New & Noteworthy

A Biological Tour de Force Reveals the Complexity of a Yeast Cell

October 11, 2016

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As anyone who has tinkered with the inside of a living cell or looked at one of those daunting and overwhelming biochemistry poster knows, life is complicated. Like thousands of gears all interconnected in some vast steampunk machine, a cell has thousands of genes making thousands of proteins that come in a variety of flavors all interacting in overlapping, complicated ways to keep the cell alive. Toss in those important RNAs and a few other bells and whistles and you begin to understand why scientists have had to just focus in on individual parts of the machine to study.

Until now that is. In a new study out in Science led by University of Toronto Professors Brenda Andrews and Charles Boone, and Professor Chad Myers of the University of Minnesota-Twin Cities, Costanzo and coworkers were able to get a first approximation of how all those genes are connected in a yeast cell. And it is as complicated as you imagined.

This was a daunting undertaking that took over 15 years to pull off but it looks like it was well worth it. They have already learned an incredible amount and people will continue to learn more and more as scientists mine this treasure trove of data for a long time to come (Stay tuned for the data to be available here at SGD). This is the sort of study that will provide an invaluable reference guide that will keep giving year after year after year.

And it isn’t just a tour de force of basic biology either. This study will, among other things, reveal what genes of unknown function are actually doing in cells, identify new targets to go after in diseases like cancer and may even help geneticists find that “missing heritability” in their genome-wide association studies (GWAS). Given that these three examples are just a subset of what we’ll get out of this study, I think you can see why it is such a game changer.

It would not have been possible without all of the yeast work that has been done and carefully curated over the years.  And there is no way it could have been done in another organism, like humans. Human cells are simply too complicated, we don’t yet have the necessary tools, and we don’t have as deep a knowledge as we do with yeast. It is only in yeast that we could begin to get this amazing peek at life happening inside of a cell.

Yeast as a model organism is alive and well and helping us better understand life and ourselves. This sort of study drives that point home in a way that benefits many, many scientists and, eventually, patients the world over.

When 1+1 Does Not Equal 2

So how did these researchers pull this off? They basically looked at what happens when they knock out two genes at once in yeast. They did this for every possible gene pair for 5416 out of the 6,000 or so yeast genes.

Of course this strategy won’t work with the 1000 or so yeast genes that are essential—the yeast dies when these are knocked out. For these genes, the researchers used temperature sensitive mutations under conditions where the gene is tweaked but not dead.

What they were looking for were combinations that did either better or worse than you might predict based upon each individual deletion on its own. And they found plenty.

They generated 23 million of these double knockout yeast (Yes, you read that right!) and found ~550,000 negative interactions and ~350,000 positive interactions—a huge amount of data about the inner workings of a cell. The next step was to take all of this data and try to make sense of it.

They did this by clustering the genes together based on who was interacting with whom. If deleting gene A with either gene B or gene C had a larger negative effect than expected, then these three genes were clustered together. And if deleting gene A had an effect on gene D, but deleting gene B or C did not have an effect on D, then A was involved in both a cluster with B and C as well as a separate cluster with D. Now repeat this over and over thousands and thousands of times.

When they did this, many things about living cells quickly came into focus. I only have the space to touch on a few.

First off, while negative interactions usually happened between genes in the same biological processes or in the same subcellular compartments, the same was not true for the positive interactions. These were much more spread out and often genes like chaperones.

They also found about 1000 genes that weren’t involved with many other genes at all. These are most likely genes that would show more interactions under different growth conditions. For example, deleting a gene critical for utilizing galactose might not show a lot of interactions if a cell is grown in glucose. Repeating this experiment under different conditions will probably uncover where these genes fit in.

This is just a taste of what they found and of what is to come in the next few years. Here is a bit more:

Finding Out What a Gene Does

You’d think having a genome sequence for over 20 years might mean you’d pretty much know what all those genes are doing, but you’d be wrong.

