March 6, 2012 § Leave a comment
Perhaps not quite as exciting as revivified dinosaurs, but still amazing: plants from the late Paleolithic era are claimed to have been regenerated from fossil material (Yashina et al. 2012. Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost. PNAS doi:10.10.73/pnas.1118386109). This has very little to do with systems biology, but I was interested and thought you would be too. Perhaps I could trace some kind of connection (did you know that our Artist-no-longer-in-Residence, Brian Knep, shared two Academy Awards for his work on the movie Jurassic Park?) but it would be forced and hardly worth it. Better to admit to mild frivolity.
The plant material in question came, not from an insect trapped in amber, but from fruits buried in burrows of an Arctic-dwelling squirrel. Some of these burrows contain hundreds of thousands of fruits and seeds. I guess when you’re a squirrel living in the Arctic, you grab what’s going while the grabbing is good. Shortly after the squirrels stored their hoards, about 30,000 years ago, the area froze, was buried deep in icy sludge, and has never since melted. Constant subzero temperatures, with all available water immobilized as ice, are the best conditions you’re likely to find for cryopreservation. Although the oldest plant seed previously germinated was only 2,000 years old, the authors were bold enough to make an attempt to grow plants from ancient frozen seeds of the plant Silene stenophylla, the arctic campion.
In the end, what Yashina et al. say that they were able to grow was not the seeds themselves but an outgrowth of the placental tissue some of the immature seeds were embedded in. The authors speculate that part of the reason they were successful with this tissue is because it has especially high levels of organic substances such as sucrose and phenolic compounds that would be expected to offer some protection against frost damage. The plants derived from these placental tissues grew to maturity and were even capable of breeding. They look somewhat different from modern Silene stenophylla, and they handle their flowering arrangements differently; flowers of the modern plants are always bisexual, whereas the ancient plants produced female flowers first, followed by bisexual flowers.
Though previous claims that plants have been grown from very old seeds have been debunked, the authors say that because the burrows were buried ~20-40 meters down and were apparently undisturbed they are confident that their samples were not contaminated with modern seeds. They also performed direct radiocarbon dating on their samples. And the plants that resulted were visibly different from their modern counterparts. It’ll be fascinating to see the DNA sequence; I’m sure it’s on its way.
Sadly, the last author of the paper, Dr. David Gilichinsky, died just 2 days before the paper was published.
Yashina, S., Gubin, S., Maksimovich, S., Yashina, A., Gakhova, E., & Gilichinsky, D. (2012). Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1118386109
December 9, 2011 § Leave a comment
The protein folding problem, as it’s called, has been confounding biologists for decades. Unlike a strand of RNA or DNA, which can be relied upon to follow a few rather simple rules dominated by base pairing, a string of amino acids seems to have so many possible ways to interact with itself as to defy analysis. But the “problem” isn’t a problem for the protein — proteins fold, for the most part, rather efficiently, even in vitro where they have no help from the rest of the contents of the cell. So the information on what the fold should look like is sitting there in the sequence, and the question is how to read the code.
The protein folding field has tried many many routes to translate the information in protein sequence into three-dimensional structure, ranging from head-on attempts to use physics plus supercomputers to work out a protein fold from first principles, to efforts to harness the vast problem-solving potential of on-line gamers by translating the rules of folding into a game, to attempts to “cheat” by using evolutionary conservation to get an idea of which parts of the sequence are essential for the shape of the fold. There’s been progress on all fronts, and it’s now possible to use computational methods of various kinds, often informed by existing structural information, to fold small proteins. Although you need a fair amount of computer power to be successful, and better models for the complicated forces affecting a biomolecule in water are clearly needed, there’s a feeling that if we keep chewing away at the problem we will eventually be able to solve it.
A new paper (Marks et al. 2011 Protein 3D structure computed from evolutionary sequence variation. PLoS One doi:10.1371/journal.pone.0028766) now provides a rather startling step forward that dramatically reduces the need for major computational resources. You can now fold a ~250 amino acid protein on your ordinary laptop. The one apparent catch is that you can’t do this with just any sequence: you need a fairly large family of homologous sequences, of around 1,000 family members. Information derived from this family of sequences about changes in one part of the protein that correlate with changes in another part of the protein — covariance — is used to infer how close the two parts of the protein are to each other. This reduces the “conformational search space”, the number of three-dimensional folds you have to evaluate before settling on the best one, and that in turn not only speeds up the process of sorting through the possibilities. but also increases the chance that you will find the right answer.
