New & Noteworthy
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.
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!
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
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).
by Maria Costanzo, Ph.D., Senior Biocurator, SGD