New & Noteworthy
December 19, 2013
Just like the chicken or milk you buy at a store, chromosomes have a shelf life too. Of course, chromosomes don’t spoil because of growing bacteria. Instead, they go bad because they lose a little of the telomeres at their ends each time they are copied. Once these telomeres get too short, the chromosome stops working and the cell dies.
Turns out food and chromosomes have another thing in common—the rates of spoilage of both can be affected by their environment. For example, we all know that chicken will last longer if you store it in a refrigerator and that it will go bad sooner if you leave it out on the counter on a hot day. In a new study out in PLoS Genetics, Romano and coworkers show a variety of ways that the loss of telomeres can be slowed down or sped up in the yeast S. cerevisiae. And importantly, they also show that some forms of environmental stress have no effect.
The authors looked at the effect of thirteen different environments on telomere length over 100-400 generations. They found that caffeine, high temperature and low levels of hydroxyurea lead to shortened telomeres, while alcohol and acetic acid lead to longer telomeres. It seems that for a long life, yeast should lay off the espresso and and try to avoid fevers, while enjoying those martinis and sauerbraten.
Romano and coworkers also found a number of conditions that had no effect on telomere length, with the most significant being oxidative stress. In contrast, previous studies in humans had suggested that the oxidative stress associated with emotional stress contributed to increased telomere loss; given these results, this may need to be looked at again. In any event, yeast can deal with the stresses of modern life with little or no impact on their telomere length.
The authors next set out to identify the genes that are impacted by these stressors. They focused on four different conditions—two that led to decreased telomere length, high temperature and caffeine, one that led to longer telomeres, ethanol, and one that had no effect, hydrogen peroxide. As a first step they identified key genes by comparing genome-wide transcript levels under each condition. They then went on to look at the effect of each stressor on strains deleted for each of the genes they identified.
Not surprisingly, the most important genes were those involved with the enzyme telomerase. This enzyme is responsible for adding to the telomeres at the ends of chromosomes. Without something like this, eukaryotes, with their linear chromosomes, would have disappeared long ago.
A key gene they identified was RIF1, encoding a negative regulator of telomerase. Deleting this gene led to decreased effects of ethanol and caffeine, suggesting that this gene is key to each stressor’s effects. The same was not true of high temperature—the strain deleted for RIF1 responded normally to high temperature. So high temperature works through a different mechanism.
Digging deeper into this pathway, Romano and coworkers found that Rap1p was the central player in ethanol’s ability to lengthen telomeres. This makes sense, as the ability of Rif1p to negatively regulate telomerase depends upon its interaction with Rap1p.
Caffeine, like ethanol, affected telomere length through Rif1p-Rap1p but with an opposite effect. As caffeine is known to be an inhibitor of phosphatydylinositol-3 kinase related kinases, the authors looked at whether known kinases in the telomerase pathway were involved in caffeine-dependent telomere shortening. They found that when they deleted both TEL1 and MEC1, caffeine no longer affected telomere length.
The authors were not so lucky in their attempts to tease out the mechanism of the ability of high temperature to shorten telomeres. They were not able to identify any single deletions that eliminated this effect of high temperature.
Whatever the mechanisms, the results presented in this study are important for a couple of different reasons. First off, they obviously teach us more about how telomere length is maintained. But this is more than a dry, academic finding.
Given that many of the 400 or so genes involved in maintaining telomere length are evolutionarily conserved, these results may also translate to humans too. This matters because telomere length is involved in a number of diseases and aging.
Studies like this may help us identify novel genes to target in diseases like cancer. And they may help us better understand how lifestyle choices can affect your telomeres and so your health. So if you have a cup of coffee, be sure to spike it with alcohol!
December 3, 2013
Our friend Saccharomyces cerevisiae has it pretty easy when it comes to sex. There is no club scene or online dating. Pretty much if an a and an α are close enough together, odds are that they will shmoo towards each other and fuse to create a diploid cell. No fuss, no muss.
Of course there aren’t any visual cues that indicate whether a yeast is a or α. Instead yeast relies on detecting gender-specific pheromones each cell puts out. The a yeast makes a pheromone and an α pheromone receptor, and the α yeast makes α pheromone and an a pheromone receptor. The way yeast finds a hottie is by looking for the yeast of the opposite sex that puts out the most pheromone.
This simple system is similar to ours in that gender is determined by gender specific gene expression. In humans this happens through the amounts of certain hormones that are made. For example, males make a lot of testosterone which turns on the androgen receptor (AR) which then turns a bunch of genes up or down. Both men and women have AR; men just make more testosterone, which causes it to be more active.
