New & Noteworthy

Happy Holidays from SGD!

December 20, 2016

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We want to take this opportunity to wish you and your family, friends and lab mates the best during the upcoming holidays.

Stanford University will be closed for two weeks from Wednesday, December 21, 2016 through Tuesday, January 3, 2017. Regular operations will resume on Wednesday, January 4, 2017.

Although SGD staff members will be taking time off, please rest assured that the website will remain up and running throughout the winter break, and we will attempt to keep connected via email should you have any questions.

Happy Holidays and best wishes for all good things in the coming New Year!

Categories: Announcements

SGD December 2016 Newsletter

December 20, 2016

SGD periodically sends out its newsletter to colleagues designated as contacts in SGD. This December 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

Budding Yeast, a Caffeine Wimp No More

December 16, 2016

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Some people get the jitters from a single espresso while others need a triple shot just to get started in the morning. Some of this is due to caffeine tolerance—a buildup of resistance to the marvelous effects of that wonderfully addictive substance, caffeine. But the rest has to do with genetic differences that affect how well each of us processes caffeine—our caffeine sensitivity.

Our best buddy Saccharomyces cerevisiae is a real wimp when it comes to caffeine. In fact, like a lot of other microorganisms, caffeine actually kills this yeast. S. cerevisiae is indeed a sensitive soul when it comes to caffeine.

In a new study in the Journal of Agricultural and Food Chemistry, Wang and coworkers were able to toughen up budding yeast against caffeine by adding bfr1, a gene from Schizosaccharomyces pombe that encodes the ABC transporter that shunts caffeine out of the cell. And then, using random mutagenesis, they were able to make bfr1 even better at its caffeine-exporting job. Although the yeast don’t get any of the pleasurable effects of caffeine, at least they can now happily grow in cultures that have more caffeine than a strong cup of coffee.

This new attribute could prove to be incredibly useful if caffeine producers ever want to start making caffeine biologically instead of synthetically. You can imagine adding the caffeine pathway from coffee to yeast and having the yeast merrily exporting caffeine to the culture medium where it can be harvested. And who knows, maybe they can have the yeast make caffeine and alcohol at the same time creating the equivalent of a vodka and Red Bull in a single step!

Previous research had shown that bfr1 was an important player in helping S. pombe deal with caffeine. When Wang and coworkers added the gene to S. cerevisiae, this newly engineered yeast could now better tolerate caffeine. For example, whereas wild type yeast barely grew with 8 mg/ml caffeine, the engineered yeast did OK.

These authors next turned to random mutagenesis of the bfr1 gene to screen for mutants that could tolerate even more caffeine. And boy did they win the lottery on this one! A mutant that they named bfr1-B did great even at concentrations of 25 mg/ml caffeine. Now they were getting somewhere.

Bfr1 doesn’t just export caffeine; it actually exports many different compounds. The authors found that bfr1-B was fairly specific for increased resistance to caffeine. For example, when they tested the bfr1-B mutant with theophylline, a structurally similar compound, and atropine, a structurally distinct compound, they found that S. cerevisiae expressing the mutant were, if anything, more sensitive to these compounds. They found what looked like a caffeine-specific mutant. 

When they looked at the mutant, Wang and coworkers found that there were 11 amino acid substitutions scattered across the protein. The next step was to figure out which ones mattered and which ones didn’t.

Using a bit of modeling with the 3-D structure of other ABC transporters, they settled on testing three mutations individually. Two of the mutations, S36 and D340 were in the nucleotide binding domain (NBD) and the third, Y497, was in the transmembrane domain (TMD). The NBD is where ATP binds to the transporter to supply the energy to move caffeine across the membrane. 

Of the three, only D340 in the nucleotide binding domain conferred caffeine resistance. While not as robust as bfr1-B, this mutant allowed yeast expressing it to tolerate caffeine concentrations up to 15 mg/ml, conditions under which cells with wild type bfr1 failed to grow. 

So while this mutation explains a lot of why bfr1-B is so good at dealing with caffeine, it is not the whole story. At least some of those other 10 mutations contribute to how well bfr1-B does with caffeine.

In the end we have a bullet-proof yeast when it comes to caffeine that should prove useful for anyone who wants yeast to synthesize caffeine for them. Of course unlike even the most grizzled 30 year coffee drinker with ideal genetics, the yeast almost certainly gets no joy from its morning Joe. But at least that cup of coffee won’t kill it!

