New & Noteworthy
Register now for the 31st International Conference on Yeast Genetics and Molecular Biology
May 16, 2023
Deadlines for #yeast2023 have been extended! Early bird registration is open until June 5, and abstract submission until July 17. For more information, go to yeastflorence2023.com.
Categories: Conferences
GENETICS Knowledgebase and Database Resources
May 08, 2023
The May 2023 issue of GENETICS features the second annual collection of Model Organism Database articles. Scientists from Alliance of Genome Resources member groups SGD, RGD, ZFIN, Gene Ontology, and Xenbase have provided updates on recent activities and innovations. Be sure to browse the issue and get acquainted with these excellent Knowledgebase and Database Resource papers at GENETICS. Cover art by Vivid Biology.
Categories: Announcements
Tags: Saccharomyces cerevisiae, yeast
GWAS Shows Potential in Yeast
August 09, 2012
Maybe GWAS will prove to be more useful in model organisms like yeast.
The idea behind a genome wide association study (GWAS) makes perfect sense. Compare the DNA of one group of people with a disease to another group that doesn’t have the disease, identify the DNA region specific to the disease group, and then find the specific gene and mutations that lead to the disease.
In theory, this sort of study should have become routine once we had the human genome sequenced. In practice, it has turned out to be less useful than everyone hoped.
Now, this doesn’t appear to be any fault with the technique itself. Instead, it has more to do with the fact that many human diseases are simply too complex for GWAS to handle.
Most common human diseases appear to result from multiple genetic pathways and/or multiple genes. Throw in environmental effects and GWAS quickly becomes overwhelmed. At least for now, too many patients and controls would be needed for this powerful technique to have a real chance at deciphering most common human diseases.
But that doesn’t mean the technique isn’t useful. It is very good at finding single genes involved in strongly expressed traits. And this might be ideal for certain model organisms.
In a study just out in the latest issue of GENETICS, Connelly and Akey set out to investigate how well GWAS would work in the yeast, Saccharomyces cerevisiae. In many respects, this yeast appears to be made for GWAS.
It has a small, easily sequenced genome, there is on average a polymorphism every 168 base pairs or so, and its linkage disequilibrium is low. There are genome sequences from 36 wild and laboratory strains publicly available, all as diverse as can be.
But this yeast isn’t perfect. The chromosomal structure between strains tends to be much more varied than between two humans. This is predicted to introduce a high error rate. And this is just what Connelly and Akey saw when they ran some simulations.
They found that the error rate was too high in the simulations to draw any meaningful conclusions. But they also found that by using a more sophisticated analytical technique called EMMA, they were able to partly correct for some of these errors.
Simulations are one thing, but how about real life? Connelly and Akey next tested the method by applying it to a practical problem: identifying the genetic reasons for differences in mitochondrial DNA (mtDNA) copy number in yeast. What they found mimicked the simulation data.
Using more traditional analytical approaches on the data obtained from GWAS, they found 73 potential causative SNPs. But when they switched to analyzing the data with EMMA, they found a single SNP that was significant. It took a bit of hand waving, but the gene associated with this SNP could possibly be implicated in mtDNA copy number. And then again, it might not.
This “significant” SNP was found amidst lots of errors and in a background of high p values. In other words, this finding may not be a real one after all. This experiment does not give confidence that GWAS can be used when all known strains of yeast are compared.
But if the strains to be included are selected more carefully, it may still prove to be a useful tool. When Connelly and Akey focused on strains that were structurally similar, they found that the error rate was much lower. Low enough that in the near term, scientists may be using GWAS to figure out how things work in model organisms.
Hopefully the findings from GWAS applied to model organisms will illuminate disease mechanisms in humans. Then maybe GWAS can realize its full potential, although not in the way it was originally envisioned.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: genome wide association study, GWAS, yeast
Ethanol from Waste
June 16, 2012
Scientists are coming up with ways for yeast to use waste like this to generate ethanol.
Biofuels hold the promise to significantly slow down global warming. But this will only be the case if they come from something besides corn.
We don’t want them to come from the parts of other plants we eat, either. Shunting food towards fuel will only jack up food prices and put the lives of the poorest at risk. Policy makers should not have to decide between feeding the poor and running their cars.
No, to make biofuels worth our time, we need to be able to turn agricultural waste, grass, saplings, etc. into ethanol. Unfortunately this stuff is mostly cellulose and lignin and we don’t have anything that can efficiently ferment this “lignocellulosic biomass.”