Yeast are simpler in that their mating loci encode transcription factors and cofactors that directly regulate a-specific and α-specific genes. Still, in both yeast and human, gender is determined by which genes are on and which are off.
Given how simple the yeast system is and how extensively it has been studied, you might think there is nothing else to learn about yeast mating. You’d be wrong. In a new study out in GENETICS, Huberman and Murray found that a gene with a previously unknown function, YLR040C, is involved in mating. They renamed this gene AFB1 (a-Factor Barrier) since it seems to interfere with a-factor secretion.
The way they found this gene was by creating, as they termed them, transvestite yeast that “pretended” to be the opposite mating type. One strain that they named the MATα-playing-a strain was α but produced a-specific mating proteins, while the other, the MATa-playing-α strain, was a but produced α-specific mating proteins. Sounds easy but it took a bit of genetic engineering to pull off.
The first steps in making the MATa-playing-α strain were to replace STE2 with STE3, MFA1 with MFα1, and MFA2 with MFα2. In addition, they had to delete BAR1 to keep it from chewing up any α factor that got made, and ASG7, which inhibits signaling from STE3. This strain still had the MATa locus, which meant that except for the manipulated genes, it still maintained an a-specific gene expression pattern.
Making the MATα-playing-a strain wasn’t much simpler. They had to replace STE3 with STE2, MFα1 with MFA1, and MFα2 with MFA2. In addition, they drove expression of BAR1 with the haploid specific FUS1 promoter and expression of the a-factor transporter STE6 with the MFα1 promoter. Maybe yeast isn’t so simple after all!
When Huberman and Murray mated the two transvestite strains to each other, they found that while these strains could produce diploid offspring, they weren’t very good at it. In fact, they were about 700-fold worse than true a and α strains! So what’s wrong?
To tease this out the researchers mated each transvestite to a wild type strain. They found that when they mated a wild type a strain to a MATa-playing-α strain, the transvestite’s mating efficiency was only down about three fold. By overexpressing α factor they quickly found that the transvestite strain’s major problem was that it simply didn’t make enough α pheromone. They hypothesized that perhaps differences in promoter strength or in the translation or processing of α-factor were to blame.
The reason for the low mating efficiency of the MATα-playing-a strain, however, wasn’t so simple. When Huberman and Murray mated the MATα-playing-a strain with an α cell, they found it was about 60-fold worse at mating. The first thing they looked for was how much a-factor this strain was producing. Because a-factor is difficult to assay biochemically, they used a novel bioassay instead and found that it secreted much less a-factor than did the wild type a strain. Further investigation showed that the transvestite strain produced something that blocked the ability of a-factor to be secreted.
By comparing the transcriptomes of MATa and MATα-playing-a cells they were able to identify YLR040C as their potential a-factor blocker. They went on to show that when this gene was present, a-factor secretion was indeed inhibited. They hypothesize that their newly named AFB1 may produce a protein that binds to and sequesters a-factor. It may be to a cells what BAR1 is to α cells, helping the yeast cell to sense the pheromone gradient and choose a mating partner.
When Huberman and Murray knocked AFB1 out of the MATα-playing-a strain, it now mated with a wild type α strain about five fold better than before. A nice increase, but it doesn’t completely correct the 60-fold reduction in this transvestite’s mating efficiency. Something else must be going on.
That something appears to be that the strain only arrests for a short time when it encounters α-factor. This would definitely impact mating efficiency, as it is very important that when a and α strains fuse they both be in the same part of the cell cycle. Pheromones usually stop the cell cycle in its tracks, but α-factor can’t seem to keep the MATα-playing-a cell arrested for very long. The researchers looked for genes involved in this transient arrest, but were not able to find any one gene that was responsible.
From all of this the authors conclude that there is a pheromone arms race raging in the yeast world. The most attractive yeast are those that make the most pheromone, so evolution favors higher and higher pheromone production. Just as people on the dating scene need to see past the makeup and trendy clothes to figure out who’s really the best partner, yeast need genes like BAR1 and AFB1 to parse out who is the best mate amid the ever increasing haze of pheromones.
November 18, 2013
Imagine you have the instructions for building a car but you don’t know what any of the specific parts do. In other words, you can build a working car but you don’t understand how it works.
One way to figure out how the car works would be to remove a part and see what happens. You would then know what role that part played in getting a car to run.
So if you remove the steering wheel, you’d see that the thing runs into a wall. That part must be for steering. When you take out the radiator, the car overheats so that part must be for cooling the engine. And so on.
Sounds like a silly way to figure out how the car works, but this is essentially one of the key ways we try to figure out how a cell works. Instead of parts, we knock out genes and see what happens. A new study by Teng and coworkers is making us rethink this approach.