There are still plenty of genes in yeast with unknown function. This kind of study can help uncover what a gene might be doing by seeing which genes it clusters together with. If gene X is clustered with known DNA repair genes, it is probably involved in something to do with DNA.

One such gene they looked at in this study was YJR141W. Not a lot was known about this gene other than it was essential.

What they found was that this gene clustered with genes in the cleavage polyadenylation factor (CPF) and cleavage factor 1A (CF1A) protein complexes suggesting this gene might have a role in mRNA 3’-end processing and polyadenylation. After showing experimentally that the protein from this gene physically interacted with CPF complex members Mpe1p and Ysh1p and that the temperature sensitive mutant had trouble processing mRNA in vitro, the researchers felt confident enough to give this gene a name based on its function. They named it IPA1 (Important for cleavage and PolyAdenylation 1).

They did this with a second gene as well (YPR153W was rechristened MAY24 because of its interactions with Mtc2p and Mtc4p). Stay tuned for lots more findings like this one in the near future.

Helping Find New Cancer Treatments

This study also has important implications for human health in lots of different ways. For example, it might help scientists find better cancer treatments.

Cancer happens when mutations in key genes cause cells to grow uncontrollably or to refuse to die. In an ideal world we’d treat these cancers by going after these genes directly. Unfortunately many of them are not particularly great targets to go after with pharmaceuticals.

This is where synthetic lethals can help. A synthetic lethal is when mutations in two separate genes do not kill a cell on their own, but they do when a single cell has both mutant genes.

The idea then is to find a pair of mutant genes that kills a cell with one of the genes being involved in cancer and the second one some other gene. You can go after the second gene and by affecting its activity kill the cancer cells that have the other gene in this pair mutated.

And of course noncancerous cells are fine with you targeting that second gene—mutating only that gene isn’t lethal. Chemotherapy with many fewer side effects!

If you think this sounds like something this study might uncover, you’d be right. This study found around 10,000 or so double knockouts that were lethal which might provide a lot of possibilities for cancer researchers.

Cancer is almost certainly just one of many possible applications to human health. And we will learn so much about the basic biology of a cell from such an exhaustive and detailed analysis of the inner workings of a cell.

This holistic portrait of the genetic interactions of a yeast cell is elegant, beautiful and useful. Not bad for the beast that gives us bread and booze.

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

Categories: Research Spotlight

Tags: double mutant, interactions, network, synthetic lethal

A Nobel Prize for Work in Yeast. Again!

October 03, 2016

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Dr. Yoshinori Ohsumi has won the 2016 Nobel Prize in Physiology or Medicine for his groundbreaking work on autophagy in yeast. This is the process whereby cells recycle their worn out parts or where a cell, like Mobius, the snake eating its own tail, eats less essential bits of itself to stay alive during times of starvation. Think Scarlett O’Hara using her drapes as a dress in Gone With the Wind (or Carol Burnett’s hilarious parody).

Like many, many Nobel Prizes in the past, Ohsumi’s work uncovered basic biological properties using a model organism. In this case he used our favorite lab workhorse, the yeast Saccharomyces cerevisiae, to piece together the steps involved in the recycling of a cell’s own internal structures.

And like many other basic biological studies, this one has important medical applications. In this case the two most obvious are chemotherapy resistance and amyloid-β aggregation in Alzheimer’s disease, but it isn’t restricted to just these two. For example, a specialized form of autophagy that targets damaged mitochondria, mitophagy, may not be working well in people with Parkinson’s disease.

The key to Ohsumi’s work was finding a way to disrupt this process in yeast so that he could find the important genes underlying autophagy using the awesome power of yeast genetics (#APOYG!). It turns out that this is trickier than it might seem because yeast and their autophagosomes, the little vesicles that surround and encase the bits to be degraded, are very small and so hard to see. In fact, they are so small that there was some question about whether yeast even had this process!

If yeast did, then it would take place in the vacuole, the recycling center in yeast. The equivalent organelle in people is the lysosome.

