improvement in fitness the bacteria experienced over its 25 years (Wiser et al., 2013). “Fitness” is measured here as growth rate relative to the ancestral strain; the faster the bacteria are able to grow in the environment of the LTEE (which has a limited amount of glucose, E. coli‘s favourite food), the fitter they are. The LTEE follows twelve populations, all from the same ancestor, evolving in parallel, so it can also determine whether something that happens to one population is a chance occurrence or a general feature of evolution.
You can draw up a plot of fitness over time for one or more populations, and then fit mathematical models to this plot. Earlier in the experiment, the group found that a simple model in which adaptation slows down over time and eventually grinds to a halt fits the data well. However, that isn’t the only promising model. Another one predicts that adaptation only slows, never stops. Now, the experiment has been running long enough to distinguish between the two, and the second one wins hands down. Thus far, even though they’ve had plenty of time to adapt to their unchanging environment, the Lenski group’s E. coli just keep getting better at living there.
Although the simple mathematical function that describes the behaviour of these populations doesn’t really explain what’s happening behind the scenes, the team was also able to reproduce the same behaviour by building a model from known evolutionary phenomena. For example, they incorporated the idea that two bacteria with two different beneficial mutations in the same bottle are going to compete and slow down overall adaptation. (This is a problem of asexual organisms. If the creatures were, say, animals, they might have sex and spread both mutations at the same time.) So the original model doesn’t just describe the data well, it also follows from sensible theory. So did the observation that the populations which evolved higher mutation rates adapted faster.
Now, one of the first things you learn about interpreting models is that extrapolating beyond your data is dangerous. Trends can’t go on forever. In this case, you’d eventually end up with bacteria that reproduced infinitely fast, which is clearly ridiculous. However, Wiser et al. suggest that the point were their trend gets ridiculous is very, very far in the future. “The 50,000 generations studied here occurred in one scientist’s laboratory in ~21 years,” they remind us, then continue: “Now imagine that the experiment continues for 50,000 generations of scientists, each overseeing 50,000 bacterial generations, for 2.5 billion generations total.”
If the current trend continues unchanged, they estimate that the bugs at that faraway time point will be able to divide roughly every 23 minutes, compared to 55 minutes for the ancestral strain. That is still a totally realistic growth rate for a happy bacterium!
I know none of us will live to see it, but I really want to know what would happen to these little guys in 2.5 billion generations…
Boraas ME et al. (1998) Phagotrophy by a flagellate selects for colonial prey: a possible origin of multicellularity. Evolutionary Ecology12:153-164
Meyer JR et al. (2012) Repeatability and contingency in the evolution of a key innovation in phage lambda. Science335:428-432
Wiser MJ et al. (2013) Long-term dynamics of adaptation in asexual populations. Science, published online 14/11/2013, doi: 10.1126/science.1243357
I’m back, and right now I can’t really decide if I should be squeeful or sad about Jiménez et al. (2013).
On the side of squeeing, I have some pretty compelling arguments.
It’s an RNA world paper. I’m an unabashedly biased fan of the RNA world. (Not that my opinion matters, seeing as that’s the only origin-of-life hypothesis I actually know anything about. It’s like voting for the only party whose campaign ads you’ve seen.)
I find the actual experiment ridiculously cool. It’s a bit like that mutation study about heat shock protein 90 that I wrote about aaaaages ago, except these guys evaluated the relative fitness of pretty much every single possible RNA molecule of 24 nucleotides. Yes, that is 4^24 different RNA molecules, each in many copies. And they did it twice, just to make sure they weren’t mistaking statistical flukes for results .
It explores the landscape of evolution and digs into Big Questions like, how inevitable/reproducible is evolution? Or, as Stephen Jay Gould would put it, what would happen if we replayed the tape of life?
On the other hand, the findings are a bit… bleak. So the experimental setup was to select from this huge pool of RNA sequences for ones that could bind GTP, which is basically a building block of RNA with an energy package attached. In each round of selection, RNAs that could attach the most strongly to GTP did best. (The relative abundances of different sequences were measured with next-generation sequencing.) The main question was the shape of the fitness landscape of these RNAs: how common are functional GTP-binding sequences, how similar do they have to be to perform this function, how easily one functional sequence might mutate into another, that sort of thing.
