New & Noteworthy

Redesigning Yeast One Chromosome at a Time

April 03, 2014

Everyone who reads our blog knows how awesome the yeast Saccharomyces cerevisiae is.  Without this little workhorse we would almost certainly not understand ourselves as well as we do now.  It is an indispensable tool in figuring out how eukaryotes work.

And of course yeast is much more than that.  It makes our bread fluffy and our drinks alcoholic.  It can be manipulated into making medicines like artemisinin, a powerful anti-malarial drug, or biofuels or whatever else we can think of.  It is the Swiss Army Knife of useful organisms.

Even with all of this fanfare, everyone knows yeast has its limitations.  It is a powerful tool but it could be improved.  For example, it would be nice if researchers could more easily manipulate its DNA to speed up the introduction of beneficial traits, add new biosynthetic pathways, or to do the kinds of experiments that will help one day cure cancer or Alzheimer’s disease.  This is where Sc2.0 comes in.

Sc2.0 is an idea that has been kicking around for the last decade or so.  First proposed by Ron Davis of Stanford University, the idea is to synthesize artificial yeast chromosomes to make yeast more useful.  Eventually the idea would be to recreate every yeast chromosome and intelligently redesign the genome for our own purposes. And maybe even to add new artificial chromosomes so we can easily add whatever genes we want.

In a new study out in Science, Annaluru and coworkers have taken a major step forward in the Sc2.0 project by replacing all 316,617 base pairs of yeast chromosome III with a 272,871 base pair synthetic version, synIII.   That leaves only 15 chromosomes and around 12.2 million base pairs before we have yeast with completely manmade DNA.

Annaluru and coworkers managed to do this with the help of a bunch of undergraduate students and yeast’s love of homologous recombination.  The first step was to have undergraduates synthesize around 30,000 base pairs each in the “Build a Genome” class at Johns Hopkins.  It took 49 students around 18 months to pull this off for synIII.

Basically they used 60-mer and 79-mer oligonucleotides to PCR up 750 base pair building blocks.  These pieces of DNA were designed so that they could be assembled into 2,000-4,000 base pair minichunks.  The final step was to transform yeast with an average of twelve of these minichunks and to let the yeast use homologous recombination to replace its native DNA sequence with the added DNA.  After 11 rounds of transformation, the yeast now had an artificial chromosome.

As you may have guessed, this chromosome is not exactly the same as the one it replaced.  To eventually free up a codon for repurposing later, all 43 of the TAG stop codons were converted to TAA.  When this is done with all of the chromosomes, researchers will now have a codon they can use to change this yeast’s fundamental genetic code.  This might allow for adding novel amino acids to proteins or even prevent viruses from infecting the new yeast.

Annaluru and coworkers also introduced 98 loxP sites which in the presence of estradiol will cause the yeast to undergo rapid DNA change.  The hope is that scientists will be able to harness SCRaMbLE (synthetic chromosome rearrangement and modification by loxP-mediated evolution), as it has been named, to more quickly evolve useful traits in yeast for both study and biotechnological uses.

As a final step, the researchers cleaned up the chromosome by removing 21 retrotransposons and many introns and by moving 11 tRNA genes to a neochromosome.  They now had created a leaner, meaner chromosome III. 

The next obvious question was whether or not all of these changes affected the yeast.  Despite looking very carefully, Annaluru and coworkers could find little that was different between strains carrying natural and synthetic chromosomes.  They both grew similarly under 21 different conditions in terms of growth curves, colony size, and cell morphology, and had very similar transcription profiles.  But they weren’t identical.

For example, the strain with synIII grew slightly less well in the presence of high sorbitol, and showed differences in expression from wild type in 10 out 6,756 transcripts.  Of these ten, eight were intentionally altered in the creation of synIII and so were expected.  The two unexpected changes were a ~16-fold decrease in the expression of HSP30 on synIII and a ~16-fold increase in the expression of PCL1 on chromosome XIV.