To see if autophagy happens in yeast, Ohsumi starved yeast that had vacuoles but couldn’t digest anything. The idea was that there would be a buildup of autophagosomes in the vacuole because the yeast would be desperately trying to eat itself but had no way to digest what it ate. He indeed saw that these poor yeast developed huge vacuoles bloated with autophagosomes.

Dr. Yoshinori Ohsumi now had the makings of a yeast screen! “All” he had to do was to look for mutants that didn’t form giant vacuoles under these conditions with the logic being that if you knocked out autophagy, you wouldn’t get a buildup of autophagosomes.

The rest, as they say, is history. Ohsumi and his lab managed to tease out the subtleties of this vital cellular process using good old baker’s yeast. What other nuggets of knowledge about ourselves will we pry out of this most useful of eukaryotes? I can’t wait to see what it reveals about us next!

Other Nobel Prizes have been awarded in recent years for work in yeast:

• Leland H. Hartwell, R. Timothy (Tim) Hunt, and Paul M. Nurse won the 2001 Nobel Prize in Physiology or Medicine for their discoveries of “key regulators of the cell cycle”.
• Roger D. Kornberg won the 2006 Nobel Prize in Chemistry “for his studies of the molecular basis of eukaryotic transcription”.
• Elizabeth H. Blackburn, Carol W. Greider, and Jack W. Szostak won the 2009 Nobel Prize in Physiology or Medicine “for the discovery of how chromosomes are protected by telomeres and the enzyme telomerase”.
• James E. Rothman, Randy W. Schekman, and Thomas C. Südhof won the 2013 Nobel Prize in Physiology or Medicine “for their discoveries of machinery regulating vesicle traffic, a major transport system in our cells”.

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

Categories: News and Views, Yeast and Human Disease

Yeast on the Red Carpet

September 28, 2016

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Awards season just kicked off with the Emmys a couple of weeks ago. Julia Louis-Dreyfus won her fifth Emmy in a row for her work on Veep, and Tatiana Maslany finally got an Emmy for her incredible work on Orphan Black. (If you haven’t seen Orphan Black, it’s a fascinating look at the ramifications of human cloning and genetic engineering.)

For a lot of people, the awards themselves are secondary to what everyone is wearing and who shows up with whom. You can learn a lot about the evolution of a performer’s career through these subtle (and not so subtle) cues.

Turns out that PLOS just had their own awards. Not quite as glamorous as the Emmys or the Oscars but almost certainly more significant.

The PLOS Genetics Research Prize for 2016 went to a fascinating paper from December of 2015 that uses yeast to explore evolution and provide ways to get at the underlying mechanisms of polygenic inheritance. And it does this by studying the subtle cues of what happens when two different yeast species show up on a yeast plate together (and mate).

The study uses a technique called a sign test. This is a way to find a set of genes that have been up- or down-regulated in response to some sort of selection pressure.

The first step is to mate two different strains or species. In this case Naranjo, Smith and coworkers used two different yeasts – Saccharomyces cerevisiae and Saccharomyces paradoxus.

The next step is to take the result of this mating, the F1 hybrid, and to compare the expression from each species’ alleles to the other’s. In other words, how does the expression of gene A compare in the two species? And gene B? And so on, through all of the genes.

What they are looking for in this allele specific expression (ASE) is a set of genes in the same pathway that are all affected in the same way. In this case they found a set of eleven genes linked to resistance to the toxin citrinin that was upregulated in S. paradoxus, but not in S. cerevisiae, in the absence of citrinin. This suggested that there was some sort of evolutionary pressure on S. paradoxus to become resistant to citrinin.

An obvious prediction from this is that their strain of S. paradoxus, CBS432, is more resistant to citrinin than is their strain of S. cerevisiae, S288C. They tested this and their S. paradoxus strain did indeed do better than S288C when citrinin was around.

