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 30, 2015
Yeast and humans diverged about a billion years ago, but there’s still enough functional conservation between some pairs of yeast and human genes that they can be substituted for each other. How cool is that?! Which genes are they? What do they do?
This two-minute video explains how to find, search, and download the yeast-human functional complementation data in SGD. You can find help with many other aspects of SGD in the tutorial videos on our YouTube channel. And as always, please be sure to contact us with any questions or suggestions.
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
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
July 15, 2014
In the art of rock balancing, the artist positions large rocks with exquisite precision. If he or she succeeds, the rocks counterbalance each other and stay in seemingly impossible positions to make a surprising and beautiful sculpture. But a little uneven pressure is enough to make the whole thing collapse.
It turns out that the cellular acetylation state is just as precisely balanced. In a new GENETICS paper, Torres-Machorro and Pillus identify Esa1p, an acetyltransferase, as the balancing artist in Saccharomyces cerevisiae cells.
Acetylation is an important type of protein modification. Histones, the proteins that interact with DNA to provide structure to chromosomes, are acetylated by histone acetyltransferases (HATs) and deacetylated by histone deacetylases (HDACs). Some HATs and HDACs also act on non-histone proteins.
The acetylation state in a cell is a dynamic process. All those HATs are adding acetyl groups at the same time that HDACs are removing them. The final level of acetylation depends on the activities of each of these classes of proteins.
Acetylation of histones has been associated with increases in gene expression and deacetylation with decreases. So to keep gene expression levels in balance, it is very important to keep acetylation balanced as well. Throwing acetylation patterns just a bit out of whack can have profound consequences on global gene expression that can ultimately lead to cell death.
The authors focused on one particular HAT, Esa1p, that acetylates histones H4 and H2A and also has non-histone targets. They were intrigued by the fact that yeast cells cannot survive without Esa1p, since no other HAT or HDAC subunit is essential in yeast.
An obvious explanation for lethality is that losing this protein leads to too low a level of acetylation. They reasoned that if they also knocked out an HDAC, then the overall acetylation levels might increase and so rescue the esa1 null mutant. And they were right.
Using a plasmid-shuffling method, they created various double mutant strains of esa1 and HDAC genes, and found that a strain that was mutant in esa1 and also in either the SDS3 or DEP1 genes was viable. SDS3 and DEP1 both encode subunits of the Rpd3L HDAC complex.
Torres-Machorro and Pillus next characterized the esa1 sds3 double mutant further. They found that although the sds3 mutation suppressed the inviability of the esa1 mutant, it did not suppress other phenotypes such as sensitivity to high temperature and DNA damaging agents.
The authors found that the sds3 mutation subtly increased histone H4 acetylation, which was low in the absence of Esa1p. However, acetylation levels of a different histone, H3, remained high even in the absence of Esa1p. This suggested that the fundamental problem in the esa1 null mutant was an imbalance in the global state of histone acetylation.
To test this hypothesis, the researchers used a variety of different genetic methods to tweak the balance of cellular acetylation in the esa1 sds3 mutant. They created mutations in histones H3 and H4 that made it seem as if acetylation was low or high, and they also mutated other genes for HDAC subunits. It is as if they were passers-by who decided to poke at a balanced rock sculpture to see what it took to bring the whole thing down.
Although the details are too numerous to report here, the results showed that by using these genetic methods to tweak the overall acetylation state of the cell, the fitness of the esa1 sds3 strain could be improved: phenotypes such as slow growth, sensitivity to high temperature or DNA damaging agents, or cell cycle defects were suppressed to some extent by the various manipulations. This lends support to the hypothesis that Esa1p is the master balancer of acetylation levels in the cell and that this is its essential function.
This balancing act may happen in human cells too. Esa1p has a human ortholog, TIP60, that has been implicated in cancer and other diseases. Like Esa1p, TIP60 is essential and is involved in the DNA damage response.
So yeast teaches us that the acetylation of proteins is balanced on a knife’s edge. Even the slightest changes can lead to a collapse in global gene regulation, which can have catastrophic effects like cancer. All that we learn about Esa1p, the acetylation balancing artist, may have much broader implications for human health.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
March 06, 2014
Imagine the heater at your house is run by a homemade copper-zinc battery. You are counting on a delivery of a copper solution that will keep the thing going. Unfortunately it fails to come, which means the battery doesn’t work and you are left out in the cold.
Turns out that something similar can happen in cells too. The respiratory chain that makes most of our energy needs copper to work. In a recent study, Ghosh and coworkers showed that if Coa6p doesn’t do its job delivering copper to the respiratory chain, the cell can’t make enough energy.
This isn’t just interesting biology. In this same study, the researchers showed that mutations in the COA6 gene cause devastating disease in humans and zebrafish. And their discovery that added copper can cure the “disease” in yeast just might have therapeutic applications for humans.
The respiratory chain is a group of large enzyme complexes that sit in the mitochondrial inner membrane and pass electrons from one to another during cellular respiration. This process generates most of the energy that a cell needs. Hundreds of genes, in both the nuclear and mitochondrial genomes, are involved in keeping this respiratory chain working.