There were only 15 fitness peaks that consistently showed up in both experiments. (A fitness peak consists of a group of similar sequences that are better at the selected function than the “masses”.) That sounds like GTP-binding RNAs of this size are pretty rare.
The peaks were generally isolated by deep valleys – that is, if you were an RNA molecule sitting on one peak and you wanted to cross to another, you’d have to endure lots of deleterious mutations to get there. In practical terms, that means you might never get there, since evolution can’t plan ahead .
On the other other hand…
This study considered only one function and only one environment. We have no idea how the look of the landscape would change if an experiment took into account that a primordial RNA molecule might have to do many jobs to “survive”, and it might “live” in an environment full of other molecules, ions, changing temperatures, whatever. (That would be a hell of an experiment. I think I might spontaneously explode into fireworks if someone did it.)
It’s not like this is really a problem from a plausibility perspective. The early earth did have a fair amount of time and potentially, quite a lot of RNA on its hands. I don’t think it originally would have had much longer RNA molecules than the ones in this experiment, not until RNA figured out how to make more of itself, but I’m pretty sure it had more than enough to explore sequence space.
4^24 molecules is about 2.8 x 10^14, or about half a nanomole (one mole is 6 x 10^23 molecules). One mole of 24-nt single-stranded RNA is roughly 8.5 kilos – I’d think you can fit quite a bit more than a billionth of that onto an entire planet with lots of places conducive to RNA synthesis. So I see no need to panic about the plausibility of random prebiotic RNA molecules performing useful (in origin-of-life terms) functions. (My first thought when I read this paper was “oh my god, creationism fodder,” but on closer inspection, you’d have to be pretty mathematically challenged to see it as such.)
So, in the end… I think I’ll settle for *SQUEEE!* After all, this is a truly fascinating experiment that doesn’t end up killing my beloved RNA world. On the question of replaying the tape, I’m not committed either way, but I am intrigued by anything that offers an insight. And this paper does – within its limited scope, it comes down on the side of evolution being very dependent on accidents of history.
Yeah. What’s not to like?
 I’ve worked a bit with RNA, and I have nothing but admiration for folks who do it all the time. The damned molecule is a total, fickle, unstable pain in the arse. And literally everything is full of almost unkillable enzymes that eat it just to mock your efforts. Or maybe I just really suck at molecular biology.
 I must point out that deleterious mutations aren’t always obstacles for evolution. They can contribute quite significantly to adaptation or even brand new functions. I’m racking my brain for studies of real living things related to this issue, but all I can find at the moment is the amazing Richard Lenski and co’s experiments with digital organisms, so Lenski et al. (2003) and Covert et al. (2013) will have to do for citations.
Covert AW et al. (2013) Experiments on the role of deleterious mutations as stepping stones in adaptive evolution. PNAS110:E3171-3178
Jiménez JI et al. (2013) Comprehensive experimental fitness landscape and evolutionary network for small RNA. PNAS advance online publication, 26/08/2013, doi: 10.1073/pnas.1307604110
Lenski RE et al. (2003) The evolutionary origin of complex features. Nature423:139-144
Evolution depends on variation, and variation depends on mutations. The evolution of new features, in particular, wouldn’t be possible without new mutations. Thus, mutation is of great interest to evolutionary biologists. More specifically, how mutations affect an organism’s fitness has been discussed and debated ever since the concept of mutations entered evolutionary theory. Relatively speaking, how many mutations are harmful, beneficial, or neither? What kinds of mutations are likely to be each in which parts of the genome? It’s hard to get a confident picture on such questions, partly because there are so many possible mutations in any given gene, let alone genome, and partly because fitness isn’t always easy to measure (see Eyre-Walker and Keightley  for a review).
Hietpas et al. (2011) did something really cool that hasn’t been done before: they took a small piece of an important gene, and examined the fitness consequences of every possible mutation in that sequence. This approach is limited in its own way, of course. Due to the sheer number of possibilities, it’s only feasible for short sequences, which might make it hard to generalise any results. But the unique window it opens on the relationship of a gene’s sequence and its owner’s success is invaluable.
What did they do?
Let’s examine the method in a bit more detail, mainly to understand what “every possible mutation” means in this context; because it’s a little more complicated than it sounds.
