New & Noteworthy

Gender Bending in Yeast

December 03, 2013

Our friend Saccharomyces cerevisiae has it pretty easy when it comes to sex.  There is no club scene or online dating.  Pretty much if an a and an α are close enough together, odds are that they will shmoo towards each other and fuse to create a diploid cell.  No fuss, no muss.

Of course there aren’t any visual cues that indicate whether a yeast is a or α.  Instead yeast relies on detecting gender-specific pheromones each cell puts out.  The a yeast makes a pheromone and an α pheromone receptor, and the α yeast makes α pheromone and an a pheromone receptor.  The way yeast finds a hottie is by looking for the yeast of the opposite sex that puts out the most pheromone.  

This simple system is similar to ours in that gender is determined by gender specific gene expression.  In humans this happens through the amounts of certain hormones that are made.  For example, males make a lot of testosterone which turns on the androgen receptor (AR) which then turns a bunch of genes up or down.  Both men and women have AR; men just make more testosterone, which causes it to be more active. 

Yeast are simpler in that their mating loci encode transcription factors and cofactors that directly regulate a-specific and α-specific genes. Still, in both yeast and human, gender is determined by which genes are on and which are off.

Given how simple the yeast system is and how extensively it has been studied, you might think there is nothing else to learn about yeast mating.  You’d be wrong.  In a new study out in GENETICS, Huberman and Murray found that a gene with a previously unknown function, YLR040C, is involved in mating.  They renamed this gene AFB1 (a-Factor Barrier) since it seems to interfere with a-factor secretion.

The way they found this gene was by creating, as they termed them, transvestite yeast that “pretended” to be the opposite mating type. One strain that they named the MATα-playing-a strain was α but produced a-specific mating proteins, while the other, the MATa-playing-α strain, was a but produced α-specific mating proteins.  Sounds easy but it took a bit of genetic engineering to pull off.

The first steps in making the MATa-playing-α strain were to replace STE2 with STE3, MFA1 with MFα1, and MFA2 with MFα2.  In addition, they had to delete BAR1 to keep it from chewing up any α factor that got made, and ASG7, which inhibits signaling from STE3.  This strain still had the MATa locus, which meant that except for the manipulated genes, it still maintained an a-specific gene expression pattern.

Making the MATα-playing-a strain wasn’t much simpler.  They had to replace STE3 with STE2, MFα1 with MFA1, and MFα2 with MFA2.  In addition, they drove expression of BAR1 with the haploid specific FUS1 promoter and expression of the a-factor transporter STE6 with the MFα1 promoter.  Maybe yeast isn’t so simple after all!

When Huberman and Murray mated the two transvestite strains to each other, they found that while these strains could produce diploid offspring, they weren’t very good at it.  In fact, they were about 700-fold worse than true a and α strains!  So what’s wrong?

To tease this out the researchers mated each transvestite to a wild type strain.  They found that when they mated a wild type a strain to a MATa-playing-α strain, the transvestite’s mating efficiency was only down about three fold.  By overexpressing α factor they quickly found that the transvestite strain’s major problem was that it simply didn’t make enough α pheromone.  They hypothesized that perhaps differences in promoter strength or in the translation or processing of α-factor were to blame. 

The reason for the low mating efficiency of the MATα-playing-a strain, however, wasn’t so simple.  When Huberman and Murray mated the MATα-playing-a strain with an α cell, they found it was about 60-fold worse at mating.  The first thing they looked for was how much a-factor this strain was producing.  Because a-factor is difficult to assay biochemically, they used a novel bioassay instead and found that it secreted much less a-factor than did the wild type a strain.  Further investigation showed that the transvestite strain produced something that blocked the ability of a-factor to be secreted. 

By comparing the transcriptomes of MATa and MATα-playing-a cells they were able to identify YLR040C as their potential a-factor blocker.  They went on to show that when this gene was present, a-factor secretion was indeed inhibited.  They hypothesize that their newly named AFB1 may produce a protein that binds to and sequesters a-factor. It may be to a cells what BAR1 is to α cells, helping the yeast cell to sense the pheromone gradient and choose a mating partner.

When Huberman and Murray knocked AFB1 out of the MATα-playing-a strain, it now mated with a wild type α strain about five fold better than before.  A nice increase, but it doesn’t completely correct the 60-fold reduction in this transvestite’s mating efficiency.  Something else must be going on.

