March 02, 2017
You may have heard the old wives’ tale of feed a cold, starve a fever. Turns out that this isn’t particularly good advice (although some studies do suggest that with a fever, you shouldn’t force-feed yourself). It also turns out to have probably originated in the 19th century and not from Chaucer in the 14th as many websites claim.
But while starving a fever is probably never a good idea, starving a cancer can be. Not by following the medical myth that since cancers use a lot of sugar, you can starve them by cutting down on sugar in your diet. Instead you can starve some cancers by denying them the amino acid asparagine (Asn).
On their way to becoming cancerous, acute lymphoblastic leukemia (ALL) cells lose their ability to make Asn. This means that unlike the cells around it, they need to pull Asn from the blood to make their proteins and to survive.
Doctors exploit this weakness by injecting L-asparaginase amidohydralase (L-ASNase) into patients which starves the cancer cell by depleting Asn levels in the blood. The cells around the cancer cells are fine because they can still make Asn.
Right now doctors use L-ASNase from two different bacterial sources: Escherichia coli and Erwinia chrysanthemi. But if a recent study by Costa and coworkers in Scientific Reports holds up, they might want to think about switching to using the Saccharomyces cerevisiae L-ASNase encoded by the ASP1 gene.
An older study had suggested that the yeast enzyme might be too weak to be useful. This new study finds that this is not the case.
The difference between the older study and this one was the purification protocol. The older study purified the native enzyme through multiple chromatography steps while this study used a single affinity chromatography step. The purified yeast and E. coli versions have comparable activity in this study.
They are also comparable in terms of being able to work with very low concentrations of Asn. This is important as Asn levels are very low in the blood.
What makes the yeast enzyme potentially better is that it is much worse at hydrolyzing a second amino acid, glutamine, than are the bacterial versions. This higher specificity for Asn is important because one of the major side effects of the current treatment is neurotoxicity caused by decreased levels of glutamine in the blood. Since the yeast version hydrolyzes glutamine at a lower rate, they predict patients may not suffer as badly from this side effect with the yeast version.
Of course this is all for naught if the yeast enzyme can’t kill cancer cells! Or if it kills cells indiscriminately.
The S. cerevisiae version was nearly as good as the E.coli version in tissue culture. After 72 hours of incubation, both versions had little effect on normal cells (HUVEC), and both were cytotoxic to the L-ASNase-sensitive cell line MOLT-4 with the E. coli version killing 95% of MOLT-4 cells and the yeast version killing 85% of them.
Taken together these results suggest that the S. cerevisiae version may be an alternative to the bacterial versions. It may be able to kill cancer cells with fewer side effects.
But the yeast version is not the only alternative in town. Another group is engineering the E. coli version to lessen its propensity for hydrolyzing glutamine. Either way it looks like certain leukemia patients may be getting an effective cancer treatment with fewer side effects.
Beer, wine, bread, chocolate, and now maybe a treatment for a nasty form of leukemia. Yeast may be humanity’s best friend. #APOYG!
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
December 12, 2016
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.
November 09, 2016
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.
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
October 24, 2016
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!
October 17, 2016
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).
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
October 03, 2016
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:
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
August 24, 2016
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
August 10, 2016
A 2013 poll identified the top 20 modern necessities British people couldn’t live without. Some we can all relate to like smartphones, daily showers, and the internet, while others are more British-specific like a cup of tea or a full English breakfast.
Of course none of these are true necessities like food, water or air. We wouldn’t be as happy, nor as competitive, without some of these modern necessities, but we’d obviously still be alive. (But is life without the Internet really living?)
It turns out that the GCN5 gene is more like water or air for most eukaryotes—they can’t live without it. But our old friend Saccharomyces cerevisiae is different. This yeast isn’t as happy without GCN5, but it soldiers on nonetheless.
This nonessentiality, combined with its powerful genetics, makes yeast a great system for exploring what GCN5 does. And this is just what Petty and coworkers did with this important member of the histone acetyltransferase (HAT) family in a new study in GENETICS.
GCN5, like other HATs, transfers an acetyl group to histones, which results in increased activity of nearby genes. Consistent with this, previous work has shown that GCN5 acetylates histone H3 in the promoters of active genes.
HATs, along with their countervailing proteins histone deacetylases, as well as kinases, phosphatases, methyltransferases and so on, all work together to change gene expression on the fly in response to all sorts of different stimuli. These can include environmental signals, entering the cell cycle, or whatever.
Understanding how HATs work is critical for understanding how we (and other beasts) change gene expression in response to these signals. Which is what makes GCN5 in yeast such a great system. A strain deleted for GCN5 is sick, but alive, so we can study what happens when it is gone. And we can explore what we can do to fix its problems.
One of the many problems that yeast lacking GCN5 have is that they grow more poorly at high temperature than do yeast with GCN5. These researchers took advantage of this and looked for high copy suppressors of this temperature sensitivity. They found multiple genes, but the most common was RTS1, one of two regulatory subunits of the PP2A phosphatase complex.
Deleting GCN5 causes more problems than temperature-sensitivity, and overexpressing RTS1 restored some, but not all, of them. For example, RTS1 overexpression helped make the Δgcn5 strain less sensitive to DNA damage, less susceptible to microtubule disruption, better able to grow on nonfermentable carbon sources like glycerol or ethanol, and more able to progress into S phase during mitosis. But lots of PP2ARts1 could not rescue the abnormal buds nor the sporulation problems seen in a diploid lacking GCN5.
When the researchers deleted RTS1 or some of the genes that code for other critical components of the PP2ARts1 complex in a Δgcn5 strain, the strain died. The same was not true of a second regulatory subunit that can be part of the PP2A complex, CDC55—its deletion was not lethal, nor did it rescue the temperature sensitivity of the Δgcn5 strain.
Petty and coworkers provided evidence that the phosphatase activity of the PP2ARts1 complex was important by showing that okadaic acid, an inhibitor of this family of phosphatases, prevented Rts1p from rescuing the Δgcn5 strain’s temperature sensitivity. The easiest explanation is that the rescue happens because of the phosphatase activity of PP2ARts1, but it is also possible that a different member of the family might be providing the phosphatase activity.
So it looks like there is something important happening between PP2A and GCN5. Petty and coworkers next set out to find out what that might be.
First they showed that deleting GCN5 causes a decrease in the levels of core histones in the cell and that RTS1 overexpression fixed this problem. This happened at the transcription level as the RNA levels of the yeast histone genes (with the exception of HTA1) all showed reduced expression in the absence of GCN5, which again was restored when RTS1 was overexpressed.
Histone genes are normally turned on at the end of G1, then shut off at the end of S phase. This makes sense, as a cell needs to make more histones when it makes a new copy of its genome and this happens during S phase!
