New & Noteworthy

Creating an Ethanol-Making ‘Super Bowl’ Championship Team

May 24, 2017


New England v. Miami NFL

Tom Brady makes a team better by making the players around him play better. The same thing can be said for a mutant RPB7 gene that makes other genes work together better as an ethanol-making team. By Paul Keleher [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)%5D, via Wikimedia Commons.

There are a few ways to turn a failing sports team around. One is to tailor individual training to make each player better. Now, the team is better overall because of the changes each player makes.

Another way to improve a team is to change a player in a key position who makes everyone better. A classic example of this is the American football team, the New England Patriots.  

On September 23, 2001, Drew Bledsoe, then the starting star quarterback of the New England Patriots, took a savage hit from New York Jets linebacker Mo Lewis. The Patriots replaced Bledsoe with his backup, Tom Brady, and some might argue, the team (whom Brady led to their first Super Bowl win that year) and the NFL, has not been the same since.

Quarterback Tom Brady, along with head coach Bill Belichick, makes whomever the New England Patriots bring in better. Wide receivers, tight ends, and running backs can be replaced in the lineup without the team missing a beat. He just makes the players around him better than they might be on another team.

In a new study, Qiu and Jiang take a “Patriots” approach to ethanol production in the yeast Saccharomyces cerevisiae. Rather than improving individual genes on their own, these authors instead decided to “bring in” a new version of RPB7, a gene that encodes a key subunit of RNA polymerase II, the molecular machine responsible for making messenger RNA (mRNA).

They hoped that changing this pivotal transcriptional player would cause lots of other genes to do “better” so that “team” yeast would make a lot more ethanol.  Their hopes were realized in their Tom Brady equivalent—a mutant they called M1. Yeast bearing this mutant RPB7 gene became the Super Bowl champs of ethanol production.

One of the keys to increasing ethanol production in yeast is to find strains that are more tolerant of high levels of ethanol. The more ethanol they can withstand, the more they can make.

These authors used error prone PCR mutagenesis of the RPB7 gene to find their game-changing mutant. They then took their library of ~108 clones and cultured them in increasing amounts of ethanol, selecting for more ethanol-resistant strains.

After 3-5 rounds of subculture, they plated the cells onto media containing ethanol. Around 30 colonies were picked and sequenced with the best mutant being the one with two mutations—Y25N and A76T. They named this mutant M1.

This mutant grew a bit better than the parental strain background, S288C, in the absence of ethanol, but where M1 really shined was when ethanol was around. It grew around twice as fast in 8% ethanol and could grow at 10%, a concentration that completely inhibited the parental strain from growing.

Being able to withstand high levels of ethanol is important, but it isn’t all that yeast have to deal with. There are multiple other stressors around when you are swimming in 20 proof media.

For example, yeast can suffer from high levels of reactive oxygen species (ROS). M1 not only tolerated 3.5 mM hydrogen peroxide, a proxy for ROS, better than the parental strain, but it also had around 37% of ROS levels inside cells than that of the parental strain. M1 can deal with high levels of ethanol and ROS.

The authors then tested how this mutant dealt with other potential fermentation problems. For example, acetate, a fermentation byproduct, and high levels of NaCl both inhibit yeast growth. M1 tolerated 80 mM acetic acid and 1.5 M NaCl better than the parental strain did.

drunk Gingy

A couple of mutations in the RPB7 gene makes yeast able to tolerate alcohol way better than this guy. By jerome Chua [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)%5D, via flickr.

M1 appeared to be a champion mutant for making ethanol, and the fermentation studies bore this out.

Under a wide variety of conditions, M1 outperformed the parental strain in terms of growth rate, cell mass, and amount of ethanol made. For example, after 54 hours, yeast containing the M1 mutation of RPB7 managed to make 122.85 g/L of ethanol, 96.58% of the theoretical yield. This is a 40% increase over the control strain. Quite the ethanol producer!

Finally, Qiu and Jiang used microarray analysis of the parental and M1 strains at high levels of ethanol to discover the genes that M1 affected. They found 369 out of a total of 6256 genes behaved differently between the two strains. Of the 369, 144 were up-regulated and 225 were down-regulated.  

I don’t have time to go over all the genes they found but a great many of them make sense. As the authors write, “…a significant set of genes are associated with energy metabolism, including glycolysis, alcoholic fermentation, hexose transport, and NAD+ synthesis.”  M1 seems fine-tuned for making ethanol.

A mutant subunit in RNA polymerase II has made yeast better at making high levels of ethanol, most likely by affecting many key genes at once. It is a fascinating way to quickly affect a whole suite of genes involved in a process. In the ethanol-making Super Bowl, we have a new champion yeast strain, M1.