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

Categories: Research Spotlight

Tags: ABC transporter, BFR1, caffeine, Schizosaccharomyces pombe

Sign Up Now! Next SGD Webinar: December 14, 2016

December 12, 2016

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Looking for human disease-related information in SGD? There is so much to find! Active areas of curation at SGD include yeast-human homology, disease associations, alleles and phenotype variants, and functional complementation relationships.

Join our upcoming webinar on December 14th, 9:30 AM PST to learn about homology and disease data in SGD. In this quick 15 minute session, we will demonstrate the best ways to research this information on our website and provide a helpful tutorial on related SGD tools and features. Our webinars are always an excellent opportunity to connect with the SGD team–be sure to bring questions if you have them!

All are welcome to this event. If you are interested attending, please register here: http://bit.ly/SGDwebinar6

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

Categories: Announcements, Homologs, Tutorial, Yeast and Human Disease

Move Along, Transcription Factor

December 07, 2016

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For an election to go smoothly, people cannot stay too long in the voting booth. If a lot of people stayed in the booth and answered emails, sent texts, etc., after they finished voting, then the whole process would grind to a halt.

There is some evidence that activating genes may work similarly. The transcription factors (TFs) that bind DNA and turn up the expression of nearby genes can’t stay too long. If they do, the activation starts to peter out.

What is thought to happen with these sorts of TFs is that they bind their preferred DNA, and then once they have attracted the cellular machinery needed to read the gene, they are targeted for destruction. Then a new TF can bind and repeat the process.

In a new study in GENETICS, Akhter and Rosonina set out to investigate the process by which the yeast transcription activator Gcn4p is removed after it has bound DNA and done its job. Gcn4p activates a number of genes in response to amino acid starvation.

They found that a key step in the process is the addition of SUMO proteins to DNA-bound Gcn4p, which gets the ball rolling on the destruction of Gcn4p. Imagine a sumo wrestler settling in next to a voter once he enters the booth and then throwing him out if he tarries too long.

Their model is that once Gcn4p binds DNA, it is sumoylated. Then the DNA-bound, sumoylated GCN4 is further modified by kinases like Cdk8p, a component of the mediator complex which acts as a bridge between TFs and the cellular machinery responsible for reading a gene. This modified TF is then sent off to the 26S proteasome where it is degraded making room for an unmodified Gcn4p.

Previous research had shown that sumoylation of GCN4 required DNA binding. The first thing these authors did in this study was to determine if Gcn4p had to bind to its target DNA sequence in order to be sumoylated. It did not.

When they fused a mutant Gcn4p that could not bind DNA to the DNA binding domain of Gal4p, they found that this molecule was sumoylated at the correct places on the Gcn4p part of the fusion protein, lysines 50 and 58, when bound to a Gal4p binding site. Therefore, Gcn4p does not need to occupy its own DNA binding site in order to be sumoylated.

Another set of experiments showed that while DNA binding was required for sumoylation, interaction with RNA polymerase II (RNAP II), the enzyme that reads the genes that Gcn4p activates, does not appear to be necessary. For one of these experiments they used a temperature sensitive mutant of the largest subunit of RNAP II, Rpb1p, and showed that even at higher temperatures when RNAP II is inactive in these cells, DNA-bound Gcn4p is still sumoylated. In the other experiment they showed that DNA-bound
Gcn4p was still sumoylated when they used the “anchor away” technique to drag Rpb1p out of the nucleus and into the cytoplasm.

So DNA binding is sufficient, and the specific site is not important. And Gcn4p doesn’t have to be activated in order to be sumoylated.

Of course, turnover like this is a delicate thing. If Gcn4p is pulled off too soon, then it can’t activate as much as it might otherwise be able to do. This might affect the cell’s response to starvation just as much as Gcn4p staying put too long. Sort of like the sumo wrestler throwing a voter out of the voting booth before they could finish their voting can muck up the election.

Akhter and Rosonina created a fusion protein of Gcn4p and the yeast SUMO peptide Smt3p. Unlike Gcn4p, this protein is sumoylated before it binds DNA.