See, one of the big differences between a machine and a cell is that the cell can react and adapt to the loss of one of its parts. And in fact, it not only can but it almost certainly will.
Each cell has gone through millions of years of evolution to adapt perfectly to its situation. If you tweak that, the cell is going to adapt through mutation of other genes. It is as if we remove the radiator from the car and it evolves an air cooling system like the one in old Volkswagen Bugs.
Teng and coworkers decided to investigate whether or not knocking out a gene causes an organism to adapt in a consistent way. In other words, does removing a gene cause a selection pressure for the same subset of mutations that allows the organism to deal with the loss of the gene. The yeast knockout (YKO) collection, which contains S. cerevisiae strains that individually have complete deletions of each nonessential gene, gave them the perfect opportunity to ask this question.
There have long been anecdotal reports of the YKO strains containing additional, secondary mutations, but the authors first needed to assess this systematically. They came up with an assay that could detect whether secondary mutations were occurring, and if so, whether separate isolates of any given YKO strain would adapt to the loss of that gene in a similar way. The assay they developed had two steps.
The first step was to fish out individual substrains from a culture of yeast that started from a single cell in which a single gene had been knocked out. This was simply done by plating the culture and picking six different, individual colonies. Each colony would have started from a single cell in the original culture.
The second step involved coming up with a way to distinguish differently adapted substrains. The first approach was to see how well each substrain responds to increasing temperatures. To do this, they looked for differences in growth at gradually increasing temperatures using a thermocycler.
They randomly selected 250 YKO strains and found that 105 of them had at least one substrain that reproducibly responded differently from the other substrains in the assay. In contrast, when they looked at 26 isolates of several different wild type strains, including the background strain for the YKO collection, there were no differences between them. This tells us that the variation they saw in the knockout substrains was due to the presence of the original knockout.
So this tells us that strains can pretty quickly develop mutations but it doesn’t tell us that they are necessarily adapting to the knocked out gene. To see if parallel evolution was indeed taking place, the authors chose to look at forty strains in which the same gene was independently knocked out. They found that 26 of these strains that had at least one substrain with the same phenotype, and fifteen of those had mutations that were in the same complementation group. So these 15 strains had evolved in similar ways to adapt to the loss of the same gene.
Teng and coworkers designed a second assay independent of the original heat sensitivity assay and tested a variety of single knockout strains. They obtained similar results that support the idea that knocking out a gene can lead cells to adapt in similar ways. This is both good and bad news.
The bad news is that it makes interpreting knockout experiments a bit trickier. Are we seeing the effect of knocking out the gene or the effect of the secondary mutations that resulted from the knockout? Are we seeing the loss of the radiator in the car or the reshaping that resulted in air cooling? We may need to revisit some earlier conclusions based on knockout phenotypes.
The good news is that not only does this help us to better understand and interpret the results from yeast and mouse (and any other model organism) knockout experiments, it also gives us an insight into evolution and maybe even into the parallel evolution that happens in cancer cells, where mutations frequently co-occur in specific pairs of genes. And while we may never be able to predict if that knock you hear in your engine really needs that $1000 repair your mechanic says it does, we may one day be able to use results like these to predict which cells containing certain mutated genes will go on to cause cancer and which ones won’t.
November 7, 2013
We all know that it’s important to get enough vitamins in our diet. Scary-sounding conditions like scurvy, rickets, and beriberi can all happen when you don’t get enough of them. And that’s not all.
Fairly recently, scientists discovered that when pregnant women get too little folate, their children are at a higher risk for neural tube defects. This connection is so strong that since 1998, the U.S. and Canada have successfully reduced the number of neural tube defects by adding extra folate to grain products.
While these kinds of effects are easy to see, it’s not always so obvious what is going on at the molecular level. But in a new study in GENETICS, Sadhu and coworkers showed that folate and methionine deficiencies can affect us right down to our DNA. And of course, they figured this out by starting with our little friend S. cerevisiae.
Folate and its related compound methionine are pretty important molecules in cellular metabolism. You need folate to make purine nucleotides, and it is essential for keeping just the right levels of methionine in a cell.
And methionine is, of course, one of the essential amino acid building blocks of proteins. But it is more than that. It’s also the precursor for S-adenosyl-methionine (SAM), which provides the methyl groups for protein methylation.
Protein methylation is a big deal for all sorts of things. But one of its most important jobs is undoubtedly controlling levels of gene expression through methylation of histones.
Since folate or methionine deficiency should affect SAM levels, in principle they could affect histone methylation too. But so far this connection had never been shown directly. Sadhu and colleagues set out to see what happens when you deprive S. cerevisiae of these nutrients.
Unlike humans, yeast can synthesize both folate and methionine. So the first step was to make folate- and methionine-requiring strains by deleting the FOL3 or MET2 genes, respectively. These mutant yeast strains couldn’t grow unless they were fed folate or methionine.
Now it was possible to starve these mutant strains by giving them low levels of the nutrients they needed. Starvation for either folate or methionine caused the methylation of a specific lysine residue (K4) of histone H3 to be reduced. Not only that, but expression of specific genes was lower, consistent with their reduced histone methylation.
To see how general this effect was, the authors performed essentially the same experiments in Schizosaccharomyces pombe, which is about as evolutionarily distant from S. cerevisiae as you can get and still be a yeast. In this beast, methionine deficiency also reduced histone methylation. For unknown reasons, folate deficiency didn’t have a significant effect.
Sadhu and coworkers wondered whether this effect was so general that they could even see it in human cells. Since humans are folate and methionine auxotrophs, this experiment was easier to set up. When they grew human cells with starvation levels of folate or methionine, their histone methylation and gene expression were both reduced. So starvation conditions have an impact right down to the level of gene expression, across a wide range of organisms.
The simple explanation for this effect would be that reduced folate leads to reduced SAM levels, and therefore fewer methyl groups are available to modify histones. But the researchers got a surprise when they measured intracellular SAM levels in S. cerevisiae under the starvation conditions: they were the same as in wild type! This conclusion was so surprising that they tried two different, sophisticated methods, but both gave the same result.
They explain this by postulating a kind of metabolic triage. Basically, the cell maintains a certain level of SAM in the cell but there is a pecking order for who gets to use it. At very low nutrient levels, the cell uses the available folate or methionine for the most essential processes such as purine synthesis or translation, and sacrifices histone methylation. As more nutrients become available, then other less critical functions can use them.
This kind of triage might provide an explanation for the link between folate deficiency and neural tube defects, and also for the effectiveness of antifolates against cancer. And it adds to the growing body of evidence that environmental conditions such as famine can have effects that persist across generations. This is an important reminder that any decisions we make today about feeding the hungry could have consequences that reach far into the future.
October 31, 2013
Folks, yeast has been on a roll lately with regard to helping to understand and finding treatments for human disease. Last week we talked about how synthetic lethal screens may find new, previously unrecognized druggable targets for cancer. And this week it is Parkinson’s disease.
Now of course yeast can’t get the traditional sort of Parkinson’s disease …it doesn’t have a brain. But it shares enough biology with us that when it expresses a mutant version of α-synuclein (α-syn) that is known to greatly increase a person’s risk for developing Parkinson’s disease, the yeast cell shows many of the same phenotypes as a diseased neuron. The yeast acts as a stand-in for the neuron.
In a new study out in Science, Tardiff and coworkers use this yeast model to identify a heretofore unknown target for Parkinson’s disease in a sort of reverse engineering process. They screened around 190,000 compounds and looked for those that rescued toxicity in this yeast model. They found one significant hit, an N-aryl-benzimidazole (NAB) compound. Working backwards from this hit they identified its target as Rsp5p, a Nedd4 E3 ubiquitin ligase.
The authors then went on to confirm this finding in C. elegans and rat neuron models where this compound halted and even managed to reverse neuronal damage. And for the coup de grace, Chung and coworkers showed in a companion paper that the compound worked in human neurons too. But not just any human neurons.
The authors used two sets of neurons derived from induced pluripotent cells from a single patient. One set of neurons had a mutation in the α-syn gene which is known to put patients at a high risk of Parkinson’s disease-induced dementia. The other set had the mutation corrected. The compound they identified in yeast reversed some of the effects in the neurons with the α-syn mutation without significantly affecting the corrected neurons. Wow.
What makes this even more exciting is that many people thought you couldn’t target α-syn with a small molecule. But as the studies here show, you can target an E3 ubiquitin ligase that can overcome the effects of mutant α-syn. It took an unbiased screen in yeast to reveal a target that would have taken much, much longer to find in human cells.
The mutant α-syn protein ends up in inclusion bodies that disrupt endosomal traffic in the cell. The NAB compound that the authors discovered restored endosomal transport and greatly decreased the numbers of these inclusion bodies. Juicing up Rsp5 seemed to clear out the mutant protein.
The next steps are those usually associated with finding a lead compound—chemical modification to make it safer and more effective, testing in clinical trial and then, if everything goes well, helping patients with Parkinson’s disease. And that may not be all.
The α-syn protein isn’t just involved in Parkinson’s disease. The dementia associated with this protein is part of a larger group of disorders called dementia with Lewy bodies that affects around 1.3 million people in the US. If everything goes according to plan, many of these patients may one day thank yeast for their treatment.