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

Categories: Research Spotlight

Tags: RPB7 , Super Bowl , NFL , RNA polymerase II , Patriots , Tom Brady , ethanol , fermentation

A Scientist Sees Transcription

September 23, 2015


While Horton uses his sensitive ears to hear a single Who, researchers need to use optical tweezers to see a single gene being transcribed. Image by Dave Parker via Flickr

In the classic Dr. Seuss tale Horton Hears a Who, the elephant Horton thinks he hears voices coming from a speck of dust. He gets into all sorts of trouble over this until all the Whos in Whoville prove they are alive when they all shout at once. Now Horton’s jungle compatriots believe him and Horton can hang out with his new friends.

Horton’s companions never get to hear an individual Who. They are not blessed with Horton’s big elephant ears and so have to just hear all the Whos shouting at once.

Up until recently, we have been in the same situation as the kangaroo and everyone else in the jungle when it comes to transcription in a cell. We can use all sorts of tools to get at what goes on when RNA polymerase II (pol II) gets ready and then starts to transcribe a gene, but we can only get an aggregate picture of lots of cells where it is happening. We can’t hear the Mayor of Whoville amidst all of the other Who voices.

In a new study out in Nature, Fazal and coworkers use the equivalent of elephant ears, optical tweezers, to study the initiation of transcription by purified pol II machinery from Saccharomyces cerevisiae on single molecules. And what they find is that at least for one part of the process, our having looked at things in the aggregate may have fooled us about how the process worked. It was important that we be able to pick out individual voices from the cacophony of the crowd.

Not surprisingly, transcribing a gene is tricky work. It is often split into three steps: initiation, elongation, and termination. And each of these can be subdivided further.

Fazal and coworkers focused on transcription initiation. Previous work had suggested that the process goes something like this:

Top image via Wikimedia Commons

Basically, an alphabet soup of general transcription factors and pol II sit down on a promoter. This complex then pries open the DNA and looks for a signal in the DNA to start transcribing. The polymerase then transcribes short transcripts until it shifts into high gear when it escapes the promoter and enters elongation phase.

This theory comes from the study of transcription in bulk. In other words, it derives from looking at many cells all at once or many promoter fragments in a test tube.

Fazal and coworkers set out to look at how well this all holds up when looking at single genes, one at a time. To do this they used a powerful technique called optical tweezers.

Optical tweezers can “see” what is going on with moving enzymes by measuring the change in force that happens when they move. For this study, the preinitiation complex bound to a longish (2.7 kb) piece of DNA was attached to one bead via pol II, the moving enzyme. The other end of the DNA was attached to a second bead. Each bead is then immobilized using lasers (how cool is that!) and the DNA is stretched between the two beads. Watch this video if you want more details on the technique.

Depending on where you attach the DNA to the bead, you can either track polymerase movement or changes in DNA by precisely measuring changes in the forces keeping the beads in place. Using this technique the researchers found that the bulk studies had done pretty well for most every step. Except for the initial transcribing complex.

The earlier studies had suggested that an open complex of around 15 nucleotides was maintained until elongation began. This study showed that in addition to the 15 base pairs, an additional 32 to 140 base pairs (mean of about 70 base pairs) was also opened before productive elongation could begin. And that this whole region was transcribed.

This result paints a very different picture of transcription initiation. Rather than maintaining a constant amount of open DNA, it looks like the DNA opens more and more until the open DNA collapses back down to the 12-14 base pair transcription bubble seen during elongation.

It turns out that this is consistent with some previous work done in both yeast and fruit flies. Using KMnO4, a probe for single stranded DNA, scientists had seen extended regions of open DNA around transcription start sites but had interpreted it as a collection of smaller, opened DNA. In other words, they thought they were seeing different polymerases at different positions along the DNA.

These new results suggest that they may have actually been seeing initial transcribing complexes poised to start processive elongation. Seeing just one complex at a time changed how we interpreted these results.

Fazal and coworkers were also able to see what happened to some of the 98% of preinitiation complexes that failed to get started. Around 20% of them did end up with an extensive region of open DNA of around 94 +/- 36 base pairs but these complexes were independent of transcription, as they didn’t require NTPs.

But since this opening did require dATP, they propose that it was due to the general transcription factor TFIIH, a helicase. It looks like in these failed complexes, TFIIH is opening the DNA without the polymerase being present.

A clearer picture of what might be going on at the promoter of genes starts to emerge from these studies. Once around 15 base pairs of DNA are pried open to form the appropriately named open complex, TFIIH unwinds an additional 70 or so base pairs. The polymerase comes along, transcribing this entire region. The whole 85 or so base pairs stays open during this process.

