New & Noteworthy

Human Disease & Fungal Homologs in YeastMine

March 4, 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.

Alternative Ways to Increase a Cell’s Shelf Life

June 5, 2013

Like milk or eggs, most cells with linear chromosomes have a shelf life. Each time these cells divide, they lose a little off the end of their chromosomes. Eventually, too much is lost and the cells crap out. Or, to use a more scientific term, they become senescent.

expiration date

Cells have lots of ways to keep their telomeres long and extend their “cell-by” dates.

But this is not the fate of every cell. Some cells, like those that go on to become sperm or eggs, use a reverse transcriptase called telomerase to extend their telomeres as part of their normal life cycle. And they aren’t the only ones. Around 85% of cancers hijack the telomerase and use it for their own nefarious ends.

The other 15% of cancers use a variety of different mechanisms to keep their telomeres from getting too short (Cesare and Reddel, 2010). All these different ways are lumped together in a single category called alternative lengthening of telomeres or ALT. The telomeres are lengthened in these cells by recombination with other telomeres, either those on other chromosomes or those that exist as shed, extrachromasomal bits. 

While telomere extension may keep cells alive, it can sometimes be a double-edged sword. A double stranded DNA break is usually recognized as DNA damage. However, if the break happens near a telomere seed (a sequence that looks like a telomere), then the DNA damage response can be suppressed and the end can be extended into a new telomere, in a process called chromosome healing. But now the cell could be in trouble, with new, partial chromosomes being created and getting pulled this way and that.

In a new study out in GENETICS, Lai and Heierhorst decided to investigate whether chromosome healing happens in yeast cells that have stayed alive because of ALT.  What they found was that chromosome healing at telomere seeds was suppressed in these post-senescence survivors.

They created these ALT dependent, post-senescence survivors from an est2 mutant strain that lacked the catalytic subunit of telomerase.  Without telomerase, the only way for these cells to survive is by using ALT. 

In the first experiment, they looked at whether the post-senescence survivors could create a new telomere by chromosome healing.  The authors used a galactose inducible HO endonuclease to create a double stranded break near an 81 base pair sequence known to be a telomere seed sequence in wild type. 

Broken DNA usually signals cells to pause the cell cycle until the damage is repaired. This is known as the DNA damage checkpoint. During chromosome healing in wild type, this checkpoint is suppressed so the chromosome break isn’t recognized as DNA damage.

In the post-senescence survivors, even after 21 hours there was no evidence of a telomere forming.  They didn’t suppress the DNA damage checkpoint either.

Lai and Heierhorst determined that these ALT-dependent cells could still repair a different break that was not near a telomere seed sequence. They just couldn’t repair the break at the telomere seed. And this wasn’t because the DNA damage checkpoint was active. When they prevented the checkpoint by using a rad53 mutant, the telomere still wasn’t repaired.

Instead, the post-senescence survivors eventually repaired the break by some other mechanism, generating lots of differing products in the process. When they repaired breaks at sites that were not telomere seeds, they were able to use homologous recombination. But homologous recombination was suppressed at the telomere seed site.

Since ALT is used in cancer cells, and happens most often in some of the least-curable types of cancer, whatever we can learn about the process in yeast is valuable. It may give us clues on how to change the expiration date of those cancer cells to “ASAP”.

Cancerous Avalanche

March 5, 2013

Cancer often gets going with chromosome instability.  Basically a cell gets a mutation that causes its chromosomes to mutate at a higher rate.  Now it and any cells that come from it build mutations faster and faster until they hit on the right combination to make the cell cancerous.  An accelerating avalanche of mutations has led to cancer.

avalanche

A mutation causing chromosomal instability can start an avalanche that leads to cancer.

There are plenty of obvious candidates for the genes that start these avalanches: genes like those involved in segregating chromosomes and repairing DNA, for example.  But there are undoubtedly sleeper genes that no one has really thought of.  In a new study out in GENETICS, Minaker and coworkers have used the yeast S. cerevisiae to identify three of these genes — GPN1 (previously named NPA3), GPN2, and GPN3.

A mutation in any one of these genes leads to chromosomal problems.  For example, mutations in GPN1 and GPN2 cause defects in sister chromatid cohesion and mutations in GPN3 confer a visible chromosome transmission defect.  All of the mutants also show increased sensitivity to hydroxyurea and ultraviolet light, two potent mutagens.  And if two of the genes are mutated at once, these defects become more severe.  Clearly, mutating GPN1, GPN2, and/or GPN3 leads to an increased risk for even more mutations!