Yeast has been the ideal experimental organism for studying these genes, because it can survive just fine without respiration. If it can’t respire for any reason, yeast simply switches over to fermentation, generating the alcohol and CO2 byproducts that we know and love.
Human cells aren’t as versatile though. Genes involved in respiration can cause mitochondrial respiratory chain disease (MRCD) when mutated. This is one of the most common kinds of genetic defect, with over 100 different genes known so far that can cause this phenotype.
Ghosh and colleagues wondered whether there were as-yet-unidentified human genes involved in maintaining the respiratory chain. They reasoned that any such genes would be highly conserved across species, because they are so important to life, and that the proteins they encoded would localize to mitochondria.
One of the candidates, C1orf31, caught their eye for a couple of reasons. First, some variations in this gene had been found in the DNA of a MRCD patient. And second, the yeast homolog, COA6, encoded a mitochondrial protein that had been implicated in assembly of one of the respiratory complexes, Complex IV or cytochrome c oxidase.
They first did some more detailed characterization of COA6 in yeast. They were able to verify that the coa6 null mutant had reduced respiratory growth because it had lower levels of fully assembled Complex IV.
They also looked to see what happens in human cell culture. When they knocked down expression of the human homolog, they also saw less assembly of Complex IV. This suggested that the function of this protein is conserved across species.
Next they turned to a sequencing study of an MRCD patient who had, sadly, died of a heart defect (hypertrophic cardiomyopathy) before reaching his first birthday. The sequence showed a mutation in a conserved cysteine-containing motif of COA6. To see whether this might be the cause of the defect, they created the analogous mutation in yeast COA6. The mutant protein was completely nonfunctional in yeast.
To nail down the physiological role of COA6 in a multicellular organism, they turned to zebrafish. The embryos of these fish are transparent, so it’s easy to follow organ development. Given the phenotype, the fact that they can live without a functional cardiovascular system for a few days after fertilization was important too.
When the researchers knocked down expression of COA6 in zebrafish, they found that the embryos’ hearts failed to develop normally and they eventually died. The abnormal development of the fish hearts paralleled that seen in the human MRCD patient carrying the C1orf31/COA6 mutation. And reduced levels of Complex IV were present in the fish embryos.
Going back to yeast for one more experiment, Ghosh and colleagues decided to see whether Coa6p might be involved in delivering copper to Complex IV. They knew that Complex IV uses copper ions as a cofactor, and furthermore Coa6p had similarities to several other yeast proteins that are known to be involved in the copper delivery.
They tested this by supplying the coa6 null mutant with large amounts of copper. Sure enough, its respiratory growth defect and Complex IV assembly problems were reversed. The delivery of copper kept the energy flowing in these cells. And this result showed that Coa6p is involved in getting copper to Complex IV.
These experiments showcase the need for model organism research even in the face of ever more sophisticated techniques applied to human cells. The mutation in human C1orf31/COA6 was discovered in a next-generation sequencing study, but yeast genetics established the relationship between the mutation and its phenotype. The zebrafish system allowed the researchers to follow the effects of the mutation in an embryo from the earliest moments after fertilization. And the rescue of the yeast mutant by copper supplementation offers an intriguing therapeutic possibility for some types of MRCD. Just another testament to the awesome power of model organism research!
YeastMine now lets you explore human homologs and disease phenotypes. Enter “COA6” into the template Yeast Gene -> OMIM Human Homolog(s) -> OMIM Disease Phenotype(s) to link to the Gene page for human COA6 (the connection between COA6 and disease is too new to be represented in OMIM). To browse some diseases related to mitochondrial function, enter “mitochondrial” into the template OMIM Disease Phenotype(s) -> Human Gene(s) -> Yeast Homolog(s).
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
February 06, 2014
Fireworks shells all pretty much look the same from the outside. They definitely all make the same boom when they’re launched. But when they burst in the air, each different kind creates a different shimmering pattern.
It turns out that the same is true for yeast strains carrying mutator alleles.
These are mutant alleles of genes that normally stop mutations from happening. When these genes are disabled, a strain eventually accumulates lots of extra mutations.
Mutator strains tend to look similar from the outside; many are deficient in DNA replication and repair pathways. But, in a new paper in GENETICS, Stirling and coworkers show that like different firework shells, each strain ends up with a distinct pattern of secondary mutations bursting across their respective genomes. Not only is this fascinating information about how yeast maintains its genomic integrity, but it may also provide valuable insights into how cancers progress.
Mutator genes have been found previously using the knockout collection of mutations in nonessential genes. But, not surprisingly, many genes required for genome maintenance are essential to life. So the first step by Stirling and coworkers was to expand the list of mutator genes by screening conditional mutant alleles of essential genes.
Using an assay for mutation frequency that counts canavanine resistance mutations arising in the CAN1 gene, they came up with 47 alleles in 38 essential genes that caused a mutator phenotype. But this standard assay for mutator phenotype has its limitations: the only mutations that can be detected are those that fall in or near the CAN1 gene, and inactivate it. So that they could look at the full spectrum of mutations arising in the mutator strains, Stirling and coworkers decided to use whole genome sequencing instead to detect them.
