New & Noteworthy

SGD December 2015 Newsletter

December 18, 2015

SGD periodically sends out a newsletter to colleagues designated as contacts in SGD. This December 2015 newsletter is also available on the community wiki. If you would like to receive the SGD newsletter in the future please use the Colleague Submission/Update form to let us know.

Categories: Newsletter

Happy Holidays from SGD

December 17, 2015

We want to take this opportunity to wish you and your family, friends and lab mates the best during the upcoming holidays. Stanford University will be closed for two weeks starting at 5:00 p.m. PST on December 18th, reopening on January 4th, 2016. Although SGD staff members will be taking time off, please rest assured that the website will remain up and running throughout the winter break, and we will attempt to keep connected via email should you have any questions.

Categories: Announcements

Yeast on Their Best Behavior

December 10, 2015

family dinner

How we act depends on whom we are with. Turns out, the same thing is true for yeast cells in a colony. Different cells do different things depending on who is near them. (Image from eyeliam on flickr.)

As the holidays approach, many of us are getting ready to crowd around the table for a big family dinner. Some of us may behave differently around family than we might with friends or coworkers.

For example, with your relatives, you might bite your tongue if your political views vary greatly from theirs. Where we are and with whom we interact can sometimes affect what we do.

Turns out that yeast growing in a colony can be the same way. Though of course they aren’t keeping their opinions to themselves. (Well, we don’t think they are…)

A yeast cell can end up acting differently depending on where it is in a colony. For example, only a narrow band of cells gets to sporulate while all the others are left to plod through mitosis.

A new study out in GENETICS by Piccirillo and coworkers shows that these cells sporulate because nearby cells “encourage” them to. They are being influenced to sporulate because of the cells around them. Just like your relatives might influence you to change your behavior at the dinner table.

The first step in showing that one set of cells signals a second set to sporulate was to find the genes involved in setting up this pattern. Since the authors were looking at Saccharomyces cerevisiae, it was pretty easy to get mutants to study. They just had to open their freezer and pull out their yeast homozygous diploid deletion library.

Initially, they looked for strains where the usual pattern of sporulating cells was disrupted. They then took these candidates and looked for those that could still sporulate normally in suspension. They wanted mutants that could sporulate but couldn’t do it in the right place.

They found seven strains that fit the bill. Three of the deleted genes, MPK1/SLT2, BCK1, and SMI1, were in the cell-wall integrity pathway (CWI). They also showed that mutation of three other genes in the pathway, SLG1/WSC1,TUS1 and RLM1, all impacted colony sporulation as well.

Further work showed that the transcription factor RLM1 was induced 1-2 days before the master regulator IME1 was turned on. IME1 is a key player in getting meiosis started so that yeast cells can sporulate.

So the story seemed to be that RLM1 is turned up which then turns on IME1, which kick starts meiosis. Makes sense except it is unlikely that Rlm1p is directly activating IME1. There is no obvious Rlm1p site in the IME1 promoter.

A close look at the colonies showed that RLM1 is upregulated in a layer of cells just under the ones where IME1 is upregulated. Deletions in the CWI pathway seemed to have disrupted a group of “feeder” cells whose job it is to get nearby cells to sporulate.

To show this, the authors used a chimeric colony assay that consisted of two strains. The first strain, which had functional Rlm1p, had a reporter, either RFP or lacZ, under the control of the IME1 promoter. The second strain was either wild type or deleted for the transcription factor RLM1.

They created colonies with equal amounts of each strain and looked at IME activation. The idea is that if RLM1 is important in the cells that sporulate, then the second strain shouldn’t matter. You should get the same number of cells in which the IME1 promoter is activated whether or not adjacent cells express RLM1.

But if it is important for RLM1 to be expressed in nearby cells, then there should be a falloff in activation if adjacent cells are deleted for RLM1. This is just what the authors found.

And it wasn’t just the artificial reporter system that was affected either. There was also a drop off in the number of cells that sporulated in the case where some of the cells lacked RLM1.

In a further set of experiments, Piccirillo and coworkers showed that these feeder cells became more osmosensitive compared to the ones that go on to sporulate. While they did not find the signal that prompted the meiosis of nearby cells, this change in osmosensitivity is consistent with the cells preparing to release something into the environment.

So it looks like activating the CWI pathway in one set of cells causes a second set to start down the road of sporulation. And if the CWI pathway is disabled in these cells, then the second set of cells no longer changes their behavior and begin to go through meiosis.

This all seems weird at first until you realize that the cells in a colony usually all share the same DNA. What is good for one set of cells is good for the survival of the DNA even if it is at the expense of other cells in the colony.

Yeast cells tend to sporulate when food grows scarce. But sporulating takes a lot of energy. Colonies may get around this paradox by having some of the cells in the colony give up nutrients or energy to a few cells that go on to sporulate. The feeder cells deprive themselves so that other cells have a better shot at survival.

Now the DNA, shared by all the cells, can live on for the next round of holiday dinners….

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

Categories: Research Spotlight

Tags: IME1 , RLM1 , sporulation , cell wall integrity pathway

Keeping Gen(i)e Drives in Their Lamps

December 02, 2015

It was important to contain Jafar to his lamp. The same is true for keeping gene drives in their cells. Image from MissRagamuffyn on flickr.

Everyone knows about genies. They have almost infinite power, can grant you three wishes, and are kept under control by the owner of their lamp.

And as we saw in Disney’s Aladdin, it is a good thing that the lamp is around! When the evil sorcerer Jafar was given the powers of a genie, he began to take over the world. Until, that is, Aladdin forced him back into his lamp where he could be kept under control.

In the last few years, scientists have come up with their own genies. While not as powerful as the “real” ones, these gene drives can still pack quite a punch. And maybe even grant us a few wishes.

Gene drives can force genes to spread quickly through a population whether those genes are good for a species or not. This means we might be able, for example, to force a “bad” gene to spread through the mosquitoes that transmit malaria. By causing the mosquito population to crash, our wish to save hundreds of thousands of lives each year would be granted!

But just like a genie, we need to keep gene drives under control. We do not want something that overrides natural selection to escape and wreak havoc with ecosystems.

Which is where, as usual, our friend yeast can help! In a new study out in Nature Biotechnology, DiCarlo and colleagues use yeast to test two different strategies to make gene drives safe enough to use. And, they argue, safe enough to research.

Gene drives are based on the idea of homing endonucleases. Basically, if a gene associated with a gene drive is on just one of the two chromosomes in a pair, the gene drive will copy and insert the gene into the other chromosome through a precise DNA cut.

Now both chromosomes end up with a copy of the gene. Which of course means all of the offspring will get the altered gene too. This copying will happen generation after generation until the new gene has swept through the population.

The idea for gene drives has been around since 2003 but really only became practical with the discovery of the CRISPR/Cas9 system. This genome editing tool, which is ludicrously simple to program to target most any DNA sequence, allows scientists to create most any gene drive they want.