Eventually the polymerase breaks free and the opened DNA collapses back down to around 12-14 base pairs. Now the polymerase can merrily elongate to its heart’s content. Until of course something happens and it stops…but that is another story. 

Categories: Research Spotlight

Tags: transcription , RNA polymerase II , Saccharomyces cerevisiae , optical tweezers

Runaway Polymerases Can Wreak Havoc in Cells

October 16, 2014

A train without working brakes can cause a lot of destruction if it careens off the tracks. And it turns out that a runaway RNA polymerase II (pol II) can cause a lot of damage too.  But it doesn’t cause destruction, so much as disease.

Working brakes are important for both large and small machines, including RNA polymerase. Image from Wikimedia Commons

Unlike a train, which has its brakes built right in, pol II has to count on outside factors to stop it in its tracks. And one of these brakes in both humans and yeast is a helicase: Sen1 in yeast and Senataxin, the product of the SETX gene, in humans. 

Mutations in SETX are associated with two devastating neurological diseases: amyotrophic lateral sclerosis type 4 (ALS4) and ataxia oculomotor apraxia type 2 (AOA2), both of which strike children and adolescents.  One idea is that these mutations may short circuit the brakes on pol II, causing it to keep on transcribing after it shouldn’t. And this is just what Chen and colleagues found in a new paper in GENETICS.

The researchers used the simple yet informative yeast model system to look at some of these mutations, and found that they disrupted the helicase function of Sen1 and caused abnormal read-through of some transcriptional terminators.  Looks like bad brakes may indeed have a role in causing these devastating diseases.

Some human proteins can function perfectly well in yeast. Unfortunately, Senataxin isn’t one of those; it could not rescue a sen1 null mutant yeast, so Chen and coworkers couldn’t study Senataxin function directly in yeast. But because Senataxin and Sen1 share significant homology,  they could instead study the yeast protein and make inferences about Senataxin from it.

First, they sliced and diced the SEN1 gene to see which regions were essential to its function. They found that the most important part, needed to keep yeast cells alive, was the helicase domain. But this wasn’t the only key region.

Some flanking residues on either side were also important, but either the N-terminal flanking region or the C-terminal flanking region was sufficient. Looking into those flanking regions more closely, the researchers found that each contained a nuclear localization sequence (NLS) that directed Sen1 into the nucleus. This makes perfect sense of course…the brakes need to go where the train is!  If we don’t put the brakes on the train, it won’t matter how well they work, the train still won’t stop.

These flanking sequences appeared to do more than direct the protein to the nuclear pol II, though.  When the authors tried to use an NLS derived from the SV40 virus instead, they found that it couldn’t completely replace the function of these flanking regions even though it did efficiently direct Sen1 to the nucleus.

Next the researchers set out to study the disease mutations found in patients affected with the neurological disease AOA2.  They re-created the equivalents of 13 AOA2-associated SETX mutations, all within the helicase domain, at the homologous codons of yeast SEN1.

Six of the 13 mutations completely destroyed the function of Sen1; yeast cells could not survive when carrying only the mutant gene. When these mutant proteins were expressed from a plasmid in otherwise wild-type cells, five of them had a dominant negative effect, interfering with transcription termination at a reporter gene. This lends support to the idea that Sen1 is important for transcription termination and that the disease mutations affected this function.

The remaining 7 of the 13 mutant genes could support life as the only copy of SEN1 in yeast. However, 5 of the mutant strains displayed heat-sensitive growth, and 4 of these showed increased transcriptional readthrough.

Taken together, these results show that the helicase domains of Senataxin and Sen1 are extremely important for their function. They also show that Sen1 can be used as a model to discover the effects of individual disease mutations in SETX, as long as those mutations are within regions that are homologous between the two proteins.

It still isn’t clear exactly how helicase activity can put the brakes on that RNA polymerase train, nor why runaway RNA polymerase can have such specific effects on the human nervous system. These questions need more investigation, and the yeast model system is now in place to help with that.

So, although it might not be obvious to the lay person (or politician) that brainless yeast cells could tell us anything about neurological diseases, in fact they can. Yeast may not have brains, but they definitely have RNA polymerase. And once we learn how the brakes work for pol II in yeast cells, we may have a clue how to repair them in humans.

by Maria Costanzo, Ph.D., Senior Biocurator, SGD

Categories: Research Spotlight Yeast and Human Disease

Tags: transcription , RNA polymerase II , helicase , ALS , Saccharomyces cerevisiae

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.

