New & Noteworthy

The Benefits of Sex

March 03, 2016

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While you doggedly swipe right and left or wait night after night at that club, you may be wondering whether it is all worth it. Biologists have been wondering something similar.

Now they haven’t been wondering about the value of sex…since everything from amoebas to zebras has sex, it must be pretty important. No, the hard part has been figuring out why it is so beneficial.

On balance it can seem that the minuses of disease risk and passing on only half of your DNA outweighs the benefits of the combining two individual sets of DNA for some brand new combination. A new study by McDonald and coworkers in Nature using our old friend S. cerevisiae provides compelling evidence for a couple of ways that sex is good for a species.

First it is a way of combining individual beneficial mutations into a single individual. Now rather than having a couple of well adapted individuals battling for supremacy, the mutations can merge into one super beast that can outcompete everyone else.

This benefit, recombination speeds adaptation by eliminating competition among beneficial mutations, had been predicted and goes by the name of the Fisher-Muller effect. But this is the first time scientists have actually seen it playing out at the DNA level.

The second big benefit of sex is freeing good mutations from a bad genetic background. Now the beneficial mutation is not weighed down by other negative mutations. It’s like finally getting rid of that concrete block tied around your ankle.

Yeast is an ideal system for studying the benefits of sex because it can happily exist as a sexual or asexual creature. This means that researchers can directly compare the two in the same experiment. Which is just what McDonald and coworkers did.

They followed 6 sexual and 12 asexual populations through about 1000 generations of adaptation. The only difference between the asexual and sexual populations was, as you might have guessed, sex.

The sexual populations included 11 bouts of sex. In other words, every 90 generations or so, an ‘alpha’ cell would swipe left and find an ‘a’ cell to hook up with.

As expected and has been seen before, the sexual populations were much better adapted to their environment than were the asexual populations. Sex is clearly a good thing! The next step was to tally up the mutations in each population to try to figure out why.

What McDonald and coworkers found was that there wasn’t a lot of difference in the mutations that crop up in each. Over time, both groups had about the same number and ratio of intergenic, synonymous, and nonsynonymous mutations.

The big difference between the asexual and the sexual populations was in the mutations that became fixed. In the sexual group, most mutations were weeded out over time. In their experiment, 78% of mutations became fixed in the asexual population while only 16% hung around in the sexual population.

Sheer numbers wasn’t the only difference between the two either. The kinds of mutations that became fixed differed significantly in both as well.

In the asexual population, each of the three kinds of mutations fixed at around the same rate. Around 75-80% of intergenic, synonymous and nonsynonymous mutations became established in this population.

It was a different story in the sexual population. Here, 22% of the nonsynonymous, 11% of the intergenic and none of the synonymous mutations became fixed. It seems like only mutations that make a difference end up getting selected for.

Further analysis revealed two big reasons why the two populations differed. First, good mutations ended up getting stuck with other bad mutations in the asexual population. This blunted the positive effects of the beneficial mutation.

And second, the various good mutations tended to be spread out among different groups in the asexual population. The end result was that instead of working together, these groups battled each other for supremacy resulting in some beneficial mutations being lost.

So no need to wonder anymore about the benefits of sex to a species. It is a strong purifier, weeding out unimportant or damaging mutations and a powerful aggregator, squirrelling all the good ones into one group. No wonder most every beast does it!

by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: Fisher-Muller effect, mutation, nonsynonymous, selection, synonymous

How Yeast Populations Make the Cut

April 15, 2014

Imagine that your dream is to be a professional basketball player.  Unfortunately for you, you are only five feet six inches tall and you can’t jump very high.  No matter how much you practice and work out, it is exceedingly unlikely you will be a starter for the Miami Heat.

Now imagine instead that you are six feet tall with a reasonable vertical jump.  Here, with enough effort you have a shot at beating out the guy with the genetic advantage of being six foot six inches high who doesn’t work as hard as you do.  Keep practicing and you might be passing the ball to LeBron James instead of him! 