The CRISPR/Cas9 system has two parts. One part is the guide RNA which leads the second part, the endonuclease Cas9, to the right spot in the genome to cut. What makes the system so powerful is that you just need to make a different guide RNA to target different sequences in the genome.

One easy way to help control a gene drive is to keep these two parts separate. Do not have the guide RNA and the Cas9 on the same piece of DNA. Then, if one part were to escape, it couldn’t do anything on its own.

This is of course easy to do in yeast. Just integrate one part into a chromosome and keep the second part on a plasmid.

This is just what DiCarlo and coworkers did. And they showed that this separation can be very effective.

They integrated a guide RNA into the ADE2 gene of a haploid yeast to create a gene drive designed to disrupt ADE2. As expected, this strain produced red colonies on adenine limiting media.

They next mated this strain to a wild type haploid. All of the resulting diploids were cream colored. This is what would be expected as both copies of ADE2 need to be disrupted to see red colonies in a diploid.

When these diploids were sporulated, the researchers got the expected 2:2 ratio of red to cream colored haploids. This all changed when they introduced a Cas9 containing plasmid into the experiment.

In the presence of Cas9, more than 99% of the resulting diploids were red. And when sporulated, these diploids produced all red haploid colonies.

The two parts of CRISPR/Cas9 together drove the disrupted ADE2 through the population. But importantly, just having the guide RNA integrated into ADE2 had no effect on how the two alleles were passed down. Once one part is removed, the gene drive stalls out.

Yeast may show us the way to wiping out these little monsters. If so, hundreds of thousands of deaths from malaria could be prevented each year. Image from Wikimedia Commons.

The same system also worked when the ADE2 gene drive included the URA3 gene so that URA3 spread through the population as well. It also worked when the essential gene ABD1 was targeted.

And genetic background did not significantly affect how well this ADE2 gene drive worked. When they mated their haploid to six different strains of yeast they saw no loss in efficiency.

So separating the two parts of the gene drive is a pretty good failsafe. But of course nothing is perfect.

Ideally we need some way to shut the system down if all of our safety features fail. We want to be able to get rid of Jafar and the lamp entirely if possible.

DiCarlo and coworkers showed that they could create a gene drive that could overwrite and correct the ADE2 they had disrupted with the guide RNA. This new gene drive targeted a synthetic sequence in the original gene which means that it would only affect altered yeast. So even if things go awry, we may be able to erase the changes we made.

These two strategies should help keep gene drives in check both in the wild and the lab. But of course, again, it is important to keep in mind that nothing is foolproof.

At the end of Aladdin, they buried Jafar and his lamp deep in the desert to keep him from causing any more trouble. But his lamp was found and Jafar reemerged to wreak havoc in the second Aladdin movie, reminding us that we must be very careful when unleashing powerful forces.

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

Categories: Research Spotlight

Tags: gene drives , CRISPR/Cas9

Pulsating Yeast

November 19, 2015

A glass of tepid water will do little for a sprained ankle. Just like adding activators and repressors to a gene will have little effect. Image from Wikimedia Commons

Sometimes when you get a minor injury, doctors will recommend alternating heat and cold as a therapy. The heat opens things up and the cold shuts them back down again.

Now obviously it would be pretty useless to apply both at the same time. Adding a bit of lukewarm water to an injury is not going to be very helpful at all.

The same thing holds true for many genes. If activators and repressors all turned on at the same time, there wouldn’t be much of an effect on the expression of a gene regulated by both. It is no way to respond to something in the environment!

Instead, if you want a gene to go up and then go back down again, you’d have the activator turn on first, followed by the repressor. Another way to put this is you’d have a pulse where all of the activators activate their genes at once and then stop working followed by a pulse where all of the repressors work at once.

This is exactly what Lin and colleagues found in their recent study in Nature. There they looked at the effect of certain external stimuli on the timing of when the activator Msn2p activated genes and when the repressor Mig1p repressed genes in our favorite yeast S. cerevisiae. These transcription factors coregulate many of the same genes.

The authors found that in the presence of either lowered glucose concentrations or 100 mM NaCl, most of the Msn2p in the cell turned on first followed closely by the Mig1p repressors. In the absence of either stimulus, there was no coordination.

So there does seem to be a carefully choreographed dance between these two transcriptional regulators with these signals. But of course gene regulation is a bit more complex than a sprained ankle.

There may be situations where a cell wants both regulators to do their jobs at the same time. Sometimes lukewarm water may be just what the doctor ordered.

And this is what Lin and colleagues found with 2.5% ethanol. Under this condition, the pulses of the two regulators overlapped—both were on at the same time. Apparently different stimuli call for different responses which means different timing of transcription factor pulses.

The authors next wanted to get at why Mig1p repression lagged behind Msn2p activation. Since both transcription factors can only enter the nucleus and do their job after they lose a few key phosphate groups, the authors reasoned that perhaps Mig1p dephosphorylation lagged behind that of Msn2p.

They decided to look at the PP1 phosphatase, Glc7p, as previous work had shown that it can indirectly regulate both Msn2p and Mig1p. And indeed, when the authors lowered the expression of GLC7, Msn2p and Mig1p no longer pulsed one after the other at lower glucose concentrations. It looks like Glc7p is a key player in controlling the pulsing of these two regulators.

Even though much of this work was done with synthetic promoters with Mig1p and Msn2p binding sites, the results were not restricted to these artificial constructs. Lin and colleagues found that around 30 endogenous targets also responded to lowered glucose concentrations in a coordinated way just like their synthetic construct. Yeast regulates genes by controlling when activators and repressors pulse.

Finally, all of these studies were done using fluorescent proteins and filming single cells in real time. (Is biology cool or what?) This makes sense because subtle signs of synchronization can be lost when averaged over a large population.

Just like a synchronized swim team, yeast regulates genes by controlling when activators and repressors can work. Image from Wikimedia Commons.

This also allowed the authors to investigate what happens in unstimulated cells. In other words, what happens when both regulators enter the nucleus at the same time? Or if a repressor gets in first?

The first thing they found was that even in the absence of stimulation, there were still pulses. So at seemingly random times, suddenly all of the Msn2p would swoop into the nucleus at the same time and then all leave a short time later. Or the same thing would happen with Mig1p.

If by chance the two entered the nucleus at the same time, both the synthetic reporter and an endogenous gene, GSY1, were not activated. But if Msn2p happens to get in there first, both were activated.

And if the repressor Mig1p managed to get into the nucleus at least 4-5 minutes before Msn2p, activation by Msn2p was muted. The presence of Mig1p beforehand seemed to keep Msn2p from activating coregulated genes to as high a level.

Taken together these results confirm that just like a synchronized swim team, yeast regulates genes by controlling when activators and repressors can work. First there is a pulse where the all of the molecules of a certain activator are primed to do their job and then, after a short time, they all stop doing their job. This can then be followed later by a pulse of repressors shutting it all down.