Now, this is far from a new idea. It has been tried and tried and tried for years and has always failed. In fact one of the authors (Chris Sander) made one of the earliest attempts, about 15 years ago. Two things are different this time: the implementation of the idea, and the number of sequences available.
August 26, 2011 § 1 Comment
When I pouted last week about the fact that other writers had beaten me to the punch in discussing an interesting recent paper on the fitness benefits of clumping in yeast, I had somehow failed to notice that another, similarly fascinating, paper on a related topic had just come out from the Bassler lab (Nadell and Bassler 2011. A fitness trade-off between local competition and dispersal in Vibrio cholerae biofilms. PNAS doi:10.1073/pnas.1111147108). This paper is looking at the formation of biofilms in the bacterium Vibrio cholerae, a nasty little bug that has been a major evolutionary force in the development of modern sewage systems. One of the factors that makes V. cholerae hard to get rid of is the fact that it can, when it chooses, grow in biofilms; it can produce a structural matrix called extracellular polysaccharide (EPS) in which the bacterial cells are embedded. EPS production has a number of benefits, including offering bacteria from many species the opportunity to collaborate and behave as a community. The puzzling thing, though, is that these community benefits are available to everyone, not just the bacteria who do the work of producing the EPS. This is a classic set-up for “cheating”; in theory, if some bacteria can gain the benefits of EPS production without paying the price for it, then those “cheating” bacteria would be expected to grow faster than the poor exploited EPS producers. At some point, the EPS producers (still struggling to build community, no doubt) would die out, and the whole system would collapse. The theoretical arguments seem very persuasive, but actually EPS-producing bacteria show no signs of going away. So clearly we need a new theory.
Kevin Foster and colleagues (including Carey Nadell, the first author of this paper) have been working for some time now on the possibility that EPS production, in addition to its benefits for the community, offers direct benefits to the cells that produce it. If you simulate the growth of EPS-producing microbes in three dimensions, including the way that nutrients and oxygen diffuse and are consumed, you can see that producing EPS can help a lineage of cells to push itself above the masses and get access to better conditions, incidentally suffocating non-EPS producing cells. This line of argument suggests that, far from being a happy “all for one, one for all” type commune, microbial biofilms are a balancing act between cooperation and competition — much like some other societies you might be aware of. It also suggests that, though there are some conditions in which “cheaters” (non-EPS producing cells, though now they look lazy and stupid rather than cunning) can win, especially when a group of cells is colonizing a new area, if a biofilm persists for a long time the EPS-producing cells have a strong advantage. And a particularly clear prediction from the 3D modeling is that, in a mixture of EPS-producers and non-producers, the EPS-producing lines should end up in skyscraper-like towers (reaching towards better oxygen conditions), suffocating the cheaters. « Read the rest of this entry »
August 18, 2011 § Leave a comment
A nice paper that emerged from a collaboration between the Murray lab and Kevin Foster (ex-Bauer Fellow, now at the Zoology Department at Oxford University and the Oxford Center for Integrative Systems Biology) just came out in PLoS Biology (Koschwanez et al. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biology 9(8): e1001122). The paper makes two interesting points that are potentially relevant to the evolution of multicellular organisms. The first is that yeast strains that grow in clumps of cells should, and do, have an advantage over strains that grow as single cells when both strains are grown in low levels of sucrose. The theoretical argument the authors make goes like this: to metabolize sucrose, cells need to secrete invertase, which chops up sucrose to give fructose and glucose; each cell then needs to absorb as much as possible of the monosaccharides produced before they diffuse away. A cell can only capture the fructose and glucose that happens to bump into the transporters on its cell wall. So, even though sucrose has the same food value as glucose + fructose, single cells should grow less effectively on sucrose than on glucose + fructose; and there should be a threshold sucrose concentration below which a single cell cannot grow at all. In contrast, cells living in a clump should benefit from the fact that all of their neighbors are producing monosaccharides too; even though an individual cell might not be able to take up all the glucose and fructose it produces using its invertase enzymes, it will capture some of the monosaccharides that escape from its neighbors. Thus, clumping cells should grow better than single cells in low concentrations of sucrose. When Koschwanez et al. did the experiments all of these predictions were confirmed.