Not the best way to run a genome either. Image from the Cornell University Library via Wikimedia Commons

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: UTR , transcription , RNA polymerase II , Saccharomyces cerevisiae

A GLAMorous New Role for Prefoldin

October 17, 2013

The glass slipper screen couldn’t find the hidden glamour of the prefoldin complex. But the GLAM assay did.

The prefoldin complex seemed like an ordinary housekeeper. It sat in the cytoplasm and folded protein after protein, just as Cinderella spent her days folding laundry for her stepsisters.

In the old story, the handsome prince searched the kingdom for a girl whose foot would fit the glass slipper. Using this crude screen, he finally found Cinderella and revealed her to be the true princess that she was.

In a new study, Millán-Zambrano and coworkers did essentially the same thing for the prefoldin complex.  They searched the genome of S. cerevisiae for new mutations that would affect transcription elongation. They found the prefoldin complex subunit PFD1 and went on to establish that in addition to its humdrum cytoplasmic role, prefoldin has a surprising and glamorous role in the nucleus facilitating transcriptional elongation.

The researchers decided to cast a wide net in their search for genes with previously undiscovered roles in transcriptional elongation. Their group had already worked out the GLAM assay (Gene Length-dependent Accumulation of mRNA), which can uncover elongation defects.

The assay uses two different reporter gene constructs that both encode Pho5p, an acid phosphatase. One generates an mRNA of average length, while the other generates an unusually long mRNA when fully transcribed. The acid phosphatase activity of Pho5p is simple to measure, and correlates well with abundance of its mRNA. If there is a problem with transcriptional elongation in a particular mutant strain, there will be much less phosphatase activity generated from the longer form than from the shorter one. So the ratio of the two gives a good indication of how well elongation is working in that mutant strain.

Millán-Zambrano and coworkers used this assay to screen the genome-wide collection of viable deletion mutants. They came up with mutations in lots of genes that were already known to affect transcriptional elongation, confirming that the assay was working. They also found some genes that hadn’t been shown to be involved in elongation before.  One of these was PFD1, a gene encoding a subunit of the prefoldin complex. As this deletion had one of the most significant effects on elongation, they decided to investigate it further.

Prefoldin is a non-essential complex made of six subunits that helps to fold proteins in the cytoplasm as they are translated. The authors tested mutants lacking the other subunits and found that most of them also had transcriptional elongation defects in the GLAM assay, although none quite as strong as the pfd1 mutant.

Since prefoldin is important in folding microtubules and actin filaments, the researchers wondered whether the GLAM assay result was the indirect effect of cytoskeletal defects. They were able to rule this out by showing that drugs that destabilize the cytoskeleton didn’t affect the GLAM ratio in wild-type cells, and that mutations in prefoldin subunits didn’t confer strong sensitivity to those drugs.

If prefoldin has a role in transcription, it would obviously need to get inside the nucleus. It had previously been seen in the cytoplasm, but when the authors took another look, they found it in the nucleus as well. Furthermore, Pfd1p was bound to the chromatin of actively transcribed genes! And besides its effect on transcription elongation, the pfd1 mutant has lower levels of RNA polymerase II occupancy and abnormal patterns of histone binding on transcribed genes.

There’s still a lot of work to be done to figure out exactly what prefoldin is doing during transcriptional elongation. Right now, the evidence points to its involvement in evicting histones from genes in order to expose them for transcription. But even before all the details of this story are worked out, this is a good reminder never to assume that an everyday housekeeper is only that.

With the right screen we can find new and exciting things about the most humdrum of characters. A glass slipper screen revealed the princess under that apron and chimney soot. And a GLAM assay revealed the sexy, exciting transcription elongation factor that is prefoldin.

by Maria Costanzo, Ph.D., Senior Biocurator, SGD

Categories: Research Spotlight

Tags: prefoldin , RNA polymerase II , transcription elongation , Saccharomyces cerevisiae

Keeping the Noise Down

May 08, 2013

When you get down to a single cell, things can get really noisy. Instead of the nice, smoothed over data that you see in populations, you see some variation from cell to cell. This is even if all the cells are identical genetically.

Too much noise is bad for individuals.

Of course this makes perfect sense if you think about it. Part of the variation comes from slightly different environments. Conditions at the bottom of the flask are bound to be different from those at the top! This goes by the name of extrinsic noise.

Another source of variation has to do with levels of reactants within the cell and the chances that they encounter each other so they can react. These effects can be especially pronounced when there aren’t a lot of reactants around. This goes by the name intrinsic noise.

One process with a lot of noise is gene regulation. It is often affected by minor fluctuations in the environment and there are usually just one or two copies of the gene itself. This is the perfect recipe for noise.

