New & Noteworthy

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

New SGD Help Video: What is GO?

September 14, 2015


The Gene Ontology (GO) is an integral part of modern biology. It provides a common language that unifies the description of gene products from all organisms, structured in a way that allows very detailed information to be captured while at the same time facilitating broad categorizations. 

Watch our new video for a brief refresher course on GO: what it is, how it’s structured, and why it’s important.

 

Categories: Tutorial

Tags: Gene Ontology , Saccharomyces cerevisiae

Yeast Researchers Turn up the Heat on Essential Genes

September 09, 2015


Looking at the phenotypes—the observable characteristics—of creatures that have mutations in various genes can give important clues to scientists trying to figure out what those genes do. And the ability to systematically mutate thousands of genes in our favorite organism, Saccharomyces cerevisiae, has made it an awesomely powerful genetic system.

Turning up the heat on temperature-sensitive mutant strains is a great way to study essential yeast genes systematically. Image via Wikimedia Commons

But this awesome system has something of an Achilles heel. If you delete an essential gene, defined as a gene necessary for life under standard growth conditions, you end up with an experimental dead end: a dead cell. How then can you study mutant phenotypes for essential genes, which are nearly 20% of the genome?

In a new paper in G3, Kofoed and colleagues address this problem by creating conditional mutations in 600 essential genes. The mutant genes function normally at standard temperatures (25-30° C) but can be inactivated by raising the temperature. This mutant collection covers about half of S. cerevisiae essential genes and gets us much closer to being able to do mutant screens that are truly genome-wide, helping us to discover unexpected connections between genes and pathways or processes.

One popular approach to studying essential genes has been to put them under control of promoters that can be turned down when you add a particular chemical or carbon source. But it can be hard to tease apart the phenotype of down-regulating transcription of your gene of interest from the effects of the other changes that you need to make in order to regulate expression of the construct.

A solution that avoids some of these issues is to use temperature-sensitive (ts) alleles of essential genes. These mutations make the resulting proteins unstable at high temperatures. If you shift a ts strain to high temperature, the mutant protein will stop functioning; by growing the cells at an intermediate temperature, you can often produce a partially active protein that allows slow growth of the strain.

Temperature-sensitive alleles are not nearly as straightforward to create as are deletion mutations. Although newer technologies are helping, it’s still a lot of work. But Kofoed and colleagues took on this challenge. Building on their previous collection of 250 genes, they targeted genes without existing ts alleles to create a mutant collection including a total of 600 genes, about half of yeast’s essential genes.

The researchers used error-prone PCR to introduce mutations into DNA fragments encoding the genes, then transformed the mutagenized fragments into heterozygous diploid cells containing one wild-type copy of the gene and one deletion allele. The flanking sequences of the mutagenized genes targeted them to integrate in place of the deletion allele. Kofoed and colleagues could then sporulate the diploids and screen the haploid progeny for temperature sensitivity.

The genetic background of these strains is S288C, the widely used strain from which the reference genome sequence is derived. Each strain contains a “barcode”, a sequence that can be used to uniquely identify it. In addition to creating new alleles, the authors also used these methods to transfer some previously isolated ts alleles into this same background. 

Kofoed and colleagues validated and stored multiple ts alleles for each gene on their list. Although they chose one allele per gene for inclusion in the collection, the other alleles are available on request and could be very informative for researchers studying those particular genes.

As a test to see whether their collection of 600 genes was biased in any way, the researchers did Gene Ontology (GO) enrichment analysis. This compares the GO terms, representing molecular function, biological process, and subcellular localization, that are associated with the genes in a set. If the genes share related GO terms, that’s an indication that they may be involved in a common process or share the same location.

Most of the time, when scientists do GO enrichment on sets of genes they’ve come up with in an experiment, they’re hoping the genes have significantly shared terms. But in this case, the researchers were happy to find no shared terms, meaning that their collection represented a wide variety of places and roles in the cell.

The mutant strains may be studied individually in classical genetics experiments or the whole set can be tested using robotic manipulation of all the strains at once. Alternatively, the strains can be pooled and grown together in a competitive situation, like a microscopic Survivor show. When the strains are forced to compete for survival, some will prosper and others will die out. Their unique barcode identifiers allow researchers to figure out which strains were the survivors and which got voted out of the chemostat.

So this collection represents an important step in our ability to survey the phenotypes of virtually all genes in the genome. In addition to creating this resource, Kofoed and colleagues provide a summary (in Table S6) of all currently available mutant collections for essential genes. The yeast research community is now poised to turn up the heat on genome-wide mutant screens and make new discoveries about the roles of these essentially important genes.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: mutant collection , community resource , Saccharomyces cerevisiae

Many Modules Make Light(er) Work

August 26, 2015


If you’ve ever put together something from IKEA you know it can be a bear. So many parts need to connect up together perfectly to build that new bookcase—if you tried to do it all at once you’d go crazy.

Complicated tasks are way easier to do if they are broken up into smaller chunks. This is true whether you are building a bookcase or a biochemical pathway. Image via Wikimedia Commons

Luckily the good folks at IKEA try to make it a bit easier (and more tolerable) by splitting the task into smaller, more manageable pieces. You can concentrate on one part without having to worry about the rest. Once that part is done you can work on the next part and so on. In the end you assemble all the pieces together into your new bookcase.

This is the approach Galanie and coworkers took in their recent Science paper where they engineered our favorite yeast S. cerevisiae to make a couple of different opioids. And it is a good thing they broke this problem up, because it was a way bigger undertaking than anything IKEA might have thrown at them. Engineering this yeast strain was a genetic tour de force.

The authors coordinated 21 different genes from mammals, plants, bacteria and yeast to get the opiate precursor thebaine made. And the semisynthetic opiate hydrocodone took an extra two genes for a grand total of 23! Trying to do all of these at once might have been very frustrating. Thank goodness they split this Herculean task into six (or seven for hydrocodone) smaller modules.

The first step was to get yeast to make (S)-reticuline, a key intermediate on the way to useful opiates. This took 4 modules made up of 17 different genes: six from rat, six from plants, four from yeast and one from bacteria.

And of course just putting these into yeast all at once would almost certainly have made a whole lot of nothing. Each gene needed to be selected from the right beast and then optimized to work in the yeast strain. Sometimes this meant picking the right variant from the right plant, and sometimes it meant mutating a gene to make it behave better. This all would have been overwhelming if the task weren’t split into four easier sections.

Even with all of their optimization, this iteration only made about 20 μg/liter of (S)-reticuline. They needed yeast to crank out more of this intermediate, so they designed a fifth module.

As its name implies, this “bottleneck” module was designed to overcome bottlenecks in the first four modules. After it was added to the strain, the yeast managed to make 82 μg/liter. This was something they could work with!

Except now they were stuck. They needed (R)-reticuline instead of the S form, but no one knew how poppies managed this feat. The gene that did this job hadn’t yet been discovered.

So Galanie and coworkers rolled up their sleeves and dug through plant transcriptome databases to find the gene they were looking for. They found a likely candidate, synthesized the gene in order to produce the enzyme, tested whether it could transform the S form of reticuline into the R form in vitro, and found that it could.

They could now make the right intermediate, which meant they could make their final module. As its name implies, this “thebaine” module would finally allow them to make the opiate precursor thebaine in yeast. This module consisted of their recently discovered gene and three other plant genes.

They had finally made thebaine from simple sugars in yeast! Except it didn’t work very well at all. There seemed to be a bottleneck right after the (R)-reticuline stage. Back to the drawing board!

