New & Noteworthy

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.

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: RNA polymerase II, Saccharomyces cerevisiae, transcription

The Rhythm of Ribosomes

February 13, 2013

We all know that some people march to the beat of a different drummer. But now we’re finding out that mRNAs also have their own particular rhythms as they move along the ribosome.

It’s long been known that some codons just work better than others. They are translated faster and more accurately mostly because they interact more strongly with their tRNAs and because there are more of their specific tRNAs around. So why hasn’t evolution gotten rid of all the “slow” codons? With only optimal codons, translation could move at a marching beat all the time.

One idea has been that a few pauses every now and then are a good thing. For example, maybe slowing down translation at the end of a stretch coding for a discrete protein domain gives that domain time to fold properly. This would make it less likely for the polypeptide chain to end up tangled, or misfolded. Great thought, but even when researchers looked in multiple organisms, they couldn’t find a consistent correlation between codons used and protein structure. Until now, that is.

In a recent study published in Nature Structural and Molecular Biology, Pechmann and Frydman took a novel approach to this question. They derived a new formula to measure codon optimality. Using it they found that codon usage was highly conserved between even distantly related species, and that this conservation reflected the domain structure of the particular protein a ribosome was translating.

First, the authors came up with a more accurate way of classifying codons as optimal or non-optimal. They took advantage of the huge amount of data available for S. cerevisiae and included a lot more of it in the calculation, such as the abundance of hundreds of mRNAs and their level of ribosome association. They also took into account competition between tRNAs based on supply and demand, something that the previous studies had not done.

Once they developed this new translational efficiency scale, they applied it to ten other yeast species – from closely related budding yeasts all the way out to the evolutionarily distant Schizosaccharomyces pombe. The authors found that positions of optimal and non-optimal codons were indeed highly conserved across the yeasts. And codon optimality was highly correlated with protein structure.

One of the better examples of this is alpha helices. These protein domains form while still inside the ribosomal tunnel. The authors found that the mRNA regions coding alpha helices use a characteristic pattern of optimal and non-optimal codons to encode the first turn of the helix. They theorize that this sets the rhythm for folding the rest of the helix. Other structural elements are coded by distinct codon signatures too.

This isn’t just interesting basic research. It has some far-reaching practical implications too.

When using yeast to make some sort of industrial product, the thought has been to use as many optimal codons as possible. This has not always worked out, and now we may know why. A gene that tailors the codon usage to the rhythm of the protein structure is probably the best way to make a lot of correctly folded protein.

And the factory isn’t the only place where this kind of information will come in handy. Protein misfolding is the known or suspected culprit in a whole slew of human neurodegenerative diseases such as Alzheimer’s, ALS, Huntington’s chorea, and Parkinson’s disease. A better understanding of its causes might give us insights into managing those diseases.

Who knew in 1971 that translation actually is a rhythmic dance?

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

Categories: Research Spotlight

Tags: evolution, protein folding, ribosome, Saccharomyces cerevisiae, translation

Giving the Keys Back to the Cell

February 06, 2013

When someone has a bit too much to drink, it is a good idea to take away their car keys. This keeps them safe until they can drive again. But the next morning, that hung over person needs to get their keys back so they can get to work.

Cells sometimes face a similar situation. Instead of being drunk though, cells have something go wrong while they are growing and dividing. When this happens, the cell stops the cell cycle at the next checkpoint, fixes what is wrong, and then starts the cell cycle back up again where it left off.

Scientists have learned a lot about how the keys are taken from cells, but not a whole lot about how they get them back. Fong and coworkers help to rectify this situation in a new study out in GENETICS. There they identified proteins key to releasing a yeast cell from its S-phase checkpoint.

If a cell’s DNA is damaged while it is growing and dividing, replication is slowed at the S-phase checkpoint. This gives the cell a chance to fix the DNA before it is copied. The authors found that in the absence of the DIA2 gene, yeast cells had trouble getting replication up and running again. This implies that this gene is required for yeast to overcome the S-phase checkpoint. The cell needs DIA2 to get its keys back.

Dia2p is an F-box protein involved in identifying certain proteins for destruction. It is one of several interchangeable subunits that provide specificity to the SCF ubiquitin ligase complex. The idea would be that Dia2p is important for degrading the “keeper of the keys,” the protein responsible for stopping the cell cycle in the S-phase.