The noisiness of gene expression can be split into two steps. One, called burst frequency, reflects how often RNA polymerase sits down and starts transcribing a gene. The second, burst size, has to do with how many proteins are produced each time a gene is turned on.

Of these two processes, the most sensitive to noise is usually burst frequency. A transcription factor (TF) has to find the promoter of the gene it is supposed to turn on and then bring the polymerase over to that gene. This is dependent on the amount of TF in a cell and the number of TF binding sites on the DNA. What this means is that most of the time, genes with low levels of expression tend to be very noisy.

There are some situations, though, where it is very important to have low expression and low noise: for example, where a cell needs at least a few copies of a protein, but can’t tolerate too many. For most promoters, low levels of expression mean high noise, which in turn means there will be some cells that lack this key protein entirely. But a new study out in PLOS Biology shows one way that a promoter can have the best of both worlds.

In this study, Carey and coworkers examined the noisiness of sixteen different naturally occurring promoters in the yeast S. cerevisiae, controlled by the TF Zap1p. This is a great system because the activity of Zap1p is determined by the concentration of zinc in the medium. This means the authors were able to look at the noisiness of these promoters under a broad range of gene activities.

Their research yields a treasure trove of information about the noisiness of these promoters at varying levels of expression. As we might predict, noise decreased at most (11/13) of the reporter genes as more active Zap1p was around. This makes sense, as cell to cell variability will decrease as genes are turned on more often. Higher burst frequency means less noise.

The opposite was true for most (2/3) of the reporters repressed by Zap1p. As more Zap1p was around, transcription of the reporter gene became less frequent, which meant that the noise effects became more prominent.

One of the more interesting findings in this study focused on an exception to this rule. The ZRT2 promoter showed a bimodal expression pattern, as it was activated at low levels of zinc and repressed at high levels. What makes it so interesting is that its noise level stays fairly constant.

As the zinc concentration increases and activity goes up, the noise goes down. This is what we would expect. But when zinc levels get high enough so that the gene is repressed, the noise levels do not increase. They stay similar to the levels seen with the activated gene.

The authors show that this promoter is repressed differently than the other two repressed promoters, ADH1 and ADH3. These promoters are repressed by decreasing the burst frequency: they fire less often when repressed. In contrast, the ZRT2 promoter fires at the same activated rate when repressed, but yields less protein with each firing: repression decreases burst size.

So this is how a cell can manage to get a gene turned on at low levels more or less uniformly through a cell’s population. If it can create a situation where the gene fires a lot but very little protein is made with each firing, then the cell will have relatively constant but low levels of that protein.

This study also provides a new tool for dissecting how a TF affects the expression of a gene. If a repressor decreases expression without an increase in noise, then it is probably affecting burst size. If on the other hand the noise goes up as expression goes down, then the repressor is affecting burst frequency.

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

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae , RNA polymerase II , cellular noise , transcription

Old Genes, New Tricks

March 14, 2013

You can’t teach an old dog new tricks, or so the saying goes. But imagine you found that your old dog knew a complicated trick and had been doing it all her life, right under your nose, without your ever noticing it! You’d be surprised – about as surprised as the Hinnebusch group at NIH when they discovered that some long-studied S. cerevisiae genes had an unexpected trick of their own.

old dog

Don’t underestimate old dogs or well studied genes. Sometimes they’ll surprise you!

They were working on the VPS* (vacuolar protein sorting) genes. While known for a very long time to be important in protein trafficking within the cell, Gaur and coworkers found that two of these genes, VPS15 and VPS34, play an important role in RNA polymerase II (pol II) transcription elongation too. Now there is an unexpected new trick…like your dog learning to use a litter box!

There had been a few hints in recent years that the VPS genes, especially VPS15 and VPS34, might have something to do with transcription. Following up on these, the researchers tested whether vps15 and vps34 null mutants were sensitive to the drugs 6-azauracil and mycophenolic acid. Sensitivity to these drugs is a hallmark of known transcription elongation factors. Sure enough, they were as sensitive as a mutant in SPT4, encoding a known transcription elongation factor. Further experiments with reporter genes and pol II occupancy studies showed that pol II had trouble getting all the way to the end of its transcripts in the vps mutant strains.

There was a bit of genetic interaction evidence that had suggested that there might be a connection between VPS15, VPS34, and the NuA4 histone acetyltransferase complex. This is important, since NuA4 is known to modify chromatin to help transcription elongation. Looking more closely, the researchers found that Vps34p and Vps15p were needed for recruitment of NuA4 to an actively transcribing reporter gene.