Given where the bottleneck was, the researchers guessed correctly that the culprit was the SalSyn enzyme which converted (R)-reticuline to salutaradine. A Western blot showed three distinct forms of this enzyme in yeast and only one form, the lowest molecular weight one, when it was expressed in tobacco. Clearly something was happening to inactivate this protein in yeast.

A close look at the protein suggested yeast was glycosylating positions that it shouldn’t, and site directed mutagenesis of these sites confirmed this. The glycosylation was causing the protein to be sorted incorrectly so that it couldn’t do its job.

Unfortunately just mutagenizing away the glycosylation sites wasn’t good enough, because this severely affected the enzyme’s ability to do its job. So the researchers created a chimeric protein with parts of another P450 enzyme they knew did great work in yeast. After optimizing its codons for yeast, this chimera performed beautifully.

Now, finally, they had a yeast strain that could make thebaine. Not a lot of it, only around 6.4 μg/liter—but amazing nonetheless.

A yeast strain would have to be millions of times better at making opioids before a Walter White character could turn it into a profitable criminal activity. But the authors advocate for starting an open dialog on synthetic biology issues now, while there’s still time to deliberate. Image by Hecziaa via deviantart.com

A final module was added that consisted of two plant enzymes that converted thebaine to the drug hydrocodone. This monster strain could crank out around 0.3 μg/liter of hydrocodone. Yes, that is as puny as it sounds; one dose of painkiller for an adult would contain 5 mg of hydrocodone.

To be competitive with poppies, they need a 100,000-fold improvement to around 5 mg/liter. In talking with Dr. Smolke, it sounds like this could happen within a couple of years. After scaling up for production, voila! An entirely new source of opiates for pain relief.

Of course the elephant in the room is a Breaking Bad-esque scene where a yeast biologist grabs ahold of an opiate-producing strain and supplies various cartels with illegal drugs. Our Walter White wannabe wouldn’t be able to use the current strain, as he would need thousands of liters of yeast to produce a single dose of Vicodin.

But this scenario will be a real concern in the next few years. Which is why the Smolke lab has crossed every t and dotted every i in setting up and creating this strain. They have made it as difficult as possible for the wrong people to get their hands on it.  

This strain represents a stunning achievement in synthetic biology. Move over poppies, there’s a new opiate producer in town.

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

Categories: Research Spotlight

Tags: pathway engineering , opiate biosynthesis , Saccharomyces cerevisiae , synthetic biology

Ancient Hybridization Causes Revision of Yeast’s Calendar

August 19, 2015


Just as this monk had trouble pinpointing when Jesus was born, so too did yeast biologists have trouble figuring out when the genome of S. cerevisiae doubled in size. But they had trouble for very different reasons… Image via Wikimedia Commons

Most people assume that Jesus Christ was born around 1 A.D. or 1 B.C. or something like that. After all, that dating system is based on when he was born. 1 A.D. is by definition “the first year of the Lord.”

Now of course no one was actually marking years this way from the get go. In fact, this dating system wasn’t really invented until 525 A.D. by the monk Dionysius Exiguus.

And as can sometimes happen when you look that far back in time, he didn’t quite get the date of Jesus’ birth right. In point of fact, Jesus was most likely born between 4 and 6 B.C.

While not nearly as momentous, yeast biologists may have done something similar with the genome of our friend Saccharomyces cerevisiae. For many years scientists have believed that this yeast underwent a whole genome duplication (WGD) event around 100 million years ago

But if the conclusions reached by Marcet-Houben and Gabaldón in a new PLOS Biology article are correct, then what looks like the WGD event actually happened before there was a S. cerevisiae around to duplicate its genome.

Which would mean that there couldn’t have been a WGD. There is no way for a species to double its genome if it doesn’t yet exist! Instead the authors propose that what looks like a WGD was actually a hybridization of two related yeasts from longer ago than 100 million years.

Once you get past the vocabulary, the idea behind this study is actually pretty easy. Basically, they took pairs of genes that most likely came from a duplication event (ohnologs) and figured out when they diverged away from one another. This is the same idea behind figuring out when chimps and humans shared a common ancestor by comparing homologous genes.

When Marcet-Houben and Gabaldón compared every potential ohnolog in S. cerevisiae, they found that a WGD event could explain the origin of only 15% of these genes. These 15% could be traced back to a time after S. cerevisiae was already around.

The other 85% all looked to have been duplicated before S. cerevisiae yet existed. Which of course means these could not have come from a WGD. A genome that does not yet exist cannot be duplicated. This set of genes must have arisen in a different way.

An origin story that makes more sense than a WGD for these genes is one in which two related species hybridize to form one new species. This kind of thing definitely happens, especially with yeast (and if you like lagers, you can be glad it does!).

Here the idea is that the related species share a subset of their genes from when they had a common ancestor. When these species fuse, the new beast has both sets of genes. A cursory look might suggest that these genes were from a duplication event, especially if there are many large tracts of them. After all, many genes share a lot of homology between species.

It is only with a closer look that you might trace these genes back to a common ancestor that came before the species you are studying even existed. This is, in essence, what Marcet-Houben and Gabaldón found.

From the phylogeny of reference species that they created, the authors were able to get a general idea about which clades these prehybridization species may have come from. The largest peak of duplication from their analysis came from before Saccharomyces split from a clade containing Kluyveromyces, Lachancea, and Eremothecium (KLE). The other major peak came before Saccharomyces separated from a clade that contains Zygosaccharomyces rouxii and Torulaspora delbrueckii (ZT). So the simplest interpretation is that at some point long ago, an ancestor of modern S. cerevisiae was formed from the hybridization of a pre-KLE and a pre-ZT species.

Showing that something like this happened so long ago is fraught with peril. All sorts of things can happen to a genome in more than a hundred million years. And duplicated genes are even trickier because they can mutate at different rates when one copy is gaining a new function. (After all, that is one way that new genes are born.)

So Marcet-Houben and Gabaldón threw everything but the kitchen sink at the genome sequences. They tried at least three different methods for comparing the various sequences, using alternative reference phylogenies and a variety of techniques to show what might happen to their results if genes were mutating at different rates.

With each method they got similar results. Many or even most of the “duplication” events happened before S. cerevisiae was even a species. And on top of all of this they were able to show that they got a similar result when they used a known hybrid, S. pastorianus, and its two founding species, S. cerevisiae and S. eubayanus.

All of this taken together argues that S. cerevisiae did not undergo a WGD in the deep, dark past. Instead, it is the result of two closely related species getting together and creating a new species.

Now this does not necessarily mean there was no WGD in our favorite yeast’s past. There are at least two ways that a hybrid species might have formed, and as you can see in this image from Marcet-Houben and Gabaldón’s article, one of them involves a duplicated genome:

Fig 6. Hybridization scenarios. From Marcet-Houben and Gabaldón (2015), PLOS Biol. 13(8):e1002220.

In the first scenario, shown on the top left, two diploids of different species fuse together to create a yeast with two sets of chromosomes. Eventually, through mutation, translocation, gene loss and whatever other genome sculpting mechanisms are handy, the yeast ends up with double the number of chromosomes of its predecessors.

In the second possibility, shown on the bottom left, two haploids fuse. This fused yeast then undergoes a whole genome duplication and then goes through similar processes as the first model to get to the current genome.

So, although there may have been a WGD, it looks unlikely that it happened to S. cerevisiae. Just as the placement of historic events in our calendar changes when more information is available, the generation of genome sequences for more and more yeast species and new methods for analyzing them are giving us deeper insight into the history of our friend S. cerevisiae.