The researchers chose 11 mutator alleles of genes representative of different processes such as homologous recombination, oxidative stress tolerance, splicing, transcription, mitochondrial function, telomere capping, and several aspects of DNA replication. They grew these strains for 200 generations and then did whole-genome sequencing of 4 to 6 independent progeny of each to find all the resulting mutations.
Under these conditions, wild-type yeast accumulated 2-4 mutations per genome. In contrast, the mutator strains ended up with 2- to 10-fold more mutations. And most every type was represented: single-nucleotide variants, structural variants (showing altered chromosome structure), copy-number variants (amplification of certain regions or entire chromosomes), and insertions or deletions.
However, while all of the mutator strains had accumulated mutations, the different types of mutation were in different proportions. For example, a mutant in the Replication Factor C subunit gene, rfc2-1, tended to give rise to transition mutations (changing a pyrimidine to a pyrimidine, or a purine to a purine). The same was true for the telomere-capping protein mutant, stn1-13.
But the pol1-ts DNA polymerase mutant instead showed more transversions (changing a purine to a pyrimidine or vice versa). And a deletion of the nonessential RAD52 gene, encoding a recombinase, tended to cause mutations in the transcribed strand of genes, suggesting that transcription-associated recombination was compromised in those cells and this affected DNA repair.
Positions of the accumulated mutations also differed between strains. The stn1-13 and pol1-ts mutants preferentially accumulated mutations in subtelomeric regions. Some of the alleles gave rise to clusters of mutations, while others did not. And, as has been seen in cancer cells, many of the mutator strains had mutations in regions of the genome that replicate late in DNA replication.
Even though this work generated a huge amount of data (much more than we can discuss here), one conclusion reached by the authors is that even more mutant progeny of mutator strains, arising under a variety of different conditions, need to be analyzed using whole-genome sequencing to give a truly comprehensive picture of the mutational spectrum associated with each allele.
But another conclusion is clear: that different mutator alleles do result in characteristic patterns of mutations. Given that some of these same genes have been found to be mutated in cancer cells, this work may help other scientists predict what mutations a cancer will develop. And that would really give us a bang for our research buck!
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
November 25, 2013
Stanford offers an innovative class, targeted at sophomore undergraduates, where students use yeast to determine how a mutation in the p53 gene affects the activity of the resulting p53 protein. What makes this class even cooler is that the p53 mutants come from actual human tumors—the undergraduates are figuring out what actual cancer mutations are doing! And the class uses what we think is the most important organism in the world, S. cerevisiae.
To learn more about the course, we decided to interview Jamie Imam, one of the instructors. After reading the interview, you will almost certainly be as excited about this class as we were and it may even get you to wishing that you could teach the class at your institution. With a little help, you can.
The creators of the course, Tim Stearns and Martha Cyert, really want as many people as possible to use this class to teach undergraduates about what real science is and how fun and exciting it can be. To that end, they are happy to help you replicate the course wherever you are. If you are interested, please contact Tim and/or Martha. You’ll be happy you did. Their contact information can be found at the Stearns lab and Cyert lab websites.
Here now is the interview with Jamie. What a great way to get undergraduates excited about the scientific process.
Sure. Bio44X is designed to be similar to an authentic research experience or as close to one as you can replicate in the classroom. During the quarter, students study mutant versions of a gene called p53, a tumor suppressor that is frequently mutated in cancer. Each partner pair in a classroom gets one p53 mutant that has been identified in a human tumor to study in our yeast system. Throughout the course of the 10 weeks, the students study the transactivation ability of their mutant compared to the wild-type version, and then work to figure out what exactly is wrong with the mutant (Can it bind DNA?, Does it localize to the nucleus properly?, etc.). Multiple sections of this course are taught during the Fall and Winter quarters, so several pairs end up studying the same mutant. We bring these students together to discuss and combine their data throughout the quarter, so there is a lot of collaboration involved. I think the students really enjoy having one topic to study in depth over the quarter rather than short individual modules, and the fact that we are studying a gene so important in cancer makes it easier to get them to care about the work they are doing.
Previously, Bio44X at Stanford was the more traditional “cookbook” type lab course. Every 2 weeks, the topic would change and students would work through set protocols that had a known correct answer. In 2010, Professors Martha Cyert and Tim Stearns set out to design and pilot a research-based course on a medically relevant topic (the tumor suppressor p53) in response to some national calls for biology lab course reform. Two years and many changes later, the new research-based lab course replaced the previous version and is now taken by all of the students that need an introductory lab course in Biology.
Students get exposed to a variety of lab techniques that can be used beyond our classroom. We start with sterile technique and pipetting during the very first week (some students have never pipetted before!). During the first class, the students also spot out some yeast strains so they can start collecting data on the transactivation ability of their p53 mutant right away. Once they have some basic information about the function of their mutant, the students then extract protein from their yeast strains. Throughout the rest of the quarter, students use this protein to conduct a kinetic assay, Western blot, and assess DNA binding ability of their mutant p53. They also get some exposure to fluorescence microscopy when they use a GFP-tagged version of their mutant to determine whether it can localize properly to the nucleus. But the most important thing of all is that students learn how to analyze the data and think critically about it. Not only do they “crunch the numbers” but they must use that information to draw some actual conclusions about what is wrong with their mutant by the end of the quarter.