Many groups are working towards creating strains of Saccharomyces yeast, the predominant fungal organism used for large-scale industrial processes, to do this job. None have yet been created that can do the job well enough to be industrially viable. They are either poor fermentors or are genetically modified so that they include non-yeast genes. Ideally any strain would include only Saccharomyces genes, to avoid the public’s fear and loathing of genetically modified organisms.
This is where a new study in GENETICS by Schwartz and coworkers comes in. This group is working towards engineering a yeast that can ferment the pentoses like xylose that make up a good chunk of this otherwise inedible biomass, using genes that are naturally occurring in Saccharomyces. They haven’t yet created such a yeast, but they have at least identified a couple of key genes involved in utilizing xylose.
The researchers took what seemed to be a straightforward approach. Collect and screen various yeast strains for their ability to grow on xylose and isolate the relevant gene(s) from the best of them. Sounds easy enough except that most of the strains they’ve found are terrible sporulators. This means that they couldn’t use conventional methods to isolate the genes they were interested in and so had to come up with new methods.
First they needed to find some way to get the strain to sporulate. They were able to force sporulation by creating a tetraploid intermediate between the xylose fermenting strain, CBS1502, and the reference strain, CBS7001, by adding an inducible HO gene. During this process, they noticed that the ability to utilize xylose segregated in a 3:1 pattern. This usually means that two genes are involved.
They next needed a way to identify these two genes. What they did was to pool 21 spores that could ferment xylose and 21 that could not. They then purified the DNA from each pool and compared them using high throughput sequencing. They eventually found two genes that were key to getting this yeast to use xylose as its carbon source. (They also found at least two other “bonus” genes that seemed to boost its ability to use xylose).
One of the genes, GRE3, was a known member of a xylose utilization pathway. But the other gene, the molecular chaperone APJ1, was not known to be involved in metabolizing xylose. The authors hypothesize that APJ1 might stabilize the GRE3 mRNA.
These two genes may not be enough to create an industrially viable, xylose fermenting Saccharomyces just yet. But the novel methods of gene isolation presented in this study may allow researchers to find additional genes that might one day get them there. Then we will have a way to get ethanol without the large carbon footprint and without the human cost.
A genetic engineering approach to getting yeast to ferment agricultural waste
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: biofuel, biomass, ethanol, fermentation, lignocellulosic biomass, Saccharomyces, Saccharomyces cerevisiae, yeast
Cancer’s Chromosomal Chaos Explained (Partly)
June 01, 2012
Because they have the wrong number of chromosomes, cancers can sample many different genetic combinations.
One reason cancer is so tricky to treat has to do with its adaptability. It can quickly try out new genetic combinations until it hits upon one that can survive whatever treatment a doctor is currently throwing at it. The result is return of the cancer after remission.
One way cancer is able to change its genetics so rapidly has to do with chromosome instability. The number of chromosomes in a cancer cell is much less stable than in a normal cell. This allows the cancer cell to constantly explore a wide range of chromosomal combinations.
It is still an open question how this dynamic instability happens. The gene-centric theory suggests that mutations in key genes are the main driving force. The chromosome-centric model says that having the wrong number of chromosomes is the critical component.
Distinguishing between these two models using cancer cells has proven difficult because these cells always have mutated genes. There is simply no way to look at just chromosome numbers in this system. This is where yeast can help.
In a recent paper published in PLoS Genetics, Zhu and coworkers used yeast to explore whether altered chromosome number was sufficient to explain chromosome instability. They found that chromosome numbers alone can explain some but not all of chromosomal instability.
The authors created various chromosomal combinations in yeast by sporulating isogenic triploid yeast cells. These cells had different numbers of genetically identical chromosomes. They then explored the stability of each chromosome number combination using both FACS and qPCR.
What they found was that chromosome number certainly impacted chromosomal stability. Chromosome number became less and less stable as the chromosome number veered further and further from the haploid state. Of course, once the cells became diploid, stability returned.
The authors explain this with the idea that there is only so much cellular machinery to move chromosomes to the proper place during mitosis. As more and more chromosomes are added to the cell, the machinery becomes increasingly taxed, resulting in more and more errors.
But once the diploid state is reached, all the genes are present to make twice as much mitotic machinery. Now stable chromosome segregation can happen.
This was the broad pattern Zhu and coworkers observed but it certainly wasn’t the whole story. The authors found islands of stability in the chromosomal chaos.
For example, very often when there were equal numbers of chromosome VII (ChrVII) and chromosome X (ChrX), the chromosome number was more stable than predicted. They explored this further and found evidence that suggested that at least part of this was due to the MAD1 gene on ChrVII and the MAD2 gene on ChrX.