Other lines of investigation all pointed to the conclusion that these VPS proteins have a role in transcription. They were required for positioning of several transcribing genes at the nuclear pore, could be cross-linked to the coding sequences of transcribing genes, and could be seen localizing at nucleus-vacuole junctions near nuclear pores.

One appealing hypothesis to explain this has to do with what both genes actually do. Vps34p synthesizes phosphatidylinositol 3-phosphate (PI(3)P) in membranes, while Vps15p is a protein kinase required for Vps34p function. The idea is that when Vps15p and Vps34p produce PI(3)P at the nuclear pore near transcribing genes, this recruits the NuA4 complex and other transcription cofactors that can bind phosphoinositides like PI(3)P. There are hints that this mechanism may also be at work in mammalian and plant cells.

There’s a lot more work to be done to nail down the exact role of these proteins in transcription. But this story is a good reminder to researchers that new and interesting discoveries may always be hiding in plain sight.

* These genes were also called VPL for Vacuolar Protein Localization and VPT for Vacuolar Protein Targeting

by Maria Costanzo, Ph.D., Senior Biocurator, SGD

Categories: Research Spotlight

Tags: transcription , VPS genes , RNA polymerase II , Saccharomyces cerevisiae

Cancerous Avalanche

March 05, 2013

Cancer often gets going with chromosome instability.  Basically a cell gets a mutation that causes its chromosomes to mutate at a higher rate.  Now it and any cells that come from it build mutations faster and faster until they hit on the right combination to make the cell cancerous.  An accelerating avalanche of mutations has led to cancer.

avalanche

A mutation causing chromosomal instability can start an avalanche that leads to cancer.

There are plenty of obvious candidates for the genes that start these avalanches: genes like those involved in segregating chromosomes and repairing DNA, for example.  But there are undoubtedly sleeper genes that no one has really thought of.  In a new study out in GENETICS, Minaker and coworkers have used the yeast S. cerevisiae to identify three of these genes — GPN1 (previously named NPA3), GPN2, and GPN3.

A mutation in any one of these genes leads to chromosomal problems.  For example, mutations in GPN1 and GPN2 cause defects in sister chromatid cohesion and mutations in GPN3 confer a visible chromosome transmission defect.  All of the mutants also show increased sensitivity to hydroxyurea and ultraviolet light, two potent mutagens.  And if two of the genes are mutated at once, these defects become more severe.  Clearly, mutating GPN1, GPN2, and/or GPN3 leads to an increased risk for even more mutations!

What makes this surprising is what these genes actually do in a cell.  They are responsible for getting RNA polymerase II (RNAPII) and RNA polymerase III (RNAPIII) into the nucleus and assembled properly.  This was known before for GPN1, but here the authors show that in gpn2 and gpn3 mutants, RNAPII and RNAPIII subunits also fail to get into the nucleus. Genetic and physical interactions between all three GPN proteins suggest that they work together in overlapping ways to get enough RNAPII and RNAPIII chugging away in the nucleus.

So it looks like having too little RNAPII and RNAPIII in the nucleus causes chromosome instability. This is consistent with previous work that shows that mutations in many of the RNAPII subunits have similar effects.  Still, these genes would not be the first ones most scientists would look at when trying to find causes of chromosomal instability. Score another point for unbiased screens in yeast leading to a better understanding of human disease.

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

Categories: Research Spotlight Yeast and Human Disease

Tags: chromosome instability , Saccharomyces cerevisiae , RNA polymerase II , RNA polymerase III , cancer

Puzzling Out Gene Expression

February 21, 2013

Have you ever put together a million piece puzzle that was all blue? That is sort of what it sometimes feels like figuring out how genes are turned on or off, up or down.

jigsaw puzzle

There are hundreds or even thousands of proteins called transcription factors (TFs) controlling gene expression. And there is a seemingly simple but frustratingly opaque string of DNA letters dictating which TFs are involved at a particular gene. Figuring out which sets of proteins bind where to control a gene’s expression can be a baffling ordeal.

Up until now most of the ways of identifying which TFs are bound at which genes have been incredibly labor intensive to do on a large scale. With all of the current techniques, researchers need to construct sets of reagents before they even get started. For example, to be able to immunoprecipitate TFs along with the DNA sequences they bind, you need to insert epitope tags in all the TF genes so an antibody can pull them down. Other techniques are just as involved.

What the field needs is a quick and dirty way to find where TFs bind in the genome. And now they just might have one.

In a new study, Mirzaei and coworkers used a modification of the well-known technique mass spectrometry (mass spec) to identify TFs that bind to a specific piece of DNA. With this technique, called selected reaction monitoring, the mass spec looks only for specific peptide sequences. This not only makes it much more sensitive and reproducible than ordinary mass spec, but it should also be relatively straightforward to do if a lab has access to the right sort of mass spec. They haven’t worked out all the bugs and it is definitely still a work in progress, but the technique looks promising.