It takes a lot of organization because we have around 200 or more students that take this class every year! Fortunately, we have a great team to help organize the setup of the labs so that the instructors can focus on the teaching. Nicole Bradon manages a small staff that sets up the classrooms and prepares all of the reagents for the lab each week. Dr. Daria Hekmat-Scafe, who is one of the instructors, constructs many of the yeast strains that we give to the students. The team of lecturers (Dr. Shyamala Malladi, Dr. Daria Hekmat-Scafe and I) all work together on lectures and other course materials so everyone gets a similar experience. All together, it takes a lot of behind-the-scenes work, but then the students really get to focus on the experiments and their results.
I love teaching this class! It is so fun to go through this research experience with so many students and they all bring their unique perspectives to the course (we get engineers, psych majors, bio majors, econ majors and others). Also, each section has only 20 students so you really get the chance to get to know them over the course of the 10 weeks. Sometimes the experiments don’t work as planned (like real science) but overall it ends up being a great learning experience.
We hope that students learn to think critically and what it really means to “think like a scientist”. Too often, science is boiled down to a series of facts that students are expected to memorize and that isn’t what science really is! Science is all about finding exciting questions and constructing experiments that try and answer those questions. The beauty of a research-based lab course is that students can also feel more in charge of their own learning. We have performed assessments of the class and have found that over the course of the quarter, students develop a more sophisticated understanding of what it means to “think like a scientist” and a large portion are more interested in becoming involved in scientific research. I think this is great, as I feel that undergraduate research helped me understand science so much more deeply than many of the courses I had taken.
Our group is willing to share our course materials and knowledge with others that are interested in replicating this at other institutions. Anyone who is interested should feel free to contact us! Also, there is a paper in preparation that will describe some of the key aspects of the course as well as more details about what we have learned from the assessments of the course over the past few years.
There you have it…a great class that uses the awesomeness of yeast to teach undergraduates how to think like scientists. Again, if you’re interested in learning more, please contact Tim Stearns and/or Martha Cyert at Stanford.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: News and Views
October 31, 2013
Folks, yeast has been on a roll lately with regard to helping to understand and finding treatments for human disease. Last week we talked about how synthetic lethal screens may find new, previously unrecognized druggable targets for cancer. And this week it is Parkinson’s disease.
Now of course yeast can’t get the traditional sort of Parkinson’s disease …it doesn’t have a brain. But it shares enough biology with us that when it expresses a mutant version of α-synuclein (α-syn) that is known to greatly increase a person’s risk for developing Parkinson’s disease, the yeast cell shows many of the same phenotypes as a diseased neuron. The yeast acts as a stand-in for the neuron.
In a new study out in Science, Tardiff and coworkers use this yeast model to identify a heretofore unknown target for Parkinson’s disease in a sort of reverse engineering process. They screened around 190,000 compounds and looked for those that rescued toxicity in this yeast model. They found one significant hit, an N-aryl-benzimidazole (NAB) compound. Working backwards from this hit they identified its target as Rsp5p, a Nedd4 E3 ubiquitin ligase.
The authors then went on to confirm this finding in C. elegans and rat neuron models where this compound halted and even managed to reverse neuronal damage. And for the coup de grace, Chung and coworkers showed in a companion paper that the compound worked in human neurons too. But not just any human neurons.
The authors used two sets of neurons derived from induced pluripotent cells from a single patient. One set of neurons had a mutation in the α-syn gene which is known to put patients at a high risk of Parkinson’s disease-induced dementia. The other set had the mutation corrected. The compound they identified in yeast reversed some of the effects in the neurons with the α-syn mutation without significantly affecting the corrected neurons. Wow.
What makes this even more exciting is that many people thought you couldn’t target α-syn with a small molecule. But as the studies here show, you can target an E3 ubiquitin ligase that can overcome the effects of mutant α-syn. It took an unbiased screen in yeast to reveal a target that would have taken much, much longer to find in human cells.
The mutant α-syn protein ends up in inclusion bodies that disrupt endosomal traffic in the cell. The NAB compound that the authors discovered restored endosomal transport and greatly decreased the numbers of these inclusion bodies. Juicing up Rsp5 seemed to clear out the mutant protein.
The next steps are those usually associated with finding a lead compound—chemical modification to make it safer and more effective, testing in clinical trial and then, if everything goes well, helping patients with Parkinson’s disease. And that may not be all.
The α-syn protein isn’t just involved in Parkinson’s disease. The dementia associated with this protein is part of a larger group of disorders called dementia with Lewy bodies that affects around 1.3 million people in the US. If everything goes according to plan, many of these patients may one day thank yeast for their treatment.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
October 10, 2013
Cheap and easy genome sequencing has been both a blessing and a curse. We are able to find an incredible wealth of variation, but for the most part we have no easy way to tell whether a difference might contribute to a disease or not.