And this isn’t just in yeast either. For example, these kinds of pulses are important in neuroscience as well.

This work suggests that in dissecting regulatory pathways, researchers may need to pay more attention to the timing of pulses. Then they can see that hot followed by cold makes much more sense than both together.

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

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae , transcription regulation

Yeast, the Spam Filter

November 11, 2015

If you don’t have a good spam filter for your email, you may be overwhelmed—just as the sheer number of variants of human genes can be overwhelming. Luckily, yeast can help us filter out the variants that matter. Image by Jean Pierre Gallot via Flickr

Imagine what our email inboxes would look like if we didn’t have spam filters! To find the meaningful emails, we’d have to wade through hundreds of messages about winning lottery tickets, discount medications, and other things that don’t interest us.

When it comes to sorting out meaningful mutations from meaningless variation in human genes, it turns out that our friend S. cerevisiae makes a pretty good spam filter. And as more and more human genomic sequence data are becoming available every day, this is becoming more and more important.

For example, when you look at the sequence of a gene from, say, a cancer cell, you may see many differences from the wild-type gene. How can you tell which changes are significant and which are not?

SuperBud to the rescue! Because many human proteins can work in yeast, simple phenotypes like viability or growth rate can be assayed to test whether variations in human genes affect the function of their gene products. This may be one answer to the increasingly thorny problem of variants of uncertain significance—those dreaded VUS’s.

In a new paper in GENETICS, Hamza and colleagues systematically screened for human genes that can replace their yeast equivalents, and went on to test the function of tumor-specific variants in several selected genes that maintain chromosome stability in S. cerevisiae. This work extends the growing catalog of human genes that can replace yeast genes.

More importantly, it also provides compelling evidence that yeast can help us tell which mutations in a cancer cell are driver mutations, the ones that are involved in tumorigenesis, and which are the passenger mutations, those that are just the consequence of a seriously messed up cell. Talk about a useful filter!

The researchers started by testing systematically for human genes that could complement yeast mutations. Other groups have done similar large-scale screens, but this study had a couple of different twists.

Previous work from the Hieter lab had identified genes in yeast that, when mutated, made chromosomes unstable: the CIN (Chromosome INstability) phenotype. Reduction-of-function alleles of a significant fraction (29%) of essential genes confer a CIN phenotype. The human orthologs of these genes could be important in cancer, since tumor cells often show chromosome rearrangements or loss. 

So in one experiment, Hamza and colleagues focused specifically on the set of CIN genes, starting with a set of 322 pairs of yeast CIN genes and their human homologs. They tested functional complementation by transforming plasmids expressing the human cDNAs into diploid yeast strains that were heterozygous null mutant for the corresponding CIN genes. Since all of the CIN genes were essential, sporulating those diploids would generate inviable spores—unless the human gene could step in and provide the missing function.

In addition to this one-to-one test, the researchers cast a wider net by doing a pool-to-pool transformation. They mixed cultures of diploid heterozygous null mutants in 621 essential yeast genes, and transformed the pooled strains with a mixture of 1010 human cDNAs. This unbiased strategy could identify unrecognized orthologs, or demonstrate complementation between non-orthologous genes.

In combination, these two screens found 65 human cDNAs that complemented null mutations in 58 essential yeast genes. Twenty of these yeast-human gene pairs were previously undiscovered.

The investigators looked at this group of “replaceable” yeast genes as a whole to see whether they shared any characteristics. Most of their gene products localized to the cytoplasm or cytoplasmic organelles rather than to the nucleus. They also tended to have enzymatic activity rather than, for example, regulatory roles. And they had relatively few physical interactions.

So yeast could “receive messages” from human genes, allowing us to see their function in yeast. But could it filter out the meaningful messages—variations that actually affect function—from the spam? 

The authors chose three CIN genes that were functionally complemented by their human orthologs and screened 35 missense mutations that are found in those orthologs in colorectal cancer cells. Four of the human missense variants failed to support the life of the corresponding yeast null mutant, pointing to these mutations as potentially the most significant of the set.

Despite the fact that these mutations block the function of the human proteins, a mutation in one of the yeast orthologs that is analogous to one of these mutations, changing the same conserved residue, doesn’t destroy the yeast protein’s function. This underscores that whenever possible, testing mutations in the context of the entire human protein is preferable to creating disease-analogous mutations in the yeast ortholog.

Another 19 of the missense mutations allowed the yeast mutants to grow, but at a different rate from the wild-type human gene. (Eighteen conferred slower growth, but one actually made the yeast grow faster!)

For those 19 human variants that did support life for the yeast mutants, Hamza and colleagues tested the sensitivity of the complemented strains to MMS and HU, two agents that cause DNA damage. Most of the alleles altered resistance to these chemicals, making the yeast either more or less resistant than did the wild-type human gene. This is consistent with the idea that the cancer-associated mutations in these human CIN gene orthologs affect chromosome dynamics.

As researchers are inundated by a tsunami of genomic data, they may be able to turn to yeast to help discover the mutations that matter for human disease. They can help us separate those emails touting the virtues of Viagra from those not-to-be-missed kitten videos. And when we know which mutations are likely to be important for disease, we’re one step closer to finding ways to alleviate their effects. 

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight Yeast and Human Disease

Tags: chromosome instability , yeast model for human disease , functional complementation , Saccharomyces cerevisiae

New SGD Help Video: Variant Viewer

November 05, 2015

Using SGD’s Variant Viewer, you can compare the nucleotide and protein sequences of your favorite genes in twelve widely-used S. cerevisiae genomes. This tool shows alignments, similarity scores, and sequence variants for open reading frames (ORFs) from the different strains relative to the S288C reference genome. Sequence data are derived from Song et al., 2015.

Take a look at our new video tutorial to get started with the Variant Viewer, and let us know if you have questions or suggestions.

Categories: Tutorial Sequence

SGD Help Video: Mutant Phenotypes

November 04, 2015

SGD’s Phenotype pages present detailed information about single mutant phenotypes for a particular gene, along with references for each observation. Phenotype pages are accessible from the ‘Phenotype’ tab of the Locus Summary and is also linked from the Mutant Phenotypes section of the Locus Summary, where the phenotype data are presented in summary form. Data are presented in tabular form on the Phenotype page.

This brief video will give you an overview of the contents and organization of SGD’s Phenotype pages.

Categories: Tutorial

SGD Help Video: Literature Page

October 29, 2015

If you’re interested in finding all the published literature about a gene or protein, there’s no need to wade through long lists of PubMed results. SGD curators have already done that for you! We review PubMed weekly for new papers about S. cerevisiae. You can find papers about a specific gene or protein on its Literature tab page (see an example).

