New & Noteworthy
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.
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!
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.
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 (firstname.lastname@example.org) 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).
December 3, 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.
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.