The poster child for this problem is autism. Lots of genome wide association studies (GWAS) have been done and lots of rare variants in lots of different genes have been found – unfortunately, way too many to pick out the ones that really matter.
Luckily our friend yeast can help. Various researchers have identified a number of variants in the human cation/proton antiporter gene NHE9 that associate with autism. In a new study, Kondapalli and coworkers used the NHE9 ortholog NHX1 from S. cerevisiae as an initial screen to identify which variants impact the activity of the NHE9 protein. They found that two of the three mutations they looked at compromised the activity of yeast Nhx1p.
They then set out to confirm these results in mammalian cells. When they looked at protein activity in glial cells, they found that all three mutations compromised the activity of NHE9. This is obviously different from what they found in yeast.
Now this doesn’t mean that yeast is useless for this approach (God forbid!). No, instead it means that it is probably only useful for a subset of autism mutations. Kondapalli and coworkers had suspected this, but apparently the subset is smaller than they initially thought.
The first thing they did was to generate a rough three dimensional map of the NHE9 protein in order to see which parts the two proteins shared. The idea is that they could then do a quick screen in yeast with mutations that affect the shared structure.
While the structure of NHE9 has not been solved, we do have the structure of its distant bacterial relative, NhaA. Kondapalli and coworkers aligned the two along with the yeast ortholog Nhx1p and identified conserved regions.
Three of the NHE9 mutations associated with autism—V176I, L236S, and S438P—were all predicted to be in shared, membrane-spanning parts of the protein. The researchers introduced the equivalent mutations into NHX1—V167I, I222S, and A438P.
A yeast deleted for NHX1 grows poorly in high salt and low pH and also has increased sensitivity to hygromycin B, as compared to a yeast with a functioning NHX1. Two of the mutant genes, carrying A438P or I222S, failed to rescue these growth defects. The other mutant gene, with the V167I change, worked as well as wild type NHX1 at rescuing the yeast. So at least in yeast, two of the three mutations appear to impact protein activity.
The next step was to see if the same was true in mammals. Easier said than done! Ideally they would want to investigate whether these mutations affected the protein in the cells where NHE9 is usually active. Too bad no one knows this protein’s natural habitat. This is why the researchers starting slicing mouse brains to figure out when and where the protein is expressed.
While we don’t have time or space to go into all the details here, Kondapalli and coworkers found that when and where in the brain NHE9 was expressed made sense as far as a possible contribution to autism. They also found that glial cells had about 1.2 fold more NHE9 transcripts than did neuronal cells. They therefore did their assays of protein activity in a type of glial cells called astrocytes.
While they couldn’t completely knock out NHE9 in mouse astrocytes, they were able to knock down its expression by over 80%. When they added back the mutant NHE9 genes, they found that all three failed to mimic the effect of adding back wild type NHE9 to these cells. This is different than what they found in yeast, where only two of the mutations impacted protein activity.
When they went back to their 3D model, they saw that the mutation that differed, V167I, affected a less defined part of the structure. This points to the fact that for the quick yeast screen to work, they need to be looking at parts of the protein where the structure is shared between the yeast and the human version. In a perfect world they would have had crystal structures of each to work off of instead of having to kludge together a model.
In any event, this is the first step towards validating yeast as a quick screen for identifying mutations that can impact protein activity and so are good candidates for being involved in disease. Yeast may help scientists separate the wheat from the chaff of GWAS and so help figure out how diseases happen and maybe help find treatments or even cures. Well done yeast.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
August 15, 2013
Suppose you had a fleet of rowboats that you wanted to split up evenly between two shores. You probably wouldn’t just set them free and hope they drifted to the right places. A better way might be to run some ropes from the distant shore and pull half the rowboats across. That way you’d be sure to get the distribution of boats that you wanted.
Dividing cells face a similar problem with their organelles. When cells divide, they need to make sure mother and daughter receive the right number of organelles. Otherwise one or both could die!
This is obviously too important to just leave to chance, which is why cells have devised ways to precisely control how many organelles end up in mother and daughter. But not every organelle is divided in the same way. In our boat analogy, some are pulled over with ropes, others with chains, some with winches and so on.
We don’t know a lot about how some organelles are distributed between mother and daughter cells. For example, the details have not been worked out for peroxisomes, the organelles that contain enzymes for beta-oxidation of fatty acids. Until now, that is…
In a recent study in The EMBO Journal, Knoblach and colleagues looked in great detail at how yeast cells distribute their peroxisomes. During budding, some peroxisomes stay tied up in the mother cell while others are transported into the bud and re-tied there.
The authors found that the structure that ties up peroxisomes is like a rope with hooks at both ends. One hook attaches to the peroxisome, while the other hook attaches to the cortical endoplasmic reticulum (ER) near the cell wall. Surprisingly, the same protein, Pex3p, acts as the hook at both ends of the rope, connecting it to both ER and peroxisomes.
