New & Noteworthy

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

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!

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

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.