To test whether Dia2p is important for checkpoint recovery, Fong and coworkers first activated the S-phase checkpoint by adding the DNA damaging agent MMS. Then they removed the MMS and measured how long it took the cells to finish copying their DNA. The dia2Δ mutant was significantly slower than wild type.

Given that Dia2p is involved in ubiquitin-mediated degradation, the authors reasoned that it may help a cell get out of S-phase arrest by degrading a protein that was keeping it there. To find this “keeper of the keys,” Fong and coworkers looked for mutations that rescued dia2Δ cells in the presence of high levels of MMS. The idea is that if they knock out the gene that is keeping the dia2Δ cells arrested, then the cells could overcome the block caused by the MMS.

One of the genes that came up in the screen was MRC1. To confirm that Dia2p and Mrc1p work together in releasing a yeast cell from the S-phase checkpoint, the authors constructed a double mutant carrying dia2Δ and a mutant version of MRC1, mrc1AQ, that they knew was checkpoint defective. Indeed, the double mutant behaved like wild type in their checkpoint recovery assay. Since the mutant Mrc1-AQp could not keep cells at the checkpoint, there was no need for Dia2p to target it for degradation. The double mutant cell never let go of its keys.

The simplest model to explain what happens in wild type is that when its DNA is damaged, a cell is prevented from progressing through S-phase by Mrc1p. Then when the DNA is repaired, Dia2p, providing specificity to the SCF ubiquitin ligase complex, targets Mrc1p for degradation. The cell is now released, allowing the cell cycle to continue.

The authors did a lot more work that we won’t go into here, but suffice it to say that Dia2p and Mrc1p are not the only players involved in releasing a cell from the S-phase checkpoint. There were other genes, both identified and unidentified, that came up in their screen. These will need to be studied as well.

And this isn’t all just interesting from a scientific standpoint. Many cancer treatments work by damaging the cancer cell’s DNA while it is growing and dividing. A better understanding of how cells are arrested and released may lead to better cancer treatments.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: DNA replication checkpoint, Saccharomyces cerevisiae

Ghosts of Centromeres Past

January 28, 2013

Every cell needs to correctly divvy up its chromosomes when it divides.  Otherwise one cell would end up with too many chromosomes, the other with too few and they’d both probably die.

Cells have developed elaborate machinery to make sure each daughter gets the right chromosomes.  One key part of the machinery is the centromere.  This is the part of the chromosome that attaches to the mitotic spindle so the chromosome gets dragged to the right place. 

Given how precise this dance is, it is surprising how sloppy the underlying centromeric DNA tends to be in most eukaryotes.  It is very long with lots of repeated sequences which make it very tricky to figure out which DNA sequences really matter.  An exception to this is the centromeres found in some budding yeasts like Saccharomyces cerevisiae.  These centromeres are around 125 base pairs long with easily identifiable important DNA sequences.

The current thought is that budding yeast used to have the usual diffuse, regional centromeres but that over time, they evolved these newer, more compact centromeres.  Work in a new study published in PLOS Genetics by Lefrançois and coworkers lends support to this idea.

These authors found that when they overexpressed a key centromeric protein, Cse4p (or CenH3 in humans), new centromere complexes formed on DNA sequences near the true centromeres. The authors termed these sequences CLR’s or Centromere-Like Regions.  And they showed that these complexes are functional.

When Lefrançois and coworkers kept the true centromere from functioning on chromosome 3 in cells overexpressing Cse4p, 82% of the cells were able to properly segregate chromosome 3.  This compares to the 62% of cells that pull this off with normal levels of Cse4p.  The advantage disappeared when the CLR on chromosome 3 was deleted.

A close look at the CLRs showed that they had a lot in common with both types of centromeres.  They had an AT-rich 90 base pair sequence that looked an awful lot like the kind of sequence that Cse4p prefers to bind and a lot like the repeats found within more traditional centromeres.  They also tended to be in areas of open chromatin and close to true centromeres. The obvious conclusion is that these are remnants of the regional centromeres budding yeast used to have. 