Articles on the Literature page are categorized by several topics. The Primary Literature section lists papers in which the gene of interest is a primary focus of the study, while the Additional Literature section lists papers in which the gene is mentioned but is more peripheral to the research. There are other categories of references, and also a cool interactive graphic that shows the relationships between papers that are about the same set, or overlapping sets, of genes. You can get to the Literature page for a gene or protein via the Literature tab, located at the top of its Locus Summary page and all of its other tab pages.

Categories: Tutorial

Tags: video

New SGD Help Video: GO Term Finder

October 26, 2015

Our GO Term Finder tool lets you start with a list of genes—perhaps a set of genes that are co-regulated, or a group of genes that can all mutate to the same phenotype—and analyze their Gene Ontology (GO) annotations to find out what else they might have in common.  GO Term Finder searches for significantly shared terms within the GO annotations associated with the genes in your list. It takes advantage of the tree structure of GO to find terms that are related to each other within the ontology.

Finding shared terms within a gene set can bring meaning to experimental results and suggest new avenues to explore. For example, if the GO Term Finder results show that most of the genes in your co-regulated set mediate steps in a pathway, this might be a hint that the uncharacterized genes in the set also participate in that pathway. Or perhaps GO Term Finder will show that a group of genes that can mutate to confer resistance to a certain drug are all annotated to a certain cellular location, suggesting a mechanism for the effects of that drug. Give it a try and see what interesting results your gene list has in store!

Our new SGD Help video gives you a quick overview of how to use the GO Term Finder. You can find all the details on our GO Term Finder help page.

Categories: Tutorial

Tags: Gene Ontology , video , GO Term Finder

Life Needs to be More Like a 1950’s Chevy

October 21, 2015

Stripped of modern bells and whistles, cars last a lot longer. The same may be true of life. It may last longer when some extra, nonessential genes are removed. Image via Wikimedia Commons

In the old days, a car came with the bare minimum of features to get from point A to point B. The windows rolled down with a crank and it usually had a radio. That was about it.

As the car has evolved, it has gained a huge number of bells and whistles. There are power windows and power brakes, a baffling number of computer-based bonus features, personal wifi hotspots, and so on. All of these have undoubtedly made cars more fun and comfortable to drive. But they have come at a cost. Many cars simply do not last as long as their predecessors because these extras break easily.

Turns out life may be like a modern car. It has lots of nice features that help it to do better in the world. But a lot of these features may shorten its life span.

This point was reinforced in a recent study by McCormick and coworkers. They painstakingly searched through a library of 4,698 single gene deletion strains in S. cerevisiae and found that 238 of these strains were able to produce significantly more buds over their lifetime. Many nonessential genes seem to shorten a yeast’s life.

And boy was it painstaking! Believe it or not, they manually dissected over 2.2 million individual yeast daughter cells to generate these results. Luckily it was worth it, as they found so many interesting things.

First off, many of the genes they found fall into a set of five pathways that includes cytosolic and mitochondrial translation, the SAGA complex, protein mannosylation, the TCA cycle, and proteasomal activity. So there are certain pathways we can target to extend the lifespan of our friend yeast. And even better, yeast may not be the only beneficiary of these studies.

Two of the pathways, cytosolic and mitochondrial translation and the TCA cycle, have also been found to be significant in extending the life of the roundworm C. elegans. These pathways are also shared with humans.

And just because the authors found no overlap with the other three pathways in other beasts doesn’t mean they may not be targets for life extension in them too. It could be that previous screens in C. elegans simply missed genes from these pathways.

It could also be that what is found in yeast may turn out to be important in people but not in C. elegans. For example, the authors failed to find any equivalent to the SAGA complex in C. elegans. Either the roundworm lost this complex during evolution, or the homologs between yeast and C. elegans are so different that they’re unrecognizable. In any event, humans at least do have an equivalent to SAGA, called STAGA.

All of this suggests that there may be common ways to make organisms, including people, live longer, healthier lives. Here’s hoping!

And these five pathways are certainly not the whole story. The majority of the genes McCormick and coworkers identified were not in these five, which means there are probably lots of other ways to get at living longer.

One fascinating example that the authors decided to look at in depth was LOS1. Deleting it had one of the biggest effects on a yeast’s reproductive life span.

At first this seems a little weird, as Los1p exports tRNAs out of the nucleus. As expected, deleting LOS1 led to a buildup in tRNAs in the nucleus. The authors confirmed that this buildup is important by showing that overexpressing MTR10, a gene involved in transporting tRNAs from the cytoplasm to the nucleus, led to a longer lived yeast with a buildup of tRNAs in its nucleus.

The next step was to figure out why having a lot of tRNA in the nucleus makes yeast live longer. It was known previously that Los1p is kept out of the nucleus under glucose starvation conditions. The authors confirmed this result.

Most everyone knows that restricting calorie intake (also called dietary restriction or DR) can extend the lives of most every beast tested so far, including yeast. The authors found that growing a los1 deletion strain at low glucose did not increase the lifespan of this strain any further. It thus appears that an important consequence of DR is keeping Los1p out of the nucleus and thereby increasing the amount of tRNA in the nucleus.

While we don’t know yet exactly why keeping tRNAs in the nucleus helps yeast live longer, it is interesting that the increased lifespan associated with the loss of LOS1 is linked to caloric restriction. Finding a way to inhibit Los1p has to be better than starving yourself!

This study has identified 238 genes to follow up on for future studies. And of course there is a whole class of genes that haven’t yet been investigated—the essential genes! Many of these may be important for extending life too. 

Stripping life down to its bare essentials may help individuals live longer at the expense of being the most fit in terms of survival in the hurly burly world of nature. After all, those “nonessential” genes undoubtedly have a function in helping yeast outcompete their less well-endowed yeast neighbors. Just like those power sliding doors are way better than the manual ones on a minivan.

But if you want a long-lived minivan, get the one with the manual doors. And if you want a long-lived yeast (or person), get rid of some of those nonessential genes that cause you to break down.

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

Categories: Research Spotlight

Tags: aging , Saccharomyces cerevisiae , lifespan

You Can Take Yeast Off of the Grapevine, But…

October 14, 2015

All over the world and through the ages, people have moved from the country into the big city to look for a better life. These folks often find that even though they can adapt to city life and city ways, they still hang on to their core country values. As the old saying goes, “You can take the boy out of the country, but you can’t take the country out of the boy.”

Even when he goes to the city, the country mouse hangs on to his country ways. The same is true for S. cerevisiae—even though it entered the lab, it still clung to genes that were most useful out in the vineyard. Illustration by Arthur Rackham (1912) via Wikimedia Commons

Our friend Saccharomyces cerevisiae didn’t migrate voluntarily into the lab. But it ended up there, and has been as lonely as a new migrant in a big city. 

Which is of course how we need it to be. One of the basic tenets of classical microbiology is that you can’t begin to study an organism until you’ve isolated it in a pure culture.

And studying pure S. cerevisiae has yielded a huge body of knowledge about molecular biology, cell biology, and genetics. But by not studying yeast in the context of its old country home, we may have missed a few things.