Consistent with the reduced histone gene expression seen earlier, the Δgcn5 strain failed to increase histone gene activity at the end of G1. Overexpressing RTS1 restored this induction. So it looks like GCN5 is involved in turning on all the histone genes, except maybe HTA1, at the right time, and that RTS1 can compensate if there is a lot of it around.
As a final set of experiments, the authors looked at what PP2ARts1 might be doing to rescue histone gene expression when GCN5 was deleted. They decided to look at histone modifications.
For this they used the SHIMA (Scanning HIstone Mutagenesis with Alanine) library, in which all key serine and threonine residues were individually mutagenized to alanine. Even though PP2ARts1 is thought to be primarily a serine/threonine phosphatase, they also looked at three tyrosine residues (Y40, Y43, and Y45) on H2B by mutating each individually to phenylalanine.
They found that two residues on histone H2B, Y40 and T91, were required for RTS1 to be able to rescue the temperature sensitivity of a Δgcn5 strain. And mimicking the permanent phosphorylation of T91, by mutating it to either aspartic or glutamic acid, slowed the growth of wild type yeast and killed the deletion strain.
This tells us a lot about what GCN5 is doing in yeast, and it might also help us better understand certain human cancers. Turns out that the residue equivalent to T91 in mammals is phosphorylated in these cancers.
Petty and coworkers were able to learn all of this because of yeast’s powerful genetic tools, and because GCN5 is not essential in yeast. Once again, the awesome power of yeast genetics (#APOYG!) can help us understand human cell biology.
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
June 28, 2016
According to an AFI poll, the best comedy of all time was the 1959 film “Some Like It Hot.” In this classic screwball comedy two men have to dress up as women to escape the mob and still make money as musicians. All sorts of hilarity ensues as one of them falls in love with a woman and a man falls in love with the other as a woman.
The key to this comedy is that the two actors, Tony Curtis and Jack Lemmon, have to play both the male and female parts. If they were played by separate actors and actresses, the movie would die at the box office. It would be a lethal mutation.
A new study by Solis and coworkers in Molecular Cell presents evidence that in yeast, the heat shock transcription factor Hsf1p is a bit like Tony Curtis and Jack Lemmon—it plays dual roles, both in maintaining basal levels of various heat shock proteins and in turning the appropriate genes up in response to a heat shock. This is different than in mammalian cells where HSF1 is only responsible for turning up heat shock genes in response to a spike in temperature. Something else maintains the levels of these proteins needed for survival.
So yeast is more like the comedy “Some Like it Hot,” or perhaps Tootsie, while mammalian cells are more conventional comedies where different actors play the male and female roles. Because Hsf1p plays a dual role in yeast, its deletion causes the cell to die. Mammalian cells can survive without HSF1 as long as it doesn’t encounter any temperature spikes.
Solis and coworkers started out by coming up with a way to dissociate the genes that Hsf1 regulates under normal conditions from those upregulated under heat shock conditions. For this they used the “Anchor-Away” approach to remove Hsf1p from the nucleus under normal conditions.
Basically, they co-expressed HSF1 fused to FRB, the FKBP rapamycin-binding domain, and a ribosomal protein L13A-FKBP12 fusion. When they add rapamycin to this strain, the two proteins heterodimerize and Hsf1p is dragged out of the nucleus. They confirmed that Hsf1p was gone from the nucleus within a few minutes.
Next, they used native elongating transcript sequencing (NET-seq) 15, 30, and 60 minutes after rapamycin addition to see which genes were affected when Hsf1p left the nucleus. They found that only 25 genes were repressed and five were induced at these time points. Using RNA-seq and ChIP of Hsf1p they showed that Hsf1p was probably responsible for the expression of 18 of the 25 repressed genes and none of the induced ones.
So yeast Hsf1p is involved in the basal expression of a number of chaperone genes. In a set of experiments that I don’t have time to go over here, they also showed that most of the heat shock response was independent of Hsf1p in yeast. Their data suggests that Msn2/4p may be the key player instead.
They next did a similar set of experiments in mammalian cells but with a couple of differences. First off, these cells can survive HSF1 deletion, meaning they didn’t need to do anything fancy—they just used CRISPR/Cas9 to delete the gene in mouse embryonic stem cells and mouse embryonic fibroblasts.
Under normal conditions they found that the deletion of this gene caused two genes to go up in expression and two to go down. This is what you might expect by chance suggesting that in mammalian cells, HSF1 isn’t involved in basal expression of any genes.
They next used RNA-seq to compare gene expression of these cells and their undeleted counterparts under normal and heat shock conditions. They found a set of nine genes that were induced in both wild type cells and repressed in the HSF1-deleted cells under heat shock conditions. Eight out of nine of these are involved in chaperone pathways and they overlap surprisingly well with the yeast genes that Hsf1p controls under basal conditions.
Taken together these experiments paint an interesting picture. In yeast, HSF1 is mostly responsible for the basal expression of chaperone genes, and in mouse cells it is a key player in the heat shock response of a similar set of genes. This suggests that deletion of HSF1 is lethal in yeast because the decreased expression of one or more of the genes it regulates under normal conditions.
They tested this by expressing 15 of the 18 genes (three are redundant to some of the others) on four different plasmids and saw that a yeast strain that is deleted for HSF1 now survives. So one or more of these genes is responsible for yeast death in the absence of HSF1.
Through a process of elimination, Solis and coworkers found that the key genes were SSA2, a member of the HSP70 family, and HSC82, an HSP90 family member. The decrease in expression of these two genes cause by the deletion of HSF1 results in a dead yeast cell.
These experiments are so cool. In yeast, HSF1 makes sure there is enough of these chaperones around in good times to fold proteins properly and has a minor role in the heat shock response, while in mouse cells, the same gene plays no real role in basal levels of expression of chaperone genes and instead is critical for responding to heat shock. The protein regulates similar genes, just under different conditions.
These neat science experiments can tell us more about diseases, like cancer too. Turns out that some cancer cells may be more like yeast cells in that deletion of HSF1 stops them from growing and causes an increase in poisonous protein aggregates which may give us a new way to target HSF1-dependent cancers. For example, it may be that targeting Hsp70 or Hsp90 could be useful for treating HSF1-dependent cancers.
In cancer cells then, HSF1, like Dustin Hoffman in Tootsie, Milton Berle in the Milton Berle Show, or Bugs Bunny in many different cartoon shorts, takes on dual roles in the cell. And as we learned from yeast, this could be these cancers’ Achilles heel.