Since all of these changes had such a small effect on the yeast, it is a green light for plowing ahead with creating yeast with completely manmade DNA.  Currently four other chromosomes, II, V, VI, and XII, are nearly done and the design work has been completed for chromosomes I, IV, VII, and XI (see an overview of the project).  It will only be a matter of time before we have a strain of yeast with completely synthetic DNA.  Scientists are making a powerful tool even better…who knows what this new strain will help us discover.

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

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae, synthetic biology, teaching

Apply Now for the CSHL Yeast Genetics & Genomics Course

March 31, 2014

For more than 40 years, the legendary Yeast Genetics & Genomics course has been taught each summer at Cold Spring Harbor Laboratory. (OK, the name didn’t include “Genomics” in the beginning…) The list of people who have taken the course reads like a Who’s Who of yeast research, including many of today’s leading scientists and two Nobel laureates (Randy Schekman in 1975 and Jim Rothman in 1985, who both won the 2013 Nobel Prize). 

Now it’s your chance to attend this summer course (July 22 – August 11) and get a comprehensive education in all things yeast, from classical genetics through up-to-the-minute genomics and the latest cytological techniques. Scientists who aren’t part of large, well-known yeast labs are especially encouraged to apply – for example, professors and instructors who want to incorporate yeast into their undergraduate genetics classrooms; scientists who want to transition from mathematical, computational, or engineering disciplines into bench science; and researchers from small labs or institutions where it would otherwise be difficult to learn the fundamentals of yeast genetics and genomics.

Besides its scientific content, the fun and camaraderie at the course is also legendary. In between all the hard work there are late-night chats at the bar and swimming at the beach. There’s a fierce competition between students at the various CSHL courses in the Plate Race, which is a relay in which teams have to carry stacks of 40 Petri dishes (used, of course).

The application deadline is April 15th, so don’t miss your chance! Find all the details here.

Categories: Conferences

Silly Sod’s Two Jobs

March 27, 2014

Most SGD users are probably too young to remember Saturday Night Live’s early years.  One very funny commercial parody involved Gilda Radner and Dan Aykroyd arguing over a product called Shimmer.  Gilda argues that it is a floor wax while Dan says it is a dessert topping.  In comes Chevy Chase to tell them that it is both.  Not quite as funny as Bassomatic, but still hilarious.

In a new study, Tsang and coworkers show something similar for the enzyme Sod1p.  Most people know Sod1p as an enzyme that protects the cell and its DNA by directly deactivating harmful reactive oxygen species (ROS) like superoxide.  Turns out that it may also be a transcription factor.

Now these two jobs aren’t quite as disconnected as a dessert topping and floor wax.  When Sod1p acts as a transcription factor, it is regulating genes that affect a cell’s response to ROS.  It is actually using its two functions to attack the same problem on multiple fronts.

Tsang and coworkers started out by looking at what happens to nuclear DNA under oxidative stress, using the Comet and TUNEL DNA damage assays. They found that endogenous and exogenous ROS caused DNA damage that was much worse in the sod1 null mutant – in other words, Sod1p protected the cells’ DNA. Using immunofluorescence, they also showed that Sod1p quickly went into the nucleus in the presence of ROS.  But if they restricted Sod1p to the cytoplasm by adding a nuclear export signal, the protein no longer protected the DNA.  In fact, it did no better than a strain deleted for SOD1.

In the course of these experiments one of the ways the researchers induced nuclear localization was with a burst of hydrogen peroxide.  But since hydrogen peroxide isn’t a substrate of the enzyme Sod1p, Tsang and coworkers next wanted to figure out how Sod1p got its signal to go nuclear.

Previous work had shown that SOD1 genetically interacted with MEC1, a yeast homolog of ATM kinases which sense oxidative stress.  They deleted MEC1 and found that Sod1p was trapped in the cytoplasm, unable to protect the cell’s DNA from damage.  This result was confirmed in human cells by showing that Sod1p only went nuclear if the cell made ATM kinase.

