New & Noteworthy

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

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

Yeast, Smarter than a Train Wreck

January 30, 2014

Imagine you run a railroad that has a single track. You need for trains to run in both directions to get your cargo where it needs to go.

One way to regulate this might be to have the trains just go whenever and count on collisions as a way to regulate traffic. Talk about a poor business model! Odds are your company would quickly go bankrupt.

Another, more sane possibility is to somehow keep the trains from running into each other. Maybe you schedule them so their paths never cross. Or maybe you have small detours where a train can wait while the other passes. Anything is better than regulation by wreckage!

Turns out that at least in some cases, nature is a better business person than many people previously thought. Instead of trains on a track, nature needs to deal with nearby genes that point towards one another, so-called convergent genes. If both genes are expressed, then the RNA polymerases will barrel towards one another and could collide.

A new study in PLoS Genetics by Wang and coworkers shows just how big a deal this issue is for our favorite yeast Saccharomyces cerevisiae. An analysis of this yeast’s genome showed that not only did 20% of its genes fit the convergent definition but that in many cases, each gene in a pair influenced the expression of the other gene. Their expression was negatively correlated: when one of the pair was turned up, the other went down, and vice versa.

One way these genes might regulate one another is the collision model. When expression of one gene is turned up and a lot of RNA polymerases are barreling down the tracks, they would crash into and derail any polymerases coming from the opposite direction. A prediction of this model is that orientation and location matter.  In other words, the negative regulation would work only in cis, not in trans.  Surprisingly, the authors show that this is clearly not the case.

Focusing on four different gene pairs, Wang and coworkers showed that if the genes in a pair were physically separated from one another, their expression was still negatively correlated.  This was true if they just flipped one of the genes so the two genes were pointed in the same direction, and it was still true if they moved one gene to a different chromosome.  Clearly, collisions were not the only way these genes regulated one another.

Using missense and deletion mutation analysis, the authors showed that neither the proteins from these genes nor the coding sequence itself was required for this regulation.  Instead, the key player was the overlapping 3’ untranslated regions (UTRs) of the transcripts.  The authors hypothesize that the regulation is happening via an anti-sense mechanism using the complementary portions of the 3’ UTRs.

This anti-sense mechanism may be S. cerevisiae’s answer to RNAi, which it lost at some point in its evolutionary history.  Given the importance of RNA-mediated regulation of gene expression in other organisms, perhaps it shouldn’t be surprising that yeast has come up with another way to use RNA.  

Instead of RNAi, it relies on genomic structure and overlapping 3’ UTRs to regulate genes.  This may be a bit more cumbersome than RNAi, but at least yeast came up with a more clever system than polymerase collisions to regulate gene expression.  

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

Categories: Research Spotlight

Tags: RNA polymerase II, Saccharomyces cerevisiae, transcription, UTR

Yeast, the New Fountain of Youth

January 23, 2014

Ponce de Leon searched the New World for the fountain of youth.  Turns out that if he had some of the tools at our disposal, he wouldn’t have even had to leave Europe.  He just needed to go to the local bakery or brewery and look inside the yeast he found there.  Of course, then he wouldn’t have found Florida…

Using in silico genome-scale metabolic models (GSMMs) in yeast, Yizhak and coworkers identified GRE3 and ADH2 as two genes that significantly increased the lifespan of yeast when knocked out.  Even more importantly, their method also allowed them to identify the mechanism behind this increased lifespan—the mild stress of increased reactive oxygen species (ROS).  This last finding may help scientists identify drug targets that they can target to increase the lifespan of people too.  If only Ponce de Leon had lots of -omics data and a powerful computer or two!

After constructing an in silico starting state, Yizhak and coworkers entered two sets of data from previous work that had been done on aging in yeast.  They next used gene expression profiling to identify which metabolic reactions were different and which were the same in young and old yeast.  They then systematically tested the effect of knocking out these reactions one at a time in their computer model to identify those that could potentially transform yeast from old to young with minimal side effects. 