Mirzaei and coworkers set up assays to detect 464 yeast proteins that are known or suspected to be involved in regulating RNA polymerase II transcription. Then they tested their assay on a 642 base pair piece of DNA known to contain signals that affect the levels of FLO11 transcription. They found fifteen proteins (out of the 222 they searched) that bound this piece of DNA. Of these, only one, Msn1p, had been previously identified as regulating the FLO11 gene. The other fourteen had not been found in any previous assays.

The authors next showed that two of these fourteen proteins, Mot3p and Azf1p, represented real regulators of the FLO11 gene. For example, deletion of MOT3 led to a threefold increase in FLO11 expression under certain conditions. And when AZF1 was deleted, FLO11 could not be activated under a different set of conditions. So Mot3p looks like a repressor of FLO11 and Azf1p looks like an activator.

This was a great proof of principle experiment, but much more work needs to be done before this will become a standard assay in the toolkit of scientists studying gene expression. They need to figure out why some known regulators of FLO11 (Flo8p, Ste12p, and Gcn4p) were missed in the assay and whether the other twelve proteins they discovered play a role in the regulation of the FLO11 gene.

Having said this, it is still important to note that even this early stage model of the assay identified two proteins that scientists did not know controlled FLO11 gene expression. At the very least this is a quick and easy way to quickly identify candidates for gene expression. We may not be able to use it to see the whole picture on the puzzle, but it will at least get us a good start on it.

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

Categories: Research Spotlight

Tags: transcription , RNA polymerase II , Saccharomyces cerevisiae

When Polymerases Collide

October 25, 2012

Lots of recent studies are showing that transcription happens over way more DNA than anyone previously thought. For example, the ENCODE project has shown that most of a genome gets transcribed into RNA in humans, fruit flies and nematodes. This transcriptional exuberance was recently confirmed in the yeast S. cerevisiae as well.

There is also a whole lot of antisense transcription going on. Taken together, these two observations suggest that there are lots of opportunities for two polymerases to run headlong into each other. And this could be a big problem if polymerases can’t easily get past one another.

Goats butting heads

What happens when RNA polymerases meet head-on?

Imagine that the two polymerases clash in the middle of some essential gene. If they can’t somehow resolve this situation, the gene would effectively be shut off. Bye bye cell!

Of course this is all theoretical at this point. After all, smaller polymerases like those from T3 and T4 bacteriophages manage to sneak past one another. It looks like this isn’t the case for RNA polymerase II (RNAPII), though.

As a new study by Hobson and coworkers in Molecular Cell shows, when two yeast RNAPII molecules meet in a head on collision on the same piece of DNA, they have real trouble getting past each other. This is true both in vitro and in vivo.

For the in vivo experiments, the authors created a situation where they could easily monitor the amount of transcription close in and far away from a promoter in yeast. Basically they pointed two inducible promoters, from the GAL10 and GAL7 genes, at one another and eliminated any transcription terminators between them. They also included G-less cassettes (regions encoding guanine-free RNA) at different positions relative to the GAL10 promoter, so that they could use RNAse T1 (which cleaves RNA at G residues) to look at how much transcription starts out and how much makes it to the end.

When they just turned on the GAL10 promoter, they saw equal amounts of transcription from both the beginning and the end of the GAL10 transcript. But when they turned on both GAL10 and GAL7, they saw only 21% of the more distant G-less cassette compared to the one closer to the GAL10 promoter.

They interpret this result as meaning the two polymerases have run into each other and stalled between the two promoters. And their in vitro data backs this up.

Using purified elongation complexes, they showed that when two polymerases charge at each other on the same template, transcripts of intermediate length are generated. They again interpret this as the polymerases stopping dead in their tracks once they run into one another. Consistent with this, they showed that these stalled polymerases are rock stable using agarose gel electrophoresis.

Left unchecked, polymerases that can’t figure out how to get past one another would obviously be bad for a cell. Even if it were a relatively rare occurrence, eventually two polymerases would clash somewhere important, with the end result being a dead cell. So how do cells get around this thorny problem?

King Arthur and the Black Knight

To get past the Black Knight, Arthur had to destroy him. Hopefully the cell has more tricks up its sleeve than that!

One way is to get rid of the polymerases. The lab previously showed that if a polymerase is permanently stalled because of some irreparable DNA lesion, the cell ubiquitinates the polymerase and targets it for destruction. In this study they used ubiquitin mutants to show that the same system can work at these paused polymerases too. Ubiquitylation-compromised yeast took longer to clear the polymerases than did their wild type brethren.