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

Categories: Research Spotlight

Tags: whole-genome duplication , evolution , Saccharomyces cerevisiae , hybridization

Redesigning Life, Ethically

August 13, 2015


Yeast is an essential ingredient in the recipes for our most delicious food and drink. But now, researchers are working on a recipe for yeast itself! Image by Maria Costanzo

For thousands of years, humans have used yeast as an essential part of recipes for bread, wine, and beer. But now we’re turning the tables on yeast. Instead of creating recipes with yeast, researchers are creating recipes for yeast.

Now of course we have been making minor tweaks here and there for years. But what we’re talking about now is changing out the whole recipe book: creating a whole new genome for S. cerevisiae.

The Synthetic Yeast 2.0 Project (Sc2.0) has the ambitious goal of re-designing and synthesizing the entire yeast genome, some 12 million base pairs. Along with the scientific challenges, the researchers face some tricky ethical issues as well. After all, they’re creating the blueprint for an entire living eukaryotic cell!

Fortunately, the Sc2.0 researchers have thought long and hard about these issues. They’ve issued a statement of ethics and governance in a new article in GENETICS that also reviews the current regulatory and ethical landscape for synthetic biology. The statement by Sliva and colleagues sets the course for Sc2.0 and serves as a model for oversight of other synthetic biology projects.

We wrote about the science in this space before, when the project published its first major milestone, the synthesis of chromosome III. It’s fascinating stuff: the scientists are not only re-synthesizing the genome, but are re-designing it to be leaner and more useful in the lab. They’re adding features like loxP sites that can be used to alter the structure of the genome for evolution experiments, and engineering the tRNA genes so that one codon can be repurposed to code for a novel amino acid.

But even though these are seemingly benign changes to a relatively harmless beast, there are ethical issues inherent in modifying a living organism in such a big way. While the authors focus on Sc2.0, the issues they discuss are relevant to other synthetic biology projects that combine genes from several organisms in novel pathways, such as the efforts to create an opiate biosynthetic pathway in yeast

While we can only touch on the highlights of their statement here, one of the principles most strongly emphasized by Sliva and colleagues is that all Sc2.0 work will be done for peaceful purposes that benefit society. To promote transparency, they are making outreach a priority, engaging with and educating the public about the project. Sc2.0 will be a public resource, with no intellectual property rights or restrictions on data or materials.

The researchers are also committed to safety. They have engineered multiple auxotrophies into all working strains so that they need special media to survive, even though it seems unlikely that an Sc2.0 strain on the loose would be harmful or would have a competitive advantage over wild strains. And although it’s not required for working with organisms like yeast that are classified “Generally Regarded as Safe” by the FDA, all participants in the project receive biosafety training.

Currently, there is relatively little official policy in place for the field of synthetic biology. Two safety measures currently recommended for DNA synthesis companies by the U.S. Department of Health and Human Services are that the companies check that the sequences they synthesize don’t correspond to toxins or harmful organisms, and that they also verify the identities and institutions of their customers. While compliance with these guidelines is voluntary, the Sc2.0 project has decided to support only companies that follow these safety measures.

To ensure that the policies outlined in their Statement of Ethics and Governance are followed, the Sc2.0 project will maintain an Executive Committee comprised of people both internal and external to the project who have broad expertise in policy, ethics, and science. All of the participants in the project are accountable to this committee, which will actively monitor the work to ensure that the guidelines are followed.

It’s obvious that this is no half-baked scheme, but rather an impressively well-planned recipe for cooking up a yeast cell from scratch. But, we expect nothing less from our friend S. cerevisiae and the talented researchers in the yeast community than to be at the forefront of modern science! 

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: synthetic biology , Saccharomyces cerevisiae

Private Nurses Help Birth Ribosomal Proteins

August 06, 2015


Some ribosomal proteins need to be closely chaperoned even as they’re being born. Image courtesy of the National Library of Medicine via Wikimedia Commons

Some kids are born troublemakers, wreaking havoc and destruction everywhere they go. They can’t help themselves; it’s in their nature to be that way. But if they have concerned and protective adults in their lives, children can overcome this tendency and grow up to become productive members of society.

Within the cell, ribosomal proteins are problem children. Although they grow up to have essential and productive roles, as newborns they can cause big trouble.

Many of them have highly charged, unstructured regions that give them a tendency to aggregate with other proteins. And they have a complicated journey to adulthood, since ribosome assembly happens in multiple cellular compartments. With an estimated 160,000 ribosomal proteins synthesized every minute in rapidly growing S. cerevisiae cells, these troublemakers could cause major problems if left to their own devices.

To help control this unruly mob, certain proteins in the cell act as designated chaperones for ribosomal proteins. In a new paper in Nature Communications, Pausch and colleagues found that, surprisingly, these specialized nurses catch their client proteins even as they’re being born.  They swaddle them from the first moment they start to emerge as nascent proteins, and keep them from causing any harm until they can be delivered safely to their final destination.

The researchers first looked at the proteins Rrb1 and Sqt1. Previous work had suggested they might act as specific chaperones for the ribosomal proteins Rpl3 and Rpl10, respectively. And Pausch and colleagues confirmed these results, showing that TAP-tagged Rrb1 pulled down only Rpl3 and Sqt1 only pulled down Rpl10. Each of these troublesome tots had its own personal chaperone!

But surprisingly, very little of the protein was needed for these chaperones to keep ahold of their respective charges. When the authors trimmed down the ribosomal proteins to shorter and shorter lengths, they saw that just the N-terminal 15-20 amino acids of each ribosomal protein were necessary and sufficient for interaction with its chaperone.

They decided to use X-ray crystallography to look in detail at the Sqt1-Rpl10 interaction. First they determined the crystal structure of Sqt1 on its own, and found that it forms an eight-bladed WD-repeat beta-propeller, looking much like a round electric fan.  The amino acids positioned on the surface of the blades are negatively charged.

Next, the authors co-crystallized Sqt1 with a peptide corresponding to amino acids 2-15 of Rpl10. The structure showed that the positively charged peptide was cradled in the negatively charged surface.

To test whether these charged residues were important for the interaction, they mutated the charged residues of Sqt1 and of the peptide and combined them in various ways. Sure enough, changing the charged residues of either partner disrupted or diminished the interaction.

The Sqt1 chaperone resembles an electric fan, with eight blades rather than four. Image via Wikimedia Commons

Pausch and colleagues went on to test whether those same charged residues are important in vivo. An sqt1 mutation changing glutamate residue 315 to lysine (E315K), that abolished the Sqt1-Rpl10 interaction in vitro, was lethal for yeast cells, confirming the importance of the interaction.

The researchers also detected many allele-specific genetic interactions between the charged residues of the two proteins, and even found that switching the charges in an interacting pair of amino acids (changing an Sqt1 residue to a positive charge and its Rpl10 binding partner to a negative charge) would improve growth compared to either single mutant.

The lethality of that sqt1-E315K mutation, and even the lethality of an sqt1 null mutation, were weakly suppressed by overproduction of Rpl10. So yeast cells can get by (just barely) with an un-chaperoned Rpl10, as long as there’s enough of it around. This result also confirmed that Rpl10 is the only client of Sqt1.

As yet another verification that Sqt1 acts as a chaperone, the authors looked to see what happens to the Rpl10 protein in sqt1 mutants. If cells carrying wild-type SQT1 are lysed and separated into a pellet and supernatant, most Rpl10 spins down in the pellet but a significant amount is soluble in the supernatant. However, if the cells carry any of several sqt1 mutant alleles that alter the charged residues and diminish the interaction with Rpl10, all of the Rpl10 is found glommed together in the pellet.

The two chaperone-ribosomal protein interactions that Pausch and colleagues investigated, Sqt1-Rpl10 and Rrb1-Rpl3, both involved the extreme N termini of the ribosomal proteins. Previous studies had also shown that two other chaperones for ribosomal proteins, Yar1 and Syo1, also interact with the N termini of their clients. So the authors wondered whether interactions between ribosomal proteins and their chaperones might even start during translation of the ribosomal proteins.

