New & Noteworthy

Yeast Researchers Take the Lion’s Share of GSA 2014 Awards

February 03, 2014

Congratulations to fellow yeasties Angelika Amon, Charlie Boone, and Robin Wright for winning three of the five annual Genetics Society of America awards for 2014! Just another confirmation that the awesome power of yeast genetics attracts excellent researchers…

Angelika Amon, of MIT and the Howard Hughes Medical Institute, has been awarded the Genetics Society of America Medal for outstanding contributions to the field of genetics during the past 15 years. Charlie Boone, of the University of Toronto and a longstanding member of SGD’s Scientific Advisory Board, received the Edward Novitski Prize for his extraordinary level of creativity and intellectual ingenuity in solving significant problems in genetics research. Robin Wright, of the University of Minnesota, has been awarded the Elizabeth W. Jones Award for Excellence in Education, which recognizes significant and sustained impact in genetics education. Find full details about the awards and recipients at the GSA website.

Categories: News and Views

Yeast, Smarter than a Train Wreck

January 30, 2014

Imagine you run a railroad that has a single track. You need for trains to run in both directions to get your cargo where it needs to go.

One way to regulate this might be to have the trains just go whenever and count on collisions as a way to regulate traffic. Talk about a poor business model! Odds are your company would quickly go bankrupt.

Another, more sane possibility is to somehow keep the trains from running into each other. Maybe you schedule them so their paths never cross. Or maybe you have small detours where a train can wait while the other passes. Anything is better than regulation by wreckage!

Turns out that at least in some cases, nature is a better business person than many people previously thought. Instead of trains on a track, nature needs to deal with nearby genes that point towards one another, so-called convergent genes. If both genes are expressed, then the RNA polymerases will barrel towards one another and could collide.

A new study in PLoS Genetics by Wang and coworkers shows just how big a deal this issue is for our favorite yeast Saccharomyces cerevisiae. An analysis of this yeast’s genome showed that not only did 20% of its genes fit the convergent definition but that in many cases, each gene in a pair influenced the expression of the other gene. Their expression was negatively correlated: when one of the pair was turned up, the other went down, and vice versa.

One way these genes might regulate one another is the collision model. When expression of one gene is turned up and a lot of RNA polymerases are barreling down the tracks, they would crash into and derail any polymerases coming from the opposite direction. A prediction of this model is that orientation and location matter.  In other words, the negative regulation would work only in cis, not in trans.  Surprisingly, the authors show that this is clearly not the case.

Focusing on four different gene pairs, Wang and coworkers showed that if the genes in a pair were physically separated from one another, their expression was still negatively correlated.  This was true if they just flipped one of the genes so the two genes were pointed in the same direction, and it was still true if they moved one gene to a different chromosome.  Clearly, collisions were not the only way these genes regulated one another.

Using missense and deletion mutation analysis, the authors showed that neither the proteins from these genes nor the coding sequence itself was required for this regulation.  Instead, the key player was the overlapping 3’ untranslated regions (UTRs) of the transcripts.  The authors hypothesize that the regulation is happening via an anti-sense mechanism using the complementary portions of the 3’ UTRs.

This anti-sense mechanism may be S. cerevisiae’s answer to RNAi, which it lost at some point in its evolutionary history.  Given the importance of RNA-mediated regulation of gene expression in other organisms, perhaps it shouldn’t be surprising that yeast has come up with another way to use RNA.  

Instead of RNAi, it relies on genomic structure and overlapping 3’ UTRs to regulate genes.  This may be a bit more cumbersome than RNAi, but at least yeast came up with a more clever system than polymerase collisions to regulate gene expression.  

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

Categories: Research Spotlight

Tags: RNA polymerase II, Saccharomyces cerevisiae, transcription, UTR

Web Primer Redesign Survey

January 27, 2014

Have you used SGD’s Web Primer tool? This tool allows you to enter the name of a yeast gene, or any DNA sequence, and design primers for sequencing or PCR. We are planning to redesign this tool and we need to hear from you to make sure that the next version meets your needs. Please let us know how you use the tool and which features are most useful by filling out the Web Primer Survey. We appreciate your feedback!

Categories: News and Views

Yeast, the New Fountain of Youth

January 23, 2014

Ponce de Leon searched the New World for the fountain of youth.  Turns out that if he had some of the tools at our disposal, he wouldn’t have even had to leave Europe.  He just needed to go to the local bakery or brewery and look inside the yeast he found there.  Of course, then he wouldn’t have found Florida…

Using in silico genome-scale metabolic models (GSMMs) in yeast, Yizhak and coworkers identified GRE3 and ADH2 as two genes that significantly increased the lifespan of yeast when knocked out.  Even more importantly, their method also allowed them to identify the mechanism behind this increased lifespan—the mild stress of increased reactive oxygen species (ROS).  This last finding may help scientists identify drug targets that they can target to increase the lifespan of people too.  If only Ponce de Leon had lots of -omics data and a powerful computer or two!

