New & Noteworthy

Crowdsourcing Genetic Disease

February 28, 2013

Remember when sequencing the human genome was going to help us better understand and treat complex diseases like Type 2 diabetes or Parkinson’s? Well, ten years later, we’re still waiting.

Sure we’ve made some progress. Using genome wide association studies (GWAS), scientists have uncovered markers here and there that explain a bit about how a genetic disease is inherited. But despite a seemingly never-ending stream of these assays, scientists simply can’t explain all of the genetics behind most of these diseases.

So now scientists need to try to explain this missing heritability. If they can find out why they aren’t getting the answers they need from GWAS, then maybe they can restructure these assays to give better results.

As usual, when things get dicey genetically, scientists turn to the yeast Saccharomyces cerevisiae to help sort things out. And in a new study out in Nature, Bloom and coworkers have done just that.

In this study, they mated a laboratory and a wine strain of yeast to get 1,008 test subjects from their progeny. They extensively genotyped each of these 1008 and came up with a colony size assay that allowed them to determine how well each strain grew under various conditions. They settled on 46 different traits to study genetically.

What they found was that none of these traits was determined by a single gene. In fact, they found that each of the 46 different traits had between 5 and 29 different loci associated with it, with a median of 12 loci. This tells us that at least in yeast, many genetic loci each contribute a bit to the final phenotype. And if this is true in people, it could be a major factor behind the missing heritability in GWAS.

If a trait is dependent on many genetic loci that each have a small effect, then researchers need large populations in order to tease them out. In fact, when Bloom and coworkers restricted their population to 100 strains, they could only detect a subset of the genetic loci. For example, the number of loci went from 16 to 2 when they looked at growth in E6 berbamine.

So it may be that scientists are missing loci in GWAS because there are simply too few participants in their assays. If true, then the obvious answer is to increase the size of the populations being studied. Thank goodness DNA technologies get cheaper every year!

Of course as the authors themselves remind us, we do need to keep in mind that humans are a bit more complex than yeast. There may be other reasons that we aren’t turning up the genetic loci involved in various traits. It may be that we can’t as accurately measure the phenotypes in humans or that human traits are more complicated than the yeast ones studied. Another possibility is that in humans, there are more rare alleles that can contribute to a given trait. These would be very hard to find in any population studies like GWAS.

Still, this study at the very least tells us that larger populations will undoubtedly uncover more loci involved in human disease. Thank you again yeast.

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

Puzzling Out Gene Expression

February 21, 2013

Have you ever put together a million piece puzzle that was all blue? That is sort of what it sometimes feels like figuring out how genes are turned on or off, up or down.

There are hundreds or even thousands of proteins called transcription factors (TFs) controlling gene expression. And there is a seemingly simple but frustratingly opaque string of DNA letters dictating which TFs are involved at a particular gene. Figuring out which sets of proteins bind where to control a gene’s expression can be a baffling ordeal.

Up until now most of the ways of identifying which TFs are bound at which genes have been incredibly labor intensive to do on a large scale. With all of the current techniques, researchers need to construct sets of reagents before they even get started. For example, to be able to immunoprecipitate TFs along with the DNA sequences they bind, you need to insert epitope tags in all the TF genes so an antibody can pull them down. Other techniques are just as involved.

What the field needs is a quick and dirty way to find where TFs bind in the genome. And now they just might have one.

In a new study, Mirzaei and coworkers used a modification of the well-known technique mass spectrometry (mass spec) to identify TFs that bind to a specific piece of DNA. With this technique, called selected reaction monitoring, the mass spec looks only for specific peptide sequences. This not only makes it much more sensitive and reproducible than ordinary mass spec, but it should also be relatively straightforward to do if a lab has access to the right sort of mass spec. They haven’t worked out all the bugs and it is definitely still a work in progress, but the technique looks promising.

Mirzaei and coworkers set up assays to detect 464 yeast proteins that are known or suspected to be involved in regulating RNA polymerase II transcription. Then they tested their assay on a 642 base pair piece of DNA known to contain signals that affect the levels of FLO11 transcription. They found fifteen proteins (out of the 222 they searched) that bound this piece of DNA. Of these, only one, Msn1p, had been previously identified as regulating the FLO11 gene. The other fourteen had not been found in any previous assays.

The authors next showed that two of these fourteen proteins, Mot3p and Azf1p, represented real regulators of the FLO11 gene. For example, deletion of MOT3 led to a threefold increase in FLO11 expression under certain conditions. And when AZF1 was deleted, FLO11 could not be activated under a different set of conditions. So Mot3p looks like a repressor of FLO11 and Azf1p looks like an activator.

This was a great proof of principle experiment, but much more work needs to be done before this will become a standard assay in the toolkit of scientists studying gene expression. They need to figure out why some known regulators of FLO11 (Flo8p, Ste12p, and Gcn4p) were missed in the assay and whether the other twelve proteins they discovered play a role in the regulation of the FLO11 gene.

Having said this, it is still important to note that even this early stage model of the assay identified two proteins that scientists did not know controlled FLO11 gene expression. At the very least this is a quick and easy way to quickly identify candidates for gene expression. We may not be able to use it to see the whole picture on the puzzle, but it will at least get us a good start on it.

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

The Rhythm of Ribosomes

February 13, 2013

We all know that some people march to the beat of a different drummer. But now we’re finding out that mRNAs also have their own particular rhythms as they move along the ribosome.

It’s long been known that some codons just work better than others. They are translated faster and more accurately mostly because they interact more strongly with their tRNAs and because there are more of their specific tRNAs around. So why hasn’t evolution gotten rid of all the “slow” codons? With only optimal codons, translation could move at a marching beat all the time.