Their first finding was that many of their best hits, like HXK2, TGL3, and FCY2, had already been identified as important in prolonging a yeast cell’s life.  They decided to look at seven genes that had not been previously identified as being involved in aging. 

When two of these seven, GRE3 and ADH2, were knocked out, these yeast strains lived significantly longer with minimal side effects.  For example, the strain lacking GRE3 lived ~100% longer than the wild type strain.

Figuring out why these yeast probably lived longer was made simpler because they used metabolic models to identify the genes.  The hormesis model of aging suggests that mild stress, like that found in caloric restriction, can lead to increased life span.  With this model in mind, the authors focused in on the possibility that knocking out GRE3 and/or ADH2 would lead to increased stress through the production of increased levels of ROS.  When they looked, they found that the two knockout strains did indeed have higher levels of two common forms of ROS, hydrogen peroxide and superoxide. 

Of course none of us is particularly interested in extending the life of a yeast!  But these results could suggest new drug targets to go after that might mimic the effects of caloric restriction without us having to starve ourselves.  And these same methods can be used on human cells to find key pathways to target in people.  In fact, the authors have started to use their computer models to investigate aging in human muscle cells and found that like in yeast, many of the genes they have identified are consistent with previous work on human aging. 

Now we probably shouldn’t get too far ahead of ourselves here.  This is a promising first step but it really isn’t much more than Ponce de Leon boarding his ship to begin his trip to the New World.  We still have a long voyage ahead of us before we find the fabled fountain of youth.  

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

Categories: Research Spotlight

Tags: aging, metabolic model, Saccharomyces cerevisiae

Cutting Down on the ChIPs

January 16, 2014

We all know that potato chips are delicious.  But we also know that eating too many of them isn’t very good for our arteries or our waistlines. And apparently these aren’t the only chips that can be too much of a good thing.

Chromatin immunoprecipitation (ChIP) is an incredibly valuable technique that lets us see where a particular protein binds in a genome. It can show us the target genes of a particular transcription factor, the distribution of RNA polymerases as they transcribe genes, the places where silencing proteins bind to turn off expression of particular regions, and lots more.

But just like potato chips, more ChIP results aren’t always better. Teytelman and coworkers, publishing in Proceedings of the National Academy of Sciences, and Park and coworkers, publishing in PLoS ONE, have discovered that highly transcribed regions of the genome consistently give false positive ChIP results. In other words, very active regions of the genome look like everything is binding there even when it almost certainly is not.  Teytelman and colleagues call these regions “hyper-ChIPable”. 

Far from being a reason to despair, though, the discovery of this artifact explains some puzzling previous results and inspires the creation of new, more reliable ChIP methods. This is exactly what Kasinathan and coworkers have done, in a recently published paper in Nature Methods. 

The idea behind the ChIP technique is that if you want to know all of the places across the genome where your protein of interest binds, you can lyse cells, shear the DNA into relatively short fragments, and immunoprecipitate your protein from the mixture. Usually the protein and DNA are cross-linked before immunoprecipitation, to strengthen their bond during the rest of the procedure.

After immunoprecipitation, the DNA fragments associated with the protein can be identified using a variety of methods. Finally, mapping the sequences of the fragments to the genomic sequence shows us all the sites that the protein occupies.

Teytelman and colleagues used ChIP-seq to ask whether the silencing complex (Sir2p, Sir3p, and Sir4p) ever binds to non-silenced regions of the genome. They thought they might see some binding, but they were astounded to find significant binding of the complex at 238 distinct euchromatic (non-silenced) loci. This didn’t really make sense, since the yeast Sir proteins are extremely well-studied and there were no biological hints that they have such a large presence at non-silenced genes. 