Tsang and coworkers suspected that this interaction might happen through a protein kinase called Dun1p, whose human homolog is a Mec effector. They confirmed a previous mass spectrometry result that showed Sod1p interacted physically with Dun1p.  And indeed, when DUN1 was deleted, Sod1p was again stranded in the cytoplasm.  Further work showed that Dun1p does its job by phosphorylating Sod1p on two serine residues, S60 and S99. When both these serines are mutated to alanine, preventing phosphorylation, less of the mutant Sod1p makes it into the nucleus. 

Using DNA microarrays, Tsang and coworkers next showed that SOD1 was required to activate 123 genes needed by the cell to respond to hydrogen peroxide.  These genes fell into five categories: oxidative stress, replication stress, DNA damage response, general stress response and Cu/Fe homeostasis.  The final experiment used chromosomal immunoprecipitation (ChIP) to show that in the presence of hydrogen peroxide more Sod1p was bound at the promoters of two of these genes, RNR3 and GRE2, but not the control gene ACT1. 

Of course, the authors have only looked at two of the 123 genes and an obvious next step is to figure out how many of the 123 have more Sod1p bound to their promoters in the presence of hydrogen peroxide.  Still, if these results can be confirmed and expanded they will suggest that Sod1p is able to combat oxidative damage in two completely different ways. 

In the first it uses its enzymatic activity to directly inactivate the ROS superoxide, while in the second it helps the cell respond to other ROS apparently by acting as a transcription factor.  While the jobs themselves are not as different as a floor wax and a dessert topping, how Sod1p goes about getting each job done is.  “Calm down you two, Sod1p is an enzyme AND a transcription factor.”

In addition to these two roles, we’ve written before about yet another regulatory role for Sod1p: it regulates glucose repression by binding to two kinases and stabilizing them. This is truly an overachiever of a protein!

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

Categories: Research Spotlight

Tags: oxidative stress, Saccharomyces cerevisiae, transcription

Explore a Large New Chemogenomics Dataset Via SGD

March 26, 2014

What happens when you cross two comprehensive deletion mutant collections with a library of more than 1800 structurally diverse chemicals? HIP HOP happens. Not the music, but a whole lot of very informative phenotype data – over 40 million data points!

The response of S. cerevisiae mutant strains to a chemical can tell us a lot about which pathways or processes the chemical affects. This is not only interesting for yeast biologists, but also has important implications for human molecular biology and disease research. So a group at The Novartis Institutes of Biomedical Research decided to test the sensitivity of nearly 6,000 mutant yeast strains to a panel of about 1,800 compounds. 

Hoepfner and colleagues have published these results and have also generously offered them to SGD.  They used the HIP and HOP methods (HIP, HaploInsufficiency Profiling, using diploid heterozygous deletion mutant strains; HOP, HOmozygous deletion Profiling, using diploid homozygous deletion mutant strains) that have proven very useful in yeast since the creation of the systematic deletion mutant collections.

To do this mammoth series of experiments they obviously needed to set up an automated pipeline. These sorts of experiments have been done before, but in this study Hoepfner et al. improved on existing procedures in many ways: the physical techniques, the controls and replicates included, and the methods for data analysis.

Phenotype annotations in SGD. We’ve incorporated a subset of these results into SGD as mutant phenotype annotations. Why a subset? Some of the chemicals that were used in these experiments are un-named proprietary compounds, so the individual phenotypes would not be very informative in the context of SGD. We’ve added the phenotypes that involve named chemicals to SGD – more than 5,500 annotations. These may be viewed on Phenotype Details pages for individual genes (see example), retrieved as a set using Yeastmine, or downloaded along with all SGD mutant phenotype annotations in our phenotype data download file.