What makes this surprising is what these genes actually do in a cell.  They are responsible for getting RNA polymerase II (RNAPII) and RNA polymerase III (RNAPIII) into the nucleus and assembled properly.  This was known before for GPN1, but here the authors show that in gpn2 and gpn3 mutants, RNAPII and RNAPIII subunits also fail to get into the nucleus. Genetic and physical interactions between all three GPN proteins suggest that they work together in overlapping ways to get enough RNAPII and RNAPIII chugging away in the nucleus.

So it looks like having too little RNAPII and RNAPIII in the nucleus causes chromosome instability. This is consistent with previous work that shows that mutations in many of the RNAPII subunits have similar effects.  Still, these genes would not be the first ones most scientists would look at when trying to find causes of chromosomal instability. Score another point for unbiased screens in yeast leading to a better understanding of human disease.

Identifying the Unstable

November 8, 2012

One of the many stumbling blocks in finding better treatments for genetic diseases is figuring out the cause of the disease.  These days, this doesn’t necessarily mean simply identifying the gene with the mutation.  No, nowadays it can mean figuring out what each specific mutation does to the gene it damages.

See, many genetic diseases are not caused by single mutations.  Instead, lots of different mutations can all damage the same gene in different ways.  And each class of mutation may require different treatments.

It’s easy to tell this bridge is unstable. It is a lot harder to tell with proteins.

Cystic fibrosis (CF) is a great example of this.  While most cases of this ultimately fatal disease are caused by mutations in the CFTR gene, not every mutation does the same thing to the CFTR protein.  Because of this, scientists have found different drugs to treat people with different classes of CFTR mutations.

So one drug, Ivacaftor, targets CFTR proteins that can’t open up as well as they should, while another investigative drug, PTC124, targets prematurely stopped CFTR proteins.  Each only treats a specific subset of CF patients who have the correct CFTR mutation.

All of this screams out for a quick and easy assay to figure out how a mutation actually disables a certain protein.  And this is where a new study by Pittman and coworkers just published in the journal GENETICS can help.

The authors have come up with a sensitive in vivo assay in S. cerevisiae that allows scientists to quickly identify mutations that lead to unstable proteins.  This kind of instability isn’t rare in human disease either.  Some of the more famous examples include a kidney disease called primary hyperoxaluria type 1 (PH1), Lou Gehrig’s disease (ALS), Parkinson’s disease, spinal muscular atrophy (SMA), and even some forms of cancer.

The assay basically inserts wild type and mutant versions of the gene of interest into the middle of the mouse dihydrofolate reductase (DHFR) gene, individually adds these chimeric genes to yeast lacking DHFR, and then measures growth rates.  The idea is that if the mutation leads to instability, the DHFR chimeric protein will be unstable too and the yeast will show growth defects under certain conditions.  This is just what they found.

Initially they focused on a gene involved in PH1, the AGT gene encoding alanine: glyoxylate aminotransferase.  They were able to show that disease causing mutations known to affect protein stability affected growth in this assay.  Not only that, but there was a strong correlation between growth and level of protein stability.  In other words, the more unstable the protein, the more severe the growth defect.

They then expanded their assay beyond known AGT mutations.  First they were able to identify a subset of disease-causing AGT mutations as affecting the stability of the AGT protein.  But the assay ran into trouble when they switched to the more stable SOD1 protein.  This protein, which is involved in most cases of ALS, is so stable that mutations that destabilized it were invisible in the assay.  The authors solved this problem by introducing a mutation into DHFR that destabilized it.  Now they could identify mutants that destabilized SOD1.

As a final step, they used their assay to screen a library of stabilizing compounds to identify those that specifically stabilized their mutant proteins.  Unfortunately, in this first attempt they only found compounds that stabilize DHFR, but the assay has the potential to find drugs that stabilize disease-related proteins as well.

Whether or not that potential is realized, this technique should still be a very useful way to determine whether a mutation affects protein stability.  Then, when drugs that stabilize the protein have been found, using this or other screens, doctors will know which patients can be helped by these compounds.  And this will be a boon for scientists and patients alike.

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