That something appears to be that the strain only arrests for a short time when it encounters α-factor.  This would definitely impact mating efficiency, as it is very important that when a and α strains fuse they both be in the same part of the cell cycle.  Pheromones usually stop the cell cycle in its tracks, but α-factor can’t seem to keep the MATα-playing-a cell arrested for very long.  The researchers looked for genes involved in this transient arrest, but were not able to find any one gene that was responsible.

From all of this the authors conclude that there is a pheromone arms race raging in the yeast world.  The most attractive yeast are those that make the most pheromone, so evolution favors higher and higher pheromone production.  Just as people on the dating scene need to see past the makeup and trendy clothes to figure out who’s really the best partner, yeast need genes like BAR1 and AFB1 to parse out who is the best mate amid the ever increasing haze of pheromones.

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

Categories: Research Spotlight

Tags: mating response, Saccharomyces cerevisiae

More Regulation Data and Redesigned Tab Pages Now Available

November 26, 2013

Transcriptional regulation data are now available on new “Regulation” tab pages for virtually every yeast gene. We are collaborating with the YEASTRACT database to display regulation annotations curated both by SGD and by YEASTRACT on these new pages. Regulation annotations are each derived from a published reference, and include a transcriptional regulator, a target gene, the experimental method used to determine the regulatory relationship, and additional data such as the strain background or experimental conditions. The relationships between regulators and the target gene are also depicted in an interactive Network Visualization diagram. The Regulation tab for DNA-binding transcription factors (TFs) includes these items and additionally contains a Regulation Summary paragraph summarizing the regulatory role of that TF, a table listing its protein domains and motifs, DNA binding site information, a table of its regulatory target genes, and an enrichment of the GO Process terms to which its target genes are annotated (view an example). In the coming months we will be adding this extra information to the Regulation pages of other classes of TFs, such as those that act by binding other TFs.

We have also completely redesigned the web display of the Interactions and Literature tab pages, which now include graphical display of data, sortable tables, interactive visualizations, and more navigation options. These pages provide seamless access to other tools at SGD such as GO tools and YeastMine. Please feel free to explore all of these new features from your favorite Locus Summary page and send us your feedback.

Categories: New Data, Website changes

A Hands-On Class That Shows Undergraduates the Power of Yeast

November 25, 2013

Stanford offers an innovative class, targeted at sophomore undergraduates, where students use yeast to determine how a mutation in the p53 gene affects the activity of the resulting p53 protein. What makes this class even cooler is that the p53 mutants come from actual human tumors—the undergraduates are figuring out what actual cancer mutations are doing! And the class uses what we think is the most important organism in the world, S. cerevisiae.

To learn more about the course, we decided to interview Jamie Imam, one of the instructors. After reading the interview, you will almost certainly be as excited about this class as we were and it may even get you to wishing that you could teach the class at your institution. With a little help, you can.

The creators of the course, Tim Stearns and Martha Cyert, really want as many people as possible to use this class to teach undergraduates about what real science is and how fun and exciting it can be. To that end, they are happy to help you replicate the course wherever you are. If you are interested, please contact Tim and/or Martha. You’ll be happy you did. Their contact information can be found at the Stearns lab and Cyert lab websites.

Here now is the interview with Jamie. What a great way to get undergraduates excited about the scientific process.

Can you describe the class?

Sure. Bio44X is designed to be similar to an authentic research experience or as close to one as you can replicate in the classroom. During the quarter, students study mutant versions of a gene called p53, a tumor suppressor that is frequently mutated in cancer. Each partner pair in a classroom gets one p53 mutant that has been identified in a human tumor to study in our yeast system. Throughout the course of the 10 weeks, the students study the transactivation ability of their mutant compared to the wild-type version, and then work to figure out what exactly is wrong with the mutant (Can it bind DNA?, Does it localize to the nucleus properly?, etc.). Multiple sections of this course are taught during the Fall and Winter quarters, so several pairs end up studying the same mutant. We bring these students together to discuss and combine their data throughout the quarter, so there is a lot of collaboration involved. I think the students really enjoy having one topic to study in depth over the quarter rather than short individual modules, and the fact that we are studying a gene so important in cancer makes it easier to get them to care about the work they are doing.

Tell me a little bit about how this class was started.