The authors think that this isn’t the only mechanism by which polymerases break free though. They are actively seeking factors that can help resolve these crashed polymerases. It will be interesting to see what cool way the cell has devised to resolve this dilemma.

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

Categories: Research Spotlight

Tags: transcription , RNA polymerase II , ubiquitin-mediated degradation , Saccharomyces cerevisiae

What Happens in Genes, Stays in Genes

September 06, 2012

Chromatin proteins, primarily histones, are a great way to control what parts of a cell’s DNA are accessible to its machinery.  These proteins coat the DNA and are marked up in certain ways to indicate how available a piece of DNA should be.  A methyl group here, an acetyl group there and a cell “knows” where the genes are that it is supposed to read!

Why doesn't RNA polymerase get a strike every time?

Of course this structure needs to be maintained or a cell might start to misread parts of its DNA as starting points of genes.  Then RNA polymerase II (RNAPII), the enzyme responsible for reading most protein-coding genes, would start making RNA from the wrong parts of the DNA, wreaking havoc in a cell.

One place where maintaining chromatin structure might be especially tricky is within the coding parts of genes.  It is easy to imagine RNAPII barreling down the DNA, knocking the proteins aside like pins in a bowling alley.  But it doesn’t.  For the most part the chromatin structure stays the same and survives the onslaught of an elongating RNAPII.

Two key marks for keeping histones in place are the trimethylation of lysine 36 of histone H3 (H3K36me3) that is mediated by Set2p, and a general deacetylation of histone H4 that is mediated by the Rpd3S histone deacetylase complex.  We know this because loss of either complex causes an increase in H4 acetylation and transcription starts from within genes.

In a recent study in Nature Structural & Molecular Biology, Smolle and coworkers identified two key components that help chromatin resist an elongating RNAPII in the yeast S. cerevisiae.  The first, called the Isw1b complex, binds H3K36me3 and the second, the Chd1 protein, binds RNAPII itself.  That these two were involved wasn’t surprising since previous work had suggested they helped prevent histone exchange at certain genes.

What makes this work unique is that the researchers showed the global importance of these proteins in the process and were able to tease out some of the fine details of what is going on at the molecular level. They used electrophoretic mobility shift assays to show that Isw1b bound the trimethylated form of H3 via its Ioc4p subunit and used chromosome immunoprecipitation coupled to microarrays (ChIP-chip) to show that Isw1b localized to the middle of genes in vivo. They also showed that when Set2p was removed, the localization disappeared (presumably because of the loss of the trimethylation of lysine 36).  They clearly demonstrated that Isw1b is found primarily in the middle of genes.

While these results indicate that the Ioc4p-containing Isw1b complex is moored to the middle of genes via its interaction with H3K36me3, it does not establish what it is doing there.  For this the researchers knocked out Isw1b and Chd1 and showed via genome tiling arrays a global increase in cryptic transcription starts.  The DNA in the middle of genes was now being used inappropriately by RNAPII as starting points for transcription.  Further investigation with Isw1b and Chd1 knockouts showed an increase in chromosome exchange and an increase in acetylated H4 in the middle of genes.

Whew.  So it appears that Isw1b and Chd1 inhibit inappropriate starts of transcription by keeping hypoacetylated histones in place over the parts of a gene that are read.   They are two of the key players in maintaining the right chromatin structure over genes.  They help keep RNAPII from railroading histones aside as it elongates, thus protecting the cell from inappropriate transcription starts.

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

Categories: Research Spotlight

Tags: Chd1 , chromatin , Isw1b , RNA polymerase II , Saccharomyces cerevisiae

New data tracks added to GBrowse

April 23, 2012

SGD has added a new mix of data tracks to our GBrowse genome viewer from seven publications covering transcriptome exploration via tiling microarrays (David et al. 2006), genomic occupancy of RNA polymerase II and III and associated factors (Kim et al. 2010; Ghavi-Helm 2008), 3′ end processing (Johnson et al. 2011), histone H2BK123 monoubiquitination (Schulze et al. 2011) and high-resolution ChIP by a novel method called ChIP-exo (Rhee et al. 2011; Rhee et al. 2012). Download data tracks, metadata and supplementary data by clicking on the ‘?’ icon on each data track within GBrowse or directly from the SGD downloads page. We welcome new data submissions pre- or post-publication and invite authors to work with us to integrate their data into our GBrowse and PBrowse viewers. Please contact us if you are interested in participating or have questions and comments. Happy browsing!

Categories: New Data

Tags: histone modifications , RNA polymerase II , RNA polymerase III , transcriptome , ChIP-exo

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