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
June 22, 2016
Model Organisms such as yeast, worm, fly, fish, and mouse are key drivers of biological research, providing experimental systems that yield insights into human biology and health. Model Organism Databases (MODs) enable researchers all over the world to uncover basic, conserved biological mechanisms relevant to new medical therapies. These discoveries have been recognized by many Nobel Prizes over the last decades.
NHGRI/NIH has recently advanced a plan in which the MODs will be integrated into a single combined database, along with a 30% reduction in funding for each MOD (see also these Nature and Science news stories). While integration presents advantages, the funding cut will cripple core functions such as high quality literature curation and genome annotation, degrading the utility of the MODs.
Leaders of several Model Organism communities, working with the Genetics Society of America (GSA), have come together to write a Statement of Support for the MODs, and to urge the NIH to revise its proposal. We ask all scientists who value the community-specific nature of the MODs to sign this ‘open letter’. The letter, along with all signatures, will be presented to NIH Director Francis Collins at a GSA-organized meeting on July 14, 2016 during The Allied Genetics Conference in Orlando. We urge you to add your name, and to spread the word to all researchers who value the MODs.
In other words, sign this letter!
April 26, 2016
If you’re not already using YeastMine to answer all your questions about S. cerevisiae genes and gene products…you should be! SGD’s YeastMine is a powerful search tool that can retrieve, compare, and analyze data on thousands of genes at a time, greatly reducing the time needed to answer real, practical research questions. Through YeastMine, questions such as “What proportion of plasma membrane proteins are essential?” or “How many different gene products physically interact with the mitochondrial ribosome?” can be answered within minutes.
Next week on May 4th, 2016 at 9:30 AM PDT, we will provide a brief webinar tutorial on how to run queries and create gene lists in SGD’s YeastMine. As a practical demonstration of YeastMine, we will also showcase a research scenario in which yeast-human homology data is used to predict potential chemotherapy targets for human cancers.
Space for this webinar is limited. To reserve your spot, please register for the event using this online form: http://bit.ly/registerFor2ndSGDwebinar
This is the second episode of the SGD Webinar Series. If you missed the first one, or if you’d like to find out more about upcoming webinars, please visit our wiki page: SGD Webinar Series.
April 06, 2016
In the book Dune, the mentat Thufir Hawat is captured by the evil Harkonnens and given a residual poison. He can only stay alive by getting a constant dose of the antidote. Once it is withdrawn, he will die.
A new study in the journal GENETICS by Dodgson and coworkers shows that the same sort of thing can happen to yeast that carry an extra chromosome. In this case, certain genes on the extra chromosome turn out to be like the residual poison. And a second gene turns out to be the antidote.
Once that second gene is deleted, the yeast cell dies. It has been deprived of its antidote.
This synthetic lethal phenotype isn’t just a cool finding in yeast either. Cancer cells invariably have extra and missing chromosomes. If scientists could find similar “antidote genes” in specific types of cancers and target them, then the cancer cell would die. And this would happen without damaging the other cells of the body that have a typical number of chromosomes.
The first thing these researchers did was to make separate yeast strains each with an extra chromosome I, V, VIII, IX, XI, XII, or XVI. The next step was to see what happens when every gene was deleted individually, one at a time, from each strain.
As expected, these yeast did pretty well when a gene on the extra chromosome was deleted. So, for example, a strain with an extra chromosome I tolerated a gene deleted from chromosome I. This makes sense as this just brings that gene back to its normal copy number.
But this was not the case with chromosomes VIII and XI. Here deleting genes on the extra chromosome often had a negative effect. This suggested that the screen probably had a high number of false positives and these researchers later confirmed this.
Likely reasons for the high number of false positives include the strain with the extra chromosome being W303 and the deletion strain being S288C, errors in the deletion collection itself, and what they refer to as neighboring gene effects. Basically this last one is the effect that deleting a gene has on nearby genes.
Once Dodgson and coworkers corrected for these problems, they found two broad sets of phenotypes – general and chromosome specific.
The general ones were the ones shared by most or all of the strains. These were deletions that affected the yeast no matter which chromosome they had an extra copy of.
For the most part, these genes were enriched for the Gene Ontology (GO) term vesicle-mediated transport, indicating that they have something to do with the transportation of substances in membrane-bounded vesicles. For example, deletion of MNN10, HOC1, and MNN11, genes all involved in protein transport and membrane-related processes, had a negative effect on many of the yeast strains with an extra chromosome. Consistent with this, brefeldin A, a drug that targets protein trafficking, negatively affected most of the strains.
Another gene that affected many of these strains when deleted was TPS1. This gene encodes a subunit of trehalose-6-phosphate synthase, a key enzyme for making trehalose, a molecule that helps yeast deal with stress. Perhaps not surprisingly, having an extra chromosome is stressful!
In addition to the genes that affect many strains with an extra chromosome, there were also genes that were chromosome specific. The best characterized of these was the EDE1 gene in the strain with an extra chromosome IX. Deleting EDE1 in this strain increased its doubling time by more than 80 minutes while only causing an increase of 5 minutes in the doubling time of wild type yeast. This was a severe phenotype in their assay.
They next tried to find which gene on chromosome IX might be responsible for the severe effect of deleting EDE1. Since EDE1 is known to be involved in endocytosis, they looked for genes involved in the same process. And they found one – PRK1.
The strain with a deleted EDE1 gene and an extra chromosome IX was rescued by deleting one copy of the PRK1 gene. The extra PRK1 gene was the poison and the EDE1 gene was the antidote.
If a similar pair could be found in cancers that often have the same set of extra chromosomes, then perhaps scientists could develop drugs that target an antidote gene. Now the cancer cells would die and the “normal” cells would be fine. Thanks again, yeast, for pointing us toward new ways to treat human disease.
by Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
November 11, 2015
Imagine what our email inboxes would look like if we didn’t have spam filters! To find the meaningful emails, we’d have to wade through hundreds of messages about winning lottery tickets, discount medications, and other things that don’t interest us.
When it comes to sorting out meaningful mutations from meaningless variation in human genes, it turns out that our friend S. cerevisiae makes a pretty good spam filter. And as more and more human genomic sequence data are becoming available every day, this is becoming more and more important.
For example, when you look at the sequence of a gene from, say, a cancer cell, you may see many differences from the wild-type gene. How can you tell which changes are significant and which are not?
SuperBud to the rescue! Because many human proteins can work in yeast, simple phenotypes like viability or growth rate can be assayed to test whether variations in human genes affect the function of their gene products. This may be one answer to the increasingly thorny problem of variants of uncertain significance—those dreaded VUS’s.
In a new paper in GENETICS, Hamza and colleagues systematically screened for human genes that can replace their yeast equivalents, and went on to test the function of tumor-specific variants in several selected genes that maintain chromosome stability in S. cerevisiae. This work extends the growing catalog of human genes that can replace yeast genes.