In a final experiment, the researchers treated yeast with cycloheximide to freeze translation and then pulled down each of the four chaperones via affinity tags. Each chaperone specifically pulled down the mRNA encoding its client protein, showing that it was binding to the nascent protein as it first started to emerge from the translating ribosome. 

So this study has defined a new step in ribosomal biogenesis. Certain specific ribosomal proteins are such troublemakers that it’s too dangerous for the cell to just release them into the cytoplasm after they’re translated.

Instead, these bouncing baby proteins are caught by their individual nurses before they’re even fully born, and wrapped up to protect both the ribosomal proteins themselves and the rest of the cell. Since ribosomal biogenesis is highly conserved across species and since defects in it are associated with many different diseases, further study of these cellular midwives could have important implications for human health. Perhaps some gentle guidance could help put wayward ribosomes on the right track.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae , ribosomal proteins , chaperones

Another Small Victory for Lamarck

July 29, 2015


Yeast gives us an example of an adaptation that is positively Lamarckian! Image of Jean Baptiste de Lamarck via Wikimedia Commons

Examples of various ways that the environment affects gene expression have become so commonplace that new examples don’t make much of a splash anymore. It is as if Lamarck and Darwin had never argued about how natural selection works.

The main reason we aren’t surprised anymore is that we have a pretty good handle on how most of these changes are happening. Something in our environment causes chemical groups to be added or removed from our DNA and/or its associated proteins, causing a change in gene expression. These kinds of epigenetic changes happen a lot and are now seen as the norm.

What doesn’t happen much at all is that the underlying DNA gets changed in a predictable way to change gene expression in response to something in the environment. Which is why a recent study in PNAS by Jack and coworkers makes you stand up and take notice.

In this study the researchers provide evidence that suggests that yeast will expand the number of copies of its rDNA locus to match the level of nutrients in the environment (presumably to make more ribosomes to take advantage of all those nutrients). The yeast is responding to the environment by changing the content of its genome rather than just changing how it is used.

This is big. It is almost as if Lamarck was right about some part of natural selection. Oh wait, that’s exactly what it is! 

What makes this so cool is that it suggests that natural selection doesn’t necessarily just happen when a random genetic variant wins out in a population. Sometimes the environment itself can induce the winning genome change–and these aren’t just epigenetic changes. (Go Lamarck!)

Jack and coworkers focused on the rDNA locus of the yeast Saccharomyces cerevisiae. This is a fairly fluid part of the yeast genome that consists of multiple copies of the 35S and 5S pre-rRNA genes. The average yeast cell has around 180 copies of this locus and there is a normal range of 150-200 per cell. If a yeast somehow ends up with 80 or fewer copies, it quickly increases the number back to that golden 150-200 range via a Fob1 dependent mechanism.

The authors created a strain of yeast that lacked Fob1 and had only 35 tandem repeats in its rDNA region. This strain, rDNA35, could not expand its rDNA unless Fob1 was added back. They now had a strain in which they could test what affected rDNA expansion, by transforming the strain with a plasmid expressing Fob1 and growing the transformants under different conditions.

The most surprising experiment was the final one of the paper. The authors grew the rDNA35 strain in either 2% or 0.5% glucose and found that rDNA amplification was slowed significantly in low glucose. The authors interpret this to mean that the genome change, the expansion of rDNA, is dependent on nutrient availability. A signaling pathway is able to adjust the rate of rDNA expansion.

Yeast will, of course, grow more slowly at low levels of glucose than they will at higher levels. But the authors were able to show that slow growth was not the reason for the slowed expansion of rDNA at low glucose. They were able to separate the two effects by overexpressing Pnc1, a nicotinamidase.

Overexpression of Pnc1 led to a decreased rate of copy number increase even at the higher glucose levels without affecting growth rate. So rDNA expansion can be separated from slow growth under the right conditions. And as you’ll see below, Pnc1 makes perfect sense given how at least part of nutrient level-dependent rDNA amplification works.

In looking for factors that might affect the rate of rDNA expansion, Jack and coworkers focused on the TOR signaling pathway, since previous work had suggested that it might be important in this process and it is known to respond to nutrient availability. The authors confirmed it was a key player by showing that rapamycin, a TOR inhibitor, kept the rDNA35 strain from expanding its rDNA in the presence of FOB1.

Again they ran into the problem of disentangling cell growth and the rDNA expansion, as rapamycin slows cell growth. The next set of experiments showed that the lack of expansion was almost certainly not due to the slower growth rate.

It is known that rapamycin affects histone deacetylases (HDACs) including Sir2. Jack and coworkers found that nicotinamide, a Sir2 inhibitor, increased the rate of expansion of rDNA without affecting growth rate. So rDNA amplification was not dependent on growth rate.

Which brings us back to Pnc1, that enzyme that cleaves nicotinamide! Presumably endogenous levels of nicotinamide are able to inhibit Sir2 and so encourage the rDNA expansion. Overexpressing Pnc1 releases Sir2 which can then impede the expansion of rDNA.

While that was a bit complicated, the idea is simple and potentially profound. Yeast can sense the level of nutrients in their environment at least partially through the TOR signaling pathway and adjust the actual content of their genome accordingly.

The involvement of nicotinamide in this regulatory process makes this result even cooler, as it has important roles in aging and cellular metabolism from yeast to man. For example, it plays a key role in the life extending properties of caloric restriction in yeast and possibly in more complicated eukaryotes as well. (Click here for a fascinating look at NAD, a compound that contains nicotinamide.)

So, in the presence of low nutrient levels, yeast expand their rDNA much more slowly than they would at higher nutrient levels. Yeast can tailor its genome in response to its environment so it can better utilize that environment.

This work raises the fascinating possibility that this process might even happen at genomic regions other than the rDNA locus. Yeast still has plenty of surprises in store—including giving a Lamarck a little boost.

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

Categories: Research Spotlight

Tags: ribosomal DNA amplification , Saccharomyces cerevisiae , nicotinamide , environmental response

Two Tales of Two Tails

July 22, 2015


Tails let animals sense and interact with their environment at a distance from their bodies. It turns out that some proteins use their “tails” in a similar way. Except they take it one step further.

Some mythical creatures have tails that coil around each other. Septin proteins are not all that different! Image via Wikimedia Commons

In addition to sensing the environment, they can also use their tails as sort of fishing poles to catch proteins they need to interact with. And they do this with great specificity…it is like having that perfect lure that always catches one type of fish.

A great example of this is the septin family of proteins which includes many members that are “tailed” proteins. Septins are highly conserved proteins that typically have a globular GTP-binding domain adjacent to an elongated C-terminal extension.

Septins form structures that act as the boundaries between different cellular parts. In budding yeast cells, they form the septum that separates the mother and bud, and recruit the cytokinesis machinery that allows the daughter cell to separate from the mother cell. In larger animals, they can be found in places such as the dendritic spines of neurons, sperm flagella, or cilia. And in humans, septin mutations have been linked to cancer and neurological diseases.

Until now, the details of how septins recruit other proteins to boundary sites have been elusive. But in two new papers in GENETICS, Finnigan and coworkers in the Thorner lab at Berkeley dove into this question and gained real insight into the lures these proteins use.

In their first paper they reported an extremely comprehensive genetic analysis to dissect the functions of two of the least characterized septins, Shs1 and Cdc11. In the second paper they used both genetic and physical methods to show how these septins recruit myosin to the septum to form the contractile ring that pinches off the bud from the mother.

