New & Noteworthy

Passing the Hog: How a Long Noncoding RNA Helps Yeast Respond to Salt

February 25, 2014

Most people know that Incans relied on human runners to get messages across their empire.  Basically they had runners stationed at various places and one runner would hand the message off to the next.  This relayed message could then quickly travel across the country.

As shown in a new study by Nadal-Ribelles and coworkers, it turns out that something similar happens in yeast when the CDC28 gene is turned up in response to high salt.  In this case, the runner is the stress activated protein kinase (SAPK) Hog1p and it is stationed at the 3’ end of the gene.  When the cell is subjected to high salt, the message is relayed from the 3’ end of the CDC28 gene to its 5’ end by the Hog1p kinase.  The end result is about a 2-fold increase in the amount of Cdc28p made, which allows the cell to enter the cell cycle more quickly after the salty insult.

Unlike the Incans who had their paths all set up in front of them, poor Hog1p has to build its own path.  It does this by activating a promoter at the 3’ end of the CDC28 gene that produces an antisense long noncoding RNA (lncRNA) that is needed for the transfer of the Hog1p.  It is as if our Incan runner had to build a bridge over a gorge to send his message.

This mechanism isn’t peculiar to the CDC28 gene either.  The authors in this study directly show that something similar happens with a second salt sensitive gene, MMF1.  And they show that a whole lot more lncRNAs are induced by high salt in yeast as well.

Nadal-Ribelles and coworkers started off by identifying coding and noncoding regions of the yeast genome that respond positively to high salt.  The authors found that 343 coding regions and 173 noncoding regions were all induced at 0.4 M NaCl.   Both coding and noncoding regions required the SAPK Hog1p for activation. 

The authors next focused on CDC28 and its associated antisense lncRNA.  After adding high salt, Nadal-Ribelles and coworkers found that Hog1p was both at the start and end of the CDC28 gene – as would be expected, since both CDC28 and the antisense lncRNA required this kinase for transcriptional activation. 

Things got interesting when they were able to prevent the lncRNA from being made.  When they did this, Hog1p was missing from both the 5′ and 3′ ends of the CDC28 gene and as expected, activation was compromised.  But Nadal-Ribelles and coworkers showed that expressing the lncRNA from a plasmid did not allow for CDC28 activation. It appears that where the lncRNA is made is just as important as whether it is made.

Through a set of clever experiments, the authors showed that not only does the lncRNA need to be made in the right place, but it needs to be activated in the right way.  When they set up a system where the lncRNA was induced in the right place using a Gal4-VP16 activator, CDC28 was not induced by high salt.  A closer look showed that this was most likely due to a lack of Hog1p at the start of the CDC28 gene.

The situation was different when they activated the lncRNA with a Gal4-Msn2p activator which uses Hog1p to increase expression.  In this case, CDC28 now responded to high salt and Hog1p was present at both the start and end of the CDC28 gene.  But this activation went away if they added a terminator which prevented the full length lncRNA from being made. 

Phew, that was a lot!  What it means is that for there to be a Hog1p at the business end of the CDC28 gene, there needs to be one at the 3’ end.  It also means that for the Hog1p to get to the start of the CDC28 gene, the antisense lncRNA needs to be made.

This would all make sense if maybe the lncRNA was involved in DNA looping, which could get the Hog1p from the end of CDC28 to the start where it can do some good.  Nadal-Ribelles and coworkers showed that this indeed was the case, as CDC28 activation required SSU72, a key looping gene.  When there was no Ssu72p in a cell, salt induction of CDC28 was severely compromised.

So it looks like an antisense lncRNA in yeast is being used as part of a looping mechanism to provide the cell with a quick way to start dividing once it has dealt with its environmental insult.  The authors show that yeast that can properly induce their CDC28 gene enter the cell cycle around 20 minutes faster than yeast that cannot induce the gene.  The cells are poised for a quick recovery.

And this is almost certainly not merely a yeast phenomenon.  Some recent work in mammalian cells has implicated lncRNAs in recruiting proteins involved in controlling gene activity through a looping mechanism as well (reviewed here).  Now that the same thing has been found in yeast, scientists can bring to bear all the powerful tools available to dissect out the mechanism(s) of lncRNA action.  And that’s far from a loopy idea…

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

Educational Resources on the SGD Community Wiki

February 21, 2014

Did you know you can find and contribute teaching and other educational resources to SGD? We have updated our Educational Resources page, found on the SGD Community Wiki. There are links to teaching resources such as classroom materials, courses, and fun sites, as well as pointers to books, dedicated learning sites, and tutorials that can help you learn more about basic genetics. Many thanks to Dr. Erin Strome and Dr. Bethany Bowling of Northern Kentucky University for being the first to contribute to this updated site by providing a series of Bioinformatics Project Modules designed to introduce undergraduates to using SGD and other bioinformatics resources.

We would like to encourage others to contribute additional teaching or general educational resources to this page. To do so, just request a wiki account by contacting us at the SGD Help desk – you will then be able to edit the SGD Community Wiki. If you prefer, we would also be happy to assist you directly with these edits.

Note that there are many other types of information you can add to the SGD Community Wiki, including information about your favorite genes, protocols, upcoming meetings, and job postings. The Community Wiki can be accessed from most SGD pages by clicking on “Community” on the main menu bar and selecting “Wiki.” The Educational Resources page is linked from the left menu bar under “Resources” from all the SGD Community Wiki pages. For more information on this newly updated page, please view the video below, “Educational Resources on the SGD Community Wiki.”