The authors already knew that some peroxisomes stayed anchored around the edges of the mother cell while others were “mobile” and moved to the daughter when yeast cells divided. They also knew that the protein Inp1p was important for anchoring peroxisomes. In the inp1 null mutant all the peroxisomes are mobile and end up in the bud, while overexpressing Inp1p causes all the peroxisomes to be anchored in the mother cell and stay there.
Knoblach and colleagues suspected that Inp1p might act as the rope that tethers peroxisomes. To test this, they fused Inp1p to a protein that sits in the mitochondrial outer membrane, Tom70p. Now peroxisomes in this strain were attached to mitochondria! This established that Inp1p is the tether.
Another major molecular player in this process is Pex3p. The pex3 null mutant phenotype looks a lot like the inp1 mutant phenotype: the mother cell loses all its peroxisomes and they end up in the bud. Pex3p is an integral membrane protein that is channeled through the ER on its way to the peroxisomal membrane – so it can be present in both places. The authors found that both the N terminus and C terminus of Inp1p bind to Pex3p. All this suggested that together, Inp1p and Pex3p might form a structure that links peroxisomes to the ER.
They were able to show that Inp1p and Pex3p interact directly both at the peroxisome and at the ER using a neat trick called bimolecular fluorescence complementation. This simply means that if the two halves of green fluorescent protein (GFP) are brought close to each other, they can fluoresce like the intact protein. The basic idea is that they fused the first half of GFP with Inp1p and the second half with Pex3p and looked for green spots to turn up in the right place of the cell. Of course this is easier said than done!
To pull this off, the authors had to first make two haploid strains of opposite mating types. The first had a pex3 mutation that caused all Pex3p to be stuck in the ER, anchoring all its peroxisomes there. It also carried a version of INP1 that was fused to half of GFP.
The second strain had a different pex3 mutation that set all peroxisomes adrift, and this mutant pex3 gene was fused to the other half of GFP. This strain also had an additional marker that made peroxisomes glow red.
When the cytoplasms of these two strains had a chance to mix after mating, the zygote had red peroxisomes with glowing green spots, showing that Inp1p-half GFP from the first strain was interacting with Pex3p-half GFP from the second strain. Because the Inp1p-half GFP of the first strain was bound to ER-localized Pex3p, and the Pex3p-half GFP of the second strain was localized to peroxisomes, this result showed that Inp1p connects peroxisomes with the ER.
The authors studied the kinetics of this process in a lot more detail and even tracked the wanderings of individual peroxisomes. The model that comes from all this work is that at start of budding, some peroxisomes are bound to Inp1p and others aren’t. Those that aren’t bound to Inp1p move to the bud, and the others stay anchored to the mother cell’s ER. Meanwhile, during budding Pex3p passes through the ER and re-emerges at the ER membrane at the bud cortex. It can then bind Inp1p, which in turn binds to the Pex3p on the surface of the migrating peroxisomes to dock them in the bud.
Not only is this cool just from a basic biology perspective, but it may also help us deal with some human peroxisome biogenesis disorders. For example, Zellweger syndrome and infantile Refsum disease are associated with specific mutations in the human ortholog of PEX3. Once again, little S. cerevisiae is helping us navigate the inner workings of eukaryotic cells.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
March 21, 2013
At first our favorite small eukaryote, S. cerevisiae, might not seem like a great model for cancer studies. After all, budding yeast can’t tell us anything about some of the pathways that go wrong in cancer, like growth factor signaling. And it clearly can’t help explain what happens in specific tissues of the human body. But in other ways, it actually turns out to be a great model.
For example, all the details of cell cycle control were originally worked out in yeast. And now a whole new batch of genes has been found that influence a phenomenon, chromosome instability (CIN), that is important in both yeast and cancer cells.
As the name implies, chromosomes are unstable in cells suffering from CIN. Big chunks of DNA are lost, or break off and fuse to different chromosomes, turning the genome into an aneuploid mess. And this mess has consequences.
CIN can cause new mutations or make old ones have a stronger effect. Eventually these mutations can affect genes that are important for keeping a cell’s growth in line. Once these are compromised, a tumor cell is born.
Since CIN is pretty common in yeast, we might be able to better understand it in cancer cells by studying it in yeast. The Hieter lab at the University of British Columbia has come up with a powerful screen to get yeast to confess why it CINs.
A previous study from the group set the stage by finding a large group of mutants that have CIN phenotypes, implying that those genes are involved in keeping chromosome structure stable. In a new paper in G3: Genes, Genomes, Genetics, van Pel et al. uncovered the network of interactions among the genes in this set, using synthetic genetic array (SGA) technology. And they confirmed that the human homologs of some of these genes interact in the same way as in yeast, making them potential targets for cancer therapies.
The idea behind SGA studies is that if two proteins are involved in the same process, then a strain carrying mutations in both of their genes will be much worse off than a strain carrying either single mutation. In the worst case, the double mutant will be dead. This is known as a synthetic lethal interaction.
Yeast is a great model for doing these sorts of studies on a very large scale. We can construct networks showing how lots of different genes interact, and most importantly, find the genes that are central to many interactions. These “hubs” are likely to be the key players in those processes.