In a new study in GENETICS, Frenkel and coworkers show that something similar can happen in yeast too.  If a population of yeast has some overwhelming advantage over a second population, the first will quickly outcompete the second every time.  But if the first population is just a bit better than the second, then the second can sometimes end up with a mutation that gives it an even better advantage than the first.  Now the first population is outcompeted and the second takes over.

Of course, when presented in a general way this is sort of obvious.  But Frenkel and coworkers set up their experiments in such a way that they got some hard numbers for just how much of an advantage one population needs to overcome to have a chance at winning.  If six feet is tall enough, what about five feet eleven inches?

The first step was to generate a number of mutants with different measured fitness advantages.  They selected mutant populations with advantages of 3, 4, 5, or 7%.  These populations were all tagged with a fluorescent marker.

They then seeded these mutants individually into 658 replicate reference populations that were tagged with a different fluorescent marker.  The mutants were seeded at a high enough level to prevent genetic drift from wiping them out.  The authors then followed each population for hundreds of generations by determining the levels of each population every 50 or so generations.

Their first finding was that mutants with a 7% advantage won out every time.  The reference population had no chance at getting a good enough mutation to beat it out.  No one is going to beat LeBron James out for his starting position with the Miami Heat.

Once the advantage was only 5%, around 16% of the time the second population won out.  As the advantage got smaller and smaller, the second population won out more and more often.   Even a genetically less gifted player has a shot at beating out the 12^th guy on the Heat’s roster!

These results can tell us quite a bit about the mutational landscape of haploid Saccharomyces cerevisiae.  For example, from these data Frenkel and coworkers figured out that only populations that get mutations that give at least a 2% advantage have a chance at outcompeting other populations.  By assuming a mutation rate of 4X10^-3, around 1 in 1000 mutations fit this bill, which might seem surprisingly high but is consistent with previous studies.  With a bit more hand waving, the authors hypothesize that disruption of something like 1 in 100 yeast genes is actually beneficial!

So yeast have a surprisingly level playing field.  Unless they are up against the equivalent of Kobe Bryant or Michael Jordan, they have a good shot at stumbling on a mutation that gives them an edge over their peers.    

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: evolution, mutation, population genetics, Saccharomyces cerevisiae

Guns vs. Butter in Cells

March 19, 2014

Life is a set of tradeoffs for people, countries, and even cells.  For example, governments need to decide how much money to dedicate to defense and how much to economic growth.  Too much on defense and your country fails, because defense spending sucks up so many resources that your country can no longer afford to pay for anything else.  And of course if you spend too little on defense, someone who spent a bit more can come and take you over.

No country lives in a vacuum though—how much to spend on defense and how much on growth depends on the country’s situation.  If you are the Ewoks living next to an Imperial shield generator, you’d better sacrifice some growth for defense.  But once the Death Star has blown up and the Empire is swept away, you probably focus more on growth (until a new Sith lord arrives). 

This guns vs. butter debate plays out at the cellular level too when it comes to protecting DNA from mutations.  If cells expend too much energy to protect their DNA they sacrifice growth, but if they spend too little, they develop too may harmful mutations to survive.  And just like with countries, how much protection a cell’s DNA needs depends on its environment.

If cells need to adapt quickly to a changing environment, a high rate of mutation is favored.  These cells are more likely to develop a mutation that gains them an advantage over their slower mutating brethren.

A new study by Herr and coworkers in the latest issue of GENETICS calculates the upper limit of the rate of mutation in a diploid yeast.  In other words, they figure out how little “spending” on defense these yeast can get away with and survive.

They find that diploid yeast can deal with a 10-fold higher rate of mutation as compared to haploid yeast.  This makes sense, since the extra gene copy afforded by being diploid can mask a recessive lethal mutation, but this study is the first to give this idea hard numbers.

The authors had previously generated a number of mutations in POL3, the yeast gene for DNA polymerase δ, that affect its ability to find and/or fix any mistakes made during DNA replication.  The study first focused on two mutations affecting accuracy, pol3-L612G and pol3-L612M, and one mutation affecting proofreading, pol3-01.  The accuracy mutations caused about a 10-fold increase in the mutation rate, while the proofreading mutation caused anywhere from a 20-100-fold increase.  Neither was enough to seriously affect a diploid’s growth.

