New & Noteworthy

Identifying the Unstable

November 08, 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.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: protein stability, Saccharomyces cerevisiae, yeast model for human disease

Using Yeast to Discover Treatments for ALS

November 01, 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.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: ALS, DBR1, Lou Gehrig's Disease, RNA binding, Saccharomyces cerevisiae, yeast model for human disease

Cancer’s Chromosomal Chaos Explained (Partly)

June 01, 2012

One reason cancer is so tricky to treat has to do with its adaptability.  It can quickly try out new genetic combinations until it hits upon one that can survive whatever treatment a doctor is currently throwing at it.  The result is return of the cancer after remission.

One way cancer is able to change its genetics so rapidly has to do with chromosome instability.  The number of chromosomes in a cancer cell is much less stable than in a normal cell.  This allows the cancer cell to constantly explore a wide range of chromosomal combinations.

It is still an open question how this dynamic instability happens.  The gene-centric theory suggests that mutations in key genes are the main driving force.  The chromosome-centric model says that having the wrong number of chromosomes is the critical component.

Distinguishing between these two models using cancer cells has proven difficult because these cells always have mutated genes.  There is simply no way to look at just chromosome numbers in this system.  This is where yeast can help.

In a recent paper published in PLoS Genetics, Zhu and coworkers used yeast to explore whether altered chromosome number was sufficient to explain chromosome instability.  They found that chromosome numbers alone can explain some but not all of chromosomal instability.

The authors created various chromosomal combinations in yeast by sporulating isogenic triploid yeast cells.  These cells had different numbers of genetically identical chromosomes.  They then explored the stability of each chromosome number combination using both FACS and qPCR.

What they found was that chromosome number certainly impacted chromosomal stability.  Chromosome number became less and less stable as the chromosome number veered further and further from the haploid state.  Of course, once the cells became diploid, stability returned. 

The authors explain this with the idea that there is only so much cellular machinery to move chromosomes to the proper place during mitosis.  As more and more chromosomes are added to the cell, the machinery becomes increasingly taxed, resulting in more and more errors. 

But once the diploid state is reached, all the genes are present to make twice as much mitotic machinery.  Now stable chromosome segregation can happen.

This was the broad pattern Zhu and coworkers observed but it certainly wasn’t the whole story.  The authors found islands of stability in the chromosomal chaos. 

For example, very often when there were equal numbers of chromosome VII (ChrVII) and chromosome X (ChrX), the chromosome number was more stable than predicted.   They explored this further and found evidence that suggested that at least part of this was due to the MAD1 gene on ChrVII and the MAD2 gene on ChrX. 

Stable chromosome numbers required that these genes be present in a 1:1 ratio.  Once the ratio strayed from one, chromosomal instability increased.  But these genes don’t explain everything.  There were unstable combinations where the MAD1/MAD2 ratio was correct.  As might be expected, there are other gene combinations that can lead to instability as well.

So incorrect chromosome number alone can explain the chromosomal instability seen in cancer cells.  But genes clearly play a role too, as evidenced by the islands of stability and the MAD1 gene and MAD2 genes.  As usual, reality is probably a combination of the two models. 

So it looks like chromosome number does play an important role in chromosomal instability.  Too many chromosomes may overtax the mitotic machinery so that chromosomes end up mis-segregated.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: cancer, chromosomal instability, chromosome, genetics, Saccharomyces cerevisiae, yeast

Yeast to the Rescue (of Personalized Medicine)

April 09, 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

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: CBS gene, CYS4, high throughput screen, homocystinuria, personalized medicine, yeast

Yeast with Dementia

January 04, 2012

Even though it doesn’t have a brain, yeast is teaching us a lot about Alzheimer’s.  Researchers are using this simple eukaryote to figure out what previously identified Alzheimer’s-related genes may be doing in humans as well as to identify new genes that might be involved in this terrible disease.  Studies like this may even one day help scientists find better treatments.

Alzheimer’s is a form of dementia that hits about 50% of people over 85.  The video below has a great summary of the how the disease progresses:

As the video states, plaques and tangles are linked to the memory loss that is associated with Alzheimer’s.  Scientists know that the plaques are  amyloids of misfolded AΒ peptides and that AΒ peptides that come from the amyloid precursor protein (APP).  What they don’t know is how AΒ peptides cause their damage and if it can be stopped.  And so far, genome wide association studies (GWAS) in humans have not shed much light on this problem either.

That isn’t to say that GWAS have been a waste of time.  They haven’t.  These studies have identified a number of alleles of a few genes that impact a person’s risk for ending up with Alzheimer’s.  They just haven’t been able to link the build up of plaques with the identified genes.  This is where yeast comes in.

Treusch and coworkers created a strain of yeast in which the AΒ peptide was sent to the endoplasmic reticulum.  This mimics what happens to the peptide in the cells of Alzheimer’s patients.  These yeast grew more slowly and developed protein complexes reminiscent of plaques.

They then added each of 5532 yeast open reading frames to this strain to identify genes that specifically affected its growth rate.  Of the 40 different yeast genes they found, two (YAP1802 and INP52) were yeast homologs of human genes (PICALM and SYNJ1) that had already been identified to be important in Alzheimer’s risks.  These results validated the screen and gave the researchers the confidence to dive deeper into their results.

The researchers decided to focus on the 12 genes that had very close human homologs.  Of these 12 genes, 10 dealt with endocytosis and the cytoskeleton and at least three had been implicated in previous genome wide association studies in humans.  Further work by these authors validated four of these genes by showing that they had similar effects on AΒ cell toxicity in the worm model C. elegans.

In one of the most interesting parts of the study, the researchers used the yeast strain to show why the GWAS-identified gene PICALM affects Alzheimer’s patients.  Rather than modifying APP trafficking as had been previously proposed, their results support a model where PICALM lessens the impact of misfolded AΒ plaques on the cell. 

This study is another example of the awesome power of yeast genetics.  Who would have thought that a brainless yeast could teach us so much about Alzheimer’s?

Simple explanation of the genetics of Alzheimer’s

More information about Alzheimer’s

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: alzheimer's, amyloid, APP, model organism, PICALM, plaque, yeast

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