The bit of DNA they chose codes for a 9-amino acid region of heat shock protein 90 (Hsp90) in brewer’s yeast. So it really is small, only 27 base pairs altogether (recall that in the genetic code, 3 base pairs [1 codon] translate to 1 amino acid). Hsp90 is a very important protein found all over the tree of life. It’s a so-called chaperone, a protein that helps other proteins fold correctly, and in eukaryotes it’s absolutely required for survival.
The team generated mutant versions of the Hsp90 gene, each of which differed from the “wild type” version in one codon out of these nine. So each “mutation” examined could actually be anywhere between one and three mutations. They generated all possible mutants like that, amounting to over 500 different sequences.
[NOTE: If you check back at the genetic code, you’ll note that most amino acids are encoded by more than one codon, so not all of the resulting proteins differed from one another. Mutations that don’t change the amino acid are called synonymous. This will become important later.]
Then came the measurement of fitness. The researchers took a strain of yeast whose own Hsp90 gene was engineered not to work at high temperatures, and infected the cells with small pieces of DNA called plasmids, each carrying either a wild type (temperature-insensitive) Hsp90 gene or one of the 500+ mutants. They then grew all cells together in a common culture. After a while, they raised the growing temperature to let the engineered genes determine the cells’ survival.
They took samples every few hours – wild type yeast populations doubled every 4 hours – and did something that would not have been possible even a few years ago: sequenced the region of interest from this mixed culture, and compared the abundance of different sequence variants. By counting how many times each mutant was sequenced at each time point, they got a very good estimate of their relative abundances. The way each mutant prospered or declined relative to others over time gave a measurement of their fitness.
What did they find?
There are so many interesting things in this study that I’m not sure where to begin. Let’s start with the result that concerns the first question posed in my introductory paragraph. How are the mutations distributed along the deleterious – beneficial axis?
Perhaps not surprisingly, most non-synonymous mutations were harmful to fitness. I say not surprisingly because this protein has been honed by selection for many, many millions of years. It is probably close to the best it can be, although the researchers tried to pick a region that contained variable as well as highly conserved amino acids.
[ASIDE: They didn’t really succeed in that – among the 400+ species they say they used for comparison, 4 of 9 positions don’t vary at all, 2 are identical in almost all species, another 2 can have two amino acids with roughly equal chance, and only one can hold three different amino acids. I’ve seen more variation in supposedly highly conserved sequences over smaller phylogenetic distances. Perhaps Hsp90 is just that conserved everywhere.]
There were a few mildly beneficial mutations, but no highly beneficial ones. Deleterious mutations could be divided into two large groups, with very few in between: mostly they were either very harmful or close to neutral. This constitutes support for the nearly neutral theory of molecular evolution, but as I said, the sequence they examined is hardly representative of all sequences under all circumstances. It would be interesting to see how (if) the distribution changes in sequences under directional selection, or sequences that don’t experience much selection at all. I’m kind of hoping that that’s their next project 😛
The second interesting observation – interesting to me, anyway – is that nonsense mutations, those that introduce an early stop codon in the sequence, were not as unfit as complete deletions of the gene. A stop codon means the end of the protein – an early stop codon eliminates everything that comes after it. Cells making a truncated protein were lousy at survival, but not quite as lousy as cells with no Hsp90 at all. This is a bit strange, given that earlier the paper states that a region of Hsp90 that comes after their 9 amino acids is necessary for its function. A nonsense mutation in the test region removes that supposedly necessary part, so why did those cells do any better than mutants lacking the gene entirely?
Looking at synonymous mutations, the team determined that these don’t affect fitness much. This has practical importance, because synonymous mutations have long been used as a “baseline” to detect signs of selection in other mutations. If they weren’t neutral, the central assumption of that approach would fall down.
Another question the study asked was whether certain positions in the protein require amino acids of a certain type. The twenty amino acids found in proteins can be loosely grouped according to their physical and chemical properties. For example, some of them are positively charged, while others carry no charge at all; some are (relatively speaking) huge and some are tiny. These properties determine how a protein folds and what its different regions can do, so one would expect that in important positions, only amino acids similar in size and chemistry could work.
