New & Noteworthy
August 27, 2012
For the most part, prions have a bad rep. They are the proverbial bad apple that spoils the whole bunch.
A prion is a protein that misfolds in a certain way that creates a chain reaction to misfold many additional copies of that particular protein in a cell. This misfolding en masse can cause severe problems like mad cow disease or Alzheimer’s.
As if that weren’t bad enough, this misfoldedness can spread from one organism to another. Once a prion gets into a cell and/or a part of the body, it will cause many of its properly folded brethren to misfold too. This is true even though the prion gene in the new host is happily churning out properly folded protein.
These things look like a nightmare. Why on Earth are prions still around? Because in addition to their bad side, they can sometimes be an advantage too (at least in yeast).
In a study published in Nature in February 2012, Halfmann and coworkers provide compelling evidence that prions can help both laboratory and wild yeast strains to adapt rapidly to a changing environment, by unlocking survival traits hidden in yeast DNA. In other words, prions are a way for a yeast population to hedge its bets against a world of changing environments.
The authors focused on the most famous prion in yeast, the translation termination protein Sup35p. When Sup35p switches to prion mode ([PSI+]), it becomes bound up in insoluble fibers, causing translation termination to become leaky. Now normally untranslated parts of mRNAs become part of their respective proteins. And this can change these proteins’ functions.
Sure, most of this newfound variation will have no effect or maybe even be harmful, but occasionally the prion will reveal a beneficial trait. This yeast can then go on to survive and even thrive in this new environment.
This mechanism may apply to other prions in addition to Sup35p. Prions tend to come from proteins that are global regulators of transcription or translation. In the non-prion form, these proteins do their usual job making sure transcription and translation are following the rules. But when these proteins become misfolded into a prion, they can no longer perform their usual function. This uncovers previously silent bits of DNA or RNA for transcription or translation.
These authors also convincingly showed that prions are not some weird phenomenon found only in laboratory strains of yeast. They found evidence for prions in 255 out of the 690 wild strains they surveyed (although only ten had Sup35p based prions). Not only that, but many of these prions also conferred new traits on the yeast that could be beneficial in certain circumstances. It looks like prions may serve an important function in yeast.
A more surprising result from the study is that these prion-derived traits carry on in later generations even after the prion has been removed. For example, the authors looked at the wine yeast UCD978. They found that when Sup35p was in its prion form in this strain, UCD978 could effectively penetrate agar surfaces and that this trait was lost when the prion was cured, reverting Sup35p to its functional form.
They then took the study further and showed that after meiosis and sporulation, 5/30 haploid progeny of UCD978 retained the trait even after the prion was removed. These five had fixed the new trait and no longer required the prion to maintain it. They got all the benefits with none of the costs.
It isn’t obvious how this trait became independent of the original inducing prion. But that is for another study (or two or ten).
If the results of this study pan out, they show that prions are not just part of a disease but are really just another way to adapt to environmental changes and to pass them down to future generations. Maybe these apples aren’t so bad after all!
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
August 21, 2012
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August 9, 2012
The idea behind a genome wide association study (GWAS) makes perfect sense. Compare the DNA of one group of people with a disease to another group that doesn’t have the disease, identify the DNA region specific to the disease group, and then find the specific gene and mutations that lead to the disease.
In theory, this sort of study should have become routine once we had the human genome sequenced. In practice, it has turned out to be less useful than everyone hoped.
Now, this doesn’t appear to be any fault with the technique itself. Instead, it has more to do with the fact that many human diseases are simply too complex for GWAS to handle.
Most common human diseases appear to result from multiple genetic pathways and/or multiple genes. Throw in environmental effects and GWAS quickly becomes overwhelmed. At least for now, too many patients and controls would be needed for this powerful technique to have a real chance at deciphering most common human diseases.
But that doesn’t mean the technique isn’t useful. It is very good at finding single genes involved in strongly expressed traits. And this might be ideal for certain model organisms.
In a study just out in the latest issue of GENETICS, Connelly and Akey set out to investigate how well GWAS would work in the yeast, Saccharomyces cerevisiae. In many respects, this yeast appears to be made for GWAS.
It has a small, easily sequenced genome, there is on average a polymorphism every 168 base pairs or so, and its linkage disequilibrium is low. There are genome sequences from 36 wild and laboratory strains publicly available, all as diverse as can be.
But this yeast isn’t perfect. The chromosomal structure between strains tends to be much more varied than between two humans. This is predicted to introduce a high error rate. And this is just what Connelly and Akey saw when they ran some simulations.
They found that the error rate was too high in the simulations to draw any meaningful conclusions. But they also found that by using a more sophisticated analytical technique called EMMA, they were able to partly correct for some of these errors.
Simulations are one thing, but how about real life? Connelly and Akey next tested the method by applying it to a practical problem: identifying the genetic reasons for differences in mitochondrial DNA (mtDNA) copy number in yeast. What they found mimicked the simulation data.
Using more traditional analytical approaches on the data obtained from GWAS, they found 73 potential causative SNPs. But when they switched to analyzing the data with EMMA, they found a single SNP that was significant. It took a bit of hand waving, but the gene associated with this SNP could possibly be implicated in mtDNA copy number. And then again, it might not.
This “significant” SNP was found amidst lots of errors and in a background of high p values. In other words, this finding may not be a real one after all. This experiment does not give confidence that GWAS can be used when all known strains of yeast are compared.
But if the strains to be included are selected more carefully, it may still prove to be a useful tool. When Connelly and Akey focused on strains that were structurally similar, they found that the error rate was much lower. Low enough that in the near term, scientists may be using GWAS to figure out how things work in model organisms.
Hopefully the findings from GWAS applied to model organisms will illuminate disease mechanisms in humans. Then maybe GWAS can realize its full potential, although not in the way it was originally envisioned.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
August 2, 2012
Translating a gene is easy, right? Hop on the end of an mRNA and start translating at the first AUG.
Of course nothing in biology is that simple! Not all AUGs in the beginning of mRNAs serve as the starts of translation and occasionally translation will start at a codon other than AUG. There is obviously more to a translation start than an AUG.
In a recent study, Kochetov and coworkers set out to better define what makes a ribosome sit down and start translating. They used a dataset compiled from S. cerevisiae in 2009 that included a wide range of translation starts ranging from the traditional to the barely recognizable.
The researchers focused on three classes of translation starts:
1) Traditional yeast gene start sites
2) AUG-containing uORFs
3) uORFs that lack an AUG
The last two sets are translation starts that happen upstream of traditional genes (hence the name upstream open reading frame or uORF). These tend to be weaker than traditional translation starts, have very short associated ORFs, and are thought to play a regulatory role in the translation of the “real” gene.
When Kochetov and coworkers analyzed the data, they confirmed some previous studies that showed that strong translation starts have an AUG, upstream RNA that is predicted to be unfolded and to be A-rich between nucleotides -6 and -1, and downstream RNA that is predicted to form a hairpin. Most of the traditional yeast genes possessed most of these attributes. The uORF translation starts were a different matter though.
The uORFs that had an AUG lacked the other features of a strong translation start. They tended to have fewer A’s in the upstream region and their RNA was structured in all the wrong ways. The uORFs that lacked an AUG apparently made up for it by having all of the other features of a strong translation start. They were A-rich between -6 and -1, had an unstructured RNA upstream and a hairpin downstream of the translation start. The thought is that translation starts that lack an AUG make up for it with all of the rest of the translation context being exceptionally strong.
These kinds of studies will make the tough job of identifying genes a bit easier. Which can only be a good thing as more and more genomes come on line.
How translation worked at Stanford in the 70′s