As a control, they looked at previously published ChIP data on the locations of two unrelated proteins, Ste12p and Cse4p, and found that their binding was enriched at the same 238 loci. Finally, they did a ChIP study using green fluorescent protein (GFP) alone. Sure enough, the ChIP data showed that this jellyfish protein apparently bound strongly to chromatin at those 238 sites! The common denominator shared by these loci: they were all very highly expressed.

Meanwhile, Park and coworkers were embarking on a similar journey. They found using ChIP-seq that several unrelated transcription factors seemed to have common targets, which didn’t make biological sense. Control experiments looking at binding sites of Mnn10p (a cytoplasmic protein not expected to have any contact with DNA), or even using nonspecific antibodies that didn’t recognize any yeast proteins, still gave the same set of ChIP targets. Again, these targets were all highly expressed genes.

Each group found several factors contributing to this artifact, although all the reasons why highly expressed regions yield false positives may not yet be uncovered.  But whatever the reasons, this finding helps explain some previously perplexing results – such as binding of Mediator complex all over the genome, or the paradoxical binding of silencing regulator Sir3p to the GAL1–GAL10 regulatory region under conditions where transcription is activated, not silenced.

In response to these issues, many researchers are actively trying to improve the ChIP technique. Kasinathan and colleagues have devised a method that they call ORGANIC (Occupied Regions of Genomes from Affinity-purified Naturally Isolated Chromatin) that eliminates crosslinking and substitutes micrococcal nuclease treatment for sonication (to shorten the DNA fragments).  In a pilot project, they mapped binding sites for the transcription factors Reb1p and Abf1p. The method looks to be both accurate and sensitive. Most binding locations that they found contained the binding motif sequence for that transcription factor, and also correlated with in vivo occupancy as determined by Dnase I footprinting – both of which support their biological relevance. Importantly, the technique shows no bias towards highly expressed regions.

The lesson for researchers is that ChIP results for highly expressed genes, particularly those done using older protocols, need to be viewed cautiously.  And of course this artifact could be an issue for organisms other than yeast. ChIP experiments are used across species, and have been valuable in elucidating the targets of disease-related proteins like the tumor suppressor p53.

The fact that yeast genetics and molecular biology have so well established the roles of certain chromatin-associated proteins was a key part of this puzzle, helping to point out the artifactual nature of some of the ChIP results. Just as a new recipe for potato chips could allow us to eat more of them while staying healthy, yeast research has led the way to a new recipe for more accurate ChIP studies.

Aside from the molecular biology behind this work, it is quite interesting from a sociological point of view as well. What is it like to make a discovery that calls into question a routinely-used technique and a lot of published results? Lenny Teytelman’s blog post on this topic provides a fascinating glimpse into this situation.

Categories: Research Spotlight

Tags: chromatin immunoprecipitation, Saccharomyces cerevisiae

The Brazor of Biology

January 08, 2014

The janitor on the U.S. comedy series Scrubs is always coming up with terrible inventions. One of his worst was the knife-wrench. It is what it sounds like—a tool with a knife at one end and a wrench at the other.

Of course not all dual purpose tools have to be so useless.  Imagine a tool like the one at the right with a razor at one end and a toothbrush on the other.  Now you can easily brush your teeth and shave in the shower or at your bathroom sink (as long as you are careful not to cut your cheek).

Turns out that biology has these dual purpose tools too except that they are almost always more useful.  For example, Lahudkar and coworkers show in the most recent issue of GENETICS that Cet1p doesn’t just help out with capping mRNA.  No, these authors found that it also helps clear RNA polymerase II (RNA pol II) away from promoters.  And what’s most interesting is that this second function has little to do with its job in mRNA capping.

Basically the two functions are probably in the same protein because they both happen in the same place, at the start site of a promoter.  Just like our brazor is useful because both jobs happen in the bathroom.

The first step was to show that in the absence of Cet1p, RNA pol II was more likely to be found near the start of transcription.  The authors showed that this was the case by using a temperature sensitive mutant of Cet1p and a chromatin immunoprecipitation (ChIP) assay targeted at RNA pol II—there was more RNA pol II crowded near the promoter at the nonpermissive temperature. 