The hope is that the yeast CLRs might make studying regional centromeres easier.  They are so long and complicated that it is very difficult to pick out which sequences matter and which don’t, but the yeast CLRs could be a simpler model system.  Even better, the CLRs might shed some light on the process of neocentromerization – the formation of new centromeres outside of centromeric regions, which happens a lot in cancer cells. Once again, simple little S. cerevisiae may hold the key to understanding what’s going on in much larger organisms.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: centromeres, evolution, Saccharomyces cerevisiae, yeast model for human disease

Autophagy’s (Atg)9th Symphony

January 17, 2013

(Please click the musical note and listen to the music while reading.) The music you’re listening to starts off with a marimba. Then a flute joins in and as the marimba fades, in comes a shamisen. The piece progresses similarly with a harp, and then ends with the reappearance of the marimba. A nice, jaunty little piece of music.

This song is actually a tool for learning about autophagy in the yeast S. cerevisiae. Autophagy is a way to break down damaged or no longer useful proteins and recycle their components for later use. It is a very important pathway in keeping starving yeast alive. Many of the proteins involved in autophagy are highly conserved, and autophagy defects are implicated in several kinds of human disease.

As described in a recent paper, Takahashi and coworkers converted the sequences of four proteins involved in a step in autophagy – Atg9, Sso1, Sec9, and Sec22 – into pieces of music using UCLA’s Gene2Music program. Each protein was then assigned a musical instrument. Atg9 was played with the marimba, Sso1 with the flute, Sec9 with the shamisen, and Sec22 with the harp. The orchestrated piece of music reflects how each protein interacts with the others in the autophagy pathway.

Atg9 is a transmembrane protein that is key to making the vesicles that carry the damaged or unused proteins to the lysosome for destruction. But it, like the marimba, is not enough. Atg9 is recruited into service by at least three other proteins, Sso1, Sec9, and Sec22. These appear in succession in the musical piece as a flute, shamisen, and harp. Just like all four are needed for the orchestral piece, all four are also needed for successful autophagy.

Now listen to the music again. With this background, did you find the piece more illuminating? If you didn’t, it may simply be because it doesn’t fit your learning style, or match the type of intelligence that is your strength. Some people may respond to music better than they do to pictures of pathways or memorizing the steps involved. It may be that these people’s understanding of complicated pathways is enhanced with a musical component.

There will need to be more research on musical representation of complex pathways to see if they actually help students and even the public better understand science. If they do, I am looking forward to hearing the Krebs Cycle put to music. Or the assembly of the RNA polymerase II preinitiation complex. Which pathways do you want put to music?

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

Categories: Research Spotlight

Tags: autophagy, music, Saccharomyces cerevisiae, teaching

P is for Protection (not Processing)

January 11, 2013

Growing and dividing are dangerous work for a cell. Making all that energy throws off free radicals that mutate DNA and wreak havoc with delicate intracellular machinery. Given this it might seem surprising that just sitting there, not growing, is dangerous too. And yet it looks like it is.

When a cell runs out of food and goes into a quiescent state, it creates ribonucleoprotein (RNP) complexes called processing bodies (P bodies). In a new study out in GENETICS, Shah and colleagues were able to control how well yeast cells could make these P bodies. What they found was that cells that had trouble making P bodies didn’t survive the quiescent state as well as those cells that were great P body makers. It looked like P bodies were doing something to protect the cell when it wasn’t growing. In other words, being quiescent is dangerous too.

The key discovery made by the authors that allowed them to do these experiments was the fact that the Ras/PKA signaling system works specifically through the Pat1 protein to make P bodies. So by controlling the sensitivity of Pat1p to the signaling system, they could control the number of P bodies in the cell.

The Ras/PKA pathway phosphorylates two serine residues on Pat1p. When they are phosphorylated, P bodies are disrupted and/or are prevented from forming. The Pat1-EE mutation replaces the serine residues with glutamic acids, mimicking the phosphorylated state. The authors found that yeast cells carrying Pat1-EE produced fewer, smaller P bodies than did yeast carrying the wild type version of Pat1.

The authors then used this constitutively active mutant to ask whether P bodies helped cells survive the quiescent state. They compared the survival rate of cells carrying either the wild type version or the Pat1-EE protein and found that cells carrying the wild type version of Pat1 were more likely to survive after quiescence than were those cells carrying the constitutive form. More P bodies led to better survival.

The authors don’t yet know why this is, but one idea is that proteins and RNAs critical for survival after quiescence are stored in these particles. The idea would be that cells that have these key components squirreled away and protected survive better than those cells where these proteins and RNAs have degraded.

