New & Noteworthy

Conformity Preferred in Yeast

March 25, 2015

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In a classic Apple ad, the world is a gray, dreary place where everyone is the same. In charges a woman dressed in brightly colored clothing who hurls a hammer into a big black and white screen, smashing the old conformist world order. Individuality can now blossom.

This is a powerful story for people, but it turns out that if Mother Nature had her druthers, she would like that woman to either stay at home or dress and act like everyone else. At least this is true if we are talking about individuals that are genetically identical. In this case, alleles that cut down on cell to cell variation tend to be the ones that prosper.

This is confirmed in a new study in Nature in which Metzger and colleagues in the Wittkopp lab showed that there is a selection against mutations that cause increased variation between individuals in the S. cerevisiae TDH3 gene. In other words, mutations that cause more “noise” are selected against. The squeaky wheel is eliminated.

The TDH3 gene encodes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), an important metabolic enzyme. Yeast cells can survive a deletion of this gene, but their fitness is greatly reduced. Overexpression of the gene also has noticeable effects. This suggested to the researchers that the level of TDH3 expression would be under selection pressure during evolution.

Metzger and coworkers compared the promoters of the TDH3 gene from 85 different strains of S. cerevisiae and found that the promoter had undergone selection out in the wild. The authors were interested in why certain polymorphisms were selected for and why others were selected against. To try to tease this out, they compared the activities of evolved changes to randomly selected ones.

The authors first used the sequences of the 27 haplotypes they saw in the 85 strains to predict what the original, ancestral promoter probably looked like. They then re-created this sequence and also sequences that represented the most likely intermediates on the way to the current promoters. They linked each of these promoter sequences to a yellow fluorescent protein (YFP) reporter and looked at mean activity and expression noise. In other words, they looked at how much promoter activity there was in aggregate and how much it varied between individual cells for the 10,000 cells in each culture.

They next set out to generate a pool of polymorphisms that didn’t make the cut during evolution, so they could compare these to the successful ones. To do this, they individually mutated 236 G:C to A:T transitions throughout the promoter region. They chose this transition because these are the most common spontaneous mutations seen in yeast and the most common SNP seen in this promoter out in the wild.

Now they were ready to do their experiment! Comparing the randomly created mutations to the evolved changes, they looked at both the overall level of expression and how much variation there was between each of the 10,000 individual cells in the tested culture.

What they found was that the effects on the mean level of activity were pretty comparable between the “selected” mutations and the random ones. But the same was not true for individual variation. The random mutations were much more likely to increase expression noise compared to the “selected” mutations.

From these results the authors conclude that there is a selection against mutations that increase the level of noise. In fact, they go a step further and conclude that at least for the TDH3 promoter, there was more of a selection against noise than there was a selection for a particular level of activity. It was more important that individuals had consistent activity than it was to have some mean level of activity.

This makes some sense, as a cell is a finely tuned machine where all the parts need to work in harmony together to succeed. If one part is erratic and shows different levels of activity in different individuals, then some of those individuals won’t do as well and so won’t survive.

This also means that certain paths to a more fit organism will be selected over others. And it could be that organisms miss out on some potential fitter states because they can’t survive the dangerous evolutionary journey that would be needed to get there.

So the cell prefers that all the parts work together in a predictable way. When you’re a population of individuals that are more or less genetically identical, nonconformists are dangerous.  The gray sameness of the Nineteen Eighty-Four world is preferable to a more bohemian atmosphere where diversity is celebrated.

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

  

From Sourdough Bread to Chemotherapeutic Drugs

March 18, 2015

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Microbes can achieve great things when they work together:

Microbiologists in the lab spend a lot of time and effort keeping each microbial strain and species separate. The conventional wisdom is that if you want to really understand an organism, you need to study it in isolation, as a pure culture. If contaminating colonies of some other bug appear on your Petri dishes, you’d better melt down those plates in the autoclave and trash them!

On the other hand, amateur microbiologists have known for centuries that mixtures of microbes can do great things. Sourdough bread, for example, is made using a culture of Lactobacilli and yeasts. They complement each other during the fermentation: the bacteria metabolize sugars that the yeast can’t use, and make new compounds that can be fermented by the yeast. The result is extremely tasty. 

A new study from Zhou and colleagues brings a microbial community into the lab to make a medicine called paclitaxel that is used to treat cancer. Yes, bread bowls for your clam chowder are cool, but this is obviously way more important for human health.

Paclitaxel is an incredibly successful drug for treating breast and ovarian cancer, but unfortunately there isn’t an easy way to make it.  It can be purified from the bark of the Pacific yew (killing the trees in the process), synthesized by plant cells cultured in vitro, or synthesized chemically. But all of these processes are expensive and complicated, which means this life saving drug is always in short supply.

The researchers wanted to produce it more cheaply and easily, and an obvious solution was to let microbes do most of the work. But neither of the most commonly used microbial workhorses, S. cerevisiae and E. coli, was exactly right for the job.