The next set of experiments showed that merely messing with the cap is not sufficient to cause the polymerase to pause. Lahudkar and coworkers found that RNA pol II occupancy was unchanged in strains carrying mutations in STO1 (also known as CBP80) or CEG1, two components of the capping machinery. Cet1p apparently has a separate, unrelated function in helping to clear polymerases away from the start site of transcription.

The final set of experiments showed that the unpausing activity of Cet1p was found in a different part of the protein from its capping function.  Cet1p be can be broadly divided into three regions—a poorly characterized N-terminal domain (amino acids 1-204), a Ceg1p interaction domain (aa 205-266), and a triphosphatase domain (aa 265-549).  The last two domains are critical to its capping function.

Lahudkar and coworkers found that deleting the 1-204 aa domain from Cet1p caused polymerase stalling at the promoter without affecting its capping ability.  And conversely, that when they impaired the ability of Cet1p to perform its capping function while retaining its 1-204 aa domain, RNA pol II escaped the promoter at the same rate as it did in the presence of wild type Cet1p.  A final experiment showed that just expressing the first 300 amino acids of Cet1p was sufficient to get the polymerases moving. 

All in all these experiments provide strong evidence that Cet1p has two separate functions—an enzymatic role in capping mRNA and an unrelated activity that helps clear RNA pol II from the regions around the promoters of genes.  Which all goes to show that even when you think you have a handle on a protein, it can still surprise you with something new.  Turn it around and you just might find a toothbrush at the end.

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

Categories: Research Spotlight

Tags: bifunctional protein, mRNA capping, Saccharomyces cerevisiae, transcription

Affecting the Shelf Life of Chromosomes

December 19, 2013

Just like the chicken or milk you buy at a store, chromosomes have a shelf life too.  Of course, chromosomes don’t spoil because of growing bacteria.  Instead, they go bad because they lose a little of the telomeres at their ends each time they are copied.  Once these telomeres get too short, the chromosome stops working and the cell dies.

Turns out food and chromosomes have another thing in common—the rates of spoilage of both can be affected by their environment.   For example, we all know that chicken will last longer if you store it in a refrigerator and that it will go bad sooner if you leave it out on the counter on a hot day.  In a new study out in PLoS Genetics, Romano and coworkers show a variety of ways that the loss of telomeres can be slowed down or sped up in the yeast S. cerevisiae.  And importantly, they also show that some forms of environmental stress have no effect.

The authors looked at the effect of thirteen different environments on telomere length over 100-400 generations.  They found that caffeine, high temperature and low levels of hydroxyurea lead to shortened telomeres, while alcohol and acetic acid lead to longer telomeres.  It seems that for a long life, yeast should lay off the espresso and and try to avoid fevers, while enjoying those martinis and sauerbraten.

Romano and coworkers also found a number of conditions that had no effect on telomere length, with the most significant being oxidative stress.  In contrast, previous studies in humans had suggested that the oxidative stress associated with emotional stress contributed to increased telomere loss; given these results, this may need to be looked at again.  In any event, yeast can deal with the stresses of modern life with little or no impact on their telomere length.

The authors next set out to identify the genes that are impacted by these stressors.  They focused on four different conditions—two that led to decreased telomere length, high temperature and caffeine, one that led to longer telomeres, ethanol, and one that had no effect, hydrogen peroxide.  As a first step they identified key genes by comparing genome-wide transcript levels under each condition.  They then went on to look at the effect of each stressor on strains deleted for each of the genes they identified.

Not surprisingly, the most important genes were those involved with the enzyme telomerase.  This enzyme is responsible for adding to the telomeres at the ends of chromosomes.  Without something like this, eukaryotes, with their linear chromosomes, would have disappeared long ago.