Previously, Bio44X at Stanford was the more traditional “cookbook” type lab course. Every 2 weeks, the topic would change and students would work through set protocols that had a known correct answer. In 2010, Professors Martha Cyert and Tim Stearns set out to design and pilot a research-based course on a medically relevant topic (the tumor suppressor p53) in response to some national calls for biology lab course reform. Two years and many changes later, the new research-based lab course replaced the previous version and is now taken by all of the students that need an introductory lab course in Biology.

What kinds of experiments do the students get to do in the class?

Students get exposed to a variety of lab techniques that can be used beyond our classroom. We start with sterile technique and pipetting during the very first week (some students have never pipetted before!). During the first class, the students also spot out some yeast strains so they can start collecting data on the transactivation ability of their p53 mutant right away. Once they have some basic information about the function of their mutant, the students then extract protein from their yeast strains. Throughout the rest of the quarter, students use this protein to conduct a kinetic assay, Western blot, and assess DNA binding ability of their mutant p53. They also get some exposure to fluorescence microscopy when they use a GFP-tagged version of their mutant to determine whether it can localize properly to the nucleus. But the most important thing of all is that students learn how to analyze the data and think critically about it. Not only do they “crunch the numbers” but they must use that information to draw some actual conclusions about what is wrong with their mutant by the end of the quarter.

How hard is it to set up and run the class?

It takes a lot of organization because we have around 200 or more students that take this class every year! Fortunately, we have a great team to help organize the setup of the labs so that the instructors can focus on the teaching. Nicole Bradon manages a small staff that sets up the classrooms and prepares all of the reagents for the lab each week. Dr. Daria Hekmat-Scafe, who is one of the instructors, constructs many of the yeast strains that we give to the students. The team of lecturers (Dr. Shyamala Malladi, Dr. Daria Hekmat-Scafe and I) all work together on lectures and other course materials so everyone gets a similar experience. All together, it takes a lot of behind-the-scenes work, but then the students really get to focus on the experiments and their results.

Do you enjoy teaching the class? What is your favorite part? Your least favorite part?

I love teaching this class! It is so fun to go through this research experience with so many students and they all bring their unique perspectives to the course (we get engineers, psych majors, bio majors, econ majors and others). Also, each section has only 20 students so you really get the chance to get to know them over the course of the 10 weeks. Sometimes the experiments don’t work as planned (like real science) but overall it ends up being a great learning experience.

What do you hope the students will learn and get out of the class? And are they learning/getting it?

We hope that students learn to think critically and what it really means to “think like a scientist”. Too often, science is boiled down to a series of facts that students are expected to memorize and that isn’t what science really is! Science is all about finding exciting questions and constructing experiments that try and answer those questions. The beauty of a research-based lab course is that students can also feel more in charge of their own learning. We have performed assessments of the class and have found that over the course of the quarter, students develop a more sophisticated understanding of what it means to “think like a scientist” and a large portion are more interested in becoming involved in scientific research. I think this is great, as I feel that undergraduate research helped me understand science so much more deeply than many of the courses I had taken.

How would someone at another University go about replicating this course? Are there resources available to help them get started and/or keep it running?

Our group is willing to share our course materials and knowledge with others that are interested in replicating this at other institutions. Anyone who is interested should feel free to contact us! Also, there is a paper in preparation that will describe some of the key aspects of the course as well as more details about what we have learned from the assessments of the course over the past few years.

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There you have it…a great class that uses the awesomeness of yeast to teach undergraduates how to think like scientists. Again, if you’re interested in learning more, please contact Tim Stearns and/or Martha Cyert at Stanford.

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

Categories: News and Views

Tags: Saccharomyces cerevisiae, undergraduate education, yeast model for human disease

Gene Knockouts May Not Be So Clean After All

November 18, 2013

Imagine you have the instructions for building a car but you don’t know what any of the specific parts do.  In other words, you can build a working car but you don’t understand how it works.

One way to figure out how the car works would be to remove a part and see what happens.  You would then know what role that part played in getting a car to run.

So if you remove the steering wheel, you’d see that the thing runs into a wall.  That part must be for steering.  When you take out the radiator, the car overheats so that part must be for cooling the engine.  And so on.

Sounds like a silly way to figure out how the car works, but this is essentially one of the key ways we try to figure out how a cell works.  Instead of parts, we knock out genes and see what happens. A new study by Teng and coworkers is making us rethink this approach.

