New & Noteworthy

Links to S. pombe Orthologs

December 20, 2013

SGD now provides links from individual S. cerevisiae genes to their Schizosaccharomyces pombe orthologs at PomBase. These links are labeled “PomBase”, and can be found on the Locus Summary Pages, within the Homologs section.

Categories: Website changes

Affecting the Shelf Life of Chromosomes

December 19, 2013

Just like the chicken or milk you buy at a store, chromosomes have a shelf life too.  Of course, chromosomes don’t spoil because of growing bacteria.  Instead, they go bad because they lose a little of the telomeres at their ends each time they are copied.  Once these telomeres get too short, the chromosome stops working and the cell dies.

You can make this chicken last longer by freezing it. You can do the same for a chromosome in yeast with a shot of alcohol. Image from Food & Spirits Magazine via Wikimedia Commons

Turns out food and chromosomes have another thing in common—the rates of spoilage of both can be affected by their environment.   For example, we all know that chicken will last longer if you store it in a refrigerator and that it will go bad sooner if you leave it out on the counter on a hot day.  In a new study out in PLoS Genetics, Romano and coworkers show a variety of ways that the loss of telomeres can be slowed down or sped up in the yeast S. cerevisiae.  And importantly, they also show that some forms of environmental stress have no effect.

The authors looked at the effect of thirteen different environments on telomere length over 100-400 generations.  They found that caffeine, high temperature and low levels of hydroxyurea lead to shortened telomeres, while alcohol and acetic acid lead to longer telomeres.  It seems that for a long life, yeast should lay off the espresso and and try to avoid fevers, while enjoying those martinis and sauerbraten.

Romano and coworkers also found a number of conditions that had no effect on telomere length, with the most significant being oxidative stress.  In contrast, previous studies in humans had suggested that the oxidative stress associated with emotional stress contributed to increased telomere loss; given these results, this may need to be looked at again.  In any event, yeast can deal with the stresses of modern life with little or no impact on their telomere length.

The authors next set out to identify the genes that are impacted by these stressors.  They focused on four different conditions—two that led to decreased telomere length, high temperature and caffeine, one that led to longer telomeres, ethanol, and one that had no effect, hydrogen peroxide.  As a first step they identified key genes by comparing genome-wide transcript levels under each condition.  They then went on to look at the effect of each stressor on strains deleted for each of the genes they identified.

Not surprisingly, the most important genes were those involved with the enzyme telomerase.  This enzyme is responsible for adding to the telomeres at the ends of chromosomes.  Without something like this, eukaryotes, with their linear chromosomes, would have disappeared long ago.

A key gene they identified was RIF1, encoding a negative regulator of telomerase.  Deleting this gene led to decreased effects of ethanol and caffeine, suggesting that this gene is key to each stressor’s effects.  The same was not true of high temperature—the strain deleted for RIF1 responded normally to high temperature.  So high temperature works through a different mechanism.

Digging deeper into this pathway, Romano and coworkers found that Rap1p was the central player in ethanol’s ability to lengthen telomeres.  This makes sense, as the ability of Rif1p to negatively regulate telomerase depends upon its interaction with Rap1p. 

The increase in telomere length by ethanol was not just dependent on genes associated with telomerase either.  The authors identified a number of other genes involved, including DOA4, SNF7, and DID4

Caffeine, like ethanol, affected telomere length through Rif1p-Rap1p but with an opposite effect.  As caffeine is known to be an inhibitor of phosphatydylinositol-3 kinase related kinases, the authors looked at whether known kinases in the telomerase pathway were involved in caffeine-dependent telomere shortening.  They found that when they deleted both TEL1 and MEC1, caffeine no longer affected telomere length. 

The authors were not so lucky in their attempts to tease out the mechanism of the ability of high temperature to shorten telomeres.  They were not able to identify any single deletions that eliminated this effect of high temperature.

Whatever the mechanisms, the results presented in this study are important for a couple of different reasons.  First off, they obviously teach us more about how telomere length is maintained.  But this is more than a dry, academic finding.

Given that many of the 400 or so genes involved in maintaining telomere length are evolutionarily conserved, these results may also translate to humans too.  This matters because telomere length is involved in a number of diseases and aging.

Studies like this may help us identify novel genes to target in diseases like cancer.  And they may help us better understand how lifestyle choices can affect your telomeres and so your health.  So if you have a cup of coffee, be sure to spike it with alcohol! 

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

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae, environmental stress, telomere

SGD Winter 2013 Newsletter

December 17, 2013

SGD periodically sends out its newsletter to colleagues designated as contacts in SGD. This Winter 2013 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

GOing Deeper into the Gene Ontology

December 12, 2013

The most interesting board games can’t be played right out of the box. You can admire the board and the game pieces, but before the fun can begin you need to spend some time reading the instructions and understanding the strategy.

A little effort put into learning the game allows you to not only play it, but master it. The same can be said for Gene Ontology! Image by Arbitrarily0 from Wikimedia Commons

Gene Ontology (GO) annotations are a little bit like that. You can get interesting information very quickly by just reading the GO terms on the Locus Summary page of your favorite yeast protein in SGD. But if you look deeper and learn just a little bit more about GO, you’ll find that you can get so much more out of it.