A key gene they identified was RIF1, encoding a negative regulator of telomerase.  Deleting this gene led to decreased effects of ethanol and caffeine, suggesting that this gene is key to each stressor’s effects.  The same was not true of high temperature—the strain deleted for RIF1 responded normally to high temperature.  So high temperature works through a different mechanism.

Digging deeper into this pathway, Romano and coworkers found that Rap1p was the central player in ethanol’s ability to lengthen telomeres.  This makes sense, as the ability of Rif1p to negatively regulate telomerase depends upon its interaction with Rap1p. 

The increase in telomere length by ethanol was not just dependent on genes associated with telomerase either.  The authors identified a number of other genes involved, including DOA4, SNF7, and DID4. 

Caffeine, like ethanol, affected telomere length through Rif1p-Rap1p but with an opposite effect.  As caffeine is known to be an inhibitor of phosphatydylinositol-3 kinase related kinases, the authors looked at whether known kinases in the telomerase pathway were involved in caffeine-dependent telomere shortening.  They found that when they deleted both TEL1 and MEC1, caffeine no longer affected telomere length. 

The authors were not so lucky in their attempts to tease out the mechanism of the ability of high temperature to shorten telomeres.  They were not able to identify any single deletions that eliminated this effect of high temperature.

Whatever the mechanisms, the results presented in this study are important for a couple of different reasons.  First off, they obviously teach us more about how telomere length is maintained.  But this is more than a dry, academic finding.

Given that many of the 400 or so genes involved in maintaining telomere length are evolutionarily conserved, these results may also translate to humans too.  This matters because telomere length is involved in a number of diseases and aging.

Studies like this may help us identify novel genes to target in diseases like cancer.  And they may help us better understand how lifestyle choices can affect your telomeres and so your health.  So if you have a cup of coffee, be sure to spike it with alcohol! 

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

Categories: Research Spotlight

Tags: environmental stress, Saccharomyces cerevisiae, telomere

Gender Bending in Yeast

December 03, 2013

Our friend Saccharomyces cerevisiae has it pretty easy when it comes to sex.  There is no club scene or online dating.  Pretty much if an a and an α are close enough together, odds are that they will shmoo towards each other and fuse to create a diploid cell.  No fuss, no muss.

Of course there aren’t any visual cues that indicate whether a yeast is a or α.  Instead yeast relies on detecting gender-specific pheromones each cell puts out.  The a yeast makes a pheromone and an α pheromone receptor, and the α yeast makes α pheromone and an a pheromone receptor.  The way yeast finds a hottie is by looking for the yeast of the opposite sex that puts out the most pheromone.  

This simple system is similar to ours in that gender is determined by gender specific gene expression.  In humans this happens through the amounts of certain hormones that are made.  For example, males make a lot of testosterone which turns on the androgen receptor (AR) which then turns a bunch of genes up or down.  Both men and women have AR; men just make more testosterone, which causes it to be more active. 

Yeast are simpler in that their mating loci encode transcription factors and cofactors that directly regulate a-specific and α-specific genes. Still, in both yeast and human, gender is determined by which genes are on and which are off.

Given how simple the yeast system is and how extensively it has been studied, you might think there is nothing else to learn about yeast mating.  You’d be wrong.  In a new study out in GENETICS, Huberman and Murray found that a gene with a previously unknown function, YLR040C, is involved in mating.  They renamed this gene AFB1 (a-Factor Barrier) since it seems to interfere with a-factor secretion.

The way they found this gene was by creating, as they termed them, transvestite yeast that “pretended” to be the opposite mating type. One strain that they named the MATα-playing-a strain was α but produced a-specific mating proteins, while the other, the MATa-playing-α strain, was a but produced α-specific mating proteins.  Sounds easy but it took a bit of genetic engineering to pull off.

The first steps in making the MATa-playing-α strain were to replace STE2 with STE3, MFA1 with MFα1, and MFA2 with MFα2.  In addition, they had to delete BAR1 to keep it from chewing up any α factor that got made, and ASG7, which inhibits signaling from STE3.  This strain still had the MATa locus, which meant that except for the manipulated genes, it still maintained an a-specific gene expression pattern.