The bottom line: their C-terminal tails are extremely important. They intertwine with other proteins’ tails like love-struck seahorses. And their specificity comes from these same tails—certain tails only coil around other tails.  

The S. cerevisiae genome encodes a family of septins that assemble with each other to form octameric rods that consist of four different septins. The rods have both end-to-end and side-to-side interactions with each other, forming a ladder-like superstructure.

The septins Cdc11 and Shs1 are the most closely related members of the septin family, and the most recently evolved. They cap the ends of the septin rods. In otherwise wild-type cells, Cdc11 is essential for life while Shs1 is not.

Because SHS1 can be deleted without causing a major phenotype, the first step in investigating its function was to find genetic conditions under which its function becomes more obvious. The authors created four different genetic backgrounds in which the function of other septins was compromised by different mutations. Cells that had mutations in both SHS1 and in other septin genes had obvious problems, such as elongated buds or the inability to grow at high temperatures.

Now Finnigan and colleagues were set to do a detailed genetic analysis to figure out what different parts of Shs1 do by testing mutant versions in these different backgrounds. We can’t possibly recapitulate all the results here, but we’ll do our best to cover the highlights.

Almost all septins, whether in yeast or mammals, end with a tail: a long stretch called the C-terminal extension (CTE) that contains sequence patterns characteristic of a coiled-coil structure. The researchers found that the coiled coil regions of Shs1 and Cdc11were essential to their functions. (And no, they didn’t create any mutations by writing their names in the coiled coil sequence!)

Finnigan and colleagues tried swapping CTEs between different septins. When Cdc11 carried the Shs1 CTE and vice versa, the cells grew just fine. However, this swappability didn’t extend to other septins that are positioned internally in the septin rods. The CTEs of the end subunits Cdc11 and Shs1 could be exchanged for each other, but these CTEs only worked when they were on the ends of the rods.

Since coiled coils are often involved in interactions between proteins, Finnigan and colleagues wondered whether the essential function of the Cdc11 and Shs1 CTEs might be to recruit other proteins to the bud neck.

To test this, they searched published data to identify proteins that are well-known to be localized to the bud neck at the time in the cell cycle when septins are present. They found 30 such proteins, and overexpressed GFP-tagged versions of each in a strain where both Cdc11 and Shs1 lacked their CTEs.

Of the 30 proteins, only overexpressed Bni5 suppressed the growth defect of this strain. To test directly whether binding to Bni5 is a critical function of Shs1, Finnigan and colleagues fused the two genes to each other, so that Bni5 replaced the CTE of Shs1. This fusion protein could compensate for the lack of both Cdc11 and Shs1 CTEs.

To confirm that the important function of the CTEs is to hold Bni5 in the right place, they came up with an alternative test using a “nanobody”, which is a very small, very high-affinity single-chain antibody. They replaced the CTE of either Cdc11 or Shs1 with a nanobody that recognized GFP, and expressed a GFP-Bni5 fusion in these strains. In both cases, tethering Bni5 to the septin via the nanobody obviated the need for the CTE.

Finally, the authors asked why it is important for Bni5 to be located on the septin rods. Previous work had suggested that Bni5 recruits Myo1 (myosin), an important component of the contractile ring at the bud neck. They used the same nanobody constructs to test this, simply expressing GFP-Myo1 in the strains where the nanobody replaced the CTEs of Cdc11 or Shs1. Sure enough, tethering Myo1 to the terminal septins eliminated the need for Bni5.

So we now know that tails are absolutely essential for the functions of the alternative terminal septins Shs1 and Cdc11. These fishing poles let them hold on to the Bni5 “bait,” which in turn catches Myo1 to provide the muscle for cytokinesis to occur. Since septins are so highly conserved, it’s probable that these results will be directly applicable to higher organisms: there are mammalian septins that also occupy the end positions of septin rods, analogous to Cdc11 and Shs1. And that’s no fish story!

Not only septins use their tails to fish.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: septins , Saccharomyces cerevisiae , cytokinesis

SGD Help Video: Gene Name Reservation

July 13, 2015


The eminent Drosophila geneticist Michael Ashburner famously said: “Biologists would rather share their toothbrush than share a gene name.” It’s true that assigning names to genes is often a sticky subject.

In the Saccharomyces cerevisiae community we’re very lucky to have well-defined guidelines for genetic nomenclature, an established system for reserving gene names, and criteria for making them “standard,” or official, names. This system was agreed upon by yeast researchers nearly two decades ago and has served the community well.

Take a look at this video to get an overview of how the gene naming system works. And as always, please contact us with any questions or suggestions.

Categories: Tutorial

Tags: Saccharomyces cerevisiae , video , gene nomenclature

The Gift for the Man Who Has Everything

July 08, 2015


Gifts can be hard to buy for some people. They have everything they need and not many outside interests. What to do?

Having trouble finding that personal gift for that impossible to buy for person? How about a vanity protein with their name written right into the amino acid sequence? Image by D. Barry Starr

You could name a star after them or get them some knick knack they don’t need. Or you could design a personalized protein that has their name in it, solve the structure and present them with the picture.

This is what Deiss and coworkers did to celebrate the 50th birthday of their colleague Andrei N. Lupas, a key figure in studying coiled-coil proteins. They created a personalized protein based on Gcn4 from Saccharomyces cerevisiae. And of course Gcn4 is a coiled-coil protein!

Coiled-coil proteins are the perfect clay for biosculpting a personalized protein. They follow a relatively simple set of rules which makes it easy to predict how they will fold. There isn’t much of the “protein folding problem” with these user-friendly proteins.

Basically these proteins consist of repeated 7 amino acid motifs that each form an alpha helix. They have hydrophobic residues down one face of the helix so that they will tend to oligomerize with each other to keep the hydrophobic residues away from the water. These helices spontaneously coil up like a rope (hence their name).

The 7 amino acids of a repeat are usually represented as abcdefg and are arranged in the pattern hxxhcxc, with h being hydrophobic residues, c being charged residues and x being most any other amino acid. So a and d must be hydrophobic, and e and g charged. That’s pretty much it!

Deiss and coworkers used the name Andrei N. Lupas to create a personalized coiled coil. They replaced 12 amino acids in Gcn4 with the amino acids represented by the letters in his name. Well, they were able to do that for most of the letters.

First off, they had to Roman things up a bit and turn the U into a V (there is no amino acid with the single amino acid code U). So here is the amino acid sequence they used and how they lined it up with the 7-amino acid repeats:

In this arrangement, the hydrophobic residues are asparagine, isoleucine, and valine, and the charged residues are aspartic acid, glutamic acid, proline, and serine. Obviously the last two are not optimal, especially the proline. Proline has an especially rigid conformation and is known to wreak havoc with alpha helices.

When the authors analyzed the protein, they found that as predicted, the proline disrupted the part of the alpha helix with which it was associated. But not enough to completely destroy the coiled coil structure. X-ray diffraction showed that this protein was still able to trimerize properly. They had created a distorted but functional personalized protein. What other kind would anyone want!

And it isn’t as if proline is completely absent from the heptad repeats of coiled-coil proteins. A quick search by the authors found two viral fusion proteins, 1ZTM and 3RRT, that could form a trimer even though they too had prolines. In both of these proteins the proline is in the f position.

They also found 4 dimers with a proline in a heptad repeat. In these cases the proline is at b or c. So no known natural coiled-coil proteins have a proline at the e position. Talk about personalized!

How cool is all of this, and who wouldn’t want a protein of their very own? Unfortunately, not everyone can easily have one.

For example, President Barack Obama would have real trouble since there are no amino acids designated with a B or an O and there is no obvious way to transform these letters into ones that are present in the single letter code. Jeb Bush is out too, but maybe we can do something with Hillary Clinton. Let’s see if we can line up the amino acids of her first name to create a personalized Gcn4 just for her.