See, one of the big differences between a machine and a cell is that the cell can react and adapt to the loss of one of its parts.  And in fact, it not only can but it almost certainly will.

Each cell has gone through millions of years of evolution to adapt perfectly to its situation.  If you tweak that, the cell is going to adapt through mutation of other genes.  It is as if we remove the radiator from the car and it evolves an air cooling system like the one in old Volkswagen Bugs.

Teng and coworkers decided to investigate whether or not knocking out a gene causes an organism to adapt in a consistent way.  In other words, does removing a gene cause a selection pressure for the same subset of mutations that allows the organism to deal with the loss of the gene.  The yeast knockout (YKO) collection, which contains S. cerevisiae strains that individually have complete deletions of each nonessential gene, gave them the perfect opportunity to ask this question.

There have long been anecdotal reports of the YKO strains containing additional, secondary mutations, but the authors first needed to assess this systematically. They came up with an assay that could detect whether secondary mutations were occurring, and if so, whether separate isolates of any given YKO strain would adapt to the loss of that gene in a similar way.  The assay they developed had two steps.

The first step was to fish out individual substrains from a culture of yeast that started from a single cell in which a single gene had been knocked out.  This was simply done by plating the culture and picking six different, individual colonies.  Each colony would have started from a single cell in the original culture.

The second step involved coming up with a way to distinguish differently adapted substrains.  The first approach was to see how well each substrain responds to increasing temperatures.  To do this, they looked for differences in growth at gradually increasing temperatures using a thermocycler.

They randomly selected 250 YKO strains and found that 105 of them had at least one substrain that reproducibly responded differently from the other substrains in the assay.  In contrast, when they looked at 26 isolates of several different wild type strains, including the background strain for the YKO collection, there were no differences between them. This tells us that the variation they saw in the knockout substrains was due to the presence of the original knockout.

So this tells us that strains can pretty quickly develop mutations but it doesn’t tell us that they are necessarily adapting to the knocked out gene.  To see if parallel evolution was indeed taking place, the authors chose to look at forty strains in which the same gene was independently knocked out.  They found that 26 of these strains that had at least one substrain with the same phenotype, and fifteen of those had mutations that were in the same complementation group.  So these 15 strains had evolved in similar ways to adapt to the loss of the same gene.

Teng and coworkers designed a second assay independent of the original heat sensitivity assay and tested a variety of single knockout strains.  They obtained similar results that support the idea that knocking out a gene can lead cells to adapt in similar ways.  This is both good and bad news.

The bad news is that it makes interpreting knockout experiments a bit trickier.  Are we seeing the effect of knocking out the gene or the effect of the secondary mutations that resulted from the knockout?  Are we seeing the loss of the radiator in the car or the reshaping that resulted in air cooling?  We may need to revisit some earlier conclusions based on knockout phenotypes.

The good news is that not only does this help us to better understand and interpret the results from yeast and mouse (and any other model organism) knockout experiments, it also gives us an insight into evolution and maybe even into the parallel evolution that happens in cancer cells, where mutations frequently co-occur in specific pairs of genes.  And while we may never be able to predict if that knock you hear in your engine really needs that $1000 repair your mechanic says it does, we may one day be able to use results like these to predict which cells containing certain mutated genes will go on to cause cancer and which ones won’t.   

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

Categories: Research Spotlight

Tags: deletion collection, evolution, Saccharomyces cerevisiae

The Cellular Hunger Games

November 07, 2013

We all know that it’s important to get enough vitamins in our diet. Scary-sounding conditions like scurvy, rickets, and beriberi can all happen when you don’t get enough of them.  And that’s not all.

Fairly recently, scientists discovered that when pregnant women get too little folate, their children are at a higher risk for neural tube defects. This connection is so strong that since 1998, the U.S. and Canada have successfully reduced the number of neural tube defects by adding extra folate to grain products.

While these kinds of effects are easy to see, it’s not always so obvious what is going on at the molecular level. But in a new study in GENETICS, Sadhu and coworkers showed that folate and methionine deficiencies can affect us right down to our DNA. And of course, they figured this out by starting with our little friend S. cerevisiae.

Folate and its related compound methionine are pretty important molecules in cellular metabolism. You need folate to make purine nucleotides, and it is essential for keeping just the right levels of methionine in a cell.