Stable chromosome numbers required that these genes be present in a 1:1 ratio. Once the ratio strayed from one, chromosomal instability increased. But these genes don’t explain everything. There were unstable combinations where the MAD1/MAD2 ratio was correct. As might be expected, there are other gene combinations that can lead to instability as well.
So incorrect chromosome number alone can explain the chromosomal instability seen in cancer cells. But genes clearly play a role too, as evidenced by the islands of stability and the MAD1 gene and MAD2 genes. As usual, reality is probably a combination of the two models.
So it looks like chromosome number does play an important role in chromosomal instability. Too many chromosomes may overtax the mitotic machinery so that chromosomes end up mis-segregated.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight, Yeast and Human Disease
Tags: cancer, chromosomal instability, chromosome, genetics, Saccharomyces cerevisiae, yeast
Yeast, Place your Bets
May 25, 2012
Life is a balancing act. An organism needs to grow and divide as fast as possible in its current environment. But it also needs to be able to survive when the environment changes.
One way nature has come up with to deal with this balancing act is called bet hedging. Basically some members in a population grow well in one set of circumstances and another set grows well in another.
Now this makes obvious sense when looking at members of a species that vary genetically. Where it gets interesting is when bet hedging happens in a clonal population.
The idea is that even though they share the same DNA, there are epigenetic differences that cause subtle variations in gene expression levels between individuals. These differences in gene expression patterns result in altered survival rates under different circumstances.
This phenomenon has been difficult to study because researchers need to focus on individuals and not populations. Growth curves in liter flasks are of little use.
But now Levy and coworkers have come up with a new high throughput assay that allows them to look at how a few individuals are growing. This has allowed them to quantitate how different individuals grow in a population and why the slower growers and/or the elderly are better able to survive stress.
The assay uses time-lapse bright-field microscopy to look at tens of thousands of microcolonies all at once. What they find is a wide range of growth rates. Somewhere between 1.3-10% of microcolonies grow at less than half the rate of the population as a whole (the number depends on the strain).
The researchers identified multiple genes that impacted the range of growth rates within a population without necessarily affecting the overall growth rate. In other words, this phenomenon isn’t simply due to chance–there are key genetic factors that help determine the amount of individual to individual variation in a population.
Levy and coworkers focused on Tsl1p, a component of the trimeric complex that synthesizes trehalose. What they found was that those cells that made more Tsl1p divided less often and so grew more slowly. Remember again, this is in a clonal population.
Trehalose is thought to help preserve proper protein folding under stress. So the idea is that some subset of individuals is primed for stress but that in turn, this preparation makes them grow more slowly. And this is just what the researchers found.
When they subjected colonies to heat stress, those that made lots of trehalose were more likely to survive. But the survivors didn’t stay slow growing for long. After multiple generations, the population returned to the original growth rate with the original individual to individual variation. The phenotype was reversible.
Finally the researchers discovered that older yeast cells tended to make more trehalose and so survived stress better. It may be that as a yeast cell gets older, it makes more Tsl1p which helps to set up the range of growth rates among individuals. This may be one way individual to individual variation has evolved in yeast.
Bet hedging is obviously a great way to ensure the survival of a clonal population. Under ideal conditions, the fast growers can grow like mad, spreading themselves far and wide. But when conditions become more hostile, a few slower, tougher individuals can survive to keep the population alive.
Video showing that slow growers survive heat shock and then revert to fast growers.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: bet hedging, epigenetics, Saccharomyces cerevisiae, Tsl1, yeast
When Heat is Not the Burning Issue
May 04, 2012
Just because a mutation looks temperature sensitive, that doesn't mean it is.
When is a temperature sensitive mutation not a temperature sensitive mutation? When the process being studied is affected by temperature too.
Mutations conferring temperature sensitivity – that is, a phenotype that only appears at higher- or lower-than-normal growth temperature due to the loss or alteration of function of a gene product at that temperature – have for decades been an invaluable tool in dissecting many biological processes in yeast and other model organisms. Now there is reason to question whether some temperature sensitive phenotypes might actually be the result of a more complex interaction between multiple genes.
This synthetic genetic interaction is easy to mistake for the effect of a single mutation. A scientist starts out with a mutation that weakens a gene in a certain process. Unfortunately for the scientist, the process being studied is itself weakened by higher temperatures. The effect of the higher temperature combines with the effect of the mutation to shut down the process. The mutation looks temperature sensitive even when it isn’t.
And this does not appear to be merely a theoretical concern. As Paschini and coworkers show in a new study out in GENETICS, something similar may have happened with key mutations used to study telomere function in yeast.