In a new article in PLOS ONE, Rossouw and colleagues uncover one of them. S. cerevisiae has a family of FLO genes that promote flocculation, the adherence of yeast cells to each other. It has always been a bit puzzling why a whole family of genes that are pretty much redundant with each other would be maintained through evolution.

When the researchers took S. cerevisiae out of its lab isolation by mixing it with other yeast species, they found that the different flocculation genes actually determine which species it can co-flocculate with. Different Flo proteins prefer different partners. 

This discovery helps us understand the evolution of this gene family and also opens the door to further study of inter-species interactions in the vineyard. And since flocculation is an important property in winemaking and brewing, there could even be tasty practical applications of this knowledge.

The researchers started by surveying 18 non-Saccharomyces yeast strains that are found in vineyards. They looked at the ability of the yeasts to flocculate both as pure strains and when mixed with either of two S. cerevisiae wine strains.

Intriguingly, certain species showed a synergistic effect when mixed with S. cerevisiae, flocculating more than either species on its own. Rossouw and colleagues used microscopy to confirm that the “flocs” did indeed contain both yeast species—a simple observation, since the cells of different species have slightly different shapes.

To test the effects of different FLO genes on co-flocculation, the authors assayed the co-flocculation ability of flo1, flo10, and flo11 deletion mutants as well as Flo1, Flo5, and Flo11 overproducers in individual combinations with six of the non-Saccharomyces yeasts. 

The results showed that Flo1 has general effects on flocculation. Overproduction increased co-flocculation across the board with all the species tested, while deletion of FLO1 consistently decreased it. In contrast, deletion of FLO10 didn’t have much effect on co-flocculation.

It was a different story for Flo5 and Flo11, though. Overproduction of each of these not only affected co-flocculation, but had species-specific or even strain-specific effects. Flo5 overproduction caused a relative increase in co-flocculation with Metchnikowia fructicola and a substantial decrease in co-flocculation with two different strains of Hanseniaspora opuntiae. Flo11 overproduction reduced co-flocculation with one of the Hanseniaspora opuntiae strains but not with the other. 

All of these experiments were done on mixtures of two species at a time. To get S. cerevisiae even further out of the lab, Rossouw and colleagues created a “consortium” of wine yeasts, a mixture of six species that are found in wine must (freshly pressed grapes) at the start of fermentation. They then added the FLO overproducer strains individually to the consortium, to see their effects in a more natural situation.

They let the yeast consortium flocculate, extracted total DNA from the flocculated or supernatant parts of the culture, and then used automated ribosomal intergenic spacer analysis (ARISA) to see which strains had co-flocculated. This technique can determine the relative abundance of different yeast species in a sample by sequencing a particular region of ribosomal DNA.

In this experiment, overexpression of each of the three FLO genes had significant effects on at least one of the species in the consortium. The species composition of the flocculated yeasts was uniquely different, depending on which gene was overexpressed.

The discovery that the flocculation genes have individual effects on association with other species goes a long way towards explaining why S. cerevisiae has maintained this gene family with so many members that apparently have the same function—at least, when you study a pure culture. Differential regulation of the FLO genes could affect the spectrum of other species that our favorite yeast interacts with. 

So, our friend S. cerevisiae didn’t actually get out of the lab in these experiments, but at least it got to rub shoulders with some of its old friends (buds?) from the vineyard. These experiments are a good reminder for researchers to think outside the lab.

And when S. cerevisiae and its friends get together outside the lab, beautiful things can happen. We’ll drink a toast to that!

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

If yeast could sing about its forced migration to the lab, it might sound like this.

Categories: Research Spotlight

Tags: vineyard , Saccharomyces cerevisiae , flocculation

Unleashing the Awesome Power of Yeast Transcription

October 07, 2015

With the right mutations, yeast can activate transcription over long distances—just as with the right social media, distance is meaningless for finding and organizing like-minded people. Image via

In the old days, before the internet, planes or even mass publishing, it was hard to spark a quick, worldwide movement. You simply couldn’t reach out to likeminded people who lived far away.

Nowadays things are very different. With the advent of social media, it is now trivially easy to spread the word. Using Twitter and Facebook, organizers can easily and effectively organize people who live on the other side of the world.

In terms of transcription activation, our friend Saccharomyces cerevisiae seems to be stuck in the old world. Its transcription factors can only turn up nearby genes. This is different from most other eukaryotic beasts, where activation at a distance is routine.

Except maybe yeast isn’t as backward as we think. It may be that yeast has the potential to activate transcription at a distance, but keeps that potential locked away.

This idea is supported in a new study out in GENETICS by Reavey and coworkers. These authors found that they could mutate away yeast’s inability to activate transcription at a distance.

In other words, this ability is there but is just prevented from being used. Yeast is keeping social media out of the hands of its genes.

Getting around social media/internet controls is not easy. Pressure might need to be applied at multiple points before people power is finally released through social media. And even when it does happen, it can sometimes be hard to figure out exactly why certain events tipped the balance.

Turns out that both of these are also true for long range transcription activation in yeast. Mutating a single gene was not enough—it took mutations in multiple genes to see any significant effect.

As you can imagine, it would be very tricky to hit all of the right mutations for a polygenic trait in one fell swoop. This is why Reavey and coworkers started their mutant hunt with a strain that could already weakly activate transcription from a distance, a strain in which the SIN4 gene was deleted. Now they just needed additional mutations to make the effect stronger.

In their screen they used a reporter in which the GAL4 upstream activating sequence (UAS) was placed 799 base pairs upstream of the HIS3 reporter. This reporter gives very low levels of activity in a wild type strain. They included a second reporter, the URA3 gene under control of the same upstream sequences, because Reavey and Winston had discovered in previous work using a single reporter that cis acting mutations and chromosomal rearrangements were a frequent source of false positive results.

The researchers put the reporter strain through multiple rounds of mutation with UV light and selection with increasing levels of 3AT, a competitive inhibitor of His3. After each round of selection, they measured mRNA levels for HIS3 and URA3 and chose strains that not only had higher 3AT resistance but also showed more transcription of the reporter genes.  In the end they found three strains that survived in the presence of galactose (to turn on the activator) and 10 mM 3AT.

As expected, each strain had multiple mutations. One strain had acquired mutations in the GRR1 and MOT3 genes. To confirm that these were the most important mutations, Reavey and colleagues engineered a fresh strain with just the original sin4 null mutation and the selected grr1-1 and mot3-1 mutations. The fresh strain completely recapitulated the selected strain, showing that these three mutations could unlock yeast’s potential for long-range transcriptional activation.

It makes sense that a grr1 mutation could affect transcriptional activation. Grr1 is a ubiquitin ligase that destabilizes Med3 (also known as Pdg1), a key component of the Mediator complex involved in transcription activation. The researchers provided evidence that this is how the grr1-1 mutation affects the process, by showing that mutating MED3 mimicked the effects of mutating GRR1.