And methionine is, of course, one of the essential amino acid building blocks of proteins. But it is more than that. It’s also the precursor for S-adenosyl-methionine (SAM), which provides the methyl groups for protein methylation.

Protein methylation is a big deal for all sorts of things.  But one of its most important jobs is undoubtedly controlling levels of gene expression through methylation of histones.   

Since folate or methionine deficiency should affect SAM levels, in principle they could affect histone methylation too. But so far this connection had never been shown directly. Sadhu and colleagues set out to see what happens when you deprive S. cerevisiae of these nutrients.

Unlike humans, yeast can synthesize both folate and methionine. So the first step was to make folate- and methionine-requiring strains by deleting the FOL3 or MET2 genes, respectively. These mutant yeast strains couldn’t grow unless they were fed folate or methionine.

Now it was possible to starve these mutant strains by giving them low levels of the nutrients they needed. Starvation for either folate or methionine caused the methylation of a specific lysine residue (K4) of histone H3 to be reduced. Not only that, but expression of specific genes was lower, consistent with their reduced histone methylation.

To see how general this effect was, the authors performed essentially the same experiments in Schizosaccharomyces pombe, which is about as evolutionarily distant from S. cerevisiae as you can get and still be a yeast. In this beast, methionine deficiency also reduced histone methylation. For unknown reasons, folate deficiency didn’t have a significant effect. 

Sadhu and coworkers wondered whether this effect was so general that they could even see it in human cells. Since humans are folate and methionine auxotrophs, this experiment was easier to set up. When they grew human cells with starvation levels of folate or methionine, their histone methylation and gene expression were both reduced. So starvation conditions have an impact right down to the level of gene expression, across a wide range of organisms.

The simple explanation for this effect would be that reduced folate leads to reduced SAM levels, and therefore fewer methyl groups are available to modify histones. But the researchers got a surprise when they measured intracellular SAM levels in S. cerevisiae under the starvation conditions: they were the same as in wild type! This conclusion was so surprising that they tried two different, sophisticated methods, but both gave the same result.

They explain this by postulating a kind of metabolic triage.  Basically, the cell maintains a certain level of SAM in the cell but there is a pecking order for who gets to use it.  At very low nutrient levels, the cell uses the available folate or methionine for the most essential processes such as purine synthesis or translation, and sacrifices histone methylation. As more nutrients become available, then other less critical functions can use them.

This kind of triage might provide an explanation for the link between folate deficiency and neural tube defects, and also for the effectiveness of antifolates against cancer. And it adds to the growing body of evidence that environmental conditions such as famine can have effects that persist across generations. This is an important reminder that any decisions we make today about feeding the hungry could have consequences that reach far into the future.

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

Categories: Research Spotlight

Tags: folate, methylation, Saccharomyces cerevisiae, starvation

Follow the CSHL Cell Biology of Yeasts Meeting on Twitter!

November 03, 2013

Wish you were going to Cold Spring Harbor for the Cell Biology of Yeasts meeting this week, November 5-9?  SGD will be live tweeting from CSHL, highlighting topics from talks and posters. Keep up with events at the meeting by following @yeastgenome on Twitter or searching #YCB2013 for all tweets!

Categories: News and Views

How Yeast May One Day Help Michael J. Fox

October 31, 2013

Folks, yeast has been on a roll lately with regard to helping to understand and finding treatments for human disease. Last week we talked about how synthetic lethal screens may find new, previously unrecognized druggable targets for cancer. And this week it is Parkinson’s disease.

Now of course yeast can’t get the traditional sort of Parkinson’s disease …it doesn’t have a brain.  But it shares enough biology with us that when it expresses a mutant version of α-synuclein (α-syn) that is known to greatly increase a person’s risk for developing Parkinson’s disease, the yeast cell shows many of the same phenotypes as a diseased neuron.  The yeast acts as a stand-in for the neuron.

In a new study out in Science, Tardiff and coworkers use this yeast model to identify a heretofore unknown target for Parkinson’s disease in a sort of reverse engineering process. They screened around 190,000 compounds and looked for those that rescued toxicity in this yeast model. They found one significant hit, an N-aryl-benzimidazole (NAB) compound. Working backwards from this hit they identified its target as Rsp5p, a Nedd4 E3 ubiquitin ligase. 

The authors then went on to confirm this finding in C. elegans and rat neuron models where this compound halted and even managed to reverse neuronal damage. And for the coup de grace, Chung and coworkers showed in a companion paper that the compound worked in human neurons too. But not just any human neurons.