To find all the amino acids that worked equally well in a given position, Hietpas et al. looked at a subset of amino acid changes: those whose fitness was very close to the wild type. Surprisingly, they found that several positions tolerated radically different amino acids without losing much fitness. Quoting from the paper,
“[t]his type of physical plasticity illustrates the degenerate relationship between physics and biology: Biology is governed by physical interactions, but biological requirements can have multiple physical solutions.”
This is kind of stating the obvious in this context, but it does echo a more general observation about life. In evolution, there is often more than one way to skin a cat.
[ASIDE: Analogous enzymes provide a striking demonstration of that. These are pairs – or even groups – of enzymes that catalyse the same reaction, without bearing any physical resemblance to one another. Their sequences are different, their 3D structures are different, and their catalytic mechanisms are different, yet they do essentially the same thing. But there are also more familiar, if less extreme, examples. For instance, within vertebrates only, we see three different solutions for powered flight and even more variations on gliding (herearesomeofthem).]
The researchers built a “fit amino acid profile” of their test sequence using these “wild type-like” mutations, then compared it to the actual pattern of amino acid substitutions observed in “real” Hsp90 proteins. It turns out the two are quite different: eight out of the nine positions are conspicuously less variable in real life than the fitness profile would predict. The paper lists a few possible explanations. Lab environments are not natural environments, and amino acids that work fine in their very controlled environment may not be so great under harsher or less stable real-world conditions. Wild type-like fitness does not mean the substitution is completely neutral – many of them are slightly deleterious, which may come out more strongly under natural circumstances, especially over the long term. And one of the substitutions would require more than one mutation at the DNA level – with strongly deleterious intermediate steps.
That last point leads me to the part of the study I personally found most interesting. Thus far, we’ve taken the genetic code as a given, and hardly paid any attention to it at all. But, in fact, the genetic code itself is a product of evolution. Most likely, it didn’t spring into existence fully formed when organisms invented protein synthesis. There is a mind-blowingly large number of possible genetic codes – why is it that organisms use this particular one, with only minor variations? We won’t go into all of the hypotheses about that, mostly because I’m not very familiar with them. It’s enough to note that in principle, the genetic code could be accidental – it just happened to be the one some distant ancestor of all living things stumbled on –, a chemical inevitability of some sort, or it could have risen to prominence by natural selection.
[ASIDE: The options are not mutually exclusive. For example, it is possible that the only important thing about the genetic code is how easy it is to mutate from particular amino acids to certain others – in other words, that it’s the structure of the code that’s under selection, while its finer details, such as which four codons stand for glycine, may be largely coincidental or determined by chemical necessity.]
For this tiny region of the Hsp90 gene/protein, it looks very much like selection had a hand in it. Hietpas et al. used their theoretical fit amino acid profile and a sample of 1000 randomly generated genetic codes – and asked how many substitutions it would take to switch between equally fit amino acids under each genetic code. Intriguingly, very few genetic codes made it as easy as the real one. In other words, the genetic code seems geared to minimise the number of deleterious mutations.
What’s really fascinating about that result is that it came from an analysis of such a tiny sequence. Earlier, I mentioned that it might be hard to generalise anything from a short sequence. But it’s hard to believe that this particular finding doesn’t have general applicability. The genetic code sets the rules for all proteins – if it weren’t optimised in general, what’s the chance that such strong optimisation would be detected in such a tiny sample? This also suggests that roughly the same amino acids are interchangeable across the board, regardless of which protein we’re talking about. (Which is not necessarily surprising if you’ve ever spent time comparing protein sequences between species, but still, it’s valuable as a new way of looking at a familiar phenomenon).
All in all, this is the kind of paper that makes me all giddy with excitement. It digs deep into fundamental questions in evolutionary theory, and it finds some intriguing answers. It’s also a great reminder of how amazingly far technology has come – merely sequencing 27 base pairs would have been a formidable task at the dawn of molecular biology, and now we can mix 500 different versions together, sequence all of them in a single experiment, and reliably count how many of each variant there are. And that’s nowhere near the limits of current sequencing technology. This is the future, folks, and it’s better than sci-fi.
Eyre-Walker A & Keightley PD (2007) The distribution of fitness effects of new mutations. Nature Reviews Genetics8:610-618
Hietpas RT et al. (2011) Experimental illumination of a fitness landscape. PNAS108:7896-7901