New & Noteworthy

It Takes More Than an AUG to Translate a Gene

August 02, 2012

Translating a gene is easy, right?  Hop on the end of an mRNA and start translating at the first AUG.

Of course nothing in biology is that simple!  Not all AUGs in the beginning of mRNAs serve as the starts of translation and occasionally translation will start at a codon other than AUG.  There is obviously more to a translation start than an AUG.

In a recent study, Kochetov and coworkers set out to better define what makes a ribosome sit down and start translating.  They used a dataset compiled from S. cerevisiae in 2009 that included a wide range of translation starts ranging from the traditional to the barely recognizable.

The researchers focused on three classes of translation starts:

1)      Traditional yeast gene start sites

2)      AUG-containing uORFs

3)      uORFs that lack an AUG

The last two sets are translation starts that happen upstream of traditional genes (hence the name upstream open reading frame or uORF).  These tend to be weaker than traditional translation starts, have very short associated ORFs, and are thought to play a regulatory role in the translation of the “real” gene.

When Kochetov and coworkers analyzed the data, they confirmed some previous studies that showed that strong translation starts have an AUG, upstream RNA that is predicted to be unfolded and to be A-rich between nucleotides -6 and -1, and downstream RNA that is predicted to form a hairpin.  Most of the traditional yeast genes possessed most of these attributes.  The uORF translation starts were a different matter though.

The uORFs that had an AUG lacked the other features of a strong translation start.  They tended to have fewer A’s in the upstream region and their RNA was structured in all the wrong ways.  The uORFs that lacked an AUG apparently made up for it by having all of the other features of a strong translation start.  They were A-rich between -6 and -1, had an unstructured RNA upstream and a hairpin downstream of the translation start.  The thought is that translation starts that lack an AUG make up for it with all of the rest of the translation context being exceptionally strong. 

These kinds of studies will make the tough job of identifying genes a bit easier.  Which can only be a good thing as more and more genomes come on line.     

How translation worked at Stanford in the 70’s

Categories: Research Spotlight

Tags: AUG, ribosome, translation, translation start

Cellular Traffic Jams

July 19, 2012

Traffic jams are a way of life in Lagos, Kuala Lampur, Berlin, Los Angeles, or pretty much anywhere with too few roads and too many cars.  If only people would learn a thing or two from cells, then traffic jams might be a thing of the past.  Which is surprising, considering how much traffic there is inside of a cell.

The inside of a cell is way more crowded than any human city.  Proteins called kinesins are delivering cargo to where it needs to go by hurtling down microtubule highways through a crowded mass of macromolecules, membranes, and organelles.  This all happens in a frenzy of activity at breakneck speed. 

And yet there are not a lot of cellular traffic jams.  We surmise this because we know that when there are lots of cellular traffic problems, diseases like ALS can result.  So cells must have some way to prevent traffic problems.

In an attempt to figure out how cells prevent traffic problems, Leduc and coworkers first set out to find out how they can happen in the first place.  They did this by setting up an in vitro system of microtubule highways and the purified yeast kinesin 8 protein, Kip3p. Using this system they figured out that traffic jams can happen when too many kinesins are on the microtubule at once (density-induced jams) or when they don’t get off the end of the microtubule fast enough (bottleneck-induced jams).  These are equivalent to too many cars at rush hour, or to the obstacles of accidents or highway construction.

From these data they hypothesize that kinesins have evolved in ways that keep their density down and prevent bottlenecks.  They suggest that bottlenecks are prevented by rapid dissociation from the ends of the microtubules and that density is kept down by having the kinesins be not too processive (i.e., not keep going and going and going…).  So kinesins avoid traffic congestion by quickly getting on and off the highway both along its length and at the end.  

They concluded all of this from their elegant “highway in a tube” assay.  This system is ideally suited for studying how traffic jams might happen because it is relatively simple to change parameters like end dissociation rate and processivity by tweaking salt and/or protein concentrations.  And it is very cool because traffic jams can be watched in real time.  A cellular traffic helicopter report!

The basic idea was to generate the microtubule pathway in the presence of a slow hydrolyzing GTP analog and taxol such that the microtubules were not easily depolymerized by Kip3p.  They then added various amounts of mCherry labeled Kip3p to a small amount of EGFP-labeled Kip3p and watched to see when the EGFP-labeled Kip3p slowed down or got stuck.

They saw that high concentrations of Kip3p led to pileups at the end of the microtubule.  These pileups disappeared when the dissociation rate of Kip3p was increased by using higher salt concentrations.  They also saw that at high concentrations, the Kip3p molecules slowed down as they got in the way of each other and that decreasing processivity eliminated this problem.

So the traffic situation in a cell and a city are remarkably similar.  In both, keeping the numbers of cars or kinesins down and making sure they can quickly get around obstacles prevents traffic problems.  Maybe civil engineers need to start looking at the cell for ideas about ways to deal with the daily grind of our commutes. 

 

Ron Vale (UCSF) Part 1: Introduction to Motor Proteins

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

Categories: Research Spotlight

Yeast with Sticking Power

July 16, 2012

Most strains of Saccharomyces cerevisiae don’t stick together very well. And hardly any of them form biofilms. But it would be very useful to have a better understanding of why some strains like to stick together and others do not. 

Stickiness helps in any process where you want the yeast to do something and then get rid of it. An obvious example is ethanol production either for energy or to make our beer and wine. After sticky yeast are done with their job of making the alcohol, they simply fall to the bottom of the fermentor or float on the surface in a biofilm (the “flor”). This makes the step of separating the yeast from the finished product that much easier.

Understanding more details of yeast stickiness would also be useful for studying harmful yeast. Adhesion to other cells and to substrates is an important factor in pathogenesis. It would be nice to investigate this phenomenon in the more tractable brewer’s yeast.

The Ibeas lab has decided to figure out why most strains of S. cerevisiae can’t flocculate by comparing one of the few that can (the “flor” strain used to make sherry) to a reference strain that can’t.  They previously showed that a key gene in the process, FLO11 (also known as MUC1), is expressed at much higher levels in flor.  They were also able to show that a large part of this increased expression comes from a 111 base pair deletion in the FLO11 promoter in this particular strain.

In a recent paper in GENETICS, Barrales and coworkers set out to investigate why the loss of these 111 base pairs leads to increased gene expression.  They were able to conclude that the deletion does not significantly affect histone occupancy at the promoter.  What they could see was that histone placement was affected and that PHO23 may play a significant role in this.

The researchers had previously shown that the histone deacetylase complex (HDAC) Rpd3L was important for maximal FLO11 activity.  They next wanted to determine if this complex was the major player in explaining the increased activity of the 111 bp deletion FLO11 promoter (Δ111) over the wild type (WT) one.  They did this by comparing the level of mRNA made by each promoter in strains lacking either the Pho23p or the Rpd3p subunits of the Rpd3L complex.  They found that the Δ111 construct was much more severely compromised by the loss of PHO23 than was the WT one.  (A bit confusingly, neither was much affected by the loss of RPD3.) 

Given that PHO23 is part of a complex that affects chromatin, the next thing the researchers did was look at the histones in and around both FLO11 promoters.  They found that PHO23 was involved in maintaining an open chromatin structure at the FLO11 promoter but that deleting the 111 base pairs didn’t affect this process significantly.

Where they started to see subtle differences was when they looked at histone placement as opposed to occupancy.  Using micrococcal nuclease protection to map chromatin structure, they found a number of differences between the two promoters, centered on the deletion and the TATA box, and deleting PHO23 affected the two promoters in different ways.

It appears that FLO11 is upregulated in the flor strain because the deletion of 111 base pairs leads to an altered chromatin structure.  The next steps will be to figure out what this means and then to use that knowledge to create stickier yeast. We’ll end up with a better understanding of transcriptional regulation and adhesion, and beer and wine makers may end up with even better self separating yeast.

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

Categories: Research Spotlight

Tags: biofilm, flocculation, Saccharomyces cerevisiae, transcription

Yeast on the Brink

July 02, 2012

How scientists are using baker’s yeast to discover the warning signs of impending financial, climate, and species collapse.

Tipping points are all the rage these days. They are discussed with regard to global warming, financial collapses, ecosystems and lots of other situations too.

A tipping point is a point from which something can’t return to what was before. In other words, it is the point at which a new equilibrium is reached.

One of the more interesting tipping points occurs when a population of organisms becomes so low that it may collapse and not be able to recover. This can happen because the beasts are all so interrelated that a disease can wipe them all out.  Or they become so few in number that potential mates have trouble finding one another. Many other reasons can bring a population to this point.

Theory makes a number of predictions about how populations at the tipping point will behave.  Dai and coworkers decided to create a model system using S. cerevisiae to study what populations at the tipping point actually look like experimentally. And to perhaps find easy to study signs that a population is veering close to one of these tipping points.

Their experiments ended up faithfully reproducing a population in the lab that was at a tipping point. This is a big deal in and of itself.  But while they were able to identify signs that a population was at a tipping point, none would be very easy to spot in a wild population. 

Their model system involved using dilutions of yeast grown in sucrose. Since sucrose is hydrolyzed by yeast outside of the cell, a sucrose molecule hydrolyzed by one yeast cell can be used by another. This cooperative effect means that yeast grow better in sucrose at higher cell densities than they do at lower ones. This mimics the effects of low population density in other systems.

The researchers then did a set of simple dilution experiments with this system. They diluted a starting population of yeast by varying amounts into replicate samples and determined how each sample did with subsequent dilutions over time. They found that they reached their tipping point in their system at dilutions of between 500 and 1600. At these dilutions, some replicates survived while others went extinct. 

They confirmed they were at a tipping point by shocking their cultures with high salt. If a population is near a tipping point, it is less able to survive environmental shocks compared to a more robust system. The researchers found that those samples near the tipping point were indeed more vulnerable to salt shock. 

Taken together, these two findings suggested that the researchers had successfully engineered a model system for tipping points. They were now ready to study their population at or near its tipping point to look for any tell tale warning signs.

They found that their model system agreed with a lot of the theory. As a population neared the tipping point it tended to fluctuate more, and to take longer to reach a new stable population. Unfortunately, neither of these is an obvious sign of an impending tipping point. Both effects require lots of observations over a long time period to see.

Given the consequences of going past a tipping point (sea level rise, coral bleaching, the Great Recession, species extinction), recognizing when we are getting close to one is of paramount importance. Perhaps research like this will help us see the warning signs before it is too late to pull back from the brink.

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

Categories: Research Spotlight

Tags: Allee effect, collapse, cooperativity, Saccharomyces cerevisiae, sucrose, tipping point

Links to DRYGIN added to SGD Locus Summary and Interactions pages

June 23, 2012

SGD now provides links from both the Locus Summary and Interactions pages for each S. cerevisiae ORF to DRYGIN (Data Repository of Yeast Genetic Interactions), a database of quantitative genetic interactions of S. cerevisiae (Koh et al., 2010). These genetic interactions were determined from SGA double-mutant arrays conducted in Charles Boone’s laboratory at the University of Toronto, and include both published data (Costanzo et al., 2010) and new interactions released by the Boone laboratory as they become available. Clicking on a DRYGIN link in SGD from an ORF’s Locus Summary or Interactions page goes directly to the DRYGIN search results page for that ORF, which lists both positive and negative genetic interactions as well as any genetic correlations for the given ORF.

Categories: New Data

Tags: DRYGIN, genetic interactions, SGA array

Ethanol from Waste

June 16, 2012

Biofuels hold the promise to significantly slow down global warming.  But this will only be the case if they come from something besides corn.

We don’t want them to come from the parts of other plants we eat, either.  Shunting food towards fuel will only jack up food prices and put the lives of the poorest at risk.  Policy makers should not have to decide between feeding the poor and running their cars.

No, to make biofuels worth our time, we need to be able to turn agricultural waste, grass, saplings, etc. into ethanol.  Unfortunately this stuff is mostly cellulose and lignin and we don’t have anything that can efficiently ferment this “lignocellulosic biomass.” 

Many groups are working towards creating strains of Saccharomyces yeast, the predominant fungal organism used for large-scale industrial processes, to do this job.  None have yet been created that can do the job well enough to be industrially viable. They are either poor fermentors or are genetically modified so that they include non-yeast genes.  Ideally any strain would include only Saccharomyces genes, to avoid the public’s fear and loathing of genetically modified organisms.  

This is where a new study in GENETICS by Schwartz and coworkers comes in.  This group is working towards engineering a yeast that can ferment the pentoses like xylose that make up a good chunk of this otherwise inedible biomass, using genes that are naturally occurring in Saccharomyces.  They haven’t yet created such a yeast, but they have at least identified a couple of key genes involved in utilizing xylose.

The researchers took what seemed to be a straightforward approach.  Collect and screen various yeast strains for their ability to grow on xylose and isolate the relevant gene(s) from the best of them.  Sounds easy enough except that most of the strains they’ve found are terrible sporulators.  This means that they couldn’t use conventional methods to isolate the genes they were interested in and so had to come up with new methods.

First they needed to find some way to get the strain to sporulate.  They were able to force sporulation by creating a tetraploid intermediate between the xylose fermenting strain, CBS1502, and the reference strain, CBS7001, by adding an inducible HO gene.  During this process, they noticed that the ability to utilize xylose segregated in a 3:1 pattern.  This usually means that two genes are involved.

They next needed a way to identify these two genes.  What they did was to pool 21 spores that could ferment xylose and 21 that could not.  They then purified the DNA from each pool and compared them using high throughput sequencing.  They eventually found two genes that were key to getting this yeast to use xylose as its carbon source.  (They also found at least two other “bonus” genes that seemed to boost its ability to use xylose).

One of the genes, GRE3, was a known member of a xylose utilization pathway.  But the other gene, the molecular chaperone APJ1, was not known to be involved in metabolizing xylose.  The authors hypothesize that APJ1 might stabilize the GRE3 mRNA.

These two genes may not be enough to create an industrially viable, xylose fermenting Saccharomyces just yet.  But the novel methods of gene isolation presented in this study may allow researchers to find additional genes that might one day get them there.  Then we will have a way to get ethanol without the large carbon footprint and without the human cost.

 

A genetic engineering approach to getting yeast to ferment agricultural waste

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

Categories: Research Spotlight

Tags: biofuel, biomass, ethanol, fermentation, lignocellulosic biomass, Saccharomyces, Saccharomyces cerevisiae, yeast

Cancer’s Chromosomal Chaos Explained (Partly)

June 01, 2012

One reason cancer is so tricky to treat has to do with its adaptability.  It can quickly try out new genetic combinations until it hits upon one that can survive whatever treatment a doctor is currently throwing at it.  The result is return of the cancer after remission.

One way cancer is able to change its genetics so rapidly has to do with chromosome instability.  The number of chromosomes in a cancer cell is much less stable than in a normal cell.  This allows the cancer cell to constantly explore a wide range of chromosomal combinations.

It is still an open question how this dynamic instability happens.  The gene-centric theory suggests that mutations in key genes are the main driving force.  The chromosome-centric model says that having the wrong number of chromosomes is the critical component.

Distinguishing between these two models using cancer cells has proven difficult because these cells always have mutated genes.  There is simply no way to look at just chromosome numbers in this system.  This is where yeast can help.

In a recent paper published in PLoS Genetics, Zhu and coworkers used yeast to explore whether altered chromosome number was sufficient to explain chromosome instability.  They found that chromosome numbers alone can explain some but not all of chromosomal instability.

The authors created various chromosomal combinations in yeast by sporulating isogenic triploid yeast cells.  These cells had different numbers of genetically identical chromosomes.  They then explored the stability of each chromosome number combination using both FACS and qPCR.

What they found was that chromosome number certainly impacted chromosomal stability.  Chromosome number became less and less stable as the chromosome number veered further and further from the haploid state.  Of course, once the cells became diploid, stability returned. 

The authors explain this with the idea that there is only so much cellular machinery to move chromosomes to the proper place during mitosis.  As more and more chromosomes are added to the cell, the machinery becomes increasingly taxed, resulting in more and more errors. 

But once the diploid state is reached, all the genes are present to make twice as much mitotic machinery.  Now stable chromosome segregation can happen.

This was the broad pattern Zhu and coworkers observed but it certainly wasn’t the whole story.  The authors found islands of stability in the chromosomal chaos. 

For example, very often when there were equal numbers of chromosome VII (ChrVII) and chromosome X (ChrX), the chromosome number was more stable than predicted.   They explored this further and found evidence that suggested that at least part of this was due to the MAD1 gene on ChrVII and the MAD2 gene on ChrX. 

Stable chromosome numbers required that these genes be present in a 1:1 ratio.  Once the ratio strayed from one, chromosomal instability increased.  But these genes don’t explain everything.  There were unstable combinations where the MAD1/MAD2 ratio was correct.  As might be expected, there are other gene combinations that can lead to instability as well.

So incorrect chromosome number alone can explain the chromosomal instability seen in cancer cells.  But genes clearly play a role too, as evidenced by the islands of stability and the MAD1 gene and MAD2 genes.  As usual, reality is probably a combination of the two models. 

So it looks like chromosome number does play an important role in chromosomal instability.  Too many chromosomes may overtax the mitotic machinery so that chromosomes end up mis-segregated.

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: cancer, chromosomal instability, chromosome, genetics, Saccharomyces cerevisiae, yeast

Yeast, Place your Bets

May 25, 2012

Life is a balancing act.  An organism needs to grow and divide as fast as possible in its current environment.  But it also needs to be able to survive when the environment changes.

One way nature has come up with to deal with this balancing act is called bet hedging.  Basically some members in a population grow well in one set of circumstances and another set grows well in another.

Now this makes obvious sense when looking at members of a species that vary genetically.  Where it gets interesting is when bet hedging happens in a clonal population.

The idea is that even though they share the same DNA, there are epigenetic differences that cause subtle variations in gene expression levels between individuals.  These differences in gene expression patterns result in altered survival rates under different circumstances.

This phenomenon has been difficult to study because researchers need to focus on individuals and not populations.  Growth curves in liter flasks are of little use.

But now Levy and coworkers have come up with a new high throughput assay that allows them to look at how a few individuals are growing.  This has allowed them to quantitate how different individuals grow in a population and why the slower growers and/or the elderly are better able to survive stress.

The assay uses time-lapse bright-field microscopy to look at tens of thousands of microcolonies all at once.  What they find is a wide range of growth rates.  Somewhere between 1.3-10% of microcolonies grow at less than half the rate of the population as a whole (the number depends on the strain). 

The researchers identified multiple genes that impacted the range of growth rates within a population without necessarily affecting the overall growth rate.  In other words, this phenomenon isn’t simply due to chance–there are key genetic factors that help determine the amount of individual to individual variation in a population.

Levy and coworkers focused on Tsl1p, a component of the trimeric complex that synthesizes trehalose.  What they found was that those cells that made more Tsl1p divided less often and so grew more slowly.  Remember again, this is in a clonal population.

Trehalose is thought to help preserve proper protein folding under stress.  So the idea is that some subset of individuals is primed for stress but that in turn, this preparation makes them grow more slowly.  And this is just what the researchers found.

When they subjected colonies to heat stress, those that made lots of trehalose were more likely to survive.  But the survivors didn’t stay slow growing for long.  After multiple generations, the population returned to the original growth rate with the original individual to individual variation.  The phenotype was reversible.

Finally the researchers discovered that older yeast cells tended to make more trehalose and so survived stress better.  It may be that as a yeast cell gets older, it makes more Tsl1p which helps to set up the range of growth rates among individuals.  This may be one way individual to individual variation has evolved in yeast.

Bet hedging is obviously a great way to ensure the survival of a clonal population. Under ideal conditions, the fast growers can grow like mad, spreading themselves far and wide.  But when conditions become more hostile, a few slower, tougher individuals can survive to keep the population alive.

Video showing that slow growers survive heat shock and then revert to fast growers.

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

Categories: Research Spotlight

Tags: bet hedging, epigenetics, Saccharomyces cerevisiae, Tsl1, yeast

Alternatives to Whale Puke

May 11, 2012

Imagine scouring the beaches of the world for balls of whale vomit.  People are willing to do this because finding one is like finding a huge gold nugget.  Perfume companies will pay around $10,000 per pound for this ambergris (which is the more scientific name for the stuff). 

Of course perfume companies would rather have a more reliable and less expensive source for their ambergris.  And it wouldn’t hurt to find one that was a little less ethically troubling than using products from an endangered animal.

Until recently their best bet was a chemical called cis-abienol from balsam firs.  While less murky ethically, this chemical is tricky to harvest and even trickier to synthesize in the lab.  The perfume industry could definitely use an alternative.  And researchers in the Bohlmann lab may be on their way to finding it.

In a recent study from this lab, Zerbe and coworkers used “…metabolite profiling, tissue-specific deep transcriptome sequencing and functional (i.e. biochemical) genomics…” to identify the key enzyme for making cis-abienol in balsam firs.  They next plan to put the gene into yeast and have the yeast crank out this chemical.

Finding the gene was not trivial.  The first thing they did was to identify that most of the cis-abienol was made in the bark and phloem of the balsam fir.  They then sequenced the transcriptome from these sources and looked for likely candidate genes.  For this last step, they used a curated library of 146 known terpene synthases.

They found 4 candidate genes (AbdiTPS1-4) and successfully cloned three of them.  They then went on to express these proteins in E. coli, purify them, and found that AbdiTPS4 catalyzed the synthesis of cis-abienol in vitro. They renamed the gene AbCAS at that point.

According to Dr. Bohlmann, the lab is now testing conditions for getting the enzyme to work in yeast.  If they can pull it off, the perfume industry will finally get a cheap and easy alternative to whale vomit.  Let’s see if they pass any savings down to the consumer…

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

Categories: Research Spotlight

Tags: ambergris, bioengineering, cis-abienol, natural products, perfume

Take SGD with you wherever you go!

May 07, 2012

SGD has just released an app for the iPhone, iPad and iPod touch ap(p)tly called “YeastGenome”, containing the latest Saccharomyces cerevisiae information from the database, available now as a free download on iTunes. Search by gene names, gene descriptions or simply browse for quick access to Gene Ontology annotations, mutant phenotypes and protein and genetic interaction data for your favorite genes – all at your fingertips!

Use YeastGenome to:

• Search using gene name or keywords
• Browse by genomic feature types
• Save your favorite genomic features
• Quickly see fundamental genomic feature information
• Find Gene Ontology terms, Phenotypes, Interactions, and References associated with genomic features

… with or without an internet connection!

How many ATP-dependent RNA helicases is the S. cerevisiae genome known to encode? Which proteins have a zinc finger motif? Now you can answer these questions and more with the YeastGenome app, whether you’re in line at the supermarket or having lunch with your colleagues or attending a seminar with no wifi-access! Get your friends and colleagues as fired up about The Awesome Power of Yeast as you are – use YeastGenome to email information about genomic features to collaborators and spread the word!

Please read our FAQ or visit iTunes for more information.

Categories: Data updates

Tags: iPad, iPhone, iPod

Next