It’s also not too hard to imagine how a mutation in Mot3, a sequence-specific transcriptional activator, could affect transcriptional activation, presumably by changing the expression of a gene under its control.

If you stay away from social media, you’ll lose opportunities for impromptu pillow fights. Who knows what yeast is missing without long-range transcription activation? Image via Wikimedia Commons

The results were not so clear-cut for two other strains that were selected. They arose from the same lineage, and each had acquired the ptr3-1 mutation on top of the original sin4 null mutation. One strain went on to further pick up the mit1-1 mutation, while the other got an msn2-1 mutation.

Again it isn’t too surprising that mutations in genes that encode sequence-specific transcriptional activators like Mit1 and Msn2 arose in these strains. But the selection of the ptr3 mutation in these lineages is something of a mystery.

It is hard to imagine how the usual job of Ptr3 in nutrient sensing and transport would be involved in keeping long range transcription activation down. Perhaps the researchers have uncovered a novel function for this gene.

And re-creating these two strains only partially restored the levels of transcription activation at a distance that were seen in the original strains. A little genetic detective work showed that a big reason for this was that both of the selected strains had acquired an extra copy of the chromosome that had the HIS3 reporter, chromosome III.

Reavey and colleagues deleted each of their identified genes to see which ones caused their effect through a loss of function mutation. Deleting GRR1, PTR3, and MSN2 all had the same effect as the original isolated mutations.

The same was not true for MOT3 and MIT1. Deleting either gene actually weakens long range transcription activation, suggesting that these two had their effect through gain of function mutations.

Finally, the researchers showed that the increase in long-distance transcriptional activation was not simply due to a general increase in transcription activation in the selected strains, by showing that their mutants did not have increased activity of a reporter with the GAL4 UAS placed 280 base pairs upstream of HIS3. In fact, if anything, the strains showed decreased activation with this reporter.

So this experimental strategy allowed Reavey and coworkers to identify some of the key genes involved in keeping transcription activation at a distance under control in yeast. In particular, they found compelling evidence that the Mediator complex is an important player. But there is still plenty of work to do. For example, which of the genes regulated by Mit1, Msn2, and Mot3 are important in long range activation? And what on Earth is Ptr3 doing in all of this?

The success of this approach also confirms that doing repeated rounds of selection in yeast is a viable way to select multiple mutants and study polygenic traits. This strategy may prove a boon for studying the many human diseases that are the result of polygenic traits.

Not only can we use yeast to uncover its activation potential, but we can also now potentially use it to uncover new treatments for human disease. Unleashing another awesome yeast power… 

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

Categories: Research Spotlight

Tags: transcription , Saccharomyces cerevisiae , polygenic trait

New SGD Help Video: GO Slim Mapper

October 05, 2015

The GO Slim Mapper is a very useful tool that maps specific Gene Ontology (GO) annotations to more general GO terms. This allows you to take a group of genes and bin them into broad categories of function, process, or localization by mapping their GO annotations to broader terms.

Watch our new video to get an overview of how the GO Slim Mapper works:

Categories: Tutorial

Tags: GO Slim Mapper , Gene Ontology , video

The Latest Buzz on Stressed-Out Mitochondria

September 30, 2015

Stinging wasps get our attention, and with good reason—getting stung hurts a lot! If you see wasps going into a nest within the walls of your house, you’ll likely try to block their access.

Just like wasps who can’t get into their nest, excess mitochondrial precursor proteins that can’t be imported into mitochondria are bad news for the cell—but it’s developed ways to deal with them. Image via

But this could backfire: instead of being able to peacefully go into their nest, a swarm of angry wasps could be buzzing around looking for trouble. It might get so bad that you’ll need to call in an exterminator to take care of the problem.

Mitochondrial proteins might seem a lot less scary than wasps, but it turns out that they can also cause trouble if they can’t get into mitochondria. In two new letters to Nature, Wrobel and colleagues and Wang and Chen used complementary approaches to ask what happens when dysfunctional mitochondria aren’t able to import all of the proteins that are waiting to get in. 

What they both found was that these piled up proteins cause real problems for the cells. In desperation, the cells slow down protein synthesis to reduce the excess, and also turn to their own exterminator, the proteasome, to keep these proteins under control.

This is a paradigm shift in thinking about how poorly functioning mitochondria cause disease. In the past, almost everyone focused on how damaged mitochondria couldn’t make enough energy for a cell. Now it looks like there are other ways for a nonworking mitochondria to do a cell in. And new targets for scientists to go after in treating mitochondrial disease.

As we all know, mitochondria are the powerhouses of the cell, where energy is generated, and they’re also the site of many other essential biochemical reactions. They’re composed of about 1,000 proteins, and nearly all of those are synthesized in the cytoplasm and then imported into mitochondria by an intricate system of transporters.

In order to find out what happens when these 1,000 proteins don’t get into mitochondria as efficiently as they should, both groups created strains whose mitochondrial import was impaired.

Wrobel and colleagues used the temperature-sensitive mia40-4int mutation, affecting an essential component of the mitochondrial import system. Wang and Chen started with the aac2-A128P mutation in PET9 (which is also known as AAC2), an ADP/ATP carrier of the mitochondrial inner membrane. Overexpression of the aac2-A128P allele causes mitochondrial dysfunction and eventual cell death.

Wrobel and colleagues decided to get a comprehensive look at what happens in the mia40-4int mutant by assaying its transcriptome and proteome, using RNA-seq and stable isotope labelling by amino acids in cell culture (SILAC), respectively. Surprisingly, one of the biggest differences from wild type that they saw in the import-defective mutant was a decrease in cytoplasmic translation. Whether they looked at the mRNAs encoding ribosomal proteins, the proteins themselves, or the polysome content and translational activity of the cells, everything pointed to down-regulation of translation. And at the same time, the proteasome—the molecular machine that breaks down unwanted proteins—was activated.

To verify that what they were seeing wasn’t peculiar to the mia40-4int mutant, Wrobel and colleagues slowed down mitochondrial import in several other ways: using different mia40 mutant alleles, other import mutants, or treatment with a chemical that destroys the mitochondrial membrane potential required for import. Under these different conditions causing the accumulation of mitochondrial precursor proteins in the cytoplasm, they still saw decreased cytoplasmic translation and increased proteasome activity.

Quick, Henry, the Flit!” When mitochondrial precursors start to swarm around the cytoplasm, the cell keeps them under control by activating the proteasome. Upper image, Flit insecticide, by Bullenwächter, lower image, structure of the yeast 26S proteasome by FridoFoe; both via Wikimedia Commons

Wang and Chen took a different approach, looking to see whether over-expression of any other genes could compensate for the lethality of overexpressing the aac2-A128P allele. The researchers transformed the mutant with a library of yeast genes on a multicopy plasmid, and found 40 genes whose expression could keep it alive.