These researchers looked at several mutations but we’ll focus on the work they did with cdc13-1. A key experiment that had been previously done with this mutation dealt with the effects of the loss of cdc13 function in the absence of RAD9.
Basically, researchers had found that prolonged incubation of a cdc13-1/Δrad9 strain at 36° severely compromised viability. These results were used to infer CDC13 function based on its loss at 36°. However, Paschini and coworkers provide compelling data that cdc13-1 behaves equally poorly at 23° and 36°.
First off, they showed that prolonged incubation of wild type yeast at the temperatures used in these studies (36°) resulted in shorter telomeres. They found very little effect on telomere length at 32°.
Next they showed that biochemically, cdc13-1 didn’t behave like a temperature sensitive mutation. Strains with cdc13-1 produced around 4-fold less protein at both 23° and 36° and the protein that was made bound telomeres equally well at both temperatures.
They argue from these two pieces of data that the loss of viability comes from a combination of the compromised cdc13-1 mutation and the effects of higher temperature on telomere function. Something is going on in the rad9 experiment but it is not due to an increased loss of CDC13 function at higher temperatures. There is some other factor involved that is being inhibited.
Of course it could be that cdc13-1 still confers temperature sensitivity, but that they didn’t have the right biochemical assay to see it. To address this issue, they generated five new mutations in cdc13 that behaved more like traditional temperature sensitive mutations.
They focused on one, cdc13-S611L, that was compromised for protein production at temperatures of 32° and above. They then repeated the rad9 double mutant experiment at 32° and 36°. They found that viability was compromised only at 36° even though Cdc13p was equally absent at both 32° and 36°. These results suggest that the loss of viability at 36° is not only the result of cdc13-1.
If this and other results hold up, scientists will need to rethink what previous experiments meant and they may need to modify their models. This should also get other researchers thinking about their temperature sensitive mutations. It is important to confirm biochemically that a mutation indeed makes a specific gene product temperature sensitive. Because sometimes even if it quacks, it isn’t a duck…
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: cdc13, synthetic lethal, telomere, temperature sensitive, yeast
Idling Transcription Factors
April 24, 2012
Transcription factors may lie in wait on the DNA, waiting for a signal to pounce. Image courtesy of DesktopNexus http://www.desktopnexus.com/.
A new study by Lickwar and coworkers suggests that many transcription factors fidget on and off the DNA, waiting for some signal to get to work. Once they get that signal, they clamp down and start affecting the activity of nearby genes.
If true this would help explain some perplexing results researchers have been getting with chromosomal immunoprecipitation (ChIP) assays. Transcription factors appear to be bound at many places where they are not affecting any nearby genes. Now we might have an idea why.
These researchers came up with this model through the use of an elegant, in vivo competition study. What they did was to set up a yeast strain that contained two different versions of the transcription factor Rap1p. One version was tagged with a FLAG epitope and was under the control of RAP1’s endogenous, constitutive promoter. The other version was tagged with a Myc epitope and was under the control of an inducible promoter.
They started out seeing where Rap1p was bound in the absence of the inducer by using an antibody against FLAG. This is the equivalent of a typical ChIP experiment. They found Rap1p was bound in many places throughout the genome including sites where it did not appear to affect any nearby genes.
Then they added the inducer galactose and at various time points repeated the ChIP experiment with antibodies against either FLAG or Myc. They were basically looking for how quickly the Myc-tagged Rap1p replaced the FLAG-tagged Rap1p with the idea that less stably bound transcription factors would be replaced more quickly.
They indeed found that some sites were better able to withstand the onslaught of Myc-tagged Rap1p. And more importantly, that these sites were near genes most influenced by Rap1p. In other words it appears that the more stably bound the Rap1p, the bigger the effect it has on nearby genes.
They then went on to show that more stable binding correlated with lower nucleosome occupancy and stronger in vitro binding. From this data they propose a model where the level of the effect on transcription is the result of a competition between nucleosome and transcription factor binding. Stronger transcription factor binding keeps nucleosomes away so transcription can proceed.
They took the model one step further and proposed that transcription factors are idling on the DNA, waiting for a signal to bind more tightly and influence the activity of nearby genes. In other words, transcription factors are ready to have an effect at a moment’s notice.
This part of the model still has to be proven though. All that has been shown so far is that a slow off rate is required for effective transcription activation by Rap1p. What we don’t know is whether this translates to other transcription factors or if idling Rap1p is ever more stably bound.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: ChIP, chromosomal immunoprecipitation, competition, off rate, stable binding, transcription factor, yeast
Yeast to the Rescue (of Personalized Medicine)
April 09, 2012
Yeast may help genomic researchers with their torrent of data.