More importantly, it also provides compelling evidence that yeast can help us tell which mutations in a cancer cell are driver mutations, the ones that are involved in tumorigenesis, and which are the passenger mutations, those that are just the consequence of a seriously messed up cell. Talk about a useful filter!
The researchers started by testing systematically for human genes that could complement yeast mutations. Other groups have done similar large-scale screens, but this study had a couple of different twists.
Previous work from the Hieter lab had identified genes in yeast that, when mutated, made chromosomes unstable: the CIN (Chromosome INstability) phenotype. Reduction-of-function alleles of a significant fraction (29%) of essential genes confer a CIN phenotype. The human orthologs of these genes could be important in cancer, since tumor cells often show chromosome rearrangements or loss.
So in one experiment, Hamza and colleagues focused specifically on the set of CIN genes, starting with a set of 322 pairs of yeast CIN genes and their human homologs. They tested functional complementation by transforming plasmids expressing the human cDNAs into diploid yeast strains that were heterozygous null mutant for the corresponding CIN genes. Since all of the CIN genes were essential, sporulating those diploids would generate inviable spores—unless the human gene could step in and provide the missing function.
In addition to this one-to-one test, the researchers cast a wider net by doing a pool-to-pool transformation. They mixed cultures of diploid heterozygous null mutants in 621 essential yeast genes, and transformed the pooled strains with a mixture of 1010 human cDNAs. This unbiased strategy could identify unrecognized orthologs, or demonstrate complementation between non-orthologous genes.
In combination, these two screens found 65 human cDNAs that complemented null mutations in 58 essential yeast genes. Twenty of these yeast-human gene pairs were previously undiscovered.
The investigators looked at this group of “replaceable” yeast genes as a whole to see whether they shared any characteristics. Most of their gene products localized to the cytoplasm or cytoplasmic organelles rather than to the nucleus. They also tended to have enzymatic activity rather than, for example, regulatory roles. And they had relatively few physical interactions.
So yeast could “receive messages” from human genes, allowing us to see their function in yeast. But could it filter out the meaningful messages—variations that actually affect function—from the spam?
The authors chose three CIN genes that were functionally complemented by their human orthologs and screened 35 missense mutations that are found in those orthologs in colorectal cancer cells. Four of the human missense variants failed to support the life of the corresponding yeast null mutant, pointing to these mutations as potentially the most significant of the set.
Despite the fact that these mutations block the function of the human proteins, a mutation in one of the yeast orthologs that is analogous to one of these mutations, changing the same conserved residue, doesn’t destroy the yeast protein’s function. This underscores that whenever possible, testing mutations in the context of the entire human protein is preferable to creating disease-analogous mutations in the yeast ortholog.
Another 19 of the missense mutations allowed the yeast mutants to grow, but at a different rate from the wild-type human gene. (Eighteen conferred slower growth, but one actually made the yeast grow faster!)
For those 19 human variants that did support life for the yeast mutants, Hamza and colleagues tested the sensitivity of the complemented strains to MMS and HU, two agents that cause DNA damage. Most of the alleles altered resistance to these chemicals, making the yeast either more or less resistant than did the wild-type human gene. This is consistent with the idea that the cancer-associated mutations in these human CIN gene orthologs affect chromosome dynamics.
As researchers are inundated by a tsunami of genomic data, they may be able to turn to yeast to help discover the mutations that matter for human disease. They can help us separate those emails touting the virtues of Viagra from those not-to-be-missed kitten videos. And when we know which mutations are likely to be important for disease, we’re one step closer to finding ways to alleviate their effects.
by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD
June 10, 2015
Yeast and humans diverged about a billion years ago. So if there’s still enough functional conservation between a pair of similar yeast and human genes that they can be substituted for each other, we know they must be critically important for life. An added bonus is that if a human protein works in yeast, all of the awesome power of yeast genetics and molecular biology can be used to study it.
To make it easier for researchers to identify these “swappable” yeast and human genes, we’ve started collecting functional complementation data in SGD. The data are all curated from the published literature, via two sources. One set of papers was curated at SGD, including the recent systematic study of functional complementation by Kachroo and colleagues. Another set was curated by Princeton Protein Orthology Database (P-POD) staff and is incorporated into SGD with their generous permission.
As a starting point, we’ve collected a relatively simple set of data: the yeast and human genes involved in a functional complementation relationship, with their respective identifiers; the direction of complementation (human gene complements yeast mutation, or vice versa); the source of curation (SGD or P-POD); the PubMed ID of the reference; and an optional free-text note adding more details. In the future we’ll incorporate more information, such as the disease involvement of the human protein and the sequence differences found in disease-associated alleles that fail to complement the yeast mutation.
You can access these data in two ways: using two new templates in YeastMine, our data warehouse; or via our Download page. Please take a look, let us know what you think, and point us to any published data that’s missing. We always appreciate your feedback!
YeastMine is a versatile tool that lets you customize searches and create and manipulate lists of search results. To help you get started with YeastMine we’ve created a series of short video tutorials explaining its features.
This template lets you query with a yeast gene or list of genes (either your own custom list, or a pre-made gene list) and retrieve the human gene(s) involved in cross-species complementation along with all of the data listed above.
This template takes either human gene names (HGNC-approved symbols) or Entrez Gene IDs for human genes and returns the yeast gene(s) involved in cross-species complementation, along with the data listed above. You can run the query using a single human gene as input, or create a custom list of human genes in YeastMine for the query. We’ve created two new pre-made lists of human genes that can also be used with this template. The list “Human genes complementing or complemented by yeast genes” includes only human genes that are currently included in the functional complementation data, while the list “Human genes with yeast homologs” includes all human genes that have a yeast homolog as predicted by any of several methods.
If you’d prefer to have all the data in one file, simply visit our Curated Data download page and download the file “functional_complementation.tab”.
June 03, 2015
Cars on the road today all look pretty similar from the outside, whether they’re gasoline-fueled or electric. On the inside, they’re fairly similar too. Even between the two kinds of car, you can probably get away with swapping parts like the air conditioner, the tires, or the seat belts. Although cars have changed over the years, these things haven’t changed all that much.
The engine, though, is a different story. All the working parts of that Nissan Leaf engine have “evolved” together into a very different engine from the one in that Ford Mustang. They both have engines, but the parts aren’t really interchangeable any more.
We can think of yeast and human cells like this too. We’ve known for a while that we humans have quite a bit in common with our favorite little workhorse S. cerevisiae. But until now, no one had any idea how common it was for yeast-human pairs of similar-looking proteins to function so similarly that they are interchangeable between organisms.