They next did RNA-seq on the F1 hybrid yeast in the presence and absence of citrinin to find the up-regulated genes responsible for the ability of S. paradoxus to better tolerate citrinin. They ultimately settled on five genes that were both more highly expressed in the absence of citrinin, and more strongly induced by citrinin.

To figure out which of these genes is critical for resistance, they next deleted each gene individually and tested each deletion strain for its ability to grow in citrinin. Four of the five genes – GPX2, FRM2, RTA1, and CIS1 – made it through this test.

They next checked to see if making more product from all of these genes at once increased the strain’s resistance to citrinin. To pull this off they turned to everyone’s favorite genetic tool, CRISPR/Cas9.

Unless you’ve been hiding under a rock, you already know that CRISPR/Cas9 uses a guide RNA to get the protein Cas9 to the specific spot in the genome you want to edit. But in this case they aren’t editing a gene. Instead they are activating genes by using a version of Cas9 with two important changes: it can’t cut DNA anymore and it has a transcription activation domain added to it.

The idea is to activate all four genes at once by providing the yeast with guide RNAs that can lead this Cas9 to each of the four genes. What a powerful and simple way to easily activate all four genes at once.

They found that this overexpression strain was able to better tolerate citrinin but that it came at a cost – the strain grew more poorly in the absence of citrinin.

They next set out to see if the mutations that distinguish S. paradoxus from S. cerevisiae were in the promoters of these four genes. First, they replaced the S. cerevisiae promoter with the S. paradoxus promoter for each gene in the citrinin-sensitive S. cerevisiae strain. This created four new strains, each with one of the promoters swapped.

They found that in all cases the S. paradoxus promoter led to increased gene activity. Expression from these genes increased by anywhere from 1.6-6.9 fold.

Their final experiment was a competition between the original S. cerevisiae parent and the four strains in which the native S. cerevisiae promoter had been swapped out with the S. paradoxus one. They found that except for the strain overexpressing RTA1, these strains did better than the original S. cerevisiae strain in the presence of citrinin, but worse in its absence. Each of the three strains alone did not provide as much advantage as all three together did.

This is pretty powerful stuff! They used the sign test and CRISPR/Cas9 to nail down the three differences between S. paradoxus and S. cerevisiae that help to explain the polygenic trait of citrinin resistance.

And this isn’t just some cool yeast experiment either (although it is definitely that – #APOYG!). Sign tests may provide a new way for all those geneticists dutifully doing genome-wide association studies (GWAS) to find the set of genes responsible for polygenic traits that have so far eluded them. This is the kind of work that definitely deserves an award!

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

Categories: Research Spotlight

Tags: adaptation, allele-specific expression, cis-regulation, natural selection

New Protein Half-life Data in SGD and YeastMine

September 08, 2016

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Ever wonder how quickly your favorite protein turns over within the cell? SGD has just incorporated half-life data for 3700 yeast proteins from a paper by Christiano et al., 2014. In this study, Christiano and colleagues pulse labeled exponentially growing wild type yeast cells in synthetic medium with a heavy lysine isotope (pulse SILAC), and followed the decay of native untagged proteins using high-resolution mass spectrometry based proteomics. The data generated in this study can be accessed by viewing the Experimental Data section of the Protein tab for your favorite gene, such as the short-lived Ctk1p or the long-lived Rsc1p.

In addition, you can retrieve this half-life data using YeastMine for one or more proteins with the Gene–>Protein Half-life template or obtain a list of proteins with half lives within a given range using the Retrieve–>Proteins with half-life in a given range template. Both of these templates can be found in the “Templates” section of YeastMine under the “Protein” category.

Thanks to Romaine Christiano and Tobias Walther for their help integrating this information into SGD.

Categories: New Data

More Than Just a Brick

September 07, 2016

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Cell phones have evolved in amazing ways. What started as a “dumbphone” that only let you make calls and, somewhat awkwardly, text, has now become a smartphone – a marvelous mini-computer.