“HILLARY” isn’t too bad by itself. All the letters are amino acids (yay) and a and d are hydrophobic (isoleucine and alanine). Aspartic acid works very well for e and while probably not perfect, histidine isn’t too bad for g. The tyrosine at position f is not ideal either but is way better than a proline. This thing might replace one heptad repeat in Gcn4 without causing too many problems.

So what about your name? Can you turn yours into a heptad repeat to create your own personalized Gcn4? 

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

Categories: Research Spotlight

Tags: coiled coil , Saccharomyces cerevisiae

Where’s That Protein?

July 01, 2015


Waldo will always be hard to find, but we now know exactly where to find more than 4,000 S. cerevisiae proteins, thanks to new methods and an analysis pipeline. Image by William Murphy via Wikimedia Commons

You might be familiar with the Where’s Waldo book series, especially (but not necessarily) if you have kids. They challenge the reader to find Waldo within huge, intricately drawn groups of people. Even though Waldo has his distinctive characteristics—glasses and a striped shirt and hat—he can be very hard to find.

Now imagine that the drawings shift under different conditions, so that Waldo could be in any of several places at different times. And imagine that you’re not just looking for Waldo, but also for thousands of other unique individuals—all tagged in the same way. This is the challenge faced by researchers who want to know where each protein in a cell is located and how its location and abundance respond to different environments.

But, as genetic, robotic, microscopic, and computational tools get more and more sophisticated, it’s becoming possible to pinpoint Waldo and his companions even as they move around within the jam-packed yeast cell.

In two new papers, scientists from the University of Toronto describe a huge effort that entailed over 9 billion quantitative measurements to find the location and measure the abundance of more than 4,000 S. cerevisiae proteins. Chong and colleagues wrote in Cell about the approach and experimental methods, while Koh and colleagues published in G3 about the computational methods and the database that houses all the data, called CYCLoPs for Collection of Yeast Cells and Localization Patterns.

This work couldn’t have been done without a valuable resource that was created some years ago: the yeast GFP collection. It’s a set of strains, each with the green fluorescent protein gene fused to the 3’ end of one open reading frame to express a GFP fusion protein from the ORF’s native promoter. Not every yeast protein can be detected this way: some are expressed too weakly, while others may actually be destabilized by their GFP tags. Still, more than 4,100 of these fusion genes—71% of the proteome—give a visible GFP signal in the cell.

The researchers started with these ~4,100 strains and transformed each with a plasmid expressing red fluorescent protein. This allowed them to visualize the boundaries of each cell. Then they got to work, taking pictures of at least 200 cells of each strain and developing an automated pipeline to analyze them. They ended up analyzing 300,000 micrographs of more than 20 million cells, beating the few dozen Where’s Waldo books by a long shot!

The scientists looked at each protein in wild type, in a mutant strain, and in the presence of two drugs. The mutant strain they studied was deleted for RPD3, which encodes a lysine deacetylase that regulates the stability and interactions of histones and other proteins. The drug treatments were done with several different concentrations of rapamycin (an inhibitor of the TORC1 complex, which is an important regulator of cell growth) or hydroxyurea (a DNA replication inhibitor).

The end result was an enormous collection of data, now stored in the CYCLoPs database, that shows the abundance of each protein in each of 16 cellular compartments under all of these different conditions. These data are much more quantitative and consistent than any protein abundance or localization data that had been obtained before. They are stored in such a way that measurements within single cells can be accessed, and the database can be searched by patterns of changes in localization or abundance as well as for data on a particular protein.

The authors came up with some innovative methods for visualizing this immense dataset to get a high-level overview. One of their most surprising findings was just how many proteins localize to multiple places. We tend to think of the cell as a tidy place where each protein has one particular location, but Chong and colleagues found that it’s extremely common for proteins to be in several spots.

Most often, when proteins are present in more than one place, those places are the nucleus and the cytoplasm. Some proteins had already been shown in small-scale studies to be present in both compartments, or to shuttle between them. But the authors saw an astounding 1,029 proteins localizing to both the nucleus and cytoplasm under standard conditions in wild-type cells.

Not counting the proteins in the nucleus and cytoplasm, another 511 proteins localized to more than one place. Some were seen in up to five different subcellular compartments.

The proteins with multiple locations, as a group, were more likely than the average protein to be phosphorylated. This made sense, because phosphorylation of proteins is known to regulate their localization. And many of these proteins themselves had regulatory roles, controlling processes such as cell division.

The fact that data were collected from single cells means that we can use them to uncover the dynamics of protein movement. For example, if a protein was scored as localizing to both the nucleus and the cytoplasm, does that mean there’s a pool of it in both places at all times, or does it move back and forth? The single-cell data for two representative proteins, Mcm2 and Whi5, showed clearly that any one cell has each of these proteins in either the nucleus or cytoplasm, but not both. But some other proteins hang out in both places at once. And the dynamics of still more roving proteins are just waiting to be revealed.

Researchers will be mining the CYCLoPs resource to find detailed information about specific proteins, pathways, and processes for years to come. The data gathered in the rpd3 mutant and under rapamycin and hydroxyurea treatment served as proof of principle that the system can be used to assess the effects of a variety of mutations and drugs.

So this study puts a spotlight on Waldo in each picture and makes it simple to find him and his friends. This mass of data on where proteins are and how they move around has far-reaching implications for yeast systems biology, and the methodology can now be applied to cells of other organisms as well. In the coming weeks, we’ll make it even simpler for you to access these data from SGD, by adding links for individual proteins to the CYCLoPs database.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: protein abundance , Saccharomyces cerevisiae , protein localization

The Sounds of Silencing

June 17, 2015


For centuries, we thought of the universe as an empty, eerily silent place. Turns out we were dead on when it came to the emptiness, not so much when it came to the silence.

Despite more and more powerful equipment, SETI has yet to find any meaningful radio signals coming from the stars. Yeast research is in a better position: new techniques applied to telomeric gene expression now make sense of the signals. Image by European Southern University (ESO) via Wikimedia Commons

Once we invented devices that could detect electromagnetic radiation—starting with the Tesla coil receiver in the 1890s—we began to realize what a noisy place the universe really is. And now with modern radio telescopes becoming more and more sensitive, we know there is a cacophony of signals out there (although the Search for Extraterrestrial Intelligence has yet to find any non-random patterns).

The ends of chromosomes, telomeres, have also long thought to be largely silent in terms of gene expression. But a new paper in GENETICS by Ellahi and colleagues challenges that idea. 

Much like surveying the universe with a high-powered radio telescope, the researchers used modern techniques to make a comprehensive survey of the telomeric landscape–and saw that the genes were not so silent. Their work revealed that there’s a lot more gene expression going on at telomeres than we thought before.

It also gave us some fascinating insights into the role of the Sir proteins, founding members of the conserved sirtuin family that is implicated in aging and cancer.

Telomeres are special structures that “cap” the ends of linear chromosomes to protect the genes near the ends from being lost during DNA replication, something like aglets, those plastic tips that keep the ends of your shoelaces from fraying. They have characteristic DNA sequence elements that we don’t have space to describe here (but you can find a short summary in SGD).

Classical genetics experiments in Drosophila fruit flies showed that telomeres had a silencing effect on the genes near them, and early work in yeast seemed to confirm this. Reporter genes became transcriptionally silenced when they were placed near artificial constructs that mimicked telomere sequences.

This early work was solid, but had a few limitations.  The artificial telomere constructs were, well, artificial; some of the reporter genes encoded enzymes that had an effect on overall cellular metabolism, such as Ura3; and the studies tended to look at just one or a few telomeres.