Genomic scientists are quickly being overwhelmed by all of the data they are generating. As trillions of A’s, T’s, C’s and G’s come pouring out of sequencers all over the world, how is anyone going to make sense of it all?
One idea is to use yeast to quickly figure out what effect certain differences have on a gene’s function. Now this won’t be that useful for differences outside of genes or in genes that aren’t shared by yeast and humans. But that still leaves an awful lot of SNPs that we might be able to better understand using the awesome power of yeast genetics.
In the most recent issue of GENETICS, Mayfield and coworkers use yeast to study a large number of variants in the human cystathione-beta synthase (CBS) gene. They chose this gene because it is involved in the metabolic disease homocystinuria, different variants respond to treatment in unpredictable ways, and it can substitute for the yeast homolog, CYS4.
The hope was that they would be able to group CBS variants based on their phenotype in yeast and that this would let them predict which treatments would work for novel variants. They were definitely able to group variants based on phenotype. Time will only tell whether they can use this to better treat patients who come into the clinic with novel variants of the gene.
They looked at 84 known alleles of CBS that affected an amino acid with a single base pair change (81 were from homocystinuria patients). They grouped these alleles based on growth phenotypes in yeast under varying conditions. For example, they determined how well each grew in the absence of glutathione. Only those alleles that were still functional would support growth. They also varied the amount of glutathione, looked at the effect of heme and vitamin B6, studied metabolite profiles with mass spectroscopy and so on.
From this they were able to group many of the alleles in clinically meaningful ways. This means that when a novel allele comes up in a patient, they can screen it in this yeast assay to see if it falls within a known group. At least 38 never before seen missense mutations have been found in the CBS gene since 2010 and undoubtedly new ones will keep appearing as more DNA is sequenced.
The study also revealed alleles that were more difficult to interpret in this assay. For example, some alleles known to cause disease did not affect yeast growth. This might mean that their particular mutation needs something human and/or patient specific to manifest itself or that the enzyme function is fine but something else is wrong.
This study provided a powerful proof of principle. The next step will be to see how well it works in practice and if any patients can benefit.
Benjamin deals with his homocystinuria
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight, Yeast and Human Disease
Tags: CBS gene, CYS4, high throughput screen, homocystinuria, personalized medicine, yeast
Studying Aging in Yeast Just Got Easier
April 02, 2012
New microfluidic dissection technique shows different classes of vacuoles in aging yeast cells.
Watching a yeast cell age can be a real pain. In budding yeast like Saccharomyces cerevisiae, the buds quickly outnumber the mom. Which means scientists need to remove the buds as they appear.
Up until now, scientists have had to use a 50-year-old method that involves removing the buds by hand. Not only is this labor intensive, but the field is held back by the inability to use high resolution microscopy to investigate the aging process.
These technical limitations may soon be swept aside with a new microfluidic dissection technique described by Lee and coworkers in a recent study out in PNAS. These researchers were able to monitor 50 aging yeast at once with a variety of microscopic techniques without having to remove the buds by hand. And unlike the older technique, they were able to keep a constant environment for the yeast cells (i.e. no decrease in nutrients and/or build up in wastes).
Basically Lee and coworkers tucked the yeast mother cells under a micropad which they then washed with a constant flow of nutrients. Because the daughter cells are smaller than the mother, they are washed away as they emerge. So no manual bud removal is required.
Sounds convenient but the researchers needed to show that this new technique gave similar results as compared to the old one. And they did.
They showed that mutant strains behaved similarly with both techniques. So a SIR2 deletion mutant still had a shorter lifespan and a FOB1 deletion mutant still lived longer with microfluidic dissection. Not only that, but the number of divisions in an average yeast’s lifetime was comparable with both techniques. At first blush the techniques do seem comparable.
Now they were ready to take their new technique out for a spin to see what it could do. First they were able to show heterogeneity in how yeast cells age. Some cells died as spheres around their 12th division while others died as ellipsoids after their 25th division. The shape of the yeast later in life correlated with how long that yeast lived.
The researchers were also able to use GFP to explore the vacuoles of aging yeast. They found three classes of vacuoles: tubular, fused, and fragmented. The tubular vacuoles were only found in the longer-lived ellipsoid yeast.
Researchers could not have discovered these properties of aging yeast without the new microfluidic dissection technique. And these findings are really just the tip of the iceberg of what can now be learned about aging by studying yeast. It will be exciting to see what else scientists will be able to learn about the twilight of a yeast cell’s life.
Life and Death of a Single Yeast
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: aging, microfluidic, vacuole, yeast