As a final point, it is important to mention why this matters (besides the excitement of figuring out how things work). Quiescent yeast cells are used as models for aging in higher eukaryotes like us. Perhaps by understanding how to make a yeast cell better survive this non-growing state, we can learn something about how to make people live longer too.

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

Categories: Research Spotlight

Tags: P bodies, quiescence, Saccharomyces cerevisiae

Be Good, For Adaptation’s Sake

December 17, 2012

You may never have herded cows. But in one way or another, you’ve certainly experienced the tragedy of the commons.

This happens when a village shares a pasture that can only feed a certain number of cows. For the system to work, everyone has to cooperate and keep the total number of cows under that limit. But inevitably, one cheater comes along and adds extra cows to his herd. At no immediate cost to himself, he gets all the benefits of the extra cows. But then tragedy kicks in as the pasture is overgrazed until no one can have any cows.

This doesn’t just happen out in the village. We can see it in the overfishing of the oceans, the production of carbon dioxide contributing to global warming, the milking of investors on Wall Street, and many other aspects of modern life. But perhaps surprisingly, we can even see it in cultures of the humble yeast S. cerevisiae.

In a recent issue of the Proceedings of the National Academy of Sciences, Waite and Shou set up a yeast system to look at the factors influencing the tragedy of the commons. In human society, sometimes cheaters cause a collapse, but other times, cooperators get together and exile the cheaters from the village. You might think that since yeast aren’t quite as smart as humans, in yeast “society” the cheaters would always win. However, Waite and Shou found that sometimes the cheaters were marginalized or even driven out, and the cooperators thrived!

To do these experiments, the researchers set up a very clever pasture in miniature. They engineered three strains, each marked with a different-colored fluorescent marker so they could be distinguished from each other.

The two “cooperator” strains needed each other to survive on minimal medium: one required lysine and produced excess adenine, while the other required adenine and produced excess lysine. The “cheater” strain required lysine but it didn’t provide any nutrients. So the cheater needed one of the cooperators to survive, but didn’t contribute anything to the common good.

As we might expect, being cooperative has a cost. The generous production of extra nutrients made the cooperator grow slower than the otherwise identical cheater strain. So you would predict that if you mixed the cooperators and the cheater in equal numbers and grew them together, the cheater would take over and collapse the culture every time.

However, when the researchers mixed all three strains in a 1:1:1 ratio and grew lots of replicate cultures, they found that cheaters didn’t always prosper. Sometimes the nice guys finished first.

Of course some of the cultures did collapse under the influence of the rapacious cheaters. After a while these cultures stopped growing and turned out to be made up of mostly dead or dying cheater cells. The cheaters had taken over the culture, selfishly using up the lysine until eventually there was not enough to continue growing.

But unexpectedly, other replicate cultures were growing much faster, at rates similar to cultures without any cheaters. In these cultures, the two cooperator strains had either dominated the culture or even driven the cheaters extinct!

To explain this, the researchers proposed that the intense selection pressure led to an adaptive race between cooperators and cheaters. In surviving cultures, the rare cooperator with a small advantage had outcompeted the cheaters.

To confirm this, they took a close look at the winning cooperators. They found that the fitness advantage could be inherited, so they used whole-genome resequencing to find out why the cooperators were outcompeting the cheaters. They kept finding mutations in the same five genes.

These genes all made sense, as mutating them would help in an environment with limited amounts of lysine. For example, most of the mutations were found in ECM21 and DOA4. Both of these gene products are important in pathways that break down proteins like permeases. Knocking them out would keep the permeases around longer, making for better lysine uptake. But this newfound advantage did not come without a price.

The researchers tested directly whether the adaptive mutations improved growth in limiting amounts of lysine. Without exception, they did. But almost all these strains grew more slowly in abundant lysine than did their ancestor strains. That explains why these mutant strains only became a significant proportion of the population late in the life of the culture, when lysine levels were very low.

The same mutations can arise in both cooperators and cheaters, of course. But when cheaters become better at growing in low lysine levels, they just become that much better at making themselves extinct. When cooperators get better at growing in low lysine levels, they are better able to keep growing and keep the cheaters at bay.

So the take-home lesson is that cooperation does pay, after all. Especially in a constantly changing environment, cooperators can often win the adaptive race and squelch the cheaters. Maybe we should take a hint from little S. cerevisiae that being kind to each other is not only a nice thing to do, it’s in all of our best interests!