To get the whole story, Ellahi and colleagues decided to look very carefully at the telomeric universe of S. cerevisiae. First, they used ChIP-seq to look at the physical locations of three proteins, Sir2, Sir3, and Sir4, on chromosomes near the telomeres.

These proteins, first characterized and named Silent Information Regulators for their role in silencing yeast’s mating type cassettes, had been seen to also mediate telomeric silencing. Scientists had hypothesized that they might be present at telomeres in a gradient, strongly repressing genes close to the chromosomal ends and petering out with increasing distance from the telomere. 

Ellahi and coworkers re-analyzed recent ChIP-seq data from their group to find where the Sir proteins were binding within the first and last 20 kb regions of every chromosome. These 20 kb regions included the telomere and the so-called subtelomeric region where genes are thought to be silenced. They found all three Sir proteins at all 32 natural telomeres.

However, the Sir proteins were not uniformly distributed across the telomeres, but rather occupied distinct positions. Typically, all three were in the same position, as would be expected since they form a complex. And they were definitely not in a gradient along the telomere.

Next the researchers asked whether gene expression was truly silenced in that subtelomeric region. They used mRNA-seq to measure gene expression from the ends of chromosomes in wild type or sir2, sir3, or sir4 null mutants.

They found that contrary to expectations, there is actually a lot of transcription going on near telomeres, even in the closest 5 kb region. The levels are lower than in other parts of the genome, but that can be partly explained by the fact that open reading frames are less dense in these regions. And only 6% of genes are silenced in a Sir-dependent manner.

The sensitivity of mRNA-seq allowed Ellahi and colleagues to uncover new patterns of gene expression in this work. They were able to detect very low-level transcription from some of the telomeric repetitive elements. Also, because the SIR genes are involved in mating type regulation, the mRNA-seq data from the sir mutants revealed a whole new set of genes that are differentially expressed in different cell types (haploids of mating types a and α, or a/α diploids).

The researchers point out that their work raises the question of why the cell would use the Sir proteins to repress transcription of a few subtelomeric genes. Wouldn’t it be more straightforward if these genes just had weaker promoters to keep their expression low?

They hypothesize that Sir repression could actually be part of a stress response mechanism, allowing a few important genes to be turned on strongly when needed. This idea could have intriguing implications for the role of Sir family proteins in aging and cancer in larger organisms. 

So, neither the universe nor the ends of our chromosomes are as silent as we thought. But unlike the disappointed SETI researchers, biologists studying everything from yeast to humans can now build on this large quantity of meaningful data from S. cerevisiae telomeres. 

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae , silencing , telomere

Yeast are People Too

June 03, 2015


Cars on the road today all look pretty similar from the outside, whether they’re gasoline-fueled or electric. On the inside, they’re fairly similar too. Even between the two kinds of car, you can probably get away with swapping parts like the air conditioner, the tires, or the seat belts. Although cars have changed over the years, these things haven’t changed all that much.

Just like these cars, yeast and human cells have some big differences under the hood but still share plenty of parts that are interchangeable. Nissan Leaf image via Wikimedia Commons; Ford Mustang image copyright Bill Nicholls via Creative Commons

The engine, though, is a different story. All the working parts of that Nissan Leaf engine have “evolved” together into a very different engine from the one in that Ford Mustang. They both have engines, but the parts aren’t really interchangeable any more.

We can think of yeast and human cells like this too. We’ve known for a while that we humans have quite a bit in common with our favorite little workhorse S. cerevisiae. But until now, no one had any idea how common it was for yeast-human pairs of similar-looking proteins to function so similarly that they are interchangeable between organisms.

In a study published last week in Science, Kachroo and colleagues looked at this question by systematically replacing a large set of essential yeast genes with their human orthologs. Amazingly, they found that almost half of the human proteins could keep the yeast mutants alive.

Also surprising was that the degree of similarity between the yeast and human proteins wasn’t always the most important factor in whether the proteins could be interchanged. Instead, membership in a gene module—a set of genes encoding proteins that act in a group, such as a complex or pathway—was an important predictor. 

The authors found that genes within a given module tended to be either mostly interchangeable or mostly not interchangeable, suggesting that if one protein changes during evolution, then the proteins with which it interacts may need to evolve as well. So we can trade air conditioner parts between the Leaf and the Mustang, but the Mustang’s spark plugs won’t do a thing in that newly evolved electric engine!

To begin their systematic survey, Kachroo and colleagues chose a set of 414 yeast genes that are essential for life and have a single human ortholog. They cloned the human cDNAs in plasmids for yeast expression, and transformed them into yeast that were mutant in the orthologous gene to see if the human gene would supply the missing yeast function.

They tested complementation using three different assays. In one, the human ortholog was transformed into a strain where expression of the yeast gene was under control of a tetracycline-repressible promoter. So if the human gene complemented the yeast mutation, it would be able to keep the yeast alive in the presence of tetracycline.

Another assay used temperature-sensitive mutants in the yeast genes and looked to see if the human orthologs could support yeast growth at the restrictive temperature. And the third assay tested whether a yeast haploid null mutant strain carrying the human gene could be recovered after sporulation of the heterozygous null diploid.

Remarkably, 176 human genes could keep the corresponding yeast mutant alive in at least one of these assays. A survey of the literature for additional examples brought the total to 199, or 47% of the tested set. After a billion years of separate evolution, yeast and humans still have hundreds of interchangeable parts!

That was the first big surprise. But the researchers didn’t stop there. They wondered what distinguished the genes that were interchangeable from those that weren’t. The simplest explanation would seem to be that the more similar the two proteins, the more likely they would work the same way. 

But biology is never so simple, is it? While it was true that human proteins with greater than 50% amino acid identity to yeast proteins were more likely to be able to replace their yeast equivalents, and that those with less than 20% amino acid identity were least likely to function in yeast, those in between did not follow the same rules. There was no correlation between similarity and interchangeability in ortholog pairs with 20-50% identity.  

After comparing 104 different types of quantitative data on each ortholog pair, including codon usage, gene expression levels, and so on, the authors found only one good predictor. If one yeast protein in a protein complex or pathway could be exchanged with its human ortholog, then usually most of the rest of the proteins in that complex or pathway could too.

This budding yeast-human drives home the point that humans and yeast share a lot in common: so much, that yeast continues (and will continue) to be the pre-eminent tool for understanding the fundamental biology of being human. Image courtesy of Stacia Engel

All of the genes that that make the proteins in these systems are said to be part of a gene module. Kachroo and colleagues found that most or all of the genes in a particular module were likely to be in the same class, either interchangeable or not. We can trade pretty much all of the parts between the radios of a Leaf and a Mustang, but none of the engine parts.

For example, none of the tested subunits of three different, conserved protein complexes (the TriC chaperone complex, origin recognition complex, and MCM complex) could complement the equivalent yeast mutations. But in contrast, 17 out of 19 tested genes in the sterol biosynthesis pathway were interchangeable.

Even within a single large complex, the proteasome, the subunits of one sub-complex, the alpha ring, were largely interchangeable while those of another sub-complex, the beta ring, were not. The researchers tested whether this trend was conserved across other species by testing complementation by proteasome subunit genes from Saccharomyces kluyveri, the nematode Caenorhabditis elegans, and the African clawed frog Xenopus laevis. Sure enough, alpha ring subunits from these organisms complemented the S. cerevisiae mutations, while beta ring subunits did not.

These results suggest that selection pressures operate similarly on all the genes in a module. And if proteins continue to interact across evolution, they can diverge widely in some regions while their interaction interfaces stay more conserved, so that orthologs from different species are more likely to be interchangeable.