They found that yeast expressing this fusion protein fared less well under starvation conditions compared to yeast cells that expressed the wild type version of GCN4. And using chromatin immunoprecipitation (ChIP) analysis they showed that at least at the ARG1 gene, this was because there was less of the fusion protein bound under activating conditions.

So cells need for TFs to stay at the right place for the right amount of time. If they are pulled off too early or stay too long, the levels of activation can fall below what is best for the cells.

Unfortunately, we don’t have time to go over other experiments that tease out which kinases are important and when, but I urge you to read about them for yourselves. They take full advantage of the genetic tools available in yeast to make this sort of study possible…#APOYG!

Integrating all of this gives the following model:

Gcn4p is only a dimer when bound to DNA and this dimerization may be the signal for sumoylation by Ubc9p. A preinitiation complex forms through its interaction with the DNA-bound, sumoylated Gcn4p which brings in the enzyme RNAP II to transcribe the gene. Once the polymerase has left the nest, the kinase Cdk8p comes in and phosphorylates Gcn4p which signals Cdc4p/Cdc34p to ubiquitinate Gcn4p. The ubiquitinated Gcn4p is then degraded by the 26S proteasome opening the upstream activator sequence (UAS) up to a fresh, new Gcn4p.

Here, with the help of our super hero Saccharomyces cerevisiae, Akhter and Rosonina have dissected out what happens to a transcription factor once it binds to DNA (at least ones that bind for short times). It will be fascinating to see if this translates to other TFs in other beasts. While I love yeast for all it can do for us for bread, wine, beer, human health, helping solve world problems like climate change, and so on, I think my favorite use is still that it allows us to better understand the basic biology of how our cells work.

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

Categories: Research Spotlight

Tags: Cdk8, Gcn4, gene activation, sumoylation, transcription

Yeast Tackles Climate Change

November 29, 2016

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One of the best parts about doing outreach with a museum is creating a successful hands on activity for the visitors. This is not an easy thing to do.

You first create something that you think will appeal to and educate visitors. When that falls flat on its face, you then do a series of tweaks until it is working smoothly. In the end you have smiling kids who understand DNA better (or at all)!

Genetic engineering can be similar. You can import the genes for a complex pathway into your beast of choice but it may not work first try. A bit of tinkering, evolution style, is often needed to get the engineering working well enough to be useful.

This is exactly what happened when a group of researchers tried to get our favorite beast, the yeast Saccharomyces cerevisiae, to turn the sugar xylose into ethanol. They added the right genes from either fungi or bacteria, but S. cerevisiae couldn’t convert enough xylose into ethanol to be useful.

And it is important for all of us that some beast be able to do this well. A yeast that can turn xylose into ethanol means a yeast that can turn a higher percent of agricultural waste into a biofuel. Which, of course, means lots of low carbon fuel to run our cars so that we have a better shot of limiting the Earth’s heating up by 1.5-2 degrees Celsius.

To get their engineered yeast to better utilize xylose, Sato and coworkers forced it to grow with xylose as its only carbon source. Ten months and hundreds of generations later, this yeast had evolved into two new strains that were much better at turning xylose into ethanol. One strain did its magic with oxygen, the other without it.

In a new study in PLOS Genetics, Sato and coworkers set out to figure out which of the mutations that came up in their evolution experiments mattered and why.

Two genes were common in both the aerobic and anaerobic strains – HOG1 and ISU1. Both needed to be nonfunctional in order to maximize ethanol yields from xylose. They confirmed this by deleting each individually and together from the parental strain.

HOG1 encodes a MAP kinase, and ISU1 encodes a mitochondrial iron-sulfur cluster chaperone. These probably would not have been the first genes to go after with a more biased approach. The benefits of evolution and natural selection!

Further experiments showed deleting each gene individually was not as good as deleting both at once when oxygen was around. In fact, while deleting only ISU1 had a small effect on the ability of this yeast to convert xylose into ethanol, deleting HOG1 alone had no effect at all. Its deletion can only help a strain already deleted for ISU1.

In the absence of oxygen, yeast needs a couple of additional genes mutated – GRE3 and IRA2. GRE3 is an aldose reductase and IRA2 is an inhibitor of RAS. Again, not very obvious genes!

Still, once you find the genes you can come up with reasonable hypotheses for why they are important.