The suppressor genes found by Wang and Chen were all involved in some aspect of synthesis or degradation of cytoplasmic proteins, just like the genes found by Wrobel and colleagues whose expression was altered in the mia40 mutant. And Wang and Chen also verified that these suppressors weren’t specific to the aac2-A128P mutation: they suppressed a variety of other mutations that decreased import.

Both groups observed precursors of mitochondrial proteins accumulating in the cytosol of the mutant strains they studied. Wang and Chen saw a couple other very interesting proteins increase in abundance: Gis2 and Nog2. These proteins are involved in regulating ribosome function, and the researchers speculate that their stabilization during this stress response contributes to the translational down-regulation. Intriguingly, their human orthologs are implicated in neuromuscular degenerative disease.

So, using orthogonal approaches, the two groups converged on the same model: a newly discovered cellular pathway that regulates cytosolic translation and protein degradation in order to deal with the stress of inefficient mitochondrial import. Wrobel and colleagues have named it UPRam, for Unfolded Protein Response activated by mistargeting of proteins, while Wang and Chen call it mPOS, mitochondrial Precursor Over-accumulation Stress.

Before this work, it was unknown whether cytosolic pathways were even affected by mitochondrial dysfunction. Now we know that the cell has a specific response when mitochondrial precursor proteins begin swarming in the cytosol, unable to get into their home: it slows down the production of those proteins and calls in the proteasome exterminator to take care of them.

We usually think of mitochondrial disease symptoms as being caused by the reduced energy generation of sick mitochondria, or by the lack of other key events that happen in mitochondria—for example, the synthesis of the iron-sulfur clusters that some vitally important enzymes need. Now, these findings raise the possibility that proteostatic stress on the cell caused by the accumulation of mitochondrial precursors could also lead to impaired cell function and disease.

Perhaps drugs that inhibited cytoplasmic translation, or activated the proteasome exterminator, would be helpful in reducing the buzzing swarm of mitochondrial precursor proteins. Wouldn’t it be wonderful if this knowledge suggested new avenues of treatment to take some of the sting out of human mitochondrial disease?

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: proteostatic stress , mitochondria , Saccharomyces cerevisiae , proteasome

A Scientist Sees Transcription

September 23, 2015

While Horton uses his sensitive ears to hear a single Who, researchers need to use optical tweezers to see a single gene being transcribed. Image by Dave Parker via Flickr

In the classic Dr. Seuss tale Horton Hears a Who, the elephant Horton thinks he hears voices coming from a speck of dust. He gets into all sorts of trouble over this until all the Whos in Whoville prove they are alive when they all shout at once. Now Horton’s jungle compatriots believe him and Horton can hang out with his new friends.

Horton’s companions never get to hear an individual Who. They are not blessed with Horton’s big elephant ears and so have to just hear all the Whos shouting at once.

Up until recently, we have been in the same situation as the kangaroo and everyone else in the jungle when it comes to transcription in a cell. We can use all sorts of tools to get at what goes on when RNA polymerase II (pol II) gets ready and then starts to transcribe a gene, but we can only get an aggregate picture of lots of cells where it is happening. We can’t hear the Mayor of Whoville amidst all of the other Who voices.

In a new study out in Nature, Fazal and coworkers use the equivalent of elephant ears, optical tweezers, to study the initiation of transcription by purified pol II machinery from Saccharomyces cerevisiae on single molecules. And what they find is that at least for one part of the process, our having looked at things in the aggregate may have fooled us about how the process worked. It was important that we be able to pick out individual voices from the cacophony of the crowd.

Not surprisingly, transcribing a gene is tricky work. It is often split into three steps: initiation, elongation, and termination. And each of these can be subdivided further.

Fazal and coworkers focused on transcription initiation. Previous work had suggested that the process goes something like this:

Top image via Wikimedia Commons

Basically, an alphabet soup of general transcription factors and pol II sit down on a promoter. This complex then pries open the DNA and looks for a signal in the DNA to start transcribing. The polymerase then transcribes short transcripts until it shifts into high gear when it escapes the promoter and enters elongation phase.

This theory comes from the study of transcription in bulk. In other words, it derives from looking at many cells all at once or many promoter fragments in a test tube.

Fazal and coworkers set out to look at how well this all holds up when looking at single genes, one at a time. To do this they used a powerful technique called optical tweezers.

Optical tweezers can “see” what is going on with moving enzymes by measuring the change in force that happens when they move. For this study, the preinitiation complex bound to a longish (2.7 kb) piece of DNA was attached to one bead via pol II, the moving enzyme. The other end of the DNA was attached to a second bead. Each bead is then immobilized using lasers (how cool is that!) and the DNA is stretched between the two beads. Watch this video if you want more details on the technique.

Depending on where you attach the DNA to the bead, you can either track polymerase movement or changes in DNA by precisely measuring changes in the forces keeping the beads in place. Using this technique the researchers found that the bulk studies had done pretty well for most every step. Except for the initial transcribing complex.

The earlier studies had suggested that an open complex of around 15 nucleotides was maintained until elongation began. This study showed that in addition to the 15 base pairs, an additional 32 to 140 base pairs (mean of about 70 base pairs) was also opened before productive elongation could begin. And that this whole region was transcribed.

This result paints a very different picture of transcription initiation. Rather than maintaining a constant amount of open DNA, it looks like the DNA opens more and more until the open DNA collapses back down to the 12-14 base pair transcription bubble seen during elongation.

It turns out that this is consistent with some previous work done in both yeast and fruit flies. Using KMnO4, a probe for single stranded DNA, scientists had seen extended regions of open DNA around transcription start sites but had interpreted it as a collection of smaller, opened DNA. In other words, they thought they were seeing different polymerases at different positions along the DNA.

These new results suggest that they may have actually been seeing initial transcribing complexes poised to start processive elongation. Seeing just one complex at a time changed how we interpreted these results.

Fazal and coworkers were also able to see what happened to some of the 98% of preinitiation complexes that failed to get started. Around 20% of them did end up with an extensive region of open DNA of around 94 +/- 36 base pairs but these complexes were independent of transcription, as they didn’t require NTPs.

But since this opening did require dATP, they propose that it was due to the general transcription factor TFIIH, a helicase. It looks like in these failed complexes, TFIIH is opening the DNA without the polymerase being present.

A clearer picture of what might be going on at the promoter of genes starts to emerge from these studies. Once around 15 base pairs of DNA are pried open to form the appropriately named open complex, TFIIH unwinds an additional 70 or so base pairs. The polymerase comes along, transcribing this entire region. The whole 85 or so base pairs stays open during this process.

Eventually the polymerase breaks free and the opened DNA collapses back down to around 12-14 base pairs. Now the polymerase can merrily elongate to its heart’s content. Until of course something happens and it stops…but that is another story. 