The second point is even more interesting. The invertase system is a classic set-up for cheating; the cells secreting invertase are effectively producing a “public good” — the monosaccharides produced by the action of the invertase on sucrose are available to anyone nearby. At the same time, producing invertase has a cost, which the authors measure as a fitness disadvantage of 0.35% in exponentially growing cells. Under some conditions, therefore, cells that don’t secrete invertase grow better than those that do, because they are able to take up the monosaccharides produced by the cells around them without producing the costly invertase enzyme. Whether this works depends on factors like cell density (high is good for cheating) and how much the culture is agitated (shaking helps spread sugars around, which is also good for cheating). But Koschwanez et al. show that the clumping strain does better than single-cell strains when competed with matched clumping or non-clumping cheaters, under a variety of conditions. Clumping allows the cells that are playing fair to stick together and share resources; conversely, if you’re a cheater growing in a clump it’s harder to escape your (similarly cheating) sisters and daughters.
When an interesting paper like this comes out from not one, but two, of our friends, I would normally make every effort to write a properly detailed post about it. But this one has been written up all over, at New Scientist, the Harvard Gazette, MSNBC and a PLoS Biology minireview, among others; and most intimidatingly of all by Ed Yong. It’s good to see so much interest, and since I can’t think of a single point that hasn’t already been made I’ll content myself with pointing you to the rest of the coverage. Enjoy!
July 21, 2011 § 1 Comment
Ah, courtship. That crazy time when you’ll do almost anything to show off for your potential mate: drink too much, fight with rivals, play chicken with cars, and generally behave in ways that make you shudder in later life. The courtship rituals of suburbia are complex enough, but they pale in comparison to the behaviors some animals show. Why do these rituals evolve? Darwin hypothesized that both sex-specific ornamentation, such as the tail of the peacock (bling, if you will), and elaborate courtship displays could be explained by selection of preferred mates by “choosy” females. The female gets to be choosy because she puts far more effort and risk into generating the progeny, so males compete to have their genes benefit from all that work.
Ever since Darwin, this idea has been discussed, debated and elaborated. One of the factors females seem to be evaluating in courtship displays is the stamina of the potential mate. The notion is that displays that involve large investments of energy (like leg-waving in spiders, or the calls of frogs) reflect the “vigor” of the individual. Thus the idea that courtship displays evolved to let the female identify the healthiest males, usually the ones with the best genes to pass on to her progeny. But that seems unsatisfying as an explanation of the most elaborate courtship displays: the complexity of the acrobatics in these rituals goes far beyond a brute test of strength. The idea that skill, not just stamina, matters for mate choice has been the subject of inconclusive discussions for decades. A recent study of the mating dance of the golden-collared manakin (Barske et al. 2011, Female choice for male motor skills. Proc. Roy. Soc B doi: 10.1098/rspb.2011.0382) now offers evidence that, at least in this one species, skill in performing the detailed choreography of the courtship display matters a lot.
Male golden-collared manakins are small birds with gold throats; they mate in a highly skewed fashion (a few males mate very frequently, the others rarely) and the males don’t share the work of raising the chicks. The courtship display seems likely to be unusually important under these conditions — there’s no other basis on which the female can choose between one male and another. And indeed, when you watch a male golden-collared manakin perform its courtship dance you definitely get the impression that he feels that what he’s doing is crucially important. The courting and male-male competition takes place in a location known as a lek. Each male clears a courting arena, and when the area is appropriately cleared he starts jumping repeatedly from tree to tree, snapping his wings together in midair. When he lands on the tree, he briefly displays his golden throat, pointing it towards the center of his courting area (where the female will sit if she’s interested), in a move called “beard up”. Sometimes he produces a series of wingsnaps so close together you can’t separate them, called a rollsnap. These are birds that are trying very, very hard… to do something.
(Data from Barske et al. 2011).
May 31, 2011 § Leave a comment
One of the great surprises of the genomic era has been how similar the coding regions of genes are between species. It seems that we have been underestimating the evolutionary role of altered regulation — increasing or decreasing the expression of a gene, in different places, at different times — relative to protein sequence changes. So the question of how evolution produces novel patterns of expression of existing genes has become one of the hot topics of the day. There are at least 4 ways you can imagine this happening via changes to the DNA near your gene of interest. First, an enhancer that drives the expression of your gene could be created out of nothing by mutation, in a region of DNA that previously had nothing to do with regulating your gene. Second, a pre-formed enhancer may “jump” into the region near your gene, carried by a transposon. Third, an enhancer that was originally driving the expression of a neighboring gene may switch its activity to the promoter of your gene. And finally, an existing enhancer that drives the expression of your gene at a particular time in development, or in a particular site in the body, may be modified by mutation such that it now causes expression in a different time and place. This is called co-option. The idea is that every functional enhancer involves many transcription factor binding sites: if an enhancer has binding sites for transcriptional activators A, B and C and repressor D, it will be active at times and places when A, B and C are present and D is not. If evolution now adds activator site E, through a random mutation of a sequence that was quite similar to E anyway, then perhaps the enhancer can activate transcription at any time or place where you have three out of the four activating transcription factors: ABC still works, but so do ABE, ACE and BCE… and perhaps if you have all four, you can over-ride the presence of D. I’m making this up, you understand, but that’s the general idea: you co-opt some of the pre-existing binding sites, add one or more new ones, and the result is that your gene of interest is expressed somewhere novel in time or space.