Signaling in a Crowd

February 18, 2014

There are two very different kinds of sports in the Winter Olympics (and in all sporting competitions really).  In one set, it is the athletes alone out on the ice or sliding down the slope, trying to get the best time they can.  They can only use themselves as the motivator.

In another set of sports, like speed skating, athletes compete directly with one another.  Here they can use each other to push themselves to go faster, farther, etc.

The key to each is obviously the proximity of other athletes.  If there are a bunch of athletes around you, you will all do better by feeding off each other’s signals.  If you are by yourself, then only you can produce the signals to motivate yourself to go faster.

Youk and Lim show in a new study that the same sort of thing happens in cells that can both secrete and sense the same signal.  If there aren’t a lot of cells around they tend to signal themselves, but in a crowded place, they are all signaling each other. 

This may seem a bit esoteric but it really isn’t.  These sorts of “secrete-and-sense” systems are common in biology.  Cell types from bacteria to our own T cells have them, and they allow for a surprisingly wide range of responses.  Understanding how these systems work will explain a lot of biology and, perhaps, help scientists create new sensing systems for bioengineered beasts.

Youk and Lim used our favorite organism Saccharomyces cerevisiae to study this widespread signaling system.  They created a bevy of strains that can either secrete and sense alpha factor or that can only sense the pheromone.  They grew varieties of these two strains together under various conditions to determine when the “secrete-and-sense” strains could also signal to the “sense only” strains.  Like our athletes, the cell concentration was important.  But so too were the levels of alpha factor and receptor.

The authors first created a strain that senses the presence of alpha factor with the Ste2p receptor and in response turns on GFP through the FUS1 promoter.  (The strain is deleted for FAR1 to prevent cell cycle arrest.) As expected, increasing amounts of alpha factor resulted in increased levels of GFP.

It is from this strain they created their “secrete-and-sense” and “sense only” strains.  The “secrete-and-sense” strain included a doxycycline inducible promoter driving the alpha factor gene.  The more doxycycline, the more alpha factor it makes, resulting in more GFP.  To tell the two strains apart in experiments, they added a second reporter, mCherry, under a constitutive promoter to the “sense only” strain.  Now in their experiments they can distinguish between the strains that glow only green and those that glow red and, sometimes, green.

The first experiment was simply to see what effect differing cell and alpha factor concentrations had on the two strains’ ability to glow green.  At low cell and doxycycline concentrations, only the “secrete-and-sense” strain glowed green.  This makes sense, as too little alpha factor was made to get to the relatively distant neighbors.  At high cell and doxycycline concentrations, both glowed green almost indistinguishably.  Here the system was flooded with enough alpha factor for everyone to respond.

The results were less binary at either low cell and high doxycycline concentrations or high cell and low doxycycline concentrations.  Under either of these conditions, the “sense only” strain did glow green although at a much slower rate.

Youk and Kim didn’t stop there.  They also tested whether the amount of receptor affected these results.  When the two strains expressed high levels of receptor, the amount of alpha factor didn’t matter at low cell concentrations—only the “secrete-and-sense” strain glowed green.  This makes sense as the strain can quickly suck up any amount of alpha factor it makes.  Again at high cell concentrations the differences disappear.

In a final set of experiments the authors created positive feedback loops and signal degradation systems, which are both very common in nature.  The positive feedback loop was created by putting the doxycycline activator, rtTA, under the control of doxycycline, and a signal degradation system was engineered using Bar1p, a protease that degrades alpha factor.  Using these systems they were able to show that at low cell concentration, low Bar1p expression, and strong positive feedback, individual cells were either on or off.  This sort of activity may be important in nature, where under certain conditions a response may be beneficial and in others a response may not.  This bet hedging means that the population can survive under both sets of conditions.

It is amazing that such a simple set of conditions can lead to so many different responses, almost as varied as the performances of Olympic athletes.  These findings not only help to explain how these deceptively simple systems work and why they are so common in nature, but might also be incredibly useful in setting up synthetic secrete-and-sense circuits for biotechnology applications.  

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

New at SGD: GO Annotation Extension Data, Redesigned GO and Phenotype Pages

February 12, 2014

Annotation Extension data for select GO annotations are now available at SGD. The Annotation Extension field (also referred to as column 16 after its position in the gene_association file of GO annotations) was introduced by the Gene Ontology Consortium (GOC) to capture details such as substrates of a protein kinase, targets of regulators, or spatial/temporal aspects of processes. The information in this field serves to provide more biological context to the GO annotation. At SGD, these data are accessible for select GO annotations via the small blue ‘i’ icon on the newly redesigned GO Details pages. See, for example, the substrate information for MEK1 kinase (image below). Currently, a limited number of GO annotations contain data in this field because we have only recently begun to capture this information; more will be added in the future.

We have also redesigned the GO Details and Phenotype Details tab pages to make it easier to understand and make connections within the data. In addition to all of the annotations that were previously displayed, these pages now include graphical summaries, interactive network diagrams displaying relationships between genes and tables that can be sorted, filtered, or downloaded. In addition, SGD Paper pages, each focusing on a particular reference that has been curated in SGD, now show all of the various types of data that are derived from that paper in addition to the list of genes covered in the paper (example). These pages provide seamless access to other tools at SGD such as GO Term Finder, GO Slim Mapper, and YeastMine. Please explore all of these new features from your favorite Locus Summary page and send us your feedback.