In a study published last week in Science, Kachroo and colleagues looked at this question by systematically replacing a large set of essential yeast genes with their human orthologs. Amazingly, they found that almost half of the human proteins could keep the yeast mutants alive.
Also surprising was that the degree of similarity between the yeast and human proteins wasn’t always the most important factor in whether the proteins could be interchanged. Instead, membership in a gene module—a set of genes encoding proteins that act in a group, such as a complex or pathway—was an important predictor.
The authors found that genes within a given module tended to be either mostly interchangeable or mostly not interchangeable, suggesting that if one protein changes during evolution, then the proteins with which it interacts may need to evolve as well. So we can trade air conditioner parts between the Leaf and the Mustang, but the Mustang’s spark plugs won’t do a thing in that newly evolved electric engine!
To begin their systematic survey, Kachroo and colleagues chose a set of 414 yeast genes that are essential for life and have a single human ortholog. They cloned the human cDNAs in plasmids for yeast expression, and transformed them into yeast that were mutant in the orthologous gene to see if the human gene would supply the missing yeast function.
They tested complementation using three different assays. In one, the human ortholog was transformed into a strain where expression of the yeast gene was under control of a tetracycline-repressible promoter. So if the human gene complemented the yeast mutation, it would be able to keep the yeast alive in the presence of tetracycline.
Another assay used temperature-sensitive mutants in the yeast genes and looked to see if the human orthologs could support yeast growth at the restrictive temperature. And the third assay tested whether a yeast haploid null mutant strain carrying the human gene could be recovered after sporulation of the heterozygous null diploid.
Remarkably, 176 human genes could keep the corresponding yeast mutant alive in at least one of these assays. A survey of the literature for additional examples brought the total to 199, or 47% of the tested set. After a billion years of separate evolution, yeast and humans still have hundreds of interchangeable parts!
That was the first big surprise. But the researchers didn’t stop there. They wondered what distinguished the genes that were interchangeable from those that weren’t. The simplest explanation would seem to be that the more similar the two proteins, the more likely they would work the same way.
But biology is never so simple, is it? While it was true that human proteins with greater than 50% amino acid identity to yeast proteins were more likely to be able to replace their yeast equivalents, and that those with less than 20% amino acid identity were least likely to function in yeast, those in between did not follow the same rules. There was no correlation between similarity and interchangeability in ortholog pairs with 20-50% identity.
After comparing 104 different types of quantitative data on each ortholog pair, including codon usage, gene expression levels, and so on, the authors found only one good predictor. If one yeast protein in a protein complex or pathway could be exchanged with its human ortholog, then usually most of the rest of the proteins in that complex or pathway could too.
All of the genes that that make the proteins in these systems are said to be part of a gene module. Kachroo and colleagues found that most or all of the genes in a particular module were likely to be in the same class, either interchangeable or not. We can trade pretty much all of the parts between the radios of a Leaf and a Mustang, but none of the engine parts.
For example, none of the tested subunits of three different, conserved protein complexes (the TriC chaperone complex, origin recognition complex, and MCM complex) could complement the equivalent yeast mutations. But in contrast, 17 out of 19 tested genes in the sterol biosynthesis pathway were interchangeable.
Even within a single large complex, the proteasome, the subunits of one sub-complex, the alpha ring, were largely interchangeable while those of another sub-complex, the beta ring, were not. The researchers tested whether this trend was conserved across other species by testing complementation by proteasome subunit genes from Saccharomyces kluyveri, the nematode Caenorhabditis elegans, and the African clawed frog Xenopus laevis. Sure enough, alpha ring subunits from these organisms complemented the S. cerevisiae mutations, while beta ring subunits did not.
These results suggest that selection pressures operate similarly on all the genes in a module. And if proteins continue to interact across evolution, they can diverge widely in some regions while their interaction interfaces stay more conserved, so that orthologs from different species are more likely to be interchangeable.
The finding that interchangeability is so common has huge implications for research on human proteins. It’s now conceivable to “humanize” an entire pathway or complex, replacing the yeast genes with their human equivalents. And that means that all of the versatile tools of yeast genetics and molecular biology can be brought to bear on the human genes and proteins.
At SGD we’ve always known that yeast has a lot to say about human health and disease. With the growing body of work in these areas, we’re expanding our coverage of yeast-human orthology, cross-species functional complementation, and studies of human disease-associated genes in yeast. Watch this space as we announce new data in YeastMine, in download files, and on SGD web pages.
by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD
January 13, 2015
Bounce houses are a great way for kids to burn off their excess energy. They can bounce off the floor and walls and scream to their hearts’ content.
Of course, adults need to keep an eye on how many kids are in the house at any one time, to keep things safe. And if one child starts to push and kick the others, it might be easier to restore calm if the adults are careful about how many kids, and which ones, they allow inside.
The yeast mitochondrion is actually a lot like a bounce house. It’s full of energy, and it has multiple gatekeepers—protein complexes in the mitochondrial membrane that imported proteins must pass through on their way in.
And, just like a bounce house, things can go very wrong inside the mitochondrion if its proteins don’t behave properly. The end result isn’t just an upset child with a black eye, either. Genetic diseases that affect mitochondrial function are among the most severe and the hardest to treat.
Now, described in a new paper in Nature Communications, Aiyar and colleagues have used a yeast model of human mitochondrial disease to discover both a drug and a genetic means to regulate a mitochondrial import complex. Surprisingly, tweaking mitochondrial import slightly by either of these methods mitigated the disease symptoms in both yeast and human cells. They found a gatekeeper who can make sure there is the right number of kids in the bounce house and that they’re all behaving properly (at least, as well as they can!).
The researchers were interested in mitochondrial disorders that affected ATP synthase. This huge molecular machine in the mitochondrial inner membrane is responsible for generating most of the cell’s energy, so if it doesn’t work properly it can be a disaster for both yeast and human cells.
Aiyar and coworkers used a genetic trick to create a yeast model that had lower amounts of functional ATP synthase. This mimics many mitochondrial disorders.
They were able to reduce the amount of functional ATP synthase by using an fmc1 null mutant. Fmc1p is involved in assembly of the complex, so the fmc1 null mutant has lower amounts of functional ATP synthase and a reduced respiration rate.
First, they looked for a drug that would mitigate the effects of the fmc1 mutation. They tested the drugs in a collection that had already been FDA approved—a drug repurposing library—to see if any would improve the mutant’s respiratory growth.
The one candidate drug that emerged from the screen was sodium pyrithione (NaPT), which is used as an antiseptic. Not only did it improve the respiration of the yeast fmc1 mutant, it also improved the respiratory growth of a human cell line carrying the atp6-T8993G mutation found in patients with neuropathy, ataxia and retinitis pigmentosa (NARP, one type of ATP synthase disorder).