After constructing an in silico starting state, Yizhak and coworkers entered two sets of data from previous work that had been done on aging in yeast.  They next used gene expression profiling to identify which metabolic reactions were different and which were the same in young and old yeast.  They then systematically tested the effect of knocking out these reactions one at a time in their computer model to identify those that could potentially transform yeast from old to young with minimal side effects. 

Their first finding was that many of their best hits, like HXK2, TGL3, and FCY2, had already been identified as important in prolonging a yeast cell’s life.  They decided to look at seven genes that had not been previously identified as being involved in aging. 

When two of these seven, GRE3 and ADH2, were knocked out, these yeast strains lived significantly longer with minimal side effects.  For example, the strain lacking GRE3 lived ~100% longer than the wild type strain.

Figuring out why these yeast probably lived longer was made simpler because they used metabolic models to identify the genes.  The hormesis model of aging suggests that mild stress, like that found in caloric restriction, can lead to increased life span.  With this model in mind, the authors focused in on the possibility that knocking out GRE3 and/or ADH2 would lead to increased stress through the production of increased levels of ROS.  When they looked, they found that the two knockout strains did indeed have higher levels of two common forms of ROS, hydrogen peroxide and superoxide. 

Of course none of us is particularly interested in extending the life of a yeast!  But these results could suggest new drug targets to go after that might mimic the effects of caloric restriction without us having to starve ourselves.  And these same methods can be used on human cells to find key pathways to target in people.  In fact, the authors have started to use their computer models to investigate aging in human muscle cells and found that like in yeast, many of the genes they have identified are consistent with previous work on human aging. 

Now we probably shouldn’t get too far ahead of ourselves here.  This is a promising first step but it really isn’t much more than Ponce de Leon boarding his ship to begin his trip to the New World.  We still have a long voyage ahead of us before we find the fabled fountain of youth.  

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

Categories: Research Spotlight

Tags: aging, metabolic model, Saccharomyces cerevisiae

Cutting Down on the ChIPs

January 16, 2014

We all know that potato chips are delicious.  But we also know that eating too many of them isn’t very good for our arteries or our waistlines. And apparently these aren’t the only chips that can be too much of a good thing.

Chromatin immunoprecipitation (ChIP) is an incredibly valuable technique that lets us see where a particular protein binds in a genome. It can show us the target genes of a particular transcription factor, the distribution of RNA polymerases as they transcribe genes, the places where silencing proteins bind to turn off expression of particular regions, and lots more.

But just like potato chips, more ChIP results aren’t always better. Teytelman and coworkers, publishing in Proceedings of the National Academy of Sciences, and Park and coworkers, publishing in PLoS ONE, have discovered that highly transcribed regions of the genome consistently give false positive ChIP results. In other words, very active regions of the genome look like everything is binding there even when it almost certainly is not.  Teytelman and colleagues call these regions “hyper-ChIPable”. 

Far from being a reason to despair, though, the discovery of this artifact explains some puzzling previous results and inspires the creation of new, more reliable ChIP methods. This is exactly what Kasinathan and coworkers have done, in a recently published paper in Nature Methods. 

The idea behind the ChIP technique is that if you want to know all of the places across the genome where your protein of interest binds, you can lyse cells, shear the DNA into relatively short fragments, and immunoprecipitate your protein from the mixture. Usually the protein and DNA are cross-linked before immunoprecipitation, to strengthen their bond during the rest of the procedure.

After immunoprecipitation, the DNA fragments associated with the protein can be identified using a variety of methods. Finally, mapping the sequences of the fragments to the genomic sequence shows us all the sites that the protein occupies.

Teytelman and colleagues used ChIP-seq to ask whether the silencing complex (Sir2p, Sir3p, and Sir4p) ever binds to non-silenced regions of the genome. They thought they might see some binding, but they were astounded to find significant binding of the complex at 238 distinct euchromatic (non-silenced) loci. This didn’t really make sense, since the yeast Sir proteins are extremely well-studied and there were no biological hints that they have such a large presence at non-silenced genes. 

As a control, they looked at previously published ChIP data on the locations of two unrelated proteins, Ste12p and Cse4p, and found that their binding was enriched at the same 238 loci. Finally, they did a ChIP study using green fluorescent protein (GFP) alone. Sure enough, the ChIP data showed that this jellyfish protein apparently bound strongly to chromatin at those 238 sites! The common denominator shared by these loci: they were all very highly expressed.

Meanwhile, Park and coworkers were embarking on a similar journey. They found using ChIP-seq that several unrelated transcription factors seemed to have common targets, which didn’t make biological sense. Control experiments looking at binding sites of Mnn10p (a cytoplasmic protein not expected to have any contact with DNA), or even using nonspecific antibodies that didn’t recognize any yeast proteins, still gave the same set of ChIP targets. Again, these targets were all highly expressed genes.