As usual for events that happened millions of years ago, specific examples of novelty are not that easy to identify with confidence. But Sean Carroll and colleagues now think that they’ve spotted a new and interesting example of co-option (Rebeiz et al. 2011. Evolutionary origin of a novel gene expression pattern through co-option of the latent activities of existing regulatory sequences. PNAS doi/01.1073/pnas.1105937108). What they did was to take a group of several closely related species of Drosophila and identified 20 genes that might be expected to evolve relatively rapidly. They then carefully examined the expression of these genes in several larval stages of each of the Drosophila species. They saw many changes in expression, most of which turned out not to meet their definition of a novel expression pattern — for example, some changes that initially looked as if something new was happening turned out to be merely shifts of the timing of an expression pattern in one species relative to another. But they did find one gene that had a unique pattern of expression in one species and no others, the Ned-1 gene. In most of the Drosophila species studied, this gene is expressed in wing, leg and central nervous system tissues. In one species, D. santomea, it’s also expressed in the developing optic lobe. Rebeiz et al. checked exhaustively that this pattern was neither a timing shift nor a remnant of an older expression pattern that had been lost in all of D. santomea‘s relatives. It really does look novel.
May 19, 2011 § 1 Comment
Quite a few stories have come out recently about microorganisms that use one type of stress as a signal that they should prepare themselves for another stress. For example, an Escherichia coli bacterium on a piece of the salad you ate for lunch (let’s hope normal E. coli, not the pathogenic sort) may find itself traversing your digestive tract. One of the first things it can observe about its new environment is that the temperature has gone up; soon afterwards, the level of oxygen goes down. It turns out that the transcription of genes associated with dealing with oxygen starvation is induced by an increase in temperature. It seems that E. coli has evolved a response that anticipates oxygen starvation when it sees a temperature elevation. Another study found that when E. coli encounter lactose they upregulate the genes required for dealing with maltose (but not vice versa), mirroring the order in which the bacteria are likely to see these sugars as they traverse our guts. In an artificial setting in which sugars are offered one after the other, wild-type E. coli grew better than a strain in which this anticipatory response is broken; in other words, the anticipatory response provided a fitness advantage. There have been similar findings in other settings, for example the response of yeast to the conditions it encounters during the process of wine production. Human pathogens such as Vibrio cholerae and Candida albicans appear to have responses like this as well.
Tzachi Pilpel and colleagues have contributed much to the idea that this so-called “predictive” behavior might be a general phenomenon. In a recent paper, they set out to develop a theoretical framework for analyzing the costs and benefits of an anticipatory response (Mitchell and Pilpel, 2011, A mathematical model for adaptive prediction of environmental changes by microorganisms. PNAS doi:10.1073/pnas.1019754108). The problem here is simple: when a cell upregulates a set of genes that it doesn’t immediately need, it starts paying a cost. Some time later, the genes start to be useful, and it gains an advantage. How far ahead can you start paying the cost, and still find the advantage worthwhile? And if the gap between the first signal and the second signal is variable, how fast does that erode your ability to gain an advantage from anticipation?
The general form of this problem may be familiar to you from the discussions people have about retirement planning. [Or perhaps it isn’t, if you live in a country where retirement planning is less of an obsession than it is in the US — every time I log into my bank account I get a screen asking me whether I’m saving enough for retirement. The answer is always “probably not”, which is disheartening. Then again, that’s what happens when you tell the calculator that you plan to live forever.] When you start saving for retirement, you pay a cost; you reduce your standard of living now in an attempt to improve your standard of living later. But, of course, you don’t know exactly how long you need your retirement savings to last. So what’s the right amount to save? I think people probably solve this problem less efficiently than bacteria.