Aiyar and colleagues wondered exactly what was being affected by the NaPT. To figure this out, they used the S. cerevisiae genome-wide heterozygous deletion mutant collection. This is a set of diploid strains, each heterozygous for a null mutation of a different gene, that has been an incredibly useful resource for all kinds of studies in yeast.
They tested the effect of NaPT on each of the mutant strains and found that strains with mutations in the TIM17 and TIM23 genes were among the most sensitive. And, when they checked the data from previous chemogenomic screens, they saw that these two mutants were much more sensitive to NaPT than to any other drug, showing that the effect was specific.
TIM17 and TIM23 are both subunits of the Tim23 complex in the mitochondrial inner membrane that acts as a gate for many of the proteins that end up in mitochondria. The researchers found that NaPT specifically inhibited the function of this mitochondrial gatekeeper complex in an in vitro mitochondrial import assay, confirming its selectivity.
So, Aiyar and coworkers had found a drug that alleviates the effects of an ATP synthase disorder by modulating the function of a mitochondrial gatekeeper. This in itself was a huge advance: the discovery that a potentially useful, already-approved drug has a specific effect on this disease phenotype.
However, the scientists took things a step further by looking to see whether a genetic therapy could accomplish the same thing as the drug. It was already known that overexpressing Tim21p, a regulatory subunit of the Tim23 complex, could modulate the function of the complex similarly to the effects they had seen for NaPT.
So the researchers tested whether overexpressing Tim21p would improve respiratory growth of the fmc1 mutant. Sure enough, it did. Consistent with this, assembly of the respiratory enzyme complexes of the mitochondrial inner membrane was more efficient when Tim21p was overexpressed.
Most importantly, overexpression of Tim21p in the fmc1 mutant cells caused their total ATP synthesis to more than double. And even more exciting was the discovery that overexpressing TIMM21, the human ortholog of TIM21, in the NARP disease human cell line improved survival of those cells.
So, just like a parent deciding how many kids should be in a bounce house so that everyone has a good time, the Tim23 complex can be made to “decide” which proteins, or perhaps how many proteins, get into mitochondria, with the end result that ATP synthesis happens as efficiently as possible. The exact mechanism of this effect is still unclear, but it is clear that modulating import in this way can improve mitochondrial health even when disease mutant proteins are present.
The next step will be to translate this discovery into therapies that will help mitochondrial disease patients. People with various mitochondrial disorders may finally be able to turn their mitochondria into safe, fun places.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
January 08, 2015
What’s for dinner tonight? For many of us the answer will be “pasta with tomato sauce”, even if we don’t have Italian roots.
But as you know, there isn’t any single recipe for homemade tomato sauce. Onions and garlic, or just garlic? Pork, beef, or no meat at all? How many bay leaves go in the pot? Every cook will use a slightly different combination of ingredients, but all will end up with tomato sauce.
When it comes to combining different allele variants to survive a lethal challenge, yeast is a lot like those cooks. In a new paper in GENETICS, Sirr and colleagues used divergent yeast strains to generate a wide range of allelic backgrounds and found that there is more than one way to survive a deadly mutation in the GAL7 gene. Just as there is more than one way to make a delicious tomato sauce…
This isn’t just an academic exercise either. GAL7 is the ortholog of the human GALT gene, which when mutated leads to the disease galactosemia. And just like in yeast, people with different genetic backgrounds may do better or worse when both copies of their GALT gene are mutated.
GAL7 and GALT encode an enzyme, galactose-1-phosphate uridyl transferase, that breaks down the sugar galactose. If people with this mutation eat galactose, the toxic compound galactose-1-phosphate accumulates; this can cause serious symptoms or even death. The same is often true for yeast with a mutated GAL7 gene.
Ideally we would want to be able to predict how severe the symptoms of galactosemia would be, based on a patient’s genetic background. So far, though, it’s been a challenge to identify comprehensively the whole set of genes that affect a given human phenotype. Which is why Sirr and colleagues turned to our friend S. cerevisiae to study alleles affecting the highly conserved galactose utilization pathway.
The researchers started with two very divergent yeast strains, one isolated from a canyon in Israel and the other from an oak tree in Pennsylvania. Both were able to utilize galactose normally, but the scientists made them “galactosemic” by knocking out the GAL7 gene in each.
After mating the strains to create a galactosemic diploid, the researchers needed to let the strain sporulate and isolate haploid progeny. But its sporulation efficiency wasn’t very good, only about 20%. And they needed to have millions of progeny to get a comprehensive look at genetic backgrounds.
To isolate a virtually pure population of haploid progeny, Sirr and coworkers came up with a neat trick. They added a green fluorescent protein gene to the strain and put it under control of a sporulation-specific promoter.
Cells that were undergoing sporulation would fluoresce and could be separated from the others by fluorescence-activated cell sorting (FACS). The FACS technique also allowed sorting by size, so they could select complete tetrads containing four haploid spores and discard incomplete products of meiosis such as dyads containing only two spores.
After using this step to isolate tetrads, the researchers broke them open to free the spores and put them on Petri dishes containing galactose—an amount that was enough to kill either of the parent strains. One in a thousand spores was able to survive the galactose toxicity. Recombination between the divergent alleles from the two parent strains somehow came up with the right combination of alleles for a survival sauce.
Sirr and colleagues individually genotyped 247 of the surviving progeny, using partial genome sequencing. They mapped QTLs (quantitative trait loci) to identify genomic regions associated with survival. If they found a particular allele in the survivors more often than would be expected by chance, that was a clue that a gene in that region had a role in survival.
We don’t have the space here to do justice to the details of the results, but we can summarize by saying that a whole variety of factors contributed to the galactose tolerance of the surviving progeny. They had three major QTLs, regions where multiple alleles were over- or under-represented. The QTLs were centered on genes involved in sugar metabolism: GAL3 and GAL80, both involved in transcriptional regulation of galactose utilization genes, and three hexose transporter genes (HXT3, HXT6, and HXT7) that are located very close to each other.
It makes sense that all of these genes could affect galactosemia. Gal3p and Gal80p are regulators of the pathway, so alleles of these genes that make galactose catabolism less active would result in less production of toxic intermediates. And although the hexose transporters don’t transport galactose as their preferred substrates, they may induce the pathway by allowing a little galactose into the cell. So less active alleles of these transporters would also result in less galactose catabolism.
Another event that occurred in over half of the surviving progeny was aneuploidy (altered chromosome number), most often an extra copy of chromosome XIII where the GAL80 gene is located. The same three QTL peaks were also seen in the disomic strains, though, leading the authors to conclude that the extra chromosome alone was not sufficient for survival of galactosemia.