One idea has been that a few pauses every now and then are a good thing. For example, maybe slowing down translation at the end of a stretch coding for a discrete protein domain gives that domain time to fold properly. This would make it less likely for the polypeptide chain to end up tangled, or misfolded. Great thought, but even when researchers looked in multiple organisms, they couldn’t find a consistent correlation between codons used and protein structure. Until now, that is.

In a recent study published in Nature Structural and Molecular Biology, Pechmann and Frydman took a novel approach to this question. They derived a new formula to measure codon optimality. Using it they found that codon usage was highly conserved between even distantly related species, and that this conservation reflected the domain structure of the particular protein a ribosome was translating.

First, the authors came up with a more accurate way of classifying codons as optimal or non-optimal. They took advantage of the huge amount of data available for S. cerevisiae and included a lot more of it in the calculation, such as the abundance of hundreds of mRNAs and their level of ribosome association. They also took into account competition between tRNAs based on supply and demand, something that the previous studies had not done.

Once they developed this new translational efficiency scale, they applied it to ten other yeast species – from closely related budding yeasts all the way out to the evolutionarily distant Schizosaccharomyces pombe. The authors found that positions of optimal and non-optimal codons were indeed highly conserved across the yeasts. And codon optimality was highly correlated with protein structure.

One of the better examples of this is alpha helices. These protein domains form while still inside the ribosomal tunnel. The authors found that the mRNA regions coding alpha helices use a characteristic pattern of optimal and non-optimal codons to encode the first turn of the helix. They theorize that this sets the rhythm for folding the rest of the helix. Other structural elements are coded by distinct codon signatures too.

This isn’t just interesting basic research. It has some far-reaching practical implications too.

When using yeast to make some sort of industrial product, the thought has been to use as many optimal codons as possible. This has not always worked out, and now we may know why. A gene that tailors the codon usage to the rhythm of the protein structure is probably the best way to make a lot of correctly folded protein.

And the factory isn’t the only place where this kind of information will come in handy. Protein misfolding is the known or suspected culprit in a whole slew of human neurodegenerative diseases such as Alzheimer’s, ALS, Huntington’s chorea, and Parkinson’s disease. A better understanding of its causes might give us insights into managing those diseases.

Who knew in 1971 that translation actually is a rhythmic dance?

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

Giving the Keys Back to the Cell

February 6, 2013

When someone has a bit too much to drink, it is a good idea to take away their car keys. This keeps them safe until they can drive again. But the next morning, that hung over person needs to get their keys back so they can get to work.

Cells sometimes face a similar situation. Instead of being drunk though, cells have something go wrong while they are growing and dividing. When this happens, the cell stops the cell cycle at the next checkpoint, fixes what is wrong, and then starts the cell cycle back up again where it left off.

Scientists have learned a lot about how the keys are taken from cells, but not a whole lot about how they get them back. Fong and coworkers help to rectify this situation in a new study out in GENETICS. There they identified proteins key to releasing a yeast cell from its S-phase checkpoint.

If a cell’s DNA is damaged while it is growing and dividing, replication is slowed at the S-phase checkpoint. This gives the cell a chance to fix the DNA before it is copied. The authors found that in the absence of the DIA2 gene, yeast cells had trouble getting replication up and running again. This implies that this gene is required for yeast to overcome the S-phase checkpoint. The cell needs DIA2 to get its keys back.

Dia2p is an F-box protein involved in identifying certain proteins for destruction. It is one of several interchangeable subunits that provide specificity to the SCF ubiquitin ligase complex. The idea would be that Dia2p is important for degrading the “keeper of the keys,” the protein responsible for stopping the cell cycle in the S-phase.

To test whether Dia2p is important for checkpoint recovery, Fong and coworkers first activated the S-phase checkpoint by adding the DNA damaging agent MMS. Then they removed the MMS and measured how long it took the cells to finish copying their DNA. The dia2Δ mutant was significantly slower than wild type.

Given that Dia2p is involved in ubiquitin-mediated degradation, the authors reasoned that it may help a cell get out of S-phase arrest by degrading a protein that was keeping it there. To find this “keeper of the keys,” Fong and coworkers looked for mutations that rescued dia2Δ cells in the presence of high levels of MMS. The idea is that if they knock out the gene that is keeping the dia2Δ cells arrested, then the cells could overcome the block caused by the MMS.

One of the genes that came up in the screen was MRC1. To confirm that Dia2p and Mrc1p work together in releasing a yeast cell from the S-phase checkpoint, the authors constructed a double mutant carrying dia2Δ and a mutant version of MRC1, mrc1AQ, that they knew was checkpoint defective. Indeed, the double mutant behaved like wild type in their checkpoint recovery assay. Since the mutant Mrc1-AQp could not keep cells at the checkpoint, there was no need for Dia2p to target it for degradation. The double mutant cell never let go of its keys.

The simplest model to explain what happens in wild type is that when its DNA is damaged, a cell is prevented from progressing through S-phase by Mrc1p. Then when the DNA is repaired, Dia2p, providing specificity to the SCF ubiquitin ligase complex, targets Mrc1p for degradation. The cell is now released, allowing the cell cycle to continue.

The authors did a lot more work that we won’t go into here, but suffice it to say that Dia2p and Mrc1p are not the only players involved in releasing a cell from the S-phase checkpoint. There were other genes, both identified and unidentified, that came up in their screen. These will need to be studied as well.

And this isn’t all just interesting from a scientific standpoint. Many cancer treatments work by damaging the cancer cell’s DNA while it is growing and dividing. A better understanding of how cells are arrested and released may lead to better cancer treatments.

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