Easy access to the full dataset and analyses. We’ve also added a new set of links to SGD that take you directly from your favorite gene to the authors’ website, which provides full access to all of the data and interesting ways to look at it (see below). When you click on a “HIP HOP Profile” link from the Locus Summary page or the Phenotype Details page of a gene in SGD, the landing page at the authors’ website allows you to explore data for mutants in that gene or for chemicals affecting that mutant strain. You can see which chemicals had the greatest effects, which other mutant strains have a similar range of phenotypes, and much more. And if a chemical that has interesting effects is proprietary, don’t worry; Hoepfner and colleagues have stated that they “encourage future academic collaborations around individual compounds used in this study.”

Information about mutant strains. In the course of this study, the authors also generated some very useful data about particular mutant strains in the deletion collection. Some of them were hypersensitive to more than 100 different chemicals. Others turned out to be carrying additional background mutations that could affect the phenotypes of the mutant strain. We are planning to display this kind of information (from this and other studies) directly on SGD Phenotype Details pages in the future.

We thank Dominic Hoepfner and colleagues for sharing these data with SGD and for helping us to incorporate the data.  And we encourage you to explore this new resource and contact us with any questions or suggestions.

Categories: New Data

Yeast Genetics Meeting Abstract and Registration Sites Now Open

March 24, 2014

The 2014 Yeast Genetics Meeting will be held July 29 – August 3 at the University of Washington, Seattle, Washington. Abstract submission and registration sites are now open.  The abstract submission deadline is April 24.   Be sure to get your abstract in by the deadline so that it will be programmed and included in the Program Book.*

The Genetics Society of America is pleased to announce the following awards and lectures will be presented:

• George Church, Harvard University – Lee Hartwell Lecture
• Olga Troyanskya, Princeton University – Ira Herskowitz Award
• Jeremy Thorner, University of California, Berkeley – Lifetime Achievement Award
• Anita Hopper, Ohio State University – Winge-Lindegren Address

In addition, there will be two special presentations: Jon Lorsch, Director of the National Institute of General Medical Sciences, NIH will discuss the future plans of the Institute and Gerry Fink, MIT, will give a retrospective look at Fred Sherman’s life and his impact on the field of yeast genetics research.

As usual, SGD staff will be at the meeting and we look forward to meeting and talking with yeast researchers. Don’t miss this premier conference of the yeast community!

*IMPORTANT NOTE: NEW THIS YEAR: The full text of all abstracts submitted by the deadline date will ONLY be available online, as a pdf and in the abstract search program,  and will not be printed in the program book. The program book will still contain the full schedule information including platform and poster sessions date, time, title, authors, gene index and a listing of exhibits. Late abstracts will only be accepted if space permits and will not be included in the online search.

Questions? Contact Anne Marie Mahoney: mahoney@genetics-gsa.org

Categories: Conferences

Guns vs. Butter in Cells

March 19, 2014

Life is a set of tradeoffs for people, countries, and even cells.  For example, governments need to decide how much money to dedicate to defense and how much to economic growth.  Too much on defense and your country fails, because defense spending sucks up so many resources that your country can no longer afford to pay for anything else.  And of course if you spend too little on defense, someone who spent a bit more can come and take you over.

No country lives in a vacuum though—how much to spend on defense and how much on growth depends on the country’s situation.  If you are the Ewoks living next to an Imperial shield generator, you’d better sacrifice some growth for defense.  But once the Death Star has blown up and the Empire is swept away, you probably focus more on growth (until a new Sith lord arrives). 

This guns vs. butter debate plays out at the cellular level too when it comes to protecting DNA from mutations.  If cells expend too much energy to protect their DNA they sacrifice growth, but if they spend too little, they develop too may harmful mutations to survive.  And just like with countries, how much protection a cell’s DNA needs depends on its environment.

If cells need to adapt quickly to a changing environment, a high rate of mutation is favored.  These cells are more likely to develop a mutation that gains them an advantage over their slower mutating brethren.

A new study by Herr and coworkers in the latest issue of GENETICS calculates the upper limit of the rate of mutation in a diploid yeast.  In other words, they figure out how little “spending” on defense these yeast can get away with and survive.