And finally, some rare non-genetic events contributed to survival of the progeny. The authors discovered this when they found that the galactose tolerance of some of the progeny wasn’t stably inherited. This could result from differences in protein levels between individual cells. For example, if one cell happened to have lower levels of a galactose transporter than other cells, it might be more resistant to galactose.
The take-home message here is that there are many different ways to get to the same phenotype. The new method that they developed allowed the researchers to see rare combinations of alleles in large numbers of individual progeny, in contrast to other genotyping methods where progeny are pooled and only the average can be detected.
For any disease or trait the ultimate goal is to identify all the alleles of all the genes that influence it. Imagine the impact on human health, if we could look at a person’s genotype and accurately predict their phenotype!
So far, it’s been a challenge to identify these large sets of human genes in a comprehensive way. But this approach using yeast could provide a feast of data to help us understand monogenic diseases like galactosemia, cystic fibrosis, porphyria, and many more, and maybe even more complex traits and diseases. Now that’s an appetizing prospect for human disease researchers. Buon appetito!
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
October 16, 2014
A train without working brakes can cause a lot of destruction if it careens off the tracks. And it turns out that a runaway RNA polymerase II (pol II) can cause a lot of damage too. But it doesn’t cause destruction, so much as disease.
Unlike a train, which has its brakes built right in, pol II has to count on outside factors to stop it in its tracks. And one of these brakes in both humans and yeast is a helicase: Sen1 in yeast and Senataxin, the product of the SETX gene, in humans.
Mutations in SETX are associated with two devastating neurological diseases: amyotrophic lateral sclerosis type 4 (ALS4) and ataxia oculomotor apraxia type 2 (AOA2), both of which strike children and adolescents. One idea is that these mutations may short circuit the brakes on pol II, causing it to keep on transcribing after it shouldn’t. And this is just what Chen and colleagues found in a new paper in GENETICS.
The researchers used the simple yet informative yeast model system to look at some of these mutations, and found that they disrupted the helicase function of Sen1 and caused abnormal read-through of some transcriptional terminators. Looks like bad brakes may indeed have a role in causing these devastating diseases.
Some human proteins can function perfectly well in yeast. Unfortunately, Senataxin isn’t one of those; it could not rescue a sen1 null mutant yeast, so Chen and coworkers couldn’t study Senataxin function directly in yeast. But because Senataxin and Sen1 share significant homology, they could instead study the yeast protein and make inferences about Senataxin from it.
First, they sliced and diced the SEN1 gene to see which regions were essential to its function. They found that the most important part, needed to keep yeast cells alive, was the helicase domain. But this wasn’t the only key region.
Some flanking residues on either side were also important, but either the N-terminal flanking region or the C-terminal flanking region was sufficient. Looking into those flanking regions more closely, the researchers found that each contained a nuclear localization sequence (NLS) that directed Sen1 into the nucleus. This makes perfect sense of course…the brakes need to go where the train is! If we don’t put the brakes on the train, it won’t matter how well they work, the train still won’t stop.
These flanking sequences appeared to do more than direct the protein to the nuclear pol II, though. When the authors tried to use an NLS derived from the SV40 virus instead, they found that it couldn’t completely replace the function of these flanking regions even though it did efficiently direct Sen1 to the nucleus.
Next the researchers set out to study the disease mutations found in patients affected with the neurological disease AOA2. They re-created the equivalents of 13 AOA2-associated SETX mutations, all within the helicase domain, at the homologous codons of yeast SEN1.
Six of the 13 mutations completely destroyed the function of Sen1; yeast cells could not survive when carrying only the mutant gene. When these mutant proteins were expressed from a plasmid in otherwise wild-type cells, five of them had a dominant negative effect, interfering with transcription termination at a reporter gene. This lends support to the idea that Sen1 is important for transcription termination and that the disease mutations affected this function.
The remaining 7 of the 13 mutant genes could support life as the only copy of SEN1 in yeast. However, 5 of the mutant strains displayed heat-sensitive growth, and 4 of these showed increased transcriptional readthrough.
Taken together, these results show that the helicase domains of Senataxin and Sen1 are extremely important for their function. They also show that Sen1 can be used as a model to discover the effects of individual disease mutations in SETX, as long as those mutations are within regions that are homologous between the two proteins.
It still isn’t clear exactly how helicase activity can put the brakes on that RNA polymerase train, nor why runaway RNA polymerase can have such specific effects on the human nervous system. These questions need more investigation, and the yeast model system is now in place to help with that.
So, although it might not be obvious to the lay person (or politician) that brainless yeast cells could tell us anything about neurological diseases, in fact they can. Yeast may not have brains, but they definitely have RNA polymerase. And once we learn how the brakes work for pol II in yeast cells, we may have a clue how to repair them in humans.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
August 21, 2014
Say you want to send a letter to your friend on the other side of the country. First off you’ll need to put the right address and postage on the envelope. Then you’ll need the U.S. Postal Service (USPS) to take your letter and deliver it to the right person. The stamp tells the USPS to deliver the letter, and the address indicates where it should be delivered (unimpeded by snow nor rain nor heat nor gloom of night, of course!).
It turns out something similar happens in human cells with aggregated proteins. Aggregated proteins are “stamped” by attachment of the small protein ubiquitin and “addressed” to the Atg8 protein. Atg8p triggers the aggregated proteins’ incorporation into autophagosomes for eventual degradation in the lysosome.
And just as it can be devastating if your mail doesn’t get to where it needs to go, so too can it be devastating for these aggregates to accumulate instead of being properly delivered. A buildup of these aggregates is a big factor in Alzheimer’s and Huntington’s diseases.
Enter the cellular USPS. Just as is the case for a prepared letter, the human cell has a service that delivers the ubiquinated proteins to the autophagosome, in the form of the protein adaptor p62 (SQSTM1) and its relative, NBR1.
These adaptor proteins can act as a postal service because they recognize both the aggregated proteins’ stamp (ubiquitin) and their addressee (Atg8p). Specifically, they each possess an ubiquitin-conjugate binding domain (UBA) and an Atg8-interacting motif (AIM). The protein p62 in particular has been shown to associate with protein aggregates linked to neurodegenerative diseases like Huntington’s disease.
In a new paper published in Cell, Lu et al. asked whether there is a link between the ubiquitin and autophagy systems in yeast. If so, yeast might provide some clues about diseases like Huntington’s. Proteins stamped with ubiquitin are known to be addressed to the proteasome for degradation in yeast, but no link between ubiquitination and autophagy had previously been seen, even though many central components of autophagy were actually first described in yeast.