The authors used two sets of neurons derived from induced pluripotent cells from a single patient.  One set of neurons had a mutation in the α-syn gene which is known to put patients at a high risk of Parkinson’s disease-induced dementia.  The other set had the mutation corrected.  The compound they identified in yeast reversed some of the effects in the neurons with the α-syn mutation without significantly affecting the corrected neurons.  Wow.

What makes this even more exciting is that many people thought you couldn’t target α-syn with a small molecule. But as the studies here show, you can target an E3 ubiquitin ligase that can overcome the effects of mutant α-syn.  It took an unbiased screen in yeast to reveal a target that would have taken much, much longer to find in human cells. 

The mutant α-syn protein ends up in inclusion bodies that disrupt endosomal traffic in the cell.  The NAB compound that the authors discovered restored endosomal transport and greatly decreased the numbers of these inclusion bodies.  Juicing up Rsp5 seemed to clear out the mutant protein.

The next steps are those usually associated with finding a lead compound—chemical modification to make it safer and more effective, testing in clinical trial and then, if everything goes well, helping patients with Parkinson’s disease.  And that may not be all.

The α-syn protein isn’t just involved in Parkinson’s disease.  The dementia associated with this protein is part of a larger group of disorders called dementia with Lewy bodies that affects around 1.3 million people in the US.  If everything goes according to plan, many of these patients may one day thank yeast for their treatment.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: alpha-synuclein, Saccharomyces cerevisiae, ubiquitin ligase, yeast model for human disease

Using Yeast to Find Better Cancer Treatments

October 24, 2013

Current cancer treatments are a lot like trying to destroy a particular red plate by letting a bull loose in a china shop.  Yes, the plate is eventually smashed, but the collateral damage is pretty severe.

Ideally we would want something a bit more discriminating than an enraged bull.  We might want an assassin that can fire a single bullet that destroys that red plate. 

One way to identify the assassin that can selectively find and destroy cancer cells is by taking advantage of the idea of synthetic lethal mutations.  “Synthetic lethal” is a genetic term that sounds a lot more complicated than it really is.  Basically the idea is that mutating certain pairs of genes kills a cell, although mutating each gene by itself has little or no effect.

A synthetic lethal strategy seems tailor made for cancer treatments.  After all, a big part of what happens when a cell becomes cancerous is that it undergoes a series of mutations.  If scientists can find and target these mutated genes’ synthetically lethal partners, then the cancer cell will die but normal cells will not.

This is just what Deshpande and coworkers set out to do in a new study in the journal Cancer Research.  They first scanned a previous screen that looked at 5.4 million pairwise interactions in the yeast S. cerevisiae to find the best synthetic lethal pairs. They found 116,000 pairs that significantly affected cell growth only if both genes in the pair were mutated.

A deeper look into the data revealed that 24,000 of these pairs had human orthologs for both genes. In 500 of these pairs, at least one of the partner genes had been shown to be mutated in certain cancers. Using a strict set of criteria (such as the strength and reproducibility of the synthetic lethal effect, and the presence of clear one-to-one orthology between yeast and human), the authors narrowed these 500 down to 21 pairs that they decided to study in mammalian cell lines.

When the authors knocked down the expression of both genes in these 21 gene pairs in a mammalian cell line, they found six that significantly affected growth.  They focused the rest of the work on the strongest two pairs, SMARCB1/PMSA4 and ASPSCR1/PSMC2.  These mammalian gene pairs correspond to the yeast orthologs SNF5/PRE9 and UBX4/RPT1, respectively.

The authors identified two separate cancer cell lines that harbored mutated versions of the SMARCB1 gene.  When this gene’s synthetic lethal partner, PMSA4, was downregulated in these cancer lines, the growth of each cell line was severely compromised. The same was not true for a cell line that had a wild type version of SMARCB1—this cell line was not affected by downregulating PMSA4.  The authors used a synthetic lethal screen in yeast to identify a new cancer target which when downregulated selectively killed the cancer without killing “normal” cells.

This proof of principle set of experiments shows how the humble yeast may one day speed up the process of finding cancer treatments without all those nasty side effects (like vomiting, hair loss, anemia and so on).  Yeast screens can first be used to identify target genes and then perhaps also to find small molecules that affect the activity of those gene products.  Yeast may one day tame the raging bull in a china shop that is current cancer treatments.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: cancer, Saccharomyces cerevisiae, synthetic lethal

Memorial Gathering for Dr. Fred Sherman

October 23, 2013

A memorial gathering in memory of Fred Sherman will be held at 10:00 am on Friday, December 6, 2013. The gathering will be held in the Ryan Case Methods Room (Rm #1-9576) of the University of Rochester School of Medicine and Dentistry, 601 Elmwood Avenue, Rochester NY 14642. This event will be a celebration of the life and science of Fred, comprised of reminiscences about Fred by some who knew him well, followed by an opportunity for any guest to say a few words about Fred. For more information, contact Mark Dumont (Mark_Dumont@urmc.rochester.edu), Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY 14642 (Phone 585-275-2466).

Categories: News and Views

A GLAMorous New Role for Prefoldin

October 17, 2013

The prefoldin complex seemed like an ordinary housekeeper. It sat in the cytoplasm and folded protein after protein, just as Cinderella spent her days folding laundry for her stepsisters.

In the old story, the handsome prince searched the kingdom for a girl whose foot would fit the glass slipper. Using this crude screen, he finally found Cinderella and revealed her to be the true princess that she was.

In a new study, Millán-Zambrano and coworkers did essentially the same thing for the prefoldin complex.  They searched the genome of S. cerevisiae for new mutations that would affect transcription elongation. They found the prefoldin complex subunit PFD1 and went on to establish that in addition to its humdrum cytoplasmic role, prefoldin has a surprising and glamorous role in the nucleus facilitating transcriptional elongation.

The researchers decided to cast a wide net in their search for genes with previously undiscovered roles in transcriptional elongation. Their group had already worked out the GLAM assay (Gene Length-dependent Accumulation of mRNA), which can uncover elongation defects.

The assay uses two different reporter gene constructs that both encode Pho5p, an acid phosphatase. One generates an mRNA of average length, while the other generates an unusually long mRNA when fully transcribed. The acid phosphatase activity of Pho5p is simple to measure, and correlates well with abundance of its mRNA. If there is a problem with transcriptional elongation in a particular mutant strain, there will be much less phosphatase activity generated from the longer form than from the shorter one. So the ratio of the two gives a good indication of how well elongation is working in that mutant strain.

Millán-Zambrano and coworkers used this assay to screen the genome-wide collection of viable deletion mutants. They came up with mutations in lots of genes that were already known to affect transcriptional elongation, confirming that the assay was working. They also found some genes that hadn’t been shown to be involved in elongation before.  One of these was PFD1, a gene encoding a subunit of the prefoldin complex. As this deletion had one of the most significant effects on elongation, they decided to investigate it further.

Prefoldin is a non-essential complex made of six subunits that helps to fold proteins in the cytoplasm as they are translated. The authors tested mutants lacking the other subunits and found that most of them also had transcriptional elongation defects in the GLAM assay, although none quite as strong as the pfd1 mutant.

Since prefoldin is important in folding microtubules and actin filaments, the researchers wondered whether the GLAM assay result was the indirect effect of cytoskeletal defects. They were able to rule this out by showing that drugs that destabilize the cytoskeleton didn’t affect the GLAM ratio in wild-type cells, and that mutations in prefoldin subunits didn’t confer strong sensitivity to those drugs.

If prefoldin has a role in transcription, it would obviously need to get inside the nucleus. It had previously been seen in the cytoplasm, but when the authors took another look, they found it in the nucleus as well. Furthermore, Pfd1p was bound to the chromatin of actively transcribed genes! And besides its effect on transcription elongation, the pfd1 mutant has lower levels of RNA polymerase II occupancy and abnormal patterns of histone binding on transcribed genes.

There’s still a lot of work to be done to figure out exactly what prefoldin is doing during transcriptional elongation. Right now, the evidence points to its involvement in evicting histones from genes in order to expose them for transcription. But even before all the details of this story are worked out, this is a good reminder never to assume that an everyday housekeeper is only that.

With the right screen we can find new and exciting things about the most humdrum of characters. A glass slipper screen revealed the princess under that apron and chimney soot. And a GLAM assay revealed the sexy, exciting transcription elongation factor that is prefoldin.

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

Categories: Research Spotlight

Tags: prefoldin, RNA polymerase II, Saccharomyces cerevisiae, transcription elongation

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