They find that diploid yeast can deal with a 10-fold higher rate of mutation as compared to haploid yeast.  This makes sense, since the extra gene copy afforded by being diploid can mask a recessive lethal mutation, but this study is the first to give this idea hard numbers.

The authors had previously generated a number of mutations in POL3, the yeast gene for DNA polymerase δ, that affect its ability to find and/or fix any mistakes made during DNA replication.  The study first focused on two mutations affecting accuracy, pol3-L612G and pol3-L612M, and one mutation affecting proofreading, pol3-01.  The accuracy mutations caused about a 10-fold increase in the mutation rate, while the proofreading mutation caused anywhere from a 20-100-fold increase.  Neither was enough to seriously affect a diploid’s growth.

The next step was to combine accuracy and proofreading mutations into the same gene to figure out if the combination resulted in a higher mutation rate.  The authors suspected that it did when they discovered that even though the heterozygotes were fine, their spores were inviable.  The POL3/pol3-01,L212M and POL3/pol3-01,L212G strains sporulated just fine, but none of the spores could germinate and grow. 

One way to explain this was that the double mutation increased the error rate to the point that it would kill off haploids but not diploids.  By looking at mutations in the hemizygous CAN1 gene they could see that the mutation rate in these diploids was indeed at around the haploid threshold. In terms of the CAN1 gene, this mutation rate was around 1X10-3 can1 mutations/cell division.

They next determined the mutation rate by sequencing the genomes of each mutant as well as the wild type.  They found a single T-G mutation in the wild type, 1535 point mutations in POL3/pol3-01,L212M and 1003 mutations in POL3/pol3-01,L212G.  From this they calculated a mutation rate of around 3-4X10-6/base pair/generation. 

Even though this level of mutation kills haploids but not diploids, this does not mean the diploids escaped unscathed.  When the heterozygous diploid colonies were subcloned the resulting colonies were variable in size, indicating that their higher mutation rate was catching up with them.  This high mutation rate was making them sick. 

Given this result, it wasn’t surprising that diploid homozygotes of each double mutant could not survive—the mutation rate was now too high.  The strains homozygous for pol3-01,L212M managed to get to around 1000 cells before petering out.  Strains homozygous for pol3-01,L212G did even worse—they only made it to around 10 cells.

In a final set of experiments Herr and coworkers used a variety of other mutations to tweak these mutation rates to find the threshold at which diploids fail to survive.  Some of these mutations were in POL3 while others were deletions of the MSH2 and/or DUN1 genes.  After testing many different combinations, they found that these yeast did pretty well up to around 1X10-3 can1 mutations/cell division (the haploid threshold rate).  Then, from 1X10-3 to 1X10-2 can1 mutations/cell division there began a rapid drop off with little to no growth at the end. 

So as might be expected, diploids can deal with a significantly higher mutation rate than can haploids.  But even though they can, wild type yeast in the lab still have a very low mutation rate.  It is like they are living near the Imperial city planet of Coruscant.  They are willing to expend the energy to keep their DNA protected.

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

Categories: Research Spotlight

Tags: DNA replication, mutation, Saccharomyces cerevisiae

Transcriptome Data in YeastMine

March 13, 2014

Towards the goal of compiling datasets to produce a complete transcriptome of yeast (the set of all RNA molecules produced in a single cell or population of cells), we have loaded a defined set of transcripts, based primarily on data from Pelechano, et al, but supported by other datasets, into SGD’s flexible search tool, YeastMine. The representative set includes transcripts which Pelechano et al. identified by simultaneous determination of the 5’ and 3’ ends of mRNA molecules whose end coordinates are supported by datasets from other laboratories.

The transcript data can be accessed in YeastMine using the ‘Gene -> Transcripts’ template, which allows you to specify a gene name or list of gene names and return the list of all associated transcripts based on the collection of data described above. The results include the start and end coordinates for each transcript, the number of counts observed for each transcript in glucose and galactose, notes, and references for the relevant datasets.