Some are easier than others. Hog1p, for example, is known to enhance a cell’s ability to turn xylose into xylitol, which shunts the xylose away from the ethanol conversion pathway. GRE3 is involved in this as well. Deleting either should make more xylose available to the yeast.

This doesn’t mean this is Hog1p’s only role in boosting this yeast’s ability to turn xylose into ethanol of course. It also probably “…relieves growth inhibition and restores glycolytic activity in response to non-glucose carbon sources.” Consistent with this, the authors found that the xylose-utilizing strain deleted for HOG1 was also better at using glycerol and acetate as carbon sources.

Other genes were less obvious. For example, perhaps mutating ISU1 frees up some iron so extra heme can be made. Or alternatively, it may have increased the mass of mitochondria available. Again, probably would not have been the first gene to go after to improve yeast’s ability to convert xylose to ethanol.

Which again underlines the importance of letting natural selection improve an engineered organism as opposed to only trying to pick and tweak the genes you think are important. Biology is simply too complicated and our understanding too limited to be able to know which are the best genes to go after. This is reminiscent of prototyping museum activities.

Some tweaks are obvious but others you would never have guessed would be needed. For example, we had visitors spreading bacteria on a plate and found that if they labeled their plate first, they almost always put their transformation mixture on the lid instead of on the LB agar. This problem was solved by having them add their mixture first and then labeling their plate.

It would be very hard to predict something like this from the get-go. The activity needed to evolve on the museum floor to work optimally. Much like the yeast engineered to utilize xylose needed to evolve in the presence of xylose to work optimally. And to perhaps take a big step towards saving the Earth from warming up too much.

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

Categories: Research Spotlight

Tags: biofuel, ethanol, evolution, fermentation, glucose metabolism, xylose

Too Much of a Good Thing

November 09, 2016

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Like a ruined cookie with too much salt, a cell can go haywire when it has too many copies of certain genes. And of course, cells can deal perfectly well with too many copies of other genes. Just like adding too many chocolate chips to your cookies might make an even better cookie!

Finding out which genes are like salt and which ones are like chocolate chips is of more than just general biological interest. It might help us to explain why cancer happens and to possibly find better treatments.

As you probably know, cancer cells are pretty messed up genetically. Their DNA is littered with mutations, rearrangements and somatic copy number amplifications (SCNAs).

A big reason for this genetic jumble is early DNA changes that increase the rate of mutations in a cell. This “mutator” trait makes a cell more likely to stumble on the mutations it needs to grow out of control or refuse to die.

In their new study in GENETICS, Ang and coworkers set out to find genes that can cause a mutator phenotype when they are part of a SCNA. In other words, which genes lead to an increased mutation rate when expressed at a higher level.

This is important because there are so many SCNAs in a typical cancer cell that it can be hard to figure out which ones matter and which ones don’t (or to put it into cancer parlance, to tell the drivers from the passengers). And despite all of the CRISPR hoopla and other mammalian resources, it would still be a very long process to find “dosage mutator” genes in cell culture and/or living animals.

Which is why Ang and coworkers used our favorite workhorse, the yeast Saccharomyces cerevisiae, to find genes that may cause an increased mutation rate when overexpressed.

The assay is conceptually simple. Yeast that have a functioning CAN1 gene do not survive in the presence of the drug canavanine. So these researchers looked for cells that did better in the presence canavanine when overexpressing a single gene. Presumably, they are surviving because that extra gene resulted in the CAN1 gene being mutated more often because of an increased mutation rate.

They found 37 genes that fit the bill, 18 of which that were involved in biological pathways known to affect genome stability. Combining this with previous studies that looked at gene deletions, this brings the grand total of suspected yeast mutator genes to 210.

Most of these 210 were identified because of mutations that made them stop working which can make figuring out why they cause the mutator phenotype relatively simple. For example, if a mutation kills a gene responsible for fixing DNA mistakes, then you are going to get more DNA mistakes in that cell. It is a little trickier to understand how extra copies of a gene might cause an increased mutation rate.

Ang and coworkers focused on trying to figure out the mechanism behind their top 5 dosage mutator genes: PIF1, MPH1, UBP12, RRM3, and DNA2. Since 4/5 of these code for helicases, they first checked to see if just being a helicase is enough to be a dosage mutator gene. It isn’t.