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

Categories: Research Spotlight

Tags: adaptation, evolution, Saccharomyces cerevisiae

As Good as the Original

December 07, 2012

If you got to choose between an original and a copy of that original, you’d undoubtedly choose the original. Because of mistakes during the copying process, the original is bound to be superior.

Cairns was the first scientist to try to apply the same logic to cells like mother cells of the budding yeast S. cerevisiae, or stem cells. The idea is that since these cells make lots of copies of themselves, they might have some mechanism for keeping the original DNA and sending the copies to the new cells. This would protect the original DNA from building up mutations.

While this seems reasonable, the many studies done to date have failed to provide any compelling evidence to support the idea. When scientists look at a cell’s DNA in bulk, they see it randomly dividing between mother and daughter. But this is not the end of the story.

Another idea people have had is that one of the strands of the double helix is kept in its original form in the mother while the other is free to be passed on to the daughter. There is mounting evidence in yeast and mammalian cells that there is some strand-specific selection going on. But in a new study out in GENETICS, Keyes and coworkers show that this selection is not dependent on a strand being an original as opposed to a copy.

They use a couple of cool tricks to be able to follow specific strands of DNA in specific cell types in these experiments. First, they engineered a strain that would incorporate BrdU (bromodeoxyuridine) into newly synthesized DNA, so that they could distinguish between the original and copied strands. Second, they used a technique to separate mother cells from daughter cells that involves arresting the mother cells with alpha factor, labeling them with biotin, and then allowing them to divide: the mothers can be pulled away from the daughters by their biotin labels.

As you can see from the image on the right, Keyes and coworkers let a population of biotin-labeled mother cells divide once in the presence of BrdU. Next they separated mothers (M) from daughters (D) and biotinylated the daughters.

Next they let the two populations divide separately one more time in unlabeled thymidine (TdR) instead of BrdU, and separated mothers from daughters again. As shown in the figure, this allowed them to isolate four different cell populations:

1) MM: the original mother cells
2) MD: daughters of the original mothers
3) DM: mother cells derived from the first daughter
4) DD: daughters of the first daughter

The image shows the prediction if the Watson strand is preferentially kept by the mother. As you follow along, remember that the red strand represents the labeled DNA.

If mother cells inherit the Watson strand specifically and the daughter cell always gets the Crick strand (C), then we would predict that only MD and DM cells should have BrdU DNA. The other two cell types should only have unlabeled DNA.

Let’s follow the mother side to see why. The mother cell would inherit the unlabeled Watson strand and a labeled Crick strand because she would keep the original from the first division. The labeled and so copied Crick strand would then be passed preferentially to the daughter.

The same sort of logic applies to the daughter cell. The daughter inherited the labeled Crick strand. Since this daughter now becomes the mother in the next division, the labeled strand now becomes the Watson strand. She would keep this labeled strand and pass the unlabeled Crick strand to her daughter.

These are not the results they got. Instead, all the cell types had pretty close to the same amounts of BrdU. The moms did not preferentially hang on to either the original Watson or Crick strand.

They then followed this up with a separate, similar experiment that looked at each individual chromosome on a microarray. The results were that there was no bias for either strand for any of the chromosomes.

It seems that mom doesn’t hang onto the original DNA even at a single chromosome. In the cases of strand-specific selection that are now being studied, there are most likely other ways to pick a strand that have nothing to do with whether it is an original or a copy. Another great idea foiled by data…

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

Categories: Research Spotlight

Tags: chromosome segregation, Saccharomyces cerevisiae

I See Dead Yeast (in My Beer Foam)

November 19, 2012

A study by Blasco and coworkers confirms that beer foam is littered with corpses of dead yeast. Or at least with bits of their cell walls.

This has been known for awhile. But what these researchers did was to identify one of the key proteins in the cell wall important for maintaining a good head on beer.

The authors knew from previous studies that certain mannose binding proteins play an important role in beer foam. So they used primers that lined up with the 5’ and 3’ ends of one of the known foam-related genes from S. cerevisiae, AWA1, to look for similar genes in the brewing yeast S. pastorianus. This allowed them to PCR out the CFG1 gene.