Categories: Research Spotlight

Tags: transcription , RNA polymerase II , Saccharomyces cerevisiae , optical tweezers

Recent network issues resolved

September 15, 2015

The SGD website was experiencing some network issues over the past few days. You may have noticed that some pages were sluggish to load, while others were completely unavailable. We apologize for this inconvenience, and appreciate your patience while we performed several rounds of network optimization. All seems to be fine now, but please let us know if you notice anything unusual. We are very happy to be back!

Categories: Maintenance

New SGD Help Video: What is GO?

September 14, 2015

The Gene Ontology (GO) is an integral part of modern biology. It provides a common language that unifies the description of gene products from all organisms, structured in a way that allows very detailed information to be captured while at the same time facilitating broad categorizations. 

Watch our new video for a brief refresher course on GO: what it is, how it’s structured, and why it’s important.


Categories: Tutorial

Tags: Gene Ontology , Saccharomyces cerevisiae

Yeast Researchers Turn up the Heat on Essential Genes

September 09, 2015

Looking at the phenotypes—the observable characteristics—of creatures that have mutations in various genes can give important clues to scientists trying to figure out what those genes do. And the ability to systematically mutate thousands of genes in our favorite organism, Saccharomyces cerevisiae, has made it an awesomely powerful genetic system.

Turning up the heat on temperature-sensitive mutant strains is a great way to study essential yeast genes systematically. Image via Wikimedia Commons

But this awesome system has something of an Achilles heel. If you delete an essential gene, defined as a gene necessary for life under standard growth conditions, you end up with an experimental dead end: a dead cell. How then can you study mutant phenotypes for essential genes, which are nearly 20% of the genome?

In a new paper in G3, Kofoed and colleagues address this problem by creating conditional mutations in 600 essential genes. The mutant genes function normally at standard temperatures (25-30° C) but can be inactivated by raising the temperature. This mutant collection covers about half of S. cerevisiae essential genes and gets us much closer to being able to do mutant screens that are truly genome-wide, helping us to discover unexpected connections between genes and pathways or processes.

One popular approach to studying essential genes has been to put them under control of promoters that can be turned down when you add a particular chemical or carbon source. But it can be hard to tease apart the phenotype of down-regulating transcription of your gene of interest from the effects of the other changes that you need to make in order to regulate expression of the construct.

A solution that avoids some of these issues is to use temperature-sensitive (ts) alleles of essential genes. These mutations make the resulting proteins unstable at high temperatures. If you shift a ts strain to high temperature, the mutant protein will stop functioning; by growing the cells at an intermediate temperature, you can often produce a partially active protein that allows slow growth of the strain.

Temperature-sensitive alleles are not nearly as straightforward to create as are deletion mutations. Although newer technologies are helping, it’s still a lot of work. But Kofoed and colleagues took on this challenge. Building on their previous collection of 250 genes, they targeted genes without existing ts alleles to create a mutant collection including a total of 600 genes, about half of yeast’s essential genes.

The researchers used error-prone PCR to introduce mutations into DNA fragments encoding the genes, then transformed the mutagenized fragments into heterozygous diploid cells containing one wild-type copy of the gene and one deletion allele. The flanking sequences of the mutagenized genes targeted them to integrate in place of the deletion allele. Kofoed and colleagues could then sporulate the diploids and screen the haploid progeny for temperature sensitivity.

The genetic background of these strains is S288C, the widely used strain from which the reference genome sequence is derived. Each strain contains a “barcode”, a sequence that can be used to uniquely identify it. In addition to creating new alleles, the authors also used these methods to transfer some previously isolated ts alleles into this same background. 

Kofoed and colleagues validated and stored multiple ts alleles for each gene on their list. Although they chose one allele per gene for inclusion in the collection, the other alleles are available on request and could be very informative for researchers studying those particular genes.

As a test to see whether their collection of 600 genes was biased in any way, the researchers did Gene Ontology (GO) enrichment analysis. This compares the GO terms, representing molecular function, biological process, and subcellular localization, that are associated with the genes in a set. If the genes share related GO terms, that’s an indication that they may be involved in a common process or share the same location.

Most of the time, when scientists do GO enrichment on sets of genes they’ve come up with in an experiment, they’re hoping the genes have significantly shared terms. But in this case, the researchers were happy to find no shared terms, meaning that their collection represented a wide variety of places and roles in the cell.

The mutant strains may be studied individually in classical genetics experiments or the whole set can be tested using robotic manipulation of all the strains at once. Alternatively, the strains can be pooled and grown together in a competitive situation, like a microscopic Survivor show. When the strains are forced to compete for survival, some will prosper and others will die out. Their unique barcode identifiers allow researchers to figure out which strains were the survivors and which got voted out of the chemostat.

So this collection represents an important step in our ability to survey the phenotypes of virtually all genes in the genome. In addition to creating this resource, Kofoed and colleagues provide a summary (in Table S6) of all currently available mutant collections for essential genes. The yeast research community is now poised to turn up the heat on genome-wide mutant screens and make new discoveries about the roles of these essentially important genes.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: mutant collection , community resource , Saccharomyces cerevisiae

Look for SGD at the 27th ICYGMB!

August 30, 2015

SGD staff will be attending the 27th International Conference on Yeast Genetics and Molecular Biology (ICYGMB), September 6-12, in Levico Terme, Italy. We will be hosting a Workshop, Posters, and an Exhibit Table. We’ll be available during the entire conference to hear your comments or suggestions about SGD and answer your questions.

Follow @yeastgenome and #Yeast2015 on Twitter for the latest research being presented at ICYGMB.

Find these SGD staff members at the conference:

Mike Cherry

Maria Costanzo
Stacia Engel
Stacia Engel
Edith Wong
Edith Wong
Giltae Song
Giltae Song

Workshop: “Getting More out of SGD”

Sunday, September 6, 4:00 – 6:00 PM

We’ll be discussing our curation efforts in capturing yeast-human functional complementation data, the new sequence Variant Viewer, new data in YeastMine, and more. Bring your questions and comments – we love feedback!


In addition to the Workshop, SGD staff will present three posters – please stop by and chat with us.

Poster no. Title Presenter Location Time/Day
PS7-9 Homology curation at SGD: budding yeast as a model for eukaryotic biology Stacia Engel Sala Belvedere 2:30-4 PM, Tuesday and Thursday, 9/8 and 9/10
PS15-24 Inferring Genome Variation Patterns in Saccharomyces cerevisiae using the Eukaryote Pan-Genome Toolset Giltae Song Sala Impero 2:30-4 PM, Monday and Thursday, 9/7 and 9/10
PS15-29 Integrating genome-wide datasets into the Saccharomyces Genome Database Edith Wong Sala Impero 2:30-4 PM, Tuesday and Thursday, 9/8 and 9/10

Exhibit Table

SGD will also have an exhibit table at the conference. Come by to take a spin on our site, learn about various features of the database, and provide us with feedback as to what we can do to improve SGD. You might even receive a prize for a good question or suggestion!

Categories: Conferences