Ready, set, mutate... and may the best microbe win

Even with modern genomic tools, it's a daunting task to find a smoking gun for Darwinian evolution. The problem lies in being able to say not just when and how a specific gene mutated but also how that one genetic change translated into real-world dominance of one population over another.

Rice University biologists, using an ingenious experiment that forced bacteria to compete in a head-to-head contest for evolutionary dominance, today offer the first glimpse of how individual genetic-level adaptations play out as Darwinian natural selection in large populations. The results appear in the May 19 issue of Molecular Cell.

"One of our most surprising findings is that an estimated 20 million point mutations gave rise to just six populations that were capable of vying for dominance," said lead researcher Yousif Shamoo, associate professor of biochemistry and cell biology. "This suggests that very few molecular pathways are available for a specific molecular response, and it points to the intriguing possibility of developing a system to predict the specific mutations that pathogens will use in order to become resistant to antibiotics."

Rice's study involved the heat-loving bacteria G. stearothermophilus, which thrives at up to 73 degrees Celsius (163 F). Shamoo and graduate students Rafael Couñago and undergraduate Stephen Chen used a mutant strain of the microbe that was unable to make a key protein that the bacteria needed to regulate its metabolism at high temperatures. They grew the bacteria for one month in fermentor, raising the temperature a half degree Celsius each day.

Over a span of 1,500 generations, the percentage of mutant strains inside the fermentor ebbed and flowed as the single-celled microbes competed for dominance. Eventually, one strain squeezed out almost all the competition by virtue of its ability to most efficiently metabolize food at high temperature.

The metabolic protein required to thrive at high-temperature could only be made in one genetic region of the bacteria's DNA, meaning the researchers had only to characterize that small region of the genome for each new strain in order to measure evolutionary progress.

The researchers sampled the fermentor for new strains every other day. Though millions of mutations in the target gene are believed to have occurred, only about 700 of those were capable of creating a new variant of the target gene. In all, the researchers identified 343 unique strains, each of which contained one of just six variants of the critical gene.

The first of the six, dubbed Q199R, arose almost immediately, and was the dominant strain through the 500 th generation. Around 62 degrees Celsius, the Q199R was unable to further cope with the rising temperature, and a new round of mutations occurred. Five new varieties - themselves mutant forms of Q199R - vied for final domination of the fermentor. Three of the five were driven to extinction within a couple of days, and the final two fought it out over the remaining three weeks of the test.

The research included a raft of additional experiments as well. The team characterized each of the mutant proteins to document precisely how it aided in metabolic regulation. The fermentor experiment was repeated and the same mutations - and no others - were observed to develop again. Three of the six genes - the "winner," it's closest competitor and Q199R - were spliced back into the original form of the bacteria and studied, to rule out the possibility that mutations in other genes were responsible for the competitive advantage.

Shamoo said it's significant that the mutations didn't arise where expected within the gene. Four of the six occurred in regions of the gene that are identical in both heat-resistant and non-heat-resistant forms of G. stearothermophilus . Shamoo said this strongly shows the dynamic nature of evolution at the molecular and atomic level.

Shamoo said the most promising finding is the fact that the follow-up test produced precisely the same mutant genes.

"The duplicate study suggests that the pathways of molecular adaptation are reproducible and not highly variable under identical conditions," Shamoo said.

The research was funded by the National Science Foundation, the Welch Foundation and the Keck Center for Computational and Structural Biology.

There are two issues here that need to be separated. They are linearity and multiplicity of variables.

As I'm sure you know, any system of purely linear differential equations in multiple variables can be solved, no matter how many variables there are. The solution is simply an eigenvalue problem; the time evolution of the system is a sum of exponentials. So, if we regard the evolution problem as the operator equation Fx = y , where F is the selective pressure, x are the genomic variables, and y the relative survival of those genes in the next generation, the fact that there are multiple selective pressures and each may affect the survival of several genes independently is irrelevant. Your 'trade-off' issue (and I must say I'd prefer to avoid Paley-esque comparison of living systems with cars) just involves two pressures having opposite signed coefficients. No problem; while a trade-off may naturally slow evolution of one gene (by countering the pressure), it won't prevent the system reaching the optimum.

The real issue is whether the system is truly linear - i.e. whether there are terms of the form Fx₁x₂. In that case, evolution, proceeding by slow steps, might genuinely not be able to find the global optimum, but might get stuck. I suspect much of speciation is in fact a result of getting stuck in local optima. However, if the 'fitness landscape', in the form of F, changes enough, you can get moved out of the local optimum. This is in essence why catastrophic events cause rapid evolution.

OK, Sorry, this is going to be another rambling post. :-(

So, if we regard the evolution problem as the operator equation Fx = y, where F is the selective pressure, x are the genomic variables, and y the relative survival of those genes in the next generation, the fact that there are multiple selective pressures and each may affect the survival of several genes independently is irrelevant.

