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New & Noteworthy

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.

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.

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.

Using Yeast to Discover Treatments for ALS

November 1, 2012

What do Lou Gehrig, Stephen Hawking, David Niven and Mao Zedong have in common?  They all suffered (or in Hawking’s case, continue to suffer) terribly from a disease called amyotrophic lateral sclerosis or ALS.  And now the humble yeast S. cerevisiae may help scientists find new treatments so that others do not need to suffer similarly.

Patients with ALS gradually lose use of their motor neurons and generally die within 3-5 years of diagnosis.  While there are some rare forms that run in families, most are sporadic.  There is no history of the disease in the family and then suddenly, it just appears.

The causes of ALS have remained a mystery for many years but recent work has suggested that RNA binding proteins and RNA processing pathways are somehow involved.  In particular, an RNA-binding protein called TDP-43 appears to be a key player.  Mutations in its gene are associated with ALS, and aggregates of the protein are found in damaged neurons of ALS patients. Unfortunately, since this protein is needed for cell survival it is not an easy target for therapies.  This is where yeast can help.

Scientists have managed to mimic the effects of TDP-43 in yeast.  When this protein is overexpressed, the yeast cells die just like the motor cell neurons do. In a recent Nature Genetics paper, Armakola and coworkers use this model system for finding better therapeutic targets.  And it looks like they may have succeeded.

These authors used two different screens to systematically look for proteins that when deleted or expressed at lower levels rescued yeast overexpressing TDP-43.  They found plenty.  One screen yielded eight suppressors while the other yielded 2,056 potential suppressors.  They decided to focus on one of the stronger suppressors, DBR1.

The first thing they wanted to do was to make sure this wasn’t a yeast specific effect.  If lowering the amount of DBR1 has no effect in mammalian models, it is obviously not worth pursuing!

To answer this question, they created a mammalian neuroblastoma cell line with an inducible system for making a mutant version of TDP-43, TDP-43 Gln331Lys, found commonly in ALS patients.  As expected, these cells quickly died in the presence of inducer.  They could be rescued, though, when DBR1 activity was inhibited with siRNA.  The authors confirmed that decreasing the activity of DBR1 in primary neurons decreased TDP-43 toxicity as well.

So decreasing the amount of DBR1 appears to rescue cells that die from the effects of mutant TDP-43.  This suggests that targeting DBR1 may be useful as a therapy for ALS.  But this study doesn’t stop there.  It also tells us a bit about how lowering DBR1 levels might be rescuing the cells.

DBR1 is an RNA processing enzyme involved in cleaning up the mess left behind by splicing.  It cleaves the 2’-5’ phosphodiester bond of the spliced-out intron (called a lariat).  Previous studies in yeast have shown that when Dbr1p levels are reduced or its catalytic activity is disrupted by a mutation, there is a build up of these lariats. This study showed directly that the accumulated lariats interact with TDP-43 in the cytoplasm to suppress its toxicity. So in ALS, the accumulated lariats may serve as a decoy for the mutant TDP-43 protein, preventing it from binding to and interfering with more essential RNAs.

This last result may also suggest another potential therapy.  If scientists can find other ways to increase the amount of decoy RNA, then they may not need to depend on reducing levels of DBR1.  There may be many possible approaches to soaking up rogue TDP-43.

Yeast to the Rescue (of Personalized Medicine)

April 9, 2012

Genomic scientists are quickly being overwhelmed by all of the data they are generating.  As trillions of A’s, T’s, C’s and G’s come pouring out of sequencers all over the world, how is anyone going to make sense of it all?

One idea is to use yeast to quickly figure out what effect certain differences have on a gene’s function.  Now this won’t be that useful for differences outside of genes or in genes that aren’t shared by yeast and humans.  But that still leaves an awful lot of SNPs that we might be able to better understand using the awesome power of yeast genetics.

In the most recent issue of GENETICS, Mayfield and coworkers use yeast to study a large number of variants in the human cystathione-beta synthase (CBS) gene.  They chose this gene because it is involved in the metabolic disease homocystinuria, different variants respond to treatment in unpredictable ways, and it can substitute for the yeast homolog, CYS4.

The hope was that they would be able to group CBS variants based on their phenotype in yeast and that this would let them predict which treatments would work for novel variants.  They were definitely able to group variants based on phenotype.  Time will only tell whether they can use this to better treat patients who come into the clinic with novel variants of the gene.

They looked at 84 known alleles of CBS that affected an amino acid with a single base pair change (81 were from homocystinuria patients).  They grouped these alleles based on growth phenotypes in yeast under varying conditions.  For example, they determined how well each grew in the absence of glutathione.  Only those alleles that were still functional would support growth.  They also varied the amount of glutathione, looked at the effect of heme and vitamin B6, studied metabolite profiles with mass spectroscopy and so on. 

From this they were able to group many of the alleles in clinically meaningful ways.  This means that when a novel allele comes up in a patient, they can screen it in this yeast assay to see if it falls within a known group.  At least 38 never before seen missense mutations have been found in the CBS gene since 2010 and undoubtedly new ones will keep appearing as more DNA is sequenced.

The study also revealed alleles that were more difficult to interpret in this assay.  For example, some alleles known to cause disease did not affect yeast growth.  This might mean that their particular mutation needs something human and/or patient specific to manifest itself or that the enzyme function is fine but something else is wrong. 

This study provided a powerful proof of principle.  The next step will be to see how well it works in practice and if any patients can benefit.

Benjamin deals with his homocystinuria