New & Noteworthy
April 30, 2012
SGD sends out its quarterly newsletter to colleagues designated as contacts in SGD. This Spring 2012 newsletter is also available online. If you would like to receive this letter in the future please use the Colleague Submission/Update form to let us know.
April 24, 2012
A new study by Lickwar and coworkers suggests that many transcription factors fidget on and off the DNA, waiting for some signal to get to work. Once they get that signal, they clamp down and start affecting the activity of nearby genes.
If true this would help explain some perplexing results researchers have been getting with chromosomal immunoprecipitation (ChIP) assays. Transcription factors appear to be bound at many places where they are not affecting any nearby genes. Now we might have an idea why.
These researchers came up with this model through the use of an elegant, in vivo competition study. What they did was to set up a yeast strain that contained two different versions of the transcription factor Rap1p. One version was tagged with a FLAG epitope and was under the control of RAP1’s endogenous, constitutive promoter. The other version was tagged with a Myc epitope and was under the control of an inducible promoter.
They started out seeing where Rap1p was bound in the absence of the inducer by using an antibody against FLAG. This is the equivalent of a typical ChIP experiment. They found Rap1p was bound in many places throughout the genome including sites where it did not appear to affect any nearby genes.
Then they added the inducer galactose and at various time points repeated the ChIP experiment with antibodies against either FLAG or Myc. They were basically looking for how quickly the Myc-tagged Rap1p replaced the FLAG-tagged Rap1p with the idea that less stably bound transcription factors would be replaced more quickly.
They indeed found that some sites were better able to withstand the onslaught of Myc-tagged Rap1p. And more importantly, that these sites were near genes most influenced by Rap1p. In other words it appears that the more stably bound the Rap1p, the bigger the effect it has on nearby genes.
They then went on to show that more stable binding correlated with lower nucleosome occupancy and stronger in vitro binding. From this data they propose a model where the level of the effect on transcription is the result of a competition between nucleosome and transcription factor binding. Stronger transcription factor binding keeps nucleosomes away so transcription can proceed.
They took the model one step further and proposed that transcription factors are idling on the DNA, waiting for a signal to bind more tightly and influence the activity of nearby genes. In other words, transcription factors are ready to have an effect at a moment’s notice.
This part of the model still has to be proven though. All that has been shown so far is that a slow off rate is required for effective transcription activation by Rap1p. What we don’t know is whether this translates to other transcription factors or if idling Rap1p is ever more stably bound.
April 23, 2012
SGD has added a new mix of data tracks to our GBrowse genome viewer from seven publications covering transcriptome exploration via tiling microarrays (David et al. 2006), genomic occupancy of RNA polymerase II and III and associated factors (Kim et al. 2010; Ghavi-Helm 2008), 3′ end processing (Johnson et al. 2011), histone H2BK123 monoubiquitination (Schulze et al. 2011) and high-resolution ChIP by a novel method called ChIP-exo (Rhee et al. 2011; Rhee et al. 2012). Download data tracks, metadata and supplementary data by clicking on the ‘?’ icon on each data track within GBrowse or directly from the SGD downloads page. We welcome new data submissions pre- or post-publication and invite authors to work with us to integrate their data into our GBrowse and PBrowse viewers. Please contact us if you are interested in participating or have questions and comments. Happy browsing!
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
April 2, 2012
Watching a yeast cell age can be a real pain. In budding yeast like Saccharomyces cerevisiae, the buds quickly outnumber the mom. Which means scientists need to remove the buds as they appear.
Up until now, scientists have had to use a 50-year-old method that involves removing the buds by hand. Not only is this labor intensive, but the field is held back by the inability to use high resolution microscopy to investigate the aging process.
These technical limitations may soon be swept aside with a new microfluidic dissection technique described by Lee and coworkers in a recent study out in PNAS. These researchers were able to monitor 50 aging yeast at once with a variety of microscopic techniques without having to remove the buds by hand. And unlike the older technique, they were able to keep a constant environment for the yeast cells (i.e. no decrease in nutrients and/or build up in wastes).
Basically Lee and coworkers tucked the yeast mother cells under a micropad which they then washed with a constant flow of nutrients. Because the daughter cells are smaller than the mother, they are washed away as they emerge. So no manual bud removal is required.
Sounds convenient but the researchers needed to show that this new technique gave similar results as compared to the old one. And they did.
They showed that mutant strains behaved similarly with both techniques. So a SIR2 deletion mutant still had a shorter lifespan and a FOB1 deletion mutant still lived longer with microfluidic dissection. Not only that, but the number of divisions in an average yeast’s lifetime was comparable with both techniques. At first blush the techniques do seem comparable.
Now they were ready to take their new technique out for a spin to see what it could do. First they were able to show heterogeneity in how yeast cells age. Some cells died as spheres around their 12th division while others died as ellipsoids after their 25th division. The shape of the yeast later in life correlated with how long that yeast lived.
The researchers were also able to use GFP to explore the vacuoles of aging yeast. They found three classes of vacuoles: tubular, fused, and fragmented. The tubular vacuoles were only found in the longer-lived ellipsoid yeast.
Researchers could not have discovered these properties of aging yeast without the new microfluidic dissection technique. And these findings are really just the tip of the iceberg of what can now be learned about aging by studying yeast. It will be exciting to see what else scientists will be able to learn about the twilight of a yeast cell’s life.
Life and Death of a Single Yeast