The next step was to combine accuracy and proofreading mutations into the same gene to figure out if the combination resulted in a higher mutation rate.  The authors suspected that it did when they discovered that even though the heterozygotes were fine, their spores were inviable.  The POL3/pol3-01,L212M and POL3/pol3-01,L212G strains sporulated just fine, but none of the spores could germinate and grow. 

One way to explain this was that the double mutation increased the error rate to the point that it would kill off haploids but not diploids.  By looking at mutations in the hemizygous CAN1 gene they could see that the mutation rate in these diploids was indeed at around the haploid threshold. In terms of the CAN1 gene, this mutation rate was around 1X10-3 can1 mutations/cell division.

They next determined the mutation rate by sequencing the genomes of each mutant as well as the wild type.  They found a single T-G mutation in the wild type, 1535 point mutations in POL3/pol3-01,L212M and 1003 mutations in POL3/pol3-01,L212G.  From this they calculated a mutation rate of around 3-4X10-6/base pair/generation. 

Even though this level of mutation kills haploids but not diploids, this does not mean the diploids escaped unscathed.  When the heterozygous diploid colonies were subcloned the resulting colonies were variable in size, indicating that their higher mutation rate was catching up with them.  This high mutation rate was making them sick. 

Given this result, it wasn’t surprising that diploid homozygotes of each double mutant could not survive—the mutation rate was now too high.  The strains homozygous for pol3-01,L212M managed to get to around 1000 cells before petering out.  Strains homozygous for pol3-01,L212G did even worse—they only made it to around 10 cells.

In a final set of experiments Herr and coworkers used a variety of other mutations to tweak these mutation rates to find the threshold at which diploids fail to survive.  Some of these mutations were in POL3 while others were deletions of the MSH2 and/or DUN1 genes.  After testing many different combinations, they found that these yeast did pretty well up to around 1X10-3 can1 mutations/cell division (the haploid threshold rate).  Then, from 1X10-3 to 1X10-2 can1 mutations/cell division there began a rapid drop off with little to no growth at the end. 

So as might be expected, diploids can deal with a significantly higher mutation rate than can haploids.  But even though they can, wild type yeast in the lab still have a very low mutation rate.  It is like they are living near the Imperial city planet of Coruscant.  They are willing to expend the energy to keep their DNA protected.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: DNA replication, mutation, Saccharomyces cerevisiae

A Simpler Way to Evolve

February 06, 2012

As life evolves there is a tendency for increased complexity.  Up until now, scientists have mostly focused on gain of function mutations as the motor for this change.  This has proven fertile ground for evolution deniers who have claimed that life’s complexity could not have arisen from these rare, gain of function mutations alone.

A new study by Finnigan and coworkers provides an important counterpunch to this argument.  These authors resurrected ancient proteins and showed that an increase in complexity can come from much more common, loss of function mutations.  Time for the deniers to find a new argument…

Making Molecular Machines

Finnigan and coworkers focused on the evolution of a protein called vacuolar H+ -ATPase or V-ATPase for short.  Like other molecular machines, this proton pump consists of many different proteins all working together in a coordinated fashion.  One key part of this machine is a rotary ring called V₀.  (This would be the ring of C proteins in the image to the right.)

In most eukaryotes, V₀ is made up of five identical subunits (called Vma3) and one subunit called Vma16.  In fungi, a third protein, Vma11, has replaced one of the Vma3 subunits.  In other words, the fungal version is a bit more complex than other eukaryotic versions.

Current theories are that these three proteins all arose through gene duplication.  Duplication of the Vma3 gene first led to the Vma16 gene and then later in fungi, Vma3 duplicated again this time becoming Vma11.  Yeast V-ATPase absolutely requires Vma11 to function and other eukaryotic Vma3 family members cannot replace Vma11.

Using the 139 family members of the Vma family available in GenBank, members of the Thornton and Stevens lab recreated the ancestral proteins that existed before and after the Vma11 gene duplication event.  Before the arrival of Vma11, there were only two proteins which the authors have named Anc.3-11 and Anc.16.  Anc.3-11 presumably has functions of both Vma3 and Vma11.  After the gene duplication event, there were three ancient proteins: Anc.3, Anc.11, and Anc.16.

Using these ancient proteins, the authors first showed that Anc.3-11 could substitute for either Vma3 or Vma11 in yeast. It could even partially rescue a yeast strain that lacked both of the other genes.  They then showed Anc.16 could replace Vma16 and most importantly, that the two ancient proteins could replace the three modern ones.  They reconstructed an ancient molecular machine that works.

The next step was to figure out what happened after Anc.3-11 duplicated again and the two genes began to evolve into the separate proteins, Anc.3 and Anc.11.  Again using the GenBank sequences, the authors predicted that two single mutations were an initial step on the way to the separation of Anc.3-11 activities into the Anc3 and the Anc11 proteins. 

The authors engineered each mutation independently into the Anc.3-11 protein and found that one mutation made Anc.3-11 more like Anc.3 and the other made Anc.3-11 more like Anc.11.  The complex now required all three Anc proteins instead of just the two for maximal activity.  The authors had recapitulated the first evolutionary steps that led to the formation of the three subunit V₀ rotary ring.

Finally the authors showed that each of these mutations were loss of function mutations.  The Anc.3-11 protein has two different interfaces that interact with Anc.16.  The first mutation weakened one interface on Anc.3 and the second mutation weakened the other interface on Anc.11 causing both proteins to now be required to reconstitute the ring.  The added complexity arose from a combination of gene duplication and relatively common loss of function mutations.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: ATPase, evolution, gain of function, gene duplication, loss of function, mutation, proton pump

Replicate Late, Mutate More

January 30, 2012

 

Variation in the DNA that results in natural selection does not come about randomly. Where a piece of DNA is in the genome and how it is used affects its chances for being mutated. The end result is that the genomes we see today are the product of these nonrandom mutation rates.

One of the first places this became apparent was in transcribed genes. Scientists found that the transcribed strand of active genes has fewer mutations than the nontranscribed strand. They found the major reason for this was transcription-coupled repair.

Now in a new study in yeast, Agier and Fischer have shown that when a piece of DNA is replicated affects its chance of being mutated too. They compared the genomes of 39 different strains of Saccharomyces cerevisiae and found that late replicating DNA is 1.3 times more likely to be mutated compared to early replicating DNA. This is consistent with a recent study by Chen and coworkers that showed a similar result in the human genome.

This means that if a piece of DNA happens to be further away from an origin of replication, it will build up more mutations over time. And while a 1.3 fold increase in mutation rate might seem small, it is predicted to have a significant impact on genomic variation and natural selection on an evolutionary time scale.

There are a number of potential models for why late replicating DNA is more likely to be mutated. One hypothesis is that cells use different repair mechanisms at different times during S phase: cells in early S-phase repair replication errors with relatively error-free repair mechanisms like template switching with newly formed sister chromatids, while cells in late S-phase tend to rely on more error-prone translesion repair pathways.

Other possible models rely on potential differences between the cellular environment in early and late S-phase. They include altered metabolism, increased presence of single stranded DNA, or even a slow decrease in DNA repair as S-phase progresses. The researchers do not know which, if any, of these mechanisms is responsible for the change in mutation rate.

It may even be that different mechanisms are responsible in yeast and humans. Agier and Fischer found that in yeast, the leading strand had higher rates of substitution towards C and A than did the lagging strand. Chen et. al. found the opposite to be true in human cells. Either they use different mechanisms or similar mechanisms can end up with opposite results.

These findings suggest that the genomes observed today are at least partly the result of the nonrandom nature of neutral mutations. Highly expressed genes near an origin of replication are much less likely to be mutated than are genes with low expression more distant from an origin of replication.

And there are other known and yet to be discovered ways that certain DNA ends up more mutated than other DNAs. Just like in real estate, the key to mutation rate is location, location, location.

Categories: Research Spotlight

Tags: DNA replication, mutation, S phase, translesion, yeast

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