E. coli had already been engineered to overproduce the compound taxadiene. The taxadiene then needs to be oxidized to create oxygenated taxanes, which are paclitaxel precursors. This oxidation can be done by membrane-bound oxidoreductase enzymes called cytochrome P450s. But these enzymes are not found naturally in bacteria, and getting them expressed and functional in E. coli is challenging.

The researchers decided to see whether they could coax these two microbes into cooperating to produce oxygenated taxanes. After creating an E. coli strain that produced taxadiene and an S. cerevisiae strain that produced a P450 oxidoreductase, they grew them together in the same culture, with glucose as the carbon source. 

As planned, the E. coli pumped out taxadiene and it was able to diffuse into the yeast cells, where it became oxygenated. However, the two species weren’t as happy together as the researchers had hoped. 

One of the things that humans love about yeast is that when it grows on glucose, it produces ethanol. However, the E. coli cells didn’t love being bathed in ethanol: their yield of taxadiene went way down as the ethanol levels in the culture went up.

So Zhou and coworkers switched the carbon source to xylose. S. cerevisiae cannot consume xylose, but E. coli can. When growing on xylose, E. coli produces acetate, which the yeast can use—and they don’t produce ethanol under these conditions.

Growing the microbes in xylose doubled the yield of oxygenated taxanes over that of the glucose-grown culture. But still, only 8% of the taxadiene that was produced was getting oxygenated.

To be sure that the yeast cells were producing the P450 enzyme as efficiently as possible, the researchers tried driving transcription of the gene using several different promoters. Using the promoter that was strongest in the co-culture conditions made a significant difference in the proportion of taxadiene that was oxygenated.

The researchers guessed that another factor limiting the final yield was that the yeast cells were not growing as well as they could. Tweaking the ratio of the two species in culture and the contents of the media resulted in a three-fold increase in oxygenated taxanes. But Zhou and colleagues hoped to improve things even more.

Thinking that the limiting step in yeast growth might be the supply of acetate, the researchers tried to beef up the acetate synthesis pathway in E. coli. They engineered the bacteria to overproduce several of the enzymes in the acetate biosynthesis pathway, but this didn’t make a large difference. 

They reasoned that if they forced the E. coli to rely on the acetate biosynthesis pathway for energy, the cells might ramp up their acetate production. To do this they blocked oxidative phosphorylation by deleting the the atpFH gene that encodes a subunit of ATP synthase. Now more acetate was produced, the yeast cells grew better, and 75% of the taxadiene that was produced got oxygenated. They were in business!

Zhou and coworkers went on to show that the co-culture environment could be modified to generate several other isoprenoids. This class of naturally-occurring molecules includes some that are in use, or in development, as pharmaceuticals (paclitaxel, and others currently in clinical trials) and compounds that have other applications, from fragrances to fuels. 

There’s much more work to be done, and the potential of microbial communities is just beginning to be realized. Harnessing the power of multiple organisms means that different steps of pathways can be optimized separately and then mixed and matched for a desired result. This approach could turn out to be the best thing since sliced bread! But then again, sourdough bakers already knew that.

by Maria Costanzo, Ph.D., Senior Biocurator, SGD

Those Yeast Got Talent

March 11, 2015

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The winners of American Idol go through quite a selection process. They start out as one of tens of thousands of people who audition, and survive each cut until they are finally crowned.

At the first cuts, those with any sort of advantage are kept in the pool and the others dropped. As the cuts continue, contestants not only need to have had that stronger initial advantage (or a bit of luck), but they also need to have picked up some new skills from all of those off- and on-air performances.

Some of these contestants start with a lot of raw talent but then progress only a little, while others are able to hone their weaker initial talent with lots of practice. Once their numbers are winnowed down to a handful, it gets close to being anyone’s game because the remaining contestants are so talented.

A new study by Levy and coworkers paints a similar sort of picture for evolving populations of yeast. Very early on a whole lot of yeast stumble upon weak, beneficial mutations that keep them going in the population. These are the yeast that make the initial cut in the hurly-burly world of the Erlenmeyer flask.

At later times a few yeast end up with strongly beneficial mutations that allow them to start to dominate. These are the pool of yeast that are the finalists of the flask.

Of course a big difference (among many) between American Idol and the yeast in this experiment is that the pool of contestants in the flask hangs around—they are not thrown off the show. This means that some cell that didn’t do too well early on can suddenly gain a strongly beneficial mutation and begin to dominate. Until, of course, that cell is usurped by another more talented yeast, in which case that finalist will fade away unless it can adapt.

And this study isn’t just a fascinating dissection of evolutionary population dynamics either. It might also have implications for treating bacterial infections and even cancer.

Bacteria and cancer cells live in large populations with each cell trying to outcompete the others. By understanding the set of mutations that allow some cells to succeed against the others and become more harmful, researchers may be able to come up with new ways to treat these devastating diseases.

One of the trickiest parts of this experiment was figuring out how to follow lots of yeast lineages all at once in a growing culture. Levy and coworkers accomplished this by adding 500,000 unique DNA barcodes to a yeast population and using high-throughput DNA sequencing to follow the lineages in real time.

They set up two replicate cultures and followed them for around 168 generations. In both cultures the researchers saw that while most of the lineages became much less common, around 5% happened upon a beneficial mutation that allowed them to increase in number by generation 112.

In other words, around 25,000 lineages ended up with beneficial mutations that let them make the first cut in both cultures. This translates to a beneficial mutation rate of around 1 X 10^-6 per cell per generation and means that around 0.04% of the yeast genome (around 5000 base pairs) can change in a way that confers a growth advantage.

But of course not all mutations are the same. Weakly beneficial mutations are very common, which means both cultures have plenty of these early on. This is why the replicate cultures behave so similarly up to around generation 80.

Eventually, though, a few yeast stumble upon stronger, more beneficial mutations. Since these are rarer and harder to get, each replicate culture gets them at different generations. This is why the cultures begin to diverge as the 100 or so of the strongest beneficial mutations begin to dominate.

The experiment did not go on for long enough to see many double mutations. In other words, it was very rare in this experiment to see a yeast lineage succeed because it had developed additive beneficial mutations. This is because there simply wasn’t enough time for a yeast cell to get a beneficial mutation and establish itself and then have one of its lineage gain and establish a second beneficial mutation. There was no Jennifer Hudson who came in 7^th but then went on to win a Grammy and an Oscar.

When a cancerous tumor is developing, however, there is plenty of time for multiple “beneficial” mutations to be established. These mutations are only beneficial for the tumor; they are devastating for the person with cancer. This is why it is so critically important to understand not only which mutations are implicated in cancer, but also the dynamics of how they accumulate in the cancer cell population during progression of the disease. Talented yeast in the hands of talented researchers are helping us figure this out.

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

Sweet or Salty? It’s Hard to Tell Just By Looking

March 5, 2015

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If you have ever accidentally added salt to your coffee, you know that sugar and salt are very different things even though they look pretty much the same. Turns out that genes can sometimes be this way too. They can look similar at the DNA level but have very different functions.

A great example of this can be found in a new study in GENETICS by Varshney and coworkers. They found that a protein kinase in Candida albicans, Sch9, is important for ensuring that chromosomes end up in the right place when this yeast reproduces by budding.

Turns out that the same is not true for the Sch9 ortholog in our favorite yeast Saccharomyces cerevisiae. There is no evidence that Sch9 has anything to do with chromosome segregation there, even though the Sch9 sequences in these two yeasts look very similar.

C. albicans Sch9 is very important for keeping filamentous growth at bay under certain conditions (hypoxia and high levels of carbon dioxide). To understand better how Sch9 does this, Varshney and coworkers used chromatin immunoprecipitation (ChIP) to figure out where the protein binds in the genome. They were surprised when they found that it bound mostly to centromeres.

Despite this binding, the authors saw no evidence that Sch9 was involved in stabilizing the kinetochore, the protein structure that forms at the spindle of sister chromatids. When a kinetochore is destabilized, a cell’s nuclear morphology changes, its centromeres decluster during the cell cycle, and the centromeric histone Cse4 delocalizes away from its centromeres. The authors saw none of these things in a C. albicans strain in which the SCH9 gene was deleted.

They did, however, find that C. albicans cells lacking Sch9 had anywhere from a 150 to a 750-fold increase in chromosome loss. They found this by using a strain of C. albicans that had an arginine marker on one copy of its chromosome 7 and a histidine marker on the other, and looking for how often cells lost one of the two markers. From this the authors concluded that like many other kinetochore associated proteins, Sch9 is involved in chromosome segregation.

As a final experiment, Varshney and coworkers used ChIP to see if the Sch9 protein bound to centromeres in S. cerevisiae. It did not. While the authors did not directly test whether Sch9 had any effect on chromosome segregation in S. cerevisiae, the presumption is that it didn’t, as it doesn’t appear to interact with centromeres and no such effect has been seen previously.

But Sch9 isn’t completely different in the two yeasts. A close look at the ChIP data showed that Sch9 bound the rDNA locus in both C. albicans and S. cerevisiae.

How did orthologous proteins in two budding yeasts end up with such different functions? One idea is that the ancestral gene to Sch9 was important for rDNA regulation and that it later gained a function in chromosome segregation in C. albicans. Another possibility is that the ancestral gene had both functions and that centromere binding was lost in S. cerevisiae. More work will need to be done to tell the difference.

Whichever explanation is correct, this study reminds us that, just like sugar and salt, even if two genes look similar they may have quite different functions. Assuming that similar appearance means identical function may lead to an experimental result that is just as unpleasant as salty coffee!

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