Categories: New Data

Changes to the SGD GAF File of Gene Ontology Annotations

March 11, 2014

The SGD Gene Associations file (GAF; gene_association.sgd) contains Gene Ontology (GO) annotations for all yeast genes, in a standard file format specified by the GO Consortium. We are changing the taxon identifier in this file to be consistent with the reference genome sequence at GenBank and protein entries at UniProt.

Until now, the taxon identifier in column 13 of SGD’s GAF has been 4932, which refers to Saccharomyces cerevisiae in general rather than to a specific S. cerevisiae strain. Starting March 8th, 2014, we have changed this to taxon ID 559292, which is specific to the S288C strain used for the S. cerevisiae reference genome sequence.

Please note that the taxon ID 559292 merely reflects the sequence (genome) to which the geneIDs in column 2 are mapped. SGD will continue to capture gene functions (GO annotations) for all strains of S. cerevisiae. Please contact us if you have any questions.
The S. cerevisiae GO annotations (GAF) can be downloaded from SGD’s Downloads site.

Categories: Data updates

A Heartfelt Need for Copper

March 06, 2014

Imagine the heater at your house is run by a homemade copper-zinc battery.  You are counting on a delivery of a copper solution that will keep the thing going.  Unfortunately it fails to come, which means the battery doesn’t work and you are left out in the cold. 

Turns out that something similar can happen in cells too.  The respiratory chain that makes most of our energy needs copper to work.  In a recent study, Ghosh and coworkers showed that if Coa6p doesn’t do its job delivering copper to the respiratory chain, the cell can’t make enough energy.

This isn’t just interesting biology.  In this same study, the researchers showed that mutations in the COA6 gene cause devastating disease in humans and zebrafish. And their discovery that added copper can cure the “disease” in yeast just might have therapeutic applications for humans.

The respiratory chain is a group of large enzyme complexes that sit in the mitochondrial inner membrane and pass electrons from one to another during cellular respiration. This process generates most of the energy that a cell needs.  Hundreds of genes, in both the nuclear and mitochondrial genomes, are involved in keeping this respiratory chain working.

Yeast has been the ideal experimental organism for studying these genes, because it can survive just fine without respiration. If it can’t respire for any reason, yeast simply switches over to fermentation, generating the alcohol and CO2 byproducts that we know and love.

Human cells aren’t as versatile though. Genes involved in respiration can cause mitochondrial respiratory chain disease (MRCD) when mutated. This is one of the most common kinds of genetic defect, with over 100 different genes known so far that can cause this phenotype.

Ghosh and colleagues wondered whether there were as-yet-unidentified human genes involved in maintaining the respiratory chain. They reasoned that any such genes would be highly conserved across species, because they are so important to life, and that the proteins they encoded would localize to mitochondria.

One of the candidates, C1orf31, caught their eye for a couple of reasons.  First, some variations in this gene had been found in the DNA of a MRCD patient.  And second, the yeast homolog, COA6, encoded a mitochondrial protein that had been implicated in assembly of one of the respiratory complexes, Complex IV or cytochrome c oxidase.

They first did some more detailed characterization of COA6 in yeast.  They were able to verify that the coa6 null mutant had reduced respiratory growth because it had lower levels of fully assembled Complex IV.

They also looked to see what happens in human cell culture.  When they knocked down expression of the human homolog, they also saw less assembly of Complex IV. This suggested that the function of this protein is conserved across species.

Next they turned to a sequencing study of an MRCD patient who had, sadly, died of a heart defect (hypertrophic cardiomyopathy) before reaching his first birthday. The sequence showed a mutation in a conserved cysteine-containing motif of COA6. To see whether this might be the cause of the defect, they created the analogous mutation in yeast COA6. The mutant protein was completely nonfunctional in yeast.

To nail down the physiological role of COA6 in a multicellular organism, they turned to zebrafish. The embryos of these fish are transparent, so it’s easy to follow organ development. Given the phenotype, the fact that they can live without a functional cardiovascular system for a few days after fertilization was important too.