What this means is that even if a smartphone’s SIM card goes bad so that you can’t make a call or send a text, you still have something that can do a lot. In fact, since it can still connect to Wi-Fi, you can even text or call! This is obviously different than a “dumbphone” which, if it can’t be used as a phone, is pretty much only useful as a hurled weapon. (Which is just what Dr. Drakken threatened Ron Stoppable with at 8:27 of this video.)

In a new study out in GENETICS, Thurtle-Schmidt and coworkers show that the histone deacetylase Sir2p is more like a smartphone than a dumb one. Even when you knock out its ability to deacetylate histones, Sir2p can, in the right background, still silence the genes it is supposed to.

Sir2p is the founding member (#APOYG!) of the important sirtuin enzyme family. It silences genes by first deacetylating acetylated lysines on histone tails (H3 and H4 specifically). This then allows the Sir-protein complex (which includes Sir2p, Sir3p, and Sir4p) to bind the nucleosome Sir2p just deacetylated. Now the Sir-protein complex deacetylates nearby histone tails and so on until the Sir-protein complex has spread across a gene, silencing it.

Obviously the ability of Sir2p to deacetylate is important in this scenario! But these researchers found that like a smartphone without a SIM card, Sir2p can sometimes do its job even without its deacetylase powers.

But instead of going around a carrier and using Wi-Fi, Sir2p needs for a second histone deacetylase, Rpd3p, to be gone. Without Rpd3p, Sir2p can now silence genes. Not as well as it could before, but some.

To find this out the researchers set up a suppressor screen. They used a reporter that replaced the a1 open reading frame (ORF) at HMR with the URA3 gene. The a1 ORF is normally silenced by Sir2p. Basically, if this gene is silenced, the yeast can grow in the presence of 5-fluoroorotic acid (5-FOA).

Next they added a mutant Sir2p that lacked its catalytic ability to this reporter strain. Finally they mutagenized this strain and looked for mutants that could again silence genes. They got 1500 5-FOA resistant mutants.

Of course many of these may have been the result of mutations in the URA3 gene. They did a secondary screen that used mating as a way to rule out this possibility. In the end they had four mutants.

One of these four was a mutation that had been found in previous screens – SUM1-1. This mutant appears to bypass the need for any of the SIR genes by setting up a different kind of silenced chromatin.

The other gene that came out of the screen was RPD3. They found three different mutations in this deacetylase that all partly restored Sir2p’s ability to silence genes and found that deleting the gene had the same effect. Follow-up work showed that this effect was indeed Sir2p-dependent. Now they had to figure out how eliminating a second deacetylase frees this mutant Sir2p to do its job.

In some ways it isn’t surprising to get RPD3 out of a screen like this. It seems to be important in keeping the Sir-protein complex from spreading too far (who wants the whole chromosome shut down?) and deleting it affects various silenced genes.

Rpd3p is found in two different complexes imaginatively named large (Rpd3L) and small (Rpd3S). When the authors deleted genes specific to either complex, their sir2 mutant did not regain its ability to silence genes. Only when they deleted a gene involved in both complexes, SIN3, were they able to mimic the effects of deleting RPD3 (“phenocopied the rpd3Δ”).

One possible idea is that since Rpd3p keeps the Sir-protein complex from spreading, its deletion might allow for increased spreading even in the absence of Sir2p’s histone deacetylase activity. This is what they found.

Using Chromatin ImmunoPrecipitation (ChIP) against Sir4p, one of the proteins in the Sir-protein complex, the authors repeated the result that in the absence of Sir2p’s histone deacetylase activity, there is less Sir4p at silenced regions. When they looked at the same strain deleted for RPD3, they found an increase of Sir4p at silenced genes. Not to the levels seen in the wild type strain, but enough to probably explain the partial silencing seen in the strain.

Makes sense so far but it isn’t the whole story. Nicotinamide (NAM) is competitive inhibitor of sirtuins like Sir2p. As such, we might predict that it should have no effect on the silencing of a gene by a catalytically inert Sir2p. We would be wrong.