To show that this protein was involved in the foaminess of beer, they next knocked the gene out of S. pastorianus and used this deletion strain to do some brewing. What they found was that while beer made with this strain had about the same amount of foam, it didn’t last as long. This strongly suggested that CFG1 was involved in maintaining a good head on a mug of beer, earning the gene its name: Carlsbergensis Foaming Gene.

As a final experiment, they added the gene back to a strain of S. cerevisiae, M12B, that makes beer without foam. When this strain expressed CFG1 and was used to brew up some beer, that beer was foamless no more. This suggests that CFG1 may be important for foam formation as well as stabilization.

What is probably happening is that during fermentation, yeast cells are autolysing, releasing their cell wall proteins into the beer. Since Cfg1p is hydrophilic on one end and hydrophobic on the other, it forms very stable bubbles. And foam is simply a bunch of stable bubbles.

Hopefully scientists can use this information to tweak the amount of foam a given beer yields. Then a drinker can choose lots or little foam, long lasting or short lived foam, or any combination he or she wants.

Root beer foams for a different reason

NPR’s take on this study

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

Categories: Research Spotlight

Tags: beer, brewing, Saccharomyces cerevisiae, Saccharomyces pastorianus

Identifying the Unstable

November 08, 2012

One of the many stumbling blocks in finding better treatments for genetic diseases is figuring out the cause of the disease.  These days, this doesn’t necessarily mean simply identifying the gene with the mutation.  No, nowadays it can mean figuring out what each specific mutation does to the gene it damages.

See, many genetic diseases are not caused by single mutations.  Instead, lots of different mutations can all damage the same gene in different ways.  And each class of mutation may require different treatments.

Cystic fibrosis (CF) is a great example of this.  While most cases of this ultimately fatal disease are caused by mutations in the CFTR gene, not every mutation does the same thing to the CFTR protein.  Because of this, scientists have found different drugs to treat people with different classes of CFTR mutations.

So one drug, Ivacaftor, targets CFTR proteins that can’t open up as well as they should, while another investigative drug, PTC124, targets prematurely stopped CFTR proteins.  Each only treats a specific subset of CF patients who have the correct CFTR mutation.

All of this screams out for a quick and easy assay to figure out how a mutation actually disables a certain protein.  And this is where a new study by Pittman and coworkers just published in the journal GENETICS can help.

The authors have come up with a sensitive in vivo assay in S. cerevisiae that allows scientists to quickly identify mutations that lead to unstable proteins.  This kind of instability isn’t rare in human disease either.  Some of the more famous examples include a kidney disease called primary hyperoxaluria type 1 (PH1), Lou Gehrig’s disease (ALS), Parkinson’s disease, spinal muscular atrophy (SMA), and even some forms of cancer.

The assay basically inserts wild type and mutant versions of the gene of interest into the middle of the mouse dihydrofolate reductase (DHFR) gene, individually adds these chimeric genes to yeast lacking DHFR, and then measures growth rates.  The idea is that if the mutation leads to instability, the DHFR chimeric protein will be unstable too and the yeast will show growth defects under certain conditions.  This is just what they found.

Initially they focused on a gene involved in PH1, the AGT gene encoding alanine: glyoxylate aminotransferase.  They were able to show that disease causing mutations known to affect protein stability affected growth in this assay.  Not only that, but there was a strong correlation between growth and level of protein stability.  In other words, the more unstable the protein, the more severe the growth defect.

They then expanded their assay beyond known AGT mutations.  First they were able to identify a subset of disease-causing AGT mutations as affecting the stability of the AGT protein.  But the assay ran into trouble when they switched to the more stable SOD1 protein.  This protein, which is involved in most cases of ALS, is so stable that mutations that destabilized it were invisible in the assay.  The authors solved this problem by introducing a mutation into DHFR that destabilized it.  Now they could identify mutants that destabilized SOD1.

As a final step, they used their assay to screen a library of stabilizing compounds to identify those that specifically stabilized their mutant proteins.  Unfortunately, in this first attempt they only found compounds that stabilize DHFR, but the assay has the potential to find drugs that stabilize disease-related proteins as well.

Whether or not that potential is realized, this technique should still be a very useful way to determine whether a mutation affects protein stability.  Then, when drugs that stabilize the protein have been found, using this or other screens, doctors will know which patients can be helped by these compounds.  And this will be a boon for scientists and patients alike.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: protein stability, Saccharomyces cerevisiae, yeast model for human disease

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