New & Noteworthy

Guns vs. Butter in Cells

March 19, 2014

Life is a set of tradeoffs for people, countries, and even cells.  For example, governments need to decide how much money to dedicate to defense and how much to economic growth.  Too much on defense and your country fails, because defense spending sucks up so many resources that your country can no longer afford to pay for anything else.  And of course if you spend too little on defense, someone who spent a bit more can come and take you over.

No country lives in a vacuum though—how much to spend on defense and how much on growth depends on the country’s situation.  If you are the Ewoks living next to an Imperial shield generator, you’d better sacrifice some growth for defense.  But once the Death Star has blown up and the Empire is swept away, you probably focus more on growth (until a new Sith lord arrives). 

This guns vs. butter debate plays out at the cellular level too when it comes to protecting DNA from mutations.  If cells expend too much energy to protect their DNA they sacrifice growth, but if they spend too little, they develop too may harmful mutations to survive.  And just like with countries, how much protection a cell’s DNA needs depends on its environment.

If cells need to adapt quickly to a changing environment, a high rate of mutation is favored.  These cells are more likely to develop a mutation that gains them an advantage over their slower mutating brethren.

A new study by Herr and coworkers in the latest issue of GENETICS calculates the upper limit of the rate of mutation in a diploid yeast.  In other words, they figure out how little “spending” on defense these yeast can get away with and survive.

They find that diploid yeast can deal with a 10-fold higher rate of mutation as compared to haploid yeast.  This makes sense, since the extra gene copy afforded by being diploid can mask a recessive lethal mutation, but this study is the first to give this idea hard numbers.

The authors had previously generated a number of mutations in POL3, the yeast gene for DNA polymerase δ, that affect its ability to find and/or fix any mistakes made during DNA replication.  The study first focused on two mutations affecting accuracy, pol3-L612G and pol3-L612M, and one mutation affecting proofreading, pol3-01.  The accuracy mutations caused about a 10-fold increase in the mutation rate, while the proofreading mutation caused anywhere from a 20-100-fold increase.  Neither was enough to seriously affect a diploid’s growth.

The next step was to combine accuracy and proofreading mutations into the same gene to figure out if the combination resulted in a higher mutation rate.  The authors suspected that it did when they discovered that even though the heterozygotes were fine, their spores were inviable.  The POL3/pol3-01,L212M and POL3/pol3-01,L212G strains sporulated just fine, but none of the spores could germinate and grow. 

One way to explain this was that the double mutation increased the error rate to the point that it would kill off haploids but not diploids.  By looking at mutations in the hemizygous CAN1 gene they could see that the mutation rate in these diploids was indeed at around the haploid threshold. In terms of the CAN1 gene, this mutation rate was around 1X10-3 can1 mutations/cell division.

They next determined the mutation rate by sequencing the genomes of each mutant as well as the wild type.  They found a single T-G mutation in the wild type, 1535 point mutations in POL3/pol3-01,L212M and 1003 mutations in POL3/pol3-01,L212G.  From this they calculated a mutation rate of around 3-4X10-6/base pair/generation. 

Even though this level of mutation kills haploids but not diploids, this does not mean the diploids escaped unscathed.  When the heterozygous diploid colonies were subcloned the resulting colonies were variable in size, indicating that their higher mutation rate was catching up with them.  This high mutation rate was making them sick. 

Given this result, it wasn’t surprising that diploid homozygotes of each double mutant could not survive—the mutation rate was now too high.  The strains homozygous for pol3-01,L212M managed to get to around 1000 cells before petering out.  Strains homozygous for pol3-01,L212G did even worse—they only made it to around 10 cells.

In a final set of experiments Herr and coworkers used a variety of other mutations to tweak these mutation rates to find the threshold at which diploids fail to survive.  Some of these mutations were in POL3 while others were deletions of the MSH2 and/or DUN1 genes.  After testing many different combinations, they found that these yeast did pretty well up to around 1X10-3 can1 mutations/cell division (the haploid threshold rate).  Then, from 1X10-3 to 1X10-2 can1 mutations/cell division there began a rapid drop off with little to no growth at the end. 