Turns out NAM does affect silencing in this strain which suggests that some other sirtuin might be playing a role. There are four homologs of SIR2: HST1, HST2, HST3, and HST4. A bit of work including creating a triple mutant strain deleted for RPD3 and HST3, and containing the mutant Sir2p, showed that Hst3p is involved in this silencing.

Whew, that was a lot! So mutating away the deacetylase activity of Sir2 unsilences genes. And deleting RPD-3 from this strain restores some of that silencing. And the restored silencing in this strain is at least partly dependent on Hst3p.

So there you have it. Like a cell phone without a SIM card using Wi-Fi, Sir2p can still do its job if Rpd3p isn’t around to interfere. As long as Hst3p, like turning the phone’s Wi-Fi on, is there to help.

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

Categories: Research Spotlight

Tags: H4K16, heterochromatin, HST3, RPD3, silencing, SIR2, sirtuin

Sign Up Now for the Next SGD Webinar: September 7, 2016

September 02, 2016

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The SPELL expression analysis tool at SGD makes it easy to find expression datasets and coexpressed genes that are relevant to your genes of interest. Just plug in a set of genes and go! Once given a query, SGD’s instance of SPELL locates informative expression datasets from over 270 published studies and identifies additional genes with similar expression profiles.

Find out how to use SPELL in our upcoming webinar on September 7th, 9:30 AM PDT. This quick 10-minute tutorial will explain how to run a multi-gene query in SPELL, locate expression datasets relevant to your query, and find genes with similar expression profiles.

If you are interested in attending this event, please register using this online form: http://bit.ly/SGDwebinar5

This is the fifth episode in the SGD Webinar Series. For more information on the SGD Webinar Series, please visit our wiki page: SGD Webinar Series.

Categories: Announcements, Tutorial

SGD August 2016 Newsletter

August 29, 2016

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SGD periodically sends out its newsletter to colleagues designated as contacts in SGD. This August 2016 newsletter is also available on the community wiki. If you would like to receive the SGD newsletter in the future please use the Colleague Submission/Update form to let us know.

Categories: Newsletter

When Running Out of Steam is a Good Thing

August 24, 2016

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Usain Bolt sprinting is a thing of beauty. It is just amazing how he can kick in the afterburners at the end of a race and just dominate the thing. I am sure Justin Gatlin of the U.S. would love for Bolt to lose this extra burst of speed so Gatlin could beat him at the Olympics.

Turns out that transcription elongation has an afterburner a bit like Bolt’s too. It goes by the name SPT4 in yeast and SUPT4H1 in you and me. The protein from this gene is needed to push through long transcripts.

A new study in Science by Kramer and coworkers suggests that like Gatlin, some people would like to see their cells lose the burst of speed that SUPT4H1 gives their polymerases. But instead of helping these folks win a race, this loss might help them deal with their amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD).

ALS is a progressive neurodegenerative disease that is always fatal. It was first made famous by Lou Gehrig and later with the bucket challenge. After Alzheimer’s, FTD is the second most common form of dementia.

Back in 2011 two groups found that a significant number of cases of FTD and ALS were associated with a gene called C9orf72 (chromosome 9 open reading frame 72). These people had hundreds or even thousands of copies of the hexanucleotide repeat GGGGCC in the first intron of their gene instead of the 30 or so that is more typical.

Later studies showed that these repeats caused two very specific problems in cells. First, the RNA (and antisense RNA) from this allele tended to build up in small bundles called foci. Some researchers think that these foci trap some of the important RNA binding proteins that the cell needs.

The second phenotype is a strange one. These RNAs get translated by a process called repeat-associated non-ATG, or RAN, which causes a buildup of dipeptide repeat proteins. Apparently there is something about the secondary structure of the RNA that allows it to get translated without a typical AUG start codon.

The idea is that these foci and weird dipeptide proteins are at least part of the reason why these folks have their ALS symptoms. Ideally you’d want to get at all three issues (the sense and antisense RNA-laden foci, and those newly translated proteins) with a single approach.