Each group found several factors contributing to this artifact, although all the reasons why highly expressed regions yield false positives may not yet be uncovered.  But whatever the reasons, this finding helps explain some previously perplexing results – such as binding of Mediator complex all over the genome, or the paradoxical binding of silencing regulator Sir3p to the GAL1–GAL10 regulatory region under conditions where transcription is activated, not silenced.

In response to these issues, many researchers are actively trying to improve the ChIP technique. Kasinathan and colleagues have devised a method that they call ORGANIC (Occupied Regions of Genomes from Affinity-purified Naturally Isolated Chromatin) that eliminates crosslinking and substitutes micrococcal nuclease treatment for sonication (to shorten the DNA fragments).  In a pilot project, they mapped binding sites for the transcription factors Reb1p and Abf1p. The method looks to be both accurate and sensitive. Most binding locations that they found contained the binding motif sequence for that transcription factor, and also correlated with in vivo occupancy as determined by Dnase I footprinting – both of which support their biological relevance. Importantly, the technique shows no bias towards highly expressed regions.

The lesson for researchers is that ChIP results for highly expressed genes, particularly those done using older protocols, need to be viewed cautiously.  And of course this artifact could be an issue for organisms other than yeast. ChIP experiments are used across species, and have been valuable in elucidating the targets of disease-related proteins like the tumor suppressor p53.

The fact that yeast genetics and molecular biology have so well established the roles of certain chromatin-associated proteins was a key part of this puzzle, helping to point out the artifactual nature of some of the ChIP results. Just as a new recipe for potato chips could allow us to eat more of them while staying healthy, yeast research has led the way to a new recipe for more accurate ChIP studies.

Aside from the molecular biology behind this work, it is quite interesting from a sociological point of view as well. What is it like to make a discovery that calls into question a routinely-used technique and a lot of published results? Lenny Teytelman’s blog post on this topic provides a fascinating glimpse into this situation.

Categories: Research Spotlight

Tags: chromatin immunoprecipitation, Saccharomyces cerevisiae

The Brazor of Biology

January 08, 2014

The janitor on the U.S. comedy series Scrubs is always coming up with terrible inventions. One of his worst was the knife-wrench. It is what it sounds like—a tool with a knife at one end and a wrench at the other.

Of course not all dual purpose tools have to be so useless.  Imagine a tool like the one at the right with a razor at one end and a toothbrush on the other.  Now you can easily brush your teeth and shave in the shower or at your bathroom sink (as long as you are careful not to cut your cheek).

Turns out that biology has these dual purpose tools too except that they are almost always more useful.  For example, Lahudkar and coworkers show in the most recent issue of GENETICS that Cet1p doesn’t just help out with capping mRNA.  No, these authors found that it also helps clear RNA polymerase II (RNA pol II) away from promoters.  And what’s most interesting is that this second function has little to do with its job in mRNA capping.

Basically the two functions are probably in the same protein because they both happen in the same place, at the start site of a promoter.  Just like our brazor is useful because both jobs happen in the bathroom.

The first step was to show that in the absence of Cet1p, RNA pol II was more likely to be found near the start of transcription.  The authors showed that this was the case by using a temperature sensitive mutant of Cet1p and a chromatin immunoprecipitation (ChIP) assay targeted at RNA pol II—there was more RNA pol II crowded near the promoter at the nonpermissive temperature. 

The next set of experiments showed that merely messing with the cap is not sufficient to cause the polymerase to pause. Lahudkar and coworkers found that RNA pol II occupancy was unchanged in strains carrying mutations in STO1 (also known as CBP80) or CEG1, two components of the capping machinery. Cet1p apparently has a separate, unrelated function in helping to clear polymerases away from the start site of transcription.

The final set of experiments showed that the unpausing activity of Cet1p was found in a different part of the protein from its capping function.  Cet1p be can be broadly divided into three regions—a poorly characterized N-terminal domain (amino acids 1-204), a Ceg1p interaction domain (aa 205-266), and a triphosphatase domain (aa 265-549).  The last two domains are critical to its capping function.

Lahudkar and coworkers found that deleting the 1-204 aa domain from Cet1p caused polymerase stalling at the promoter without affecting its capping ability.  And conversely, that when they impaired the ability of Cet1p to perform its capping function while retaining its 1-204 aa domain, RNA pol II escaped the promoter at the same rate as it did in the presence of wild type Cet1p.  A final experiment showed that just expressing the first 300 amino acids of Cet1p was sufficient to get the polymerases moving. 

All in all these experiments provide strong evidence that Cet1p has two separate functions—an enzymatic role in capping mRNA and an unrelated activity that helps clear RNA pol II from the regions around the promoters of genes.  Which all goes to show that even when you think you have a handle on a protein, it can still surprise you with something new.  Turn it around and you just might find a toothbrush at the end.

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

Categories: Research Spotlight

Tags: bifunctional protein, mRNA capping, Saccharomyces cerevisiae, transcription

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.

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: environmental stress, Saccharomyces cerevisiae, 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.

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 (sgd-helpdesk@lists.stanford.edu) 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.

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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: Gene Ontology, GO

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