So as might be expected, diploids can deal with a significantly higher mutation rate than can haploids.  But even though they can, wild type yeast in the lab still have a very low mutation rate.  It is like they are living near the Imperial city planet of Coruscant.  They are willing to expend the energy to keep their DNA protected.

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

Categories: Research Spotlight

Tags: DNA replication, mutation, Saccharomyces cerevisiae

Transcriptome Data in YeastMine

March 13, 2014

Towards the goal of compiling datasets to produce a complete transcriptome of yeast (the set of all RNA molecules produced in a single cell or population of cells), we have loaded a defined set of transcripts, based primarily on data from Pelechano, et al, but supported by other datasets, into SGD’s flexible search tool, YeastMine. The representative set includes transcripts which Pelechano et al. identified by simultaneous determination of the 5’ and 3’ ends of mRNA molecules whose end coordinates are supported by datasets from other laboratories.

The transcript data can be accessed in YeastMine using the ‘Gene -> Transcripts’ template, which allows you to specify a gene name or list of gene names and return the list of all associated transcripts based on the collection of data described above. The results include the start and end coordinates for each transcript, the number of counts observed for each transcript in glucose and galactose, notes, and references for the relevant datasets.

Categories: New Data

Changes to the SGD GAF File of Gene Ontology Annotations

March 11, 2014

The SGD Gene Associations file (GAF; gene_association.sgd) contains Gene Ontology (GO) annotations for all yeast genes, in a standard file format specified by the GO Consortium. We are changing the taxon identifier in this file to be consistent with the reference genome sequence at GenBank and protein entries at UniProt.

Until now, the taxon identifier in column 13 of SGD’s GAF has been 4932, which refers to Saccharomyces cerevisiae in general rather than to a specific S. cerevisiae strain. Starting March 8th, 2014, we have changed this to taxon ID 559292, which is specific to the S288C strain used for the S. cerevisiae reference genome sequence.

Please note that the taxon ID 559292 merely reflects the sequence (genome) to which the geneIDs in column 2 are mapped. SGD will continue to capture gene functions (GO annotations) for all strains of S. cerevisiae. Please contact us if you have any questions.
The S. cerevisiae GO annotations (GAF) can be downloaded from SGD’s Downloads site.

Categories: Data updates

A Heartfelt Need for Copper

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: respiration, Saccharomyces cerevisiae, yeast model for human disease, zebrafish

Human Disease & Fungal Homologs in YeastMine

March 04, 2014

You can now use SGD’s advanced search tool, YeastMine, to find the human homolog(s) of your favorite yeast gene and their corresponding disease associations. Or, begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search).

We have recently loaded data from OMIM (Online Mendelian Inheritance in Man) into our fast, flexible search resource, YeastMine, and provided 3 predefined queries (templates) that make it simple to perform the above searches. Newly updated HomoloGene, Ensembl, TreeFam, and Panther data sets are used to define the homology between S. cerevisiae and human genes. The results table provides identifiers and standard names for the yeast and human genes, as well as OMIM gene and disease identifiers and names. As with other YeastMine templates, results can be saved as lists and analyzed further. You can also now create a list of human names and/or identifiers using the updated Create Lists feature that allows you to specify the organism representing the genes in your list. The query for yeast homologs can then be made against this list.

In addition to human disease homologs, we have incorporated fungal homolog data for 24 additional species of fungi. You can now query for the fungal homologs of a given S. cerevisiae gene using the template “Gene –> Fungal Homologs.” This fungal homology data comes from various sources including FungiDB, the Candida Gene Order Browser (CGOB), and PomBase, and the results link directly to the corresponding gene pages in the relevant databases, including Candida Genome Database (CGD) and Aspergillus Genome Database (AspGD).