Making the MATα-playing-a strain wasn’t much simpler.  They had to replace STE3 with STE2, MFα1 with MFA1, and MFα2 with MFA2.  In addition, they drove expression of BAR1 with the haploid specific FUS1 promoter and expression of the a-factor transporter STE6 with the MFα1 promoter.  Maybe yeast isn’t so simple after all!

When Huberman and Murray mated the two transvestite strains to each other, they found that while these strains could produce diploid offspring, they weren’t very good at it.  In fact, they were about 700-fold worse than true a and α strains!  So what’s wrong?

To tease this out the researchers mated each transvestite to a wild type strain.  They found that when they mated a wild type a strain to a MATa-playing-α strain, the transvestite’s mating efficiency was only down about three fold.  By overexpressing α factor they quickly found that the transvestite strain’s major problem was that it simply didn’t make enough α pheromone.  They hypothesized that perhaps differences in promoter strength or in the translation or processing of α-factor were to blame. 

The reason for the low mating efficiency of the MATα-playing-a strain, however, wasn’t so simple.  When Huberman and Murray mated the MATα-playing-a strain with an α cell, they found it was about 60-fold worse at mating.  The first thing they looked for was how much a-factor this strain was producing.  Because a-factor is difficult to assay biochemically, they used a novel bioassay instead and found that it secreted much less a-factor than did the wild type a strain.  Further investigation showed that the transvestite strain produced something that blocked the ability of a-factor to be secreted. 

By comparing the transcriptomes of MATa and MATα-playing-a cells they were able to identify YLR040C as their potential a-factor blocker.  They went on to show that when this gene was present, a-factor secretion was indeed inhibited.  They hypothesize that their newly named AFB1 may produce a protein that binds to and sequesters a-factor. It may be to a cells what BAR1 is to α cells, helping the yeast cell to sense the pheromone gradient and choose a mating partner.

When Huberman and Murray knocked AFB1 out of the MATα-playing-a strain, it now mated with a wild type α strain about five fold better than before.  A nice increase, but it doesn’t completely correct the 60-fold reduction in this transvestite’s mating efficiency.  Something else must be going on.

That something appears to be that the strain only arrests for a short time when it encounters α-factor.  This would definitely impact mating efficiency, as it is very important that when a and α strains fuse they both be in the same part of the cell cycle.  Pheromones usually stop the cell cycle in its tracks, but α-factor can’t seem to keep the MATα-playing-a cell arrested for very long.  The researchers looked for genes involved in this transient arrest, but were not able to find any one gene that was responsible.

From all of this the authors conclude that there is a pheromone arms race raging in the yeast world.  The most attractive yeast are those that make the most pheromone, so evolution favors higher and higher pheromone production.  Just as people on the dating scene need to see past the makeup and trendy clothes to figure out who’s really the best partner, yeast need genes like BAR1 and AFB1 to parse out who is the best mate amid the ever increasing haze of pheromones.

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

Categories: Research Spotlight

Tags: mating response, Saccharomyces cerevisiae

A Hands-On Class That Shows Undergraduates the Power of Yeast

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.

Can you describe the class?

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.

Tell me a little bit about how this class was started.

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.

What kinds of experiments do the students get to do in the class?

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.

How hard is it to set up and run the class?

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.

Do you enjoy teaching the class? What is your favorite part? Your least favorite part?

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.

What do you hope the students will learn and get out of the class? And are they learning/getting it?

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.

How would someone at another University go about replicating this course? Are there resources available to help them get started and/or keep it running?

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.

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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

Tags: Saccharomyces cerevisiae, undergraduate education, yeast model for human disease

Gene Knockouts May Not Be So Clean After All

November 18, 2013

Imagine you have the instructions for building a car but you don’t know what any of the specific parts do.  In other words, you can build a working car but you don’t understand how it works.

One way to figure out how the car works would be to remove a part and see what happens.  You would then know what role that part played in getting a car to run.

So if you remove the steering wheel, you’d see that the thing runs into a wall.  That part must be for steering.  When you take out the radiator, the car overheats so that part must be for cooling the engine.  And so on.

Sounds like a silly way to figure out how the car works, but this is essentially one of the key ways we try to figure out how a cell works.  Instead of parts, we knock out genes and see what happens. A new study by Teng and coworkers is making us rethink this approach.

See, one of the big differences between a machine and a cell is that the cell can react and adapt to the loss of one of its parts.  And in fact, it not only can but it almost certainly will.

Each cell has gone through millions of years of evolution to adapt perfectly to its situation.  If you tweak that, the cell is going to adapt through mutation of other genes.  It is as if we remove the radiator from the car and it evolves an air cooling system like the one in old Volkswagen Bugs.

Teng and coworkers decided to investigate whether or not knocking out a gene causes an organism to adapt in a consistent way.  In other words, does removing a gene cause a selection pressure for the same subset of mutations that allows the organism to deal with the loss of the gene.  The yeast knockout (YKO) collection, which contains S. cerevisiae strains that individually have complete deletions of each nonessential gene, gave them the perfect opportunity to ask this question.

There have long been anecdotal reports of the YKO strains containing additional, secondary mutations, but the authors first needed to assess this systematically. They came up with an assay that could detect whether secondary mutations were occurring, and if so, whether separate isolates of any given YKO strain would adapt to the loss of that gene in a similar way.  The assay they developed had two steps.

The first step was to fish out individual substrains from a culture of yeast that started from a single cell in which a single gene had been knocked out.  This was simply done by plating the culture and picking six different, individual colonies.  Each colony would have started from a single cell in the original culture.

The second step involved coming up with a way to distinguish differently adapted substrains.  The first approach was to see how well each substrain responds to increasing temperatures.  To do this, they looked for differences in growth at gradually increasing temperatures using a thermocycler.

They randomly selected 250 YKO strains and found that 105 of them had at least one substrain that reproducibly responded differently from the other substrains in the assay.  In contrast, when they looked at 26 isolates of several different wild type strains, including the background strain for the YKO collection, there were no differences between them. This tells us that the variation they saw in the knockout substrains was due to the presence of the original knockout.

So this tells us that strains can pretty quickly develop mutations but it doesn’t tell us that they are necessarily adapting to the knocked out gene.  To see if parallel evolution was indeed taking place, the authors chose to look at forty strains in which the same gene was independently knocked out.  They found that 26 of these strains that had at least one substrain with the same phenotype, and fifteen of those had mutations that were in the same complementation group.  So these 15 strains had evolved in similar ways to adapt to the loss of the same gene.

Teng and coworkers designed a second assay independent of the original heat sensitivity assay and tested a variety of single knockout strains.  They obtained similar results that support the idea that knocking out a gene can lead cells to adapt in similar ways.  This is both good and bad news.

The bad news is that it makes interpreting knockout experiments a bit trickier.  Are we seeing the effect of knocking out the gene or the effect of the secondary mutations that resulted from the knockout?  Are we seeing the loss of the radiator in the car or the reshaping that resulted in air cooling?  We may need to revisit some earlier conclusions based on knockout phenotypes.

The good news is that not only does this help us to better understand and interpret the results from yeast and mouse (and any other model organism) knockout experiments, it also gives us an insight into evolution and maybe even into the parallel evolution that happens in cancer cells, where mutations frequently co-occur in specific pairs of genes.  And while we may never be able to predict if that knock you hear in your engine really needs that $1000 repair your mechanic says it does, we may one day be able to use results like these to predict which cells containing certain mutated genes will go on to cause cancer and which ones won’t.   

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

Categories: Research Spotlight

Tags: deletion collection, evolution, Saccharomyces cerevisiae

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