When the researchers knocked down expression of COA6 in zebrafish, they found that the embryos’ hearts failed to develop normally and they eventually died. The abnormal development of the fish hearts paralleled that seen in the human MRCD patient carrying the C1orf31/COA6 mutation. And reduced levels of Complex IV were present in the fish embryos.

Going back to yeast for one more experiment, Ghosh and colleagues decided to see whether Coa6p might be involved in delivering copper to Complex IV. They knew that Complex IV uses copper ions as a cofactor, and furthermore Coa6p had similarities to several other yeast proteins that are known to be involved in the copper delivery.

They tested this by supplying the coa6 null mutant with large amounts of copper. Sure enough, its respiratory growth defect and Complex IV assembly problems were reversed.  The delivery of copper kept the energy flowing in these cells. And this result showed that Coa6p is involved in getting copper to Complex IV.

These experiments showcase the need for model organism research even in the face of ever more sophisticated techniques applied to human cells. The mutation in human C1orf31/COA6 was discovered in a next-generation sequencing study, but yeast genetics established the relationship between the mutation and its phenotype. The zebrafish system allowed the researchers to follow the effects of the mutation in an embryo from the earliest moments after fertilization. And the rescue of the yeast mutant by copper supplementation offers an intriguing therapeutic possibility for some types of MRCD. Just another testament to the awesome power of model organism research!

YeastMine now lets you explore human homologs and disease phenotypes.  Enter “COA6” into the template Yeast Gene -> OMIM Human Homolog(s) -> OMIM Disease Phenotype(s) to link to the Gene page for human COA6 (the connection between COA6 and disease is too new to be represented in OMIM).  To browse some diseases related to mitochondrial function, enter “mitochondrial” into the template OMIM Disease Phenotype(s) -> Human Gene(s) -> Yeast Homolog(s).

by Maria Costanzo, Ph.D., Senior Biocurator, SGD

Categories: Research Spotlight, Yeast and Human Disease

Tags: respiration, Saccharomyces cerevisiae, yeast model for human disease, zebrafish

Human Disease & Fungal Homologs in YeastMine

March 04, 2014

You can now use SGD’s advanced search tool, YeastMine, to find the human homolog(s) of your favorite yeast gene and their corresponding disease associations. Or, begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search).

We have recently loaded data from OMIM (Online Mendelian Inheritance in Man) into our fast, flexible search resource, YeastMine, and provided 3 predefined queries (templates) that make it simple to perform the above searches. Newly updated HomoloGene, Ensembl, TreeFam, and Panther data sets are used to define the homology between S. cerevisiae and human genes. The results table provides identifiers and standard names for the yeast and human genes, as well as OMIM gene and disease identifiers and names. As with other YeastMine templates, results can be saved as lists and analyzed further. You can also now create a list of human names and/or identifiers using the updated Create Lists feature that allows you to specify the organism representing the genes in your list. The query for yeast homologs can then be made against this list.

In addition to human disease homologs, we have incorporated fungal homolog data for 24 additional species of fungi. You can now query for the fungal homologs of a given S. cerevisiae gene using the template “Gene –> Fungal Homologs.” This fungal homology data comes from various sources including FungiDB, the Candida Gene Order Browser (CGOB), and PomBase, and the results link directly to the corresponding gene pages in the relevant databases, including Candida Genome Database (CGD) and Aspergillus Genome Database (AspGD).

All of the new templates that query human and fungal homolog data can be found on the YeastMine Home page under the new tab “Homology.” These templates complement the template “Gene → Non-Fungal and S. cerevisiae Homologs” that retrieves homologs of S. cerevisiae genes in human, rat, mouse, worm, fly, mosquito, and zebrafish.

Watch the Human Disease & Fungal Homologs in SGD’s YeastMine tutorial (below) to learn how to find and use these new templates.

Categories: New Data, Yeast and Human Disease

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