Indeed, the authors showed that cells specifically deficient in the autophagy pathway (atg8∆, atg1∆, or atg7∆), accumulated ubiquitin conjugates under autophagy-inducing conditions. This suggests that the ubiquitin and autophagy pathways are connected in yeast, as is the case for humans.
Next, the researchers looked to see if there is an adaptor in yeast analogous to p62 in humans. When they pulled down proteins that bind yeast Atg8p under starvation conditions, they found ubiquitin conjugates and, using mass spectrometry, further identified peptides from a few other proteins – one of which was Cue5p.
Could Cue5p, like p62 in humans, be the postal service that recognizes both stamped ubiquitin conjugates and the addressee Atg8p in yeast? Strikingly, Cue5p had both a CUE domain that binds ubiquitin and an Atg8p-interacting motif (AIM). The authors confirmed in vivo that Cue5p binds ubiquitin conjugates and Atg8p using these domains, particularly under starvation conditions. They also showed that it acts specifically at the stage of ubiquitin-conjugate recognition and on aggregated proteins, without affecting the process of autophagy itself.
Given that Cue5p functions similarly to p62 and p62 is known to associate with protein aggregates involved in neurodegenerative disease, Lu et al. were quick to look for Cue5p substrates. Analyzing ubiquitin-conjugated proteins that accumulated in cue5 mutant cells, they identified 24 different proteins. Although these 24 Cue5p substrates had diverse functions, the common thread was that many had a tendency to aggregate under certain conditions such as high temperature.
Could Cue5p then actually facilitate removal of cytotoxic protein aggregates in neurodegenerative diseases? Indeed, the authors showed that CUE5 helped clear cytotoxic variants of the human huntingtin protein (Htt-96Q) when it was expressed in yeast, and that Htt-96Q is ubiquitinated in yeast.
These experiments started with an observation in human cells that prompted discovery of an analogous system and adaptor protein in yeast. Now the authors turned the tables and used yeast to look for new adaptor proteins in human cells. Using bioinformatics, they identified a human CUE-domain protein, Tollip, which, although different in its domain organization from Cue5p, contains 2 AIM motifs.
To make a long story (and a lot of work!) short, they showed that Tollip binds both human Atg8p and ubiquitin conjugates and clears cytotoxic variants of huntingtin in human cells. Expressed in yeast, it similarly binds ubiquitin conjugates and Atg8p and suppresses the hypersensitivity of cue5∆ cells to the variant huntingtin protein Htt-96Q. So Tollip is a newly defined adaptor protein and functional homolog of Cue5p!
Letter carriers of one sort or another have been around for as long as human civilization has existed, from homing pigeons to FedEx. Now we know that for even longer, cells from yeast to human have been using similar ways to recognize stamped proteins and deliver them to the right address. And once again, yeast has helped us understand the inner secrets of human cells.
by Selina Dwight, Ph.D., Senior Biocurator, SGD
August 14, 2014
One way to think about the cell is that organelles float around in it like those globs in a lava lamp. This is obviously a simplification, but it’s also true that organelles aren’t locked into place. As usual, the real picture lies somewhere in between these two extremes.
What we know about the architecture of the cell has mostly been discovered using classical cell biology and genetic techniques. But in a paper published in Molecular BioSystems, Cohen et al. uncovered some very interesting small details using a very large-scale approach.
The authors were interested in peroxisomes, where a lot of critical metabolic reactions happen (or fail to happen, in several human diseases). The researchers were able to see that peroxisomes not only interact with other organelles, but they contact the endoplasmic reticulum (ER) and mitochondria in a way that could be extremely important for cellular metabolism. And surprisingly, it was by combining a variety of different high-throughput techniques that Cohen and colleagues could uncover this fine structure.
The first step was to set up two reporter constructs to look for genes involved in two different peroxisomal processes.
One reporter was a red fluorescent protein, mCherry, modified to carry a peroxisomal targeting signal and show whether import into peroxisomes was normal. Another reporter, a peroxisomal membrane protein (Ant1p) tagged with green fluorescent protein (GFP), would show whether peroxisomal membranes were normal.
The reporters were crossed into mutant collections, creating one strain for each gene in the genome that had either a complete deletion (for nonessential genes) or a knock-down allele (for essential genes), plus both reporters. Now the researchers could systematically test for genes that, when mutated, affected one or both of these aspects of peroxisomal biogenesis.
To visualize the mutant phenotypes, they used a sophisticated technique termed “high-content screening.” This is an automated way to analyze micrographs that both pinpoints the intracellular location of a fluorescent reporter and measures its quantity. Screening the mutant collection in this way showed that 56 strains had altered distribution of the two different reporter proteins. Some had a reduction in peroxisomal protein import (mCherry fluorescence), while some had fewer or no peroxisomes and some had peroxisomes that were smaller than normal (GFP fluorescence).
One result that caught the researchers’ eyes was that one of the strains with smaller peroxisomes had a mutation in the MDM10 gene. Mdm10p is part of the ERMES (ER-Mitochondria Encounter Structure) complex that tethers mitochondria to the ER, and this wasn’t previously known to have any connection with peroxisomes. Strains that were mutant in other ERMES subunits had the same phenotype, confirming that the complex has something to do with peroxisome structure. Other results from the screens added weight to the idea of a three-way connection between peroxisomes, the ER, and mitochondria, and the authors went on to show that peroxisomes often sit at the ERMES complex where mitochondria contact the ER.
Next, to test whether mitochondria might have specific subdomains where peroxisomes interact, the authors used yet another large-scale screen. In the C-terminal GFP fusion library, where each yeast open reading frame is C-terminally tagged with GFP, 96 strains showed a punctate pattern of the fluorescent signal – meaning that the protein was concentrated in spots, rather than evenly distributed. They labeled the mitochondria with a red fluorescent marker protein in these strains and, again using the high-content screening system, identified protein spots that co-localized with mitochondria. The most intense hit was for Pda1p, a subunit of the mitochondrial enzyme pyruvate dehydrogenase (PDH), and a similar result was obtained for another PDH subunit. So PDH isn’t distributed uniformly in the mitochondrion, but is instead concentrated in clusters.
Looking more closely using the various reporter constructs in their collections, the authors found that peroxisomes and the ERMES complex most often co-localized with those mitochondrial globs of PDH. It would make metabolic sense for peroxisomes to hang out near PDH on mitochondria because this could increase the local concentration of metabolites that they both use.
Intriguingly, Cohen et al. also found that mitochondria and peroxisomes co-localized in mammalian cells. Given that many diseases are linked to peroxisomal metabolism, this is an important avenue to investigate.
So while organelles don’t float around in the cell quite as fluidly as the globs in a lava lamp, the data generated from large-scale approaches boiled down to learning some very fine-grained detail about cellular architecture. We think that’s, like, groovy.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD