New & Noteworthy
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.
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.
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.