The researchers looked specifically for interactions between genes that are involved with CIN in yeast and are also similar to human cancer-related genes. They came up with various interaction hubs that will be interesting research subjects for a long time to come. In this study, they focused on one of these: genes involved with the DNA replication fork.
One of these in particular, CTF4, is a hub for both physical and genetic interactions. Unfortunately, Ctf4p doesn’t look like a good target for chemotherapy. It’s thought to act as a scaffold, and lacks any known activity that could potentially be inhibited by a drug. However, the interaction network around CTF4 that van Pel et al. uncovered suggests another way to target this hub. If a gene that interacts with CTF4 itself has a synthetic lethal interaction with another gene, and we could re-create the synthetic lethal phenotype in a cancer cell, we might be able to knock out the whole process. And that is just what they found in human cells.
First the authors identified a couple of human genes that were predicted from the yeast screen to be close to human CTF4 in the interaction network and to have a synthetic lethal interaction with each other. They then lowered the expression of one using small interfering RNA (siRNA), and reduced the activity of the other with a known inhibitor. Neither treatment alone had much effect, but combining them significantly reduced cell viability.
Since cancer cells frequently carry mutations in CIN genes, it should be possible to create a synthetic lethal interaction, guided by the yeast interaction network, where one partner is mutated in cancer cells (equivalent to using siRNA in this study) and the other partner is inhibited with a drug. Since it relies on a cancer-specific mutation, this approach has the potential to selectively target cancer cells while not disturbing normal cells, the ultimate goal for chemotherapy.
by Maria Costanzo, Ph.D., Senior Biocurator, SGD
January 28, 2013
Every cell needs to correctly divvy up its chromosomes when it divides. Otherwise one cell would end up with too many chromosomes, the other with too few and they’d both probably die.
Cells have developed elaborate machinery to make sure each daughter gets the right chromosomes. One key part of the machinery is the centromere. This is the part of the chromosome that attaches to the mitotic spindle so the chromosome gets dragged to the right place.
Given how precise this dance is, it is surprising how sloppy the underlying centromeric DNA tends to be in most eukaryotes. It is very long with lots of repeated sequences which make it very tricky to figure out which DNA sequences really matter. An exception to this is the centromeres found in some budding yeasts like Saccharomyces cerevisiae. These centromeres are around 125 base pairs long with easily identifiable important DNA sequences.
The current thought is that budding yeast used to have the usual diffuse, regional centromeres but that over time, they evolved these newer, more compact centromeres. Work in a new study published in PLOS Genetics by Lefrançois and coworkers lends support to this idea.
These authors found that when they overexpressed a key centromeric protein, Cse4p (or CenH3 in humans), new centromere complexes formed on DNA sequences near the true centromeres. The authors termed these sequences CLR’s or Centromere-Like Regions. And they showed that these complexes are functional.
When Lefrançois and coworkers kept the true centromere from functioning on chromosome 3 in cells overexpressing Cse4p, 82% of the cells were able to properly segregate chromosome 3. This compares to the 62% of cells that pull this off with normal levels of Cse4p. The advantage disappeared when the CLR on chromosome 3 was deleted.
A close look at the CLRs showed that they had a lot in common with both types of centromeres. They had an AT-rich 90 base pair sequence that looked an awful lot like the kind of sequence that Cse4p prefers to bind and a lot like the repeats found within more traditional centromeres. They also tended to be in areas of open chromatin and close to true centromeres. The obvious conclusion is that these are remnants of the regional centromeres budding yeast used to have.
The hope is that the yeast CLRs might make studying regional centromeres easier. They are so long and complicated that it is very difficult to pick out which sequences matter and which don’t, but the yeast CLRs could be a simpler model system. Even better, the CLRs might shed some light on the process of neocentromerization – the formation of new centromeres outside of centromeric regions, which happens a lot in cancer cells. Once again, simple little S. cerevisiae may hold the key to understanding what’s going on in much larger organisms.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
November 08, 2012
One of the many stumbling blocks in finding better treatments for genetic diseases is figuring out the cause of the disease. These days, this doesn’t necessarily mean simply identifying the gene with the mutation. No, nowadays it can mean figuring out what each specific mutation does to the gene it damages.
See, many genetic diseases are not caused by single mutations. Instead, lots of different mutations can all damage the same gene in different ways. And each class of mutation may require different treatments.
Cystic fibrosis (CF) is a great example of this. While most cases of this ultimately fatal disease are caused by mutations in the CFTR gene, not every mutation does the same thing to the CFTR protein. Because of this, scientists have found different drugs to treat people with different classes of CFTR mutations.
So one drug, Ivacaftor, targets CFTR proteins that can’t open up as well as they should, while another investigative drug, PTC124, targets prematurely stopped CFTR proteins. Each only treats a specific subset of CF patients who have the correct CFTR mutation.
All of this screams out for a quick and easy assay to figure out how a mutation actually disables a certain protein. And this is where a new study by Pittman and coworkers just published in the journal GENETICS can help.
The authors have come up with a sensitive in vivo assay in S. cerevisiae that allows scientists to quickly identify mutations that lead to unstable proteins. This kind of instability isn’t rare in human disease either. Some of the more famous examples include a kidney disease called primary hyperoxaluria type 1 (PH1), Lou Gehrig’s disease (ALS), Parkinson’s disease, spinal muscular atrophy (SMA), and even some forms of cancer.
The assay basically inserts wild type and mutant versions of the gene of interest into the middle of the mouse dihydrofolate reductase (DHFR) gene, individually adds these chimeric genes to yeast lacking DHFR, and then measures growth rates. The idea is that if the mutation leads to instability, the DHFR chimeric protein will be unstable too and the yeast will show growth defects under certain conditions. This is just what they found.
Initially they focused on a gene involved in PH1, the AGT gene encoding alanine: glyoxylate aminotransferase. They were able to show that disease causing mutations known to affect protein stability affected growth in this assay. Not only that, but there was a strong correlation between growth and level of protein stability. In other words, the more unstable the protein, the more severe the growth defect.
They then expanded their assay beyond known AGT mutations. First they were able to identify a subset of disease-causing AGT mutations as affecting the stability of the AGT protein. But the assay ran into trouble when they switched to the more stable SOD1 protein. This protein, which is involved in most cases of ALS, is so stable that mutations that destabilized it were invisible in the assay. The authors solved this problem by introducing a mutation into DHFR that destabilized it. Now they could identify mutants that destabilized SOD1.
As a final step, they used their assay to screen a library of stabilizing compounds to identify those that specifically stabilized their mutant proteins. Unfortunately, in this first attempt they only found compounds that stabilize DHFR, but the assay has the potential to find drugs that stabilize disease-related proteins as well.
Whether or not that potential is realized, this technique should still be a very useful way to determine whether a mutation affects protein stability. Then, when drugs that stabilize the protein have been found, using this or other screens, doctors will know which patients can be helped by these compounds. And this will be a boon for scientists and patients alike.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
November 01, 2012
What do Lou Gehrig, Stephen Hawking, David Niven and Mao Zedong have in common? They all suffered (or in Hawking’s case, continue to suffer) terribly from a disease called amyotrophic lateral sclerosis or ALS. And now the humble yeast S. cerevisiae may help scientists find new treatments so that others do not need to suffer similarly.
Patients with ALS gradually lose use of their motor neurons and generally die within 3-5 years of diagnosis. While there are some rare forms that run in families, most are sporadic. There is no history of the disease in the family and then suddenly, it just appears.
The causes of ALS have remained a mystery for many years but recent work has suggested that RNA binding proteins and RNA processing pathways are somehow involved. In particular, an RNA-binding protein called TDP-43 appears to be a key player. Mutations in its gene are associated with ALS, and aggregates of the protein are found in damaged neurons of ALS patients. Unfortunately, since this protein is needed for cell survival it is not an easy target for therapies. This is where yeast can help.
Scientists have managed to mimic the effects of TDP-43 in yeast. When this protein is overexpressed, the yeast cells die just like the motor cell neurons do. In a recent Nature Genetics paper, Armakola and coworkers use this model system for finding better therapeutic targets. And it looks like they may have succeeded.
These authors used two different screens to systematically look for proteins that when deleted or expressed at lower levels rescued yeast overexpressing TDP-43. They found plenty. One screen yielded eight suppressors while the other yielded 2,056 potential suppressors. They decided to focus on one of the stronger suppressors, DBR1.
The first thing they wanted to do was to make sure this wasn’t a yeast specific effect. If lowering the amount of DBR1 has no effect in mammalian models, it is obviously not worth pursuing!
To answer this question, they created a mammalian neuroblastoma cell line with an inducible system for making a mutant version of TDP-43, TDP-43 Gln331Lys, found commonly in ALS patients. As expected, these cells quickly died in the presence of inducer. They could be rescued, though, when DBR1 activity was inhibited with siRNA. The authors confirmed that decreasing the activity of DBR1 in primary neurons decreased TDP-43 toxicity as well.
So decreasing the amount of DBR1 appears to rescue cells that die from the effects of mutant TDP-43. This suggests that targeting DBR1 may be useful as a therapy for ALS. But this study doesn’t stop there. It also tells us a bit about how lowering DBR1 levels might be rescuing the cells.
DBR1 is an RNA processing enzyme involved in cleaning up the mess left behind by splicing. It cleaves the 2’-5’ phosphodiester bond of the spliced-out intron (called a lariat). Previous studies in yeast have shown that when Dbr1p levels are reduced or its catalytic activity is disrupted by a mutation, there is a build up of these lariats. This study showed directly that the accumulated lariats interact with TDP-43 in the cytoplasm to suppress its toxicity. So in ALS, the accumulated lariats may serve as a decoy for the mutant TDP-43 protein, preventing it from binding to and interfering with more essential RNAs.
This last result may also suggest another potential therapy. If scientists can find other ways to increase the amount of decoy RNA, then they may not need to depend on reducing levels of DBR1. There may be many possible approaches to soaking up rogue TDP-43.
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics