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Cellular Traffic Jams

July 19, 2012

Traffic JamTraffic 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

Yeast with Sticking Power

July 16, 2012

Stickier yeast might make beer brewing easier.

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

Yeast on the Brink

July 2, 2012

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

Yeast might help us recognize when we are on the edge of a cliff.

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