They retested 48 DNA helicases in their assay and found that none of them caused an increased mutation rate when mutated. There is more to a dosage mutator than being a helicase!

In the next set of experiments, they wanted to determine if the five strains, each overexpressing one of these five genes, had a higher mutation rate by the same mechanism. They tested this by determining the sensitivity of these 5 strains to 3 different DNA damaging agents. The idea is that if they share the same mechanism, they should have the same sensitivity profiles to each of these agents. They did not.

For example, overexpressing MPH1 resulted in a higher sensitivity to all three agents while overexpressing UBP12 only increased sensitivity to two of them. So each strain probably has an increased mutation rate for a different reason.

They next wanted to see if the increased mutation rate was due to a loss or gain of function. They did this by comparing the profiles of strains either deleted for or overexpressing the dosage mutator genes. The idea is that if overexpression leads to a loss of function, then deleting and overexpressing the genes should have the same profile. The three they could test like this did not.

The authors conclude from this that the increased mutation rate for MPH1, UBP12, and RRM3 is most likely due to the gain of an inappropriate function as opposed to a loss of function. In a final set of experiments, Ang and coworkers focused on what that new function might be in their strongest mutant, MPH1.

First they showed that of the three activities associated with Mph1p, only DNA binding and not its ATPase or helicase activities were important for it causing an increased mutation rate when overexpressed. From this they reasoned that perhaps Mph1p was displacing some other important DNA binding protein and that it was this displacement that was causing the increased mutation rate.

Through a set of experiments we don’t have time to go into here, they provided evidence that Mph1p was outcompeting the flap endonuclease Rad27p for DNA binding. This makes some sense as previous work had shown that deleting RAD27 causes mutation rates to go way up. So too much Mph1p keeps Rad27p from getting to where it needs to be with the end result being an increased mutation rate.

All this MPH1 work may have important implications in some human cancers. Nonsense or missense mutations in FANCM, the human homolog of MPH1, are known to make people more likely to get cancer. And there are examples of cancers where FANCM is overexpressed. Perhaps that overexpression results in an increased mutation rate in these cancers.

Yet again yeast is giving researchers new targets for, and new ways to think about, human disease. Thanks, yeast, for finding all of these mutator genes for us to investigate further! #APOYG!

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: cancer, forward mutation, genome-wide, MPH1, mutator, overexpression

Personalized Essential Genes

November 02, 2016

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Every smartphone has a few, key functions. For example, they all let you make calls, send texts, take pictures, and search the web. But smartphones also have their own specialized functions depending on who owns them.

Maybe you’re an avid Candy Crusher and so you have every iteration of that game on your phone. Or maybe you have an ESPN app that lets you watch football highlights. There are pretty much an infinite number of possible combinations to personalize your phone.

A new study out in PLOS Pathogens finds something similar for the XRN1 gene in yeast. Rowley and coworkers found that in terms of basic function, they could swap one XRN1 gene for another across 4 different Saccharomyces species. All these different versions of the XRN1 did their main job of degrading RNA just fine no matter which yeast species they were in.

But a closer look revealed that each version of this key gene had a personalized function that did not swap as well. And this specialization wasn’t something trivial like Apple Music vs. Spotify. The personalized XRN1 genes protected their own species of yeast against species-specific viruses better than the XRN1 genes from other species.

It will be interesting to see if something similar happens in people. Perhaps the human version of XRN1, which also plays a role in taming RNA viruses, works better with human-specific RNA viruses too. This might help to explain our poor response to viruses that move from one species to ours.

Rowley and coworkers used four different assays to show that XRN1 from Saccharomyces species cerevisiae, mikatae, kudriavzevii and bayanus were all interchangeable for rescuing a variety of growth defects present in a S. cerevisiae strain deleted for XRN1. These XRN1 genes are indistinguishable in terms of the basic function of degrading damaged or old RNA in the cell.

The same was not true of each gene’s ability to deal with RNA viruses.

The main virus that infects Saccharomyces goes by the name of the L-A virus. These RNA viruses are a little different from many other viruses in that they don’t spread from one yeast cell to another. Instead, they stay within their host cell and spread only when the infected yeast cell buds off a new daughter.