Don't know how to answer here without giving the wrong impression...

I understand completely about time evolution and linear differential equations being sums of exponentials. And I agree that the use of such a formulation is applicable to the survival problem. I merely do not know how applicable it is to a problem with multiple survival pressures: if you can combine the survival pressures into a single F, or if you have multiple F's which commute, then OK.

But I don't know enough about modeling evolutionary survival in this way to know if my question is even germane to the issue. If you have any references I'll put them on my "to read" pile. [Warning, I'm currently getting to reading put on my list 5 months ago. It does make me look like a jerk to admit that, but I'd rather do that than give a misleading impression.]

Note: What I'm trying to get at here is the modeling of mutation / survival rates in the face of something like Malaria and sickle cell anemia. You have multiple competing causes of morbidity / mortality; and either one can kill at a young enough age to prevent passing on of genes. Whichever survival pressure is 'stronger' for a given population will (I suspect) depend sensitively on the particular environmental conditions (# of mosquitos, # of predators you have to run away from so sickle cell means you're out of breath and get eaten, etc., etc.)

Your 'trade-off' issue (and I must say I'd prefer to avoid Paley-esque comparison of living systems with cars) just involves two pressures having opposite signed coefficients.

I hate to admit it, but I never read Paley. I chose cars for two reasons: 1) textbook description of classes and inheritance in Java, 2) it's a great way to illustrate 'design' tradeoffs in a way non-experts can appreciate. No, I don't necessarily mean "Design" in the ID sense, but in the "what is the way to minimize some global error given a number of competing constraints"? I was thinking of several talks I attended within the Dept. of Defence, concerning design tradeoffs for various armored vehicles. What enhances survivability in a classic Warsaw Pact mass attack might get your rear handed to you in a tight urban environment. (*)

No problem; while a trade-off may naturally slow evolution of one gene (by countering the pressure), it won't prevent the system reaching the optimum.

Well said, indeed. The first sentence is EXACTLY my point. By using an artificially simplified system such as the rising-temperature bacterial tank, you obtain estimates of the effective mutation rate for a given environmental pressure. That's fantastic, that is real-life empirical data. My only point was, outside the laboratory, there are more likely to be other selection pressures, so your estimate of the rate from *this* experiment is likely inexact. What *are* the error bars? Not to 'disprove' selection, but just to make sure you are aware of any approximations. The breakdown of the ideal gas law does not invalidate the atomic / molecular theory of matter: but you'd still better keep in mind its limitations.

In that case, evolution, proceeding by slow steps, might genuinely not be able to find the global optimum, but might get stuck.

One of the military symposia I attended had a fascinating discussion of just these issues with regards to simulated evolutionary methods for target acquisition...and of course (IIRC I first read about it in a paper by Monty Pettit) you have a similar problem in protein folding when you cannot sample all of configuration space, a "steepest descents" method may get caught in a local minimum. Matching the sampling step size to the size over which the topography of the potential energy varies is important (**). Think 'broad shallow minium vs. deep narrow minimum'. With a broad minimum, you are likely to find it but end up in a configuration which is a physically uninteresting local minimum; with a deep narrow minimum, it is probably very important but your steps are likely to pass right by it. The use of a simulated evolutionary method avoids this by randomly sampling 'all over' the configuration space.

The steepest descent method can be thought of as taking a golf ball on the golf course, setting it down (assume the greens are fast!), and watching where it rolls.

The simulated evolution method is akin to going out on the course in the rain, and looking where the water has puddled.

Real life evolution seems to be somewhere between these two: you can take arbitrary jumps from your starting position (like the nylon bug) but you're not *truly* sampling over the "whole" of the configuration space.

This topic has made me very happy. I'm going to wander off somewhere to purr for awhile ;-)

Cheers!

(*) Think of modeling a function, using a couple of parameters. Is it better to minimize the RMS error throughout the entire function, or is it better to get "almost exact" everywhere except in a limited region, where your fit *sucks* ? The answer, as usual, is "it depends."

(**) THIS is what I think is what is missing in evolutionary theory. There must be a sensitive interplay between raw mutation rates, birthrate, environmental changes, and other populations (say of food or predators). The selection pressures will to keep the target population "on the edge" (so they need to evolve), but not be so strong that they just drive everybody extinct.

But even in the case of mass extinctions, the mass extinctions are not the end of the story. In fact, the dying out of other species within a habitat or region merely becomes just one more "selection pressure" for the folks left behind...

Disclaimer: Opinions posted on Free Republic are those of the individual posters and do not necessarily represent the opinion of Free Republic or its management. All materials posted herein are protected by copyright law and the exemption for fair use of copyrighted works.