Kramer and coworkers reasoned that they might get such a result if they could get the cell to make a whole lot less of SUPT4H1 (or Spt4p in yeast). They reasoned correctly.

Previous research had shown that its deletion didn’t affect too many genes except for those involved in diseases like Huntington’s – those with long CAG repeats. Perhaps, then, deleting it might also just affect the copies of the C9orf72 gene with those hexanucleotide repeats without affecting too many other genes.

When they forced yeast, nematode, fruit fly and human cells to make less Spt4p or SUPT4H1, the number of RNA foci went down or even disappeared in all of these different cells. There was also much less of those dipeptide repeat proteins lurking about the cell as well.

They first set out to do some experiments in everyone’s favorite workhorse, Saccharomyces cerevisiae. They found that expressing either the sense or antisense RNA with the 66 hexanucleotide repeats caused both the RNA foci and the dipeptide repeat proteins seen in the cells of ALS patients to form in yeast too. Neither happened with the sense or antisense 2 repeat constructs.

Next they showed that deleting SPT4 greatly reduced the level of 66 repeat RNA but had little effect on the 2 repeat RNA. These researchers also saw no RNA foci and much less dipeptide repeat proteins in the deletion strain expressing the 66 hexanucleotide repeats. All without much affecting any other genes.

This yeast work suggests that targeting SUPT4H1 might reduce the effects of the ALS version of the C9orf72 gene without affecting the more typical version. Now Kramer and coworkers were ready to see what happens in bigger beasts.

When they expressed the 66 repeat in Caenorhabditis elegans neurons, these nematodes lived for a shorter time and their neurons had RNA foci and the dipeptide repeat proteins. Expressing human SUPT4H1 in these worms’ neurons worsened their condition while feeding them RNAi against nematode SPT4 helped.

The RNAi let these worms live longer and it decreased the number of RNAi foci and the amount of dipeptide repeat proteins. They saw similar results with a Drosophila system.

Finally they moved to the main stage—human cells from ALS patients who had the C9orf72 protein with too many hexanucleotide repeats. RNAi against either SUPT4H1 or its partner in crime, SUPT5H, reduced the number of RNA foci and reduced the amount of dipeptide repeat proteins with no “overt toxicity.” RNA-seq showed that only a small subset of genes was affected with the RNAi treatment.

So it looks like targeting SUPT4H1 may be a good strategy for dealing with ALS if the RNA foci and dipeptide repeat proteins are a big part of the problem. This is a big if.

But if it all does work out, we can thank yeast yet again (#APOYG!) for showing us the way to a new treatment for a devastating disease. Of course, though, yeast can’t do everything. It is unlikely to show sprinters the best way to beat Usain Bolt in a race!

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

Categories: Research Spotlight, Yeast and Human Disease

SGD: New Search, New Style

August 22, 2016

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• Superior search box functionality: New autocomplete options connect to information faster than ever
• Sorting options: Results sorted alphabetically, by relevance, and by number of annotations
• Download gene lists: Export gene lists from search results into a text file with the “Wrapped” and “Download” options
• Faceted searching: Narrow search results by using categories like Genes, Molecular Functions, Phenotypes, and more!

Categories: Announcements, Website changes

Coming Soon – New Search and New Styling

August 15, 2016

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SGD is planning to release a new faceted search on Monday, August 22, 2016, along with some new site styling optimized for mobile use. The refactored search has been available for the last few months on our beta site: sgd-beta.stanford.edu.

New features of the search include:

• an expanded selection of fields included in the autocomplete list
• the ability to enter the SGD site and explore data and pages without an initial query – just click the Explore button and go!
• narrowing of search results by categories such as genes, molecular functions, and phenotypes (and more!)

Navigating SGD will soon be easier than ever. Please explore the new search, try some different queries, view the new styling on your favorite pages, and send us your feedback via email, or through this short survey.

Categories: Announcements, Website changes

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