These researchers used three different assays to show that the XRN1 genes from different species worked less well than the cerevisiae XRN1 to reduce the viral load in Saccharomyces cerevisiae. All three assays were consistent with the S. cerevisiae version working best.

First, they used an assay that looked directly at the dsRNA of the L-A virus. In a cerevisiae strain deleted for XRN1, they saw a fat, juicy band on their gel. This band was unaffected when they added back a dead version of XRN1 (either E176G or Δ1206-1528) and severely reduced when they added back the complete XRN1 gene from S. cerevisiae.

The effect of XRN1 from other species depended on how closely related they were to S. cerevisiae. For example, the XRN1 from the more closely related S. mikatae was able to reduce the band a bit while the genes from the more distantly related S. kudriavzevii and S. bayanus had no discernible effect.

The second assay took advantage of a very cool RNA virus known as “killer”. It basically has the instructions for making a secreted toxin that kills any yeast around the host cell while sparing the host. It is completely dependent on the L-A virus.

Previous research showed that XRN1 affects how well yeast carrying both viruses kill off surrounding yeast. They measured this with something called a kill zone. The idea was to put a spot containing 6×10⁵ killer S. cerevisiae on a lawn of S. cerevisiae lacking the killer virus and to measure how big the “death” circle was that surrounded the spot.

Consistent with the results looking directly at the double stranded RNA of the L-A virus, Rowley and coworkers found that XRN1 from other Saccharomyces species were less able to negatively affect the ability of the killer virus to kill. This is presumably because they are less likely to degrade the virus meaning there is more of it around.

When they used S. cerevisiae XRN1, the kill zone averaged about 0.68 cm². S. mikatae, S. kudriavzevii, and S. bayanus average 0.92 cm², 0.96 cm², and 0.97 cm². S. cerevisiae harboring XRN1 from a different species were better killers.

The final experiment tested the ability of overexpressed XRN1 to cure S. cerevisiae of the L-A virus. In the absence of XRN1, 0/103 yeast managed to get rid of their virus. This number rose to 49% (78/159) when the XRN1 from S. cerevisiae was overexpressed. The XRN1 genes from other species fared much worse: only 12% (20/129) were cured with S. mikatae, 9% (11/123) with S. kudriavzevii, and 8% (10/120) with S. bayanus.

So it looks like the S. cerevisiae XRN1 gene has evolved to combat the L-A viruses that infect S. cerevisiae best. But is the reverse true? Are the XRN1 genes from the other species also specialized in their viral attacks? Looks like the answer is yes, at least for S. kudriavzevii.

The tricky part of answering this question is the only well characterized L-A virus is from S. cerevisiae. So their first experiment was to find a virus in another Saccharomyces species. With a little work they found one in S. kudriavzevii FM1138. The experiments they did with this strain weren’t as clean as the ones they did with S. cerevisiae because S. kudriavzevii is a trickier yeast to work with, but they found that XRN1 from S. kudriavzevii did best at reducing the amount of S. kudriavzevii-specific virus compared to the XRN1 genes from the other species.

So XRN1 does some important basic things in the cell like clearing out old and damaged RNA and these functions are pretty similar no matter which Saccharomyces species they come from. However, the same is not true for its role in keeping viruses in check. At least in S. cerevisae, its XRN1 does a way better job at keeping its endemic viruses manageable than do the XRN1 genes from three other Saccharomyces species.

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

Categories: Research Spotlight

Tags: pathogen, ribonuclease, virus

New SGD Help Video: Finding Human Homology & Disease Information

October 24, 2016

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Looking for human disease-related information in SGD? There is so much to find! Active areas of curation at SGD include yeast-human homology, alleles and phenotype variants, functional complementation relationships, and disease associations. There are plenty of ways to find this information on our website, and it takes just 90 seconds to learn how – what are you waiting for?

For more SGD Help Videos, visit our YouTube channel, and be sure to subscribe so you don’t miss anything!

Categories: Announcements, Homologs, Tutorial, Yeast and Human Disease

Winter is Coming (for Cancer)

October 17, 2016

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I may be a little late to the game, but over the last few weeks I have started consuming episodes of Game of Thrones voraciously. It is such a fun show to watch! And this isn’t the only HBO show I enjoy. Veep, Silicon Valley, and Last Week Tonight with John Oliver all have my attention as well.