A new article by Judith Blake in PLoS Computational Biology is intended to help you do just that. Dr. Blake very succinctly summarizes the most important points in her article, “Ten Quick Tips for Using the Gene Ontology”.

If you’re a molecular or cell biologist, a geneticist, or a computational biologist (or are studying one of those fields), you’re probably already aware of GO. But still, you may be wondering, “Where did these annotations come from? What do those three-letter acronyms mean? How can this help me in my research?” This short and sweet article is a great place to start getting answers to these questions.

We recommend that everyone devote a few minutes to reading this brief article, even if you think you already understand GO.  Based on the most frequent questions that we get from researchers who use GO annotations at SGD, we can distill it even further into these top three points as seen from an SGD perspective.

There are people behind these annotations. GO terms are assigned either by real, live humans called biocurators, or computationally using automated methods (each annotation is marked, so you can easily see which is which). At SGD, biocurators are Ph.D. biologists who read the yeast literature and capture experimental results as GO annotations; SGD biocurators are also involved in developing the structure of the GO. We try our best, but like all human beings, we are not infallible. So if you see an annotation that looks wrong or confusing, or if you think an area of the GO could better represent the biology, please contact us ( to talk about it. The more expert help we can get, the better the GO and our GO annotations will be.

The details matter.  Those three-letter codes that accompany each annotation mean something. Imagine you are deciding how to allocate your lab’s resources and a critical experiment will be based on a particular protein having a particular function. You see a GO annotation for that function and that protein, so you’re good to go! But wait a minute…

Those codes tell you the experimental evidence behind the assignment of a GO term to a gene product. If that annotation has an IDA (Inferred from Direct Assay) evidence code, then the function was shown in an actual experiment, so you probably are good to go. On the other hand, if the annotation has an ISS (Inferred from Sequence Similarity) evidence code, then it was made solely based on resemblance to another protein. This is still valuable information, but you might not want to bet the farm (or the lab) on it.

Dates are very important too. Both the annotations and the GO itself are constantly updated to keep up with new biological knowledge. Because of this, everything related to GO – from a single annotation shown on an SGD GO Details page, to the downloadable files that contain all GO annotations or the ontology itself – is associated with the date it was created. So if you do any analysis using GO annotations it’s important to note the dates of both the annotation and ontology files that you used. This is especially important if you repeat a GO term enrichment for a gene set over time.  The results will definitely change, as significant enrichments become more strongly supported while marginally significant enrichments may not be reproduced.

Go deeper. GO is not just a list of terms. GO terms have defined relationships to each other, with some being broader (parent terms) and some more specific (child terms). If you really understand the structure of GO, you’ll be able to make much better use of the annotations.

For example, if you look for gene products in SGD annotated to the GO term “mitochondrion,” you’ll currently find 1055 of them1. Does that mean that there are exactly 1055 proteins or noncoding RNAs known to be in yeast mitochondria? Noooo!

There are more than that, because the term “mitochondrion” has more specific child terms such as “mitochondrial matrix”; some proteins are annotated directly to those terms and not to the parent term. If you had used the original list of proteins annotated to “mitochondrion”, you’d be missing 92 gene products2 that are so well-studied that their precise locations in the organelle are known! The structure of the GO allows you to gather all the gene products annotated to a term and to all its child terms (YeastMine has a template tailored to this kind of query).

As you can tell, there is a lot more to GO annotations than a lot of people think.  And as you dig deeper, you begin to be able to use them in ever more sophisticated ways.  Sort of like the natural progression with a strategy board game like Settlers of Catan.  At first, even after reading the instructions, you are just trying to work through the game.  But as you play more and more, you quickly learn where to build your roads, which islands to colonize and so much more.  So get out there and master GO.  You’ll be glad you did.

1As of December 2013, using YeastMine template “GO Term -> All genes” (includes Manually curated and High-throughput annotation types).

 2As of December 2013, using YeastMine template “GO Term Name [and children of this term] -> All genes” (filtered to exclude Computational annotation type so that only Manually curated and High-throughput annotation types are included).

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

Categories: News and Views

Tags: GO, Gene Ontology

Gender Bending in Yeast

December 03, 2013

Even after all these years of studying the mating response in yeast, there is still more to be learned! Image courtesy of Lori B. Huberman and GENETICS

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.

Dr. Jamie Imam

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.

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

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.

If a cell were a car and you removed its radiator, it might adapt by evolving an air cooled system. If it happened soon enough, you might never figure out what the radiator did. Image by Joe Mazzola obtained from Wikimedia Commons.

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

In the Hunger Games, limited resources mean only the privileged get them. The same is true for methyl groups in yeast and human cells…when in short supply, they are only available to the chosen few. Image by Eva Rinaldi obtained from Wikimedia Commons.

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: starvation, methylation, Saccharomyces cerevisiae, folate

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