All of the new templates that query human and fungal homolog data can be found on the YeastMine Home page under the new tab “Homology.” These templates complement the template “Gene → Non-Fungal and S. cerevisiae Homologs” that retrieves homologs of S. cerevisiae genes in human, rat, mouse, worm, fly, mosquito, and zebrafish.

Watch the Human Disease & Fungal Homologs in SGD’s YeastMine tutorial (below) to learn how to find and use these new templates.

Categories: New Data, Yeast and Human Disease

Passing the Hog: How a Long Noncoding RNA Helps Yeast Respond to Salt

February 25, 2014

Most people know that Incans relied on human runners to get messages across their empire.  Basically they had runners stationed at various places and one runner would hand the message off to the next.  This relayed message could then quickly travel across the country.

As shown in a new study by Nadal-Ribelles and coworkers, it turns out that something similar happens in yeast when the CDC28 gene is turned up in response to high salt.  In this case, the runner is the stress activated protein kinase (SAPK) Hog1p and it is stationed at the 3’ end of the gene.  When the cell is subjected to high salt, the message is relayed from the 3’ end of the CDC28 gene to its 5’ end by the Hog1p kinase.  The end result is about a 2-fold increase in the amount of Cdc28p made, which allows the cell to enter the cell cycle more quickly after the salty insult.

Unlike the Incans who had their paths all set up in front of them, poor Hog1p has to build its own path.  It does this by activating a promoter at the 3’ end of the CDC28 gene that produces an antisense long noncoding RNA (lncRNA) that is needed for the transfer of the Hog1p.  It is as if our Incan runner had to build a bridge over a gorge to send his message.

This mechanism isn’t peculiar to the CDC28 gene either.  The authors in this study directly show that something similar happens with a second salt sensitive gene, MMF1.  And they show that a whole lot more lncRNAs are induced by high salt in yeast as well.

Nadal-Ribelles and coworkers started off by identifying coding and noncoding regions of the yeast genome that respond positively to high salt.  The authors found that 343 coding regions and 173 noncoding regions were all induced at 0.4 M NaCl.   Both coding and noncoding regions required the SAPK Hog1p for activation. 

The authors next focused on CDC28 and its associated antisense lncRNA.  After adding high salt, Nadal-Ribelles and coworkers found that Hog1p was both at the start and end of the CDC28 gene – as would be expected, since both CDC28 and the antisense lncRNA required this kinase for transcriptional activation. 

Things got interesting when they were able to prevent the lncRNA from being made.  When they did this, Hog1p was missing from both the 5′ and 3′ ends of the CDC28 gene and as expected, activation was compromised.  But Nadal-Ribelles and coworkers showed that expressing the lncRNA from a plasmid did not allow for CDC28 activation. It appears that where the lncRNA is made is just as important as whether it is made.

Through a set of clever experiments, the authors showed that not only does the lncRNA need to be made in the right place, but it needs to be activated in the right way.  When they set up a system where the lncRNA was induced in the right place using a Gal4-VP16 activator, CDC28 was not induced by high salt.  A closer look showed that this was most likely due to a lack of Hog1p at the start of the CDC28 gene.

The situation was different when they activated the lncRNA with a Gal4-Msn2p activator which uses Hog1p to increase expression.  In this case, CDC28 now responded to high salt and Hog1p was present at both the start and end of the CDC28 gene.  But this activation went away if they added a terminator which prevented the full length lncRNA from being made. 

Phew, that was a lot!  What it means is that for there to be a Hog1p at the business end of the CDC28 gene, there needs to be one at the 3’ end.  It also means that for the Hog1p to get to the start of the CDC28 gene, the antisense lncRNA needs to be made.

This would all make sense if maybe the lncRNA was involved in DNA looping, which could get the Hog1p from the end of CDC28 to the start where it can do some good.  Nadal-Ribelles and coworkers showed that this indeed was the case, as CDC28 activation required SSU72, a key looping gene.  When there was no Ssu72p in a cell, salt induction of CDC28 was severely compromised.

So it looks like an antisense lncRNA in yeast is being used as part of a looping mechanism to provide the cell with a quick way to start dividing once it has dealt with its environmental insult.  The authors show that yeast that can properly induce their CDC28 gene enter the cell cycle around 20 minutes faster than yeast that cannot induce the gene.  The cells are poised for a quick recovery.

And this is almost certainly not merely a yeast phenomenon.  Some recent work in mammalian cells has implicated lncRNAs in recruiting proteins involved in controlling gene activity through a looping mechanism as well (reviewed here).  Now that the same thing has been found in yeast, scientists can bring to bear all the powerful tools available to dissect out the mechanism(s) of lncRNA action.  And that’s far from a loopy idea…

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

Categories: Research Spotlight

Tags: DNA looping, lncRNA, Saccharomyces cerevisiae, transcription

Educational Resources on the SGD Community Wiki

February 21, 2014

Did you know you can find and contribute teaching and other educational resources to SGD? We have updated our Educational Resources page, found on the SGD Community Wiki. There are links to teaching resources such as classroom materials, courses, and fun sites, as well as pointers to books, dedicated learning sites, and tutorials that can help you learn more about basic genetics. Many thanks to Dr. Erin Strome and Dr. Bethany Bowling of Northern Kentucky University for being the first to contribute to this updated site by providing a series of Bioinformatics Project Modules designed to introduce undergraduates to using SGD and other bioinformatics resources.

We would like to encourage others to contribute additional teaching or general educational resources to this page. To do so, just request a wiki account by contacting us at the SGD Help desk – you will then be able to edit the SGD Community Wiki. If you prefer, we would also be happy to assist you directly with these edits.

Note that there are many other types of information you can add to the SGD Community Wiki, including information about your favorite genes, protocols, upcoming meetings, and job postings. The Community Wiki can be accessed from most SGD pages by clicking on “Community” on the main menu bar and selecting “Wiki.” The Educational Resources page is linked from the left menu bar under “Resources” from all the SGD Community Wiki pages. For more information on this newly updated page, please view the video below, “Educational Resources on the SGD Community Wiki.”

Categories: New Data, Website changes

Tags: educational, genetics, Saccharomyces cerevisiae, teaching

Signaling in a Crowd

February 18, 2014

There are two very different kinds of sports in the Winter Olympics (and in all sporting competitions really).  In one set, it is the athletes alone out on the ice or sliding down the slope, trying to get the best time they can.  They can only use themselves as the motivator.

In another set of sports, like speed skating, athletes compete directly with one another.  Here they can use each other to push themselves to go faster, farther, etc.

The key to each is obviously the proximity of other athletes.  If there are a bunch of athletes around you, you will all do better by feeding off each other’s signals.  If you are by yourself, then only you can produce the signals to motivate yourself to go faster.

Youk and Lim show in a new study that the same sort of thing happens in cells that can both secrete and sense the same signal.  If there aren’t a lot of cells around they tend to signal themselves, but in a crowded place, they are all signaling each other. 

This may seem a bit esoteric but it really isn’t.  These sorts of “secrete-and-sense” systems are common in biology.  Cell types from bacteria to our own T cells have them, and they allow for a surprisingly wide range of responses.  Understanding how these systems work will explain a lot of biology and, perhaps, help scientists create new sensing systems for bioengineered beasts.

Youk and Lim used our favorite organism Saccharomyces cerevisiae to study this widespread signaling system.  They created a bevy of strains that can either secrete and sense alpha factor or that can only sense the pheromone.  They grew varieties of these two strains together under various conditions to determine when the “secrete-and-sense” strains could also signal to the “sense only” strains.  Like our athletes, the cell concentration was important.  But so too were the levels of alpha factor and receptor.

The authors first created a strain that senses the presence of alpha factor with the Ste2p receptor and in response turns on GFP through the FUS1 promoter.  (The strain is deleted for FAR1 to prevent cell cycle arrest.) As expected, increasing amounts of alpha factor resulted in increased levels of GFP.

It is from this strain they created their “secrete-and-sense” and “sense only” strains.  The “secrete-and-sense” strain included a doxycycline inducible promoter driving the alpha factor gene.  The more doxycycline, the more alpha factor it makes, resulting in more GFP.  To tell the two strains apart in experiments, they added a second reporter, mCherry, under a constitutive promoter to the “sense only” strain.  Now in their experiments they can distinguish between the strains that glow only green and those that glow red and, sometimes, green.

The first experiment was simply to see what effect differing cell and alpha factor concentrations had on the two strains’ ability to glow green.  At low cell and doxycycline concentrations, only the “secrete-and-sense” strain glowed green.  This makes sense, as too little alpha factor was made to get to the relatively distant neighbors.  At high cell and doxycycline concentrations, both glowed green almost indistinguishably.  Here the system was flooded with enough alpha factor for everyone to respond.

The results were less binary at either low cell and high doxycycline concentrations or high cell and low doxycycline concentrations.  Under either of these conditions, the “sense only” strain did glow green although at a much slower rate.

Youk and Kim didn’t stop there.  They also tested whether the amount of receptor affected these results.  When the two strains expressed high levels of receptor, the amount of alpha factor didn’t matter at low cell concentrations—only the “secrete-and-sense” strain glowed green.  This makes sense as the strain can quickly suck up any amount of alpha factor it makes.  Again at high cell concentrations the differences disappear.

In a final set of experiments the authors created positive feedback loops and signal degradation systems, which are both very common in nature.  The positive feedback loop was created by putting the doxycycline activator, rtTA, under the control of doxycycline, and a signal degradation system was engineered using Bar1p, a protease that degrades alpha factor.  Using these systems they were able to show that at low cell concentration, low Bar1p expression, and strong positive feedback, individual cells were either on or off.  This sort of activity may be important in nature, where under certain conditions a response may be beneficial and in others a response may not.  This bet hedging means that the population can survive under both sets of conditions.

It is amazing that such a simple set of conditions can lead to so many different responses, almost as varied as the performances of Olympic athletes.  These findings not only help to explain how these deceptively simple systems work and why they are so common in nature, but might also be incredibly useful in setting up synthetic secrete-and-sense circuits for biotechnology applications.  

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

Categories: Research Spotlight

Tags: pheromone, Saccharomyces cerevisiae, signal transduction

New at SGD: GO Annotation Extension Data, Redesigned GO and Phenotype Pages

February 12, 2014

Annotation Extension data for select GO annotations are now available at SGD. The Annotation Extension field (also referred to as column 16 after its position in the gene_association file of GO annotations) was introduced by the Gene Ontology Consortium (GOC) to capture details such as substrates of a protein kinase, targets of regulators, or spatial/temporal aspects of processes. The information in this field serves to provide more biological context to the GO annotation. At SGD, these data are accessible for select GO annotations via the small blue ‘i’ icon on the newly redesigned GO Details pages. See, for example, the substrate information for MEK1 kinase (image below). Currently, a limited number of GO annotations contain data in this field because we have only recently begun to capture this information; more will be added in the future.

We have also redesigned the GO Details and Phenotype Details tab pages to make it easier to understand and make connections within the data. In addition to all of the annotations that were previously displayed, these pages now include graphical summaries, interactive network diagrams displaying relationships between genes and tables that can be sorted, filtered, or downloaded. In addition, SGD Paper pages, each focusing on a particular reference that has been curated in SGD, now show all of the various types of data that are derived from that paper in addition to the list of genes covered in the paper (example). These pages provide seamless access to other tools at SGD such as GO Term Finder, GO Slim Mapper, and YeastMine. Please explore all of these new features from your favorite Locus Summary page and send us your feedback.

Categories: New Data, Website changes

Studying the Ballistics of Yeast Mutagenesis

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

Tags: mutator phenotype, Saccharomyces cerevisiae, yeast model for human disease

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