You might say that I need HBO, because without it I can’t get my fill of these shows. (Well, there are other routes, but HBO or HBO GO are the easiest). My over watching of these shows has made me dependent on HBO.

Something similar can happen in cancers. Sometimes a key player in keeping a cell cancerous is an overexpressed gene. And just like my binging of Game of Thrones makes me dependent on HBO, so this overexpressed gene (the TV show) makes the cancer cell dependent on another gene (HBO).

A real life example might make this clearer. Most folks have heard of BRCA1 and BRCA2 especially since the Angelina Jolie story. When either of these genes is damaged, you can end up with cancer.

Both of these genes are involved in DNA repair and damaging them means the cell builds up mutations. Making lots of DNA mistakes is a good thing for cancers but only up to a point. Too much damage and the cancer cell dies.

What this means is that these cancer cells are now more dependent on other DNA repair genes. Which means these other DNA repair genes are now targets to go after to selectively kill the cancer cells.

For cancer cells lacking BRCA1 or BRCA2 function, research has shown that these cells are now dependent on a second gene, PARP1. If PARP1 expression is turned down, normal cells survive but BRCA1/BRCA2-dependent cancers die. So, we can kill cancer cells, or end their TV show watching, by going after PARP1, their HBO.

Finding these sorts of genes is not easy unless, of course, you turn to our favorite lab workhorse, the yeast Saccharomyces cerevisiae. Given all of the genetic techniques and tools available with this yeast, it is possible to quickly do a synthetic dosage lethality assay – to look for genes that are lethal only in combination with deleting your gene of interest.

This is just what Reid and coworkers did in a new study just out in GENETICS for CKS1B, a gene that is amplified and overexpressed in many cases of breast, lung, and liver cancers. And they found a more “druggable” target to go after, the kinase PLK1 (the human homolog of yeast CDC5). PLK1 even comes with its own kinase inhibitor, Volasertib.

Reid and coworkers transformed a low copy plasmid containing the CKS1 gene, the yeast homolog of CKS1B, under the control of the galactose promoter into two different yeast strain libraries. The first screen used 9600 yeast deletion strains, each with a single gene deleted in either a MATa or MATα strain. The second screen used strains with temperature sensitive mutants of essential genes. They now looked to see which yeast strains did poorly or couldn’t survive when they were overexpressing CSK1 in the presence of galactose.

In the end they came up with 44 different genes that, when deleted or weakened, had a severe effect on the growth of yeast that overexpressed CKS1. Given that CKS1 plays an important role in cell cycle progression, they focused on the 15 genes that affect mitotic progression. Eventually, through a set of experiments that I don’t have time to go into here, they settled in on CDC5, a polo-like kinase involved in both mitotic entry and exit.

The next step was to see if what they learned about in yeast has any bearing on cancer. It did.

First Reid and coworkers looked at a variety of cancer cells in The Cancer Genome Atlas (TCGA) and found that it was very rare for both PLK1 and CKS1B to be overexpressed in the same cancer at the same time. Next they looked at a data set of short hairpin RNA (shRNA) knockdowns of ~16,000 human genes and found that knocking down PLK1 had negative effects on cancers overexpressing CKS1B. These are consistent with the two genes having a synthetic lethal relationship.

They then took eight breast cancer lines where the shRNA against PLK1 had a negative effect on growth and tested the effects of targeting PLK1 on apoptosis. Did decreasing expression of PLK1 in cells that overexpress CKS1B cause an increase in apoptosis in their hands?

The short answer is yes. They repeated the experiments with the shRNA and also tested the PLK1-specific kinase inhibitor Volasertib and found that both treatments increased apoptosis in CKS1B overexpressing cancer cells. It looks like they may have uncovered a way to go after a subset of cancers using yeast!

Which shouldn’t surprise us. Yeast and other model organisms have been teaching us about cancer at least since the days when Hartwell, Hunt and Nurse first identified cyclins and CDKs (for which they got the 2001 Nobel Prize in Physiology or Medicine), and will continue to school us for years to come.

Hopefully researchers will continue to turn to yeast to continue to better understand and find new treatments for cancer. Yeast has so much more to teach us! #APOYG!

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: CKS1, cyclin-dependent kinase, polo-like kinase, synthetic dosage lethal

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