The finding that interchangeability is so common has huge implications for research on human proteins. It’s now conceivable to “humanize” an entire pathway or complex, replacing the yeast genes with their human equivalents. And that means that all of the versatile tools of yeast genetics and molecular biology can be brought to bear on the human genes and proteins.

At SGD we’ve always known that yeast has a lot to say about human health and disease. With the growing body of work in these areas, we’re expanding our coverage of yeast-human orthology, cross-species functional complementation, and studies of human disease-associated genes in yeast. Watch this space as we announce new data in YeastMine, in download files, and on SGD web pages.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight Yeast and Human Disease

Tags: yeast model for human disease , evolution , Saccharomyces cerevisiae , functional complementation

The Failed Hook Up: A Sitcom Starring S. cerevisiae

May 27, 2015


As anyone who watches a situation comedy knows, long range relationships are tricky. The longer the couple is separated, the more they drift apart. Eventually they are just too different, and they break up.

Jerry Seinfeld finds girlfriends incompatible for seemingly minor reasons like eating peas one at a time. Different yeast strains become incompatible over small differences in certain genes as well. Image by Dano Nicholson via Flickr

Of course if this were the end of the story, it would be the plot of the worst comedy ever. What usually happens in the sitcom is that one or both of them find someone more compatible and live happily ever after (with lots of silliness and high jinks).

Turns out that according to a new study by Hou and coworkers, our friend Saccharomyces cerevisiae could star in this sitcom. When different populations live in different environments, they drift apart. Eventually, because they accumulate chromosomal translocations and other serious mutations, they have trouble mating and having healthy offspring.

Now researchers already knew that big changes in yeast, like chromosomal translocations, affect hybrid offspring. But what was controversial before and what this study shows is that, as is known for plants and animals, smaller changes like point mutations can affect the ability of distinct populations of yeast to have healthy progeny. It is like Jerry Seinfeld being incompatible with a girl because she eats her peas one at a time (click here for other silly reasons Jerry breaks up with girlfriends).  

The key to finding that yeast can be Seinfeldesque was to grow hybrid offspring in different environments. Hybrids that did great on rich media like YPD sometimes suffered under certain, specific growth conditions. Relying on the standard medium YPD masked mutations that could have heralded the beginnings of a new species of yeast.

See, genetic isolation is a powerful way for speciation to happen. One population generates a mutation in a gene and the second population has a mutation in a second gene. In combination, these two mutations cause a growth defect or even death. Now each population must evolve on its own, eventually separating into two species.

To show that this is a route that yeast can take to new species, Hou and coworkers mated 27 different Saccharomyces cerevisiae isolates with the reference laboratory strain S288C and grew their progeny under 20 different conditions. These strains were chosen because they were all able to produce spores with S288C that were viable on rich medium (YPD).

Once they eliminated the 59 pairings that involved parental strains that could not grow under certain conditions, they found that 117 out of 481 or 24.3% of crosses showed at least some negative effect on the growth of the progeny under at least some environmental conditions. And some of these were pretty bad. In 32 cases, at least 20% of the spores could not survive.

The authors decided to focus on crosses between S288C and a clinical isolate, YJM241, where around 25% of spores were inviable under growth conditions that required good respiration, such as the nonfermentable carbon source glycerol. They found that rather than each strain having a variant that affected respiration, the growth defect happened because of two complementary mutations in the clinical isolate.

The first mutation was a nonsense mutation in COX15, a protein involved in maturation of the mitochondrial cytochrome c oxidase complex, which is essential for respiration. The second was a nonsense suppressor mutation in a tyrosine tRNA, SUP7. So YJM241 was fine because it had both the mutation and the mechanism for suppressing the mutation. Its offspring with S288C were not so lucky.

Around 1 in 4 progeny got the mutated COX15 gene without SUP7 and so could not survive under conditions that required respiration. Which of course is why this was missed when the two strains were mated on YPD, where respiration isn’t required for growth.

So this is a case where the separated population, the clinical isolate YJM241, changed on its own such that it would have difficulty producing viable progeny with any other yeast strains. Like the narrator in that old Simon and Garfunkel song, it had become an island unto itself.

The researchers wondered whether this kind of change—a nonsense mutation combined with a suppressor—occurs frequently in natural yeast populations. They surveyed 100 different S. cerevisiae genome sequences and found that nonsense mutations are actually pretty common. Nonsense suppressor mutations were another story, though: they found exactly zero.

Apparently nonsense suppressor mutations are really rare in the yeast world, and Hou and colleagues wondered whether this was because they had a negative effect on growth. They added the SUP7 suppressor mutant gene to 23 natural isolates. It had negative effects on most of the isolates during growth on rich media, but it was more of a mixed bag under various stress conditions. Sometimes the mutation had negative effects and sometimes it had positive effects.

The fact that a suppressor mutation can provide a growth advantage under the right circumstances, combined with the fact that they are very rare, suggests that a new suppressor arising might help a yeast population out of a jam, but once the environment improves the yeast are free to jettison it. Suppressor mutations may be a transitory phenomenon, a momentary dalliance.

So, separate populations of yeast can change over time in subtle ways that prevent them from mating with one another. This can eventually lead to the formation of new species as the changes cause the two to drift too far apart genetically. It is satisfying to know that yeast drift apart like any other plant, animal, or sitcom character.

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

Categories: Research Spotlight

Tags: evolution , Saccharomyces cerevisiae

Getting the Big Picture from 100 Genomes

May 20, 2015


Like the Peruvian Hairless dog, in some ways the S288C genome looks quite different from other members of its species. Image via Wikimedia Commons

Imagine if aliens visited the earth to learn about dogs, but they stumbled upon a colony of the very rare Peruvian Hairless. Taking a sample for DNA analysis, they would retreat to their home planet, do their studies, and conclude that all dogs had smooth, mottled skin and a stiff mohawk—as well as whatever crazy mutations the Peruvian Hairless happens to carry. 

Until recently, S. cerevisiae researchers have been a bit like those aliens. The genomic sequence of the reference strain S288C was completed in 1996, and for a long time it was the only sequence available. Scientists knew a lot about the S288C genome, but they didn’t have any perspective on the species as a whole.

In the past few years, genomic sequences have become available from a handful of other strains. But now, as described in a new paper in Genome Research, Strope and colleagues have determined the genomic sequences of 93 additional S. cerevisiae strains to make the number an even hundred.

This collection of strains and sequences has already provided new insights into yeast phenotypic and genotypic variation, and represents an incredible resource for future studies. And the comparison with this collection of other strains suggests that in some ways, S288C may be just as unusual as the Peruvian Hairless.

This collection of strains and their sequences gave the researchers a much broader perspective across the whole S. cerevisiae species. It’s as if the aliens discovered Golden Retrievers, Great Danes, Chihuahuas, and more. We only have space here to touch upon a few of the highlights.

First off, they confirmed what many yeast researchers have suspected for a while—S288C is a bit odd.  We already knew that a S288C carries polymorphisms in several genes that affect its phenotype. For example, the MIP1 gene in S288C encodes a mitochondrial DNA polymerase that is less efficient than in other strains, making its mitochondrial genome less stable.

Back when fewer strain sequences were available, it wasn’t clear whether the S288C polymorphisms in other genes like MKT1, SSD1, MIP1, AMN1, FLO8, HAP1, BUL2, and SAL1 were the exception or the rule. Now that Strope and colleagues had 100 genomes in hand, they could see that these differences are indeed peculiar to S288C and its close relative W303.  They might have arisen because of the long genetic isolation of the strains, or because of special selective pressures they faced during growth in the lab.

They also found a lot of variation in how often S. cerevisiae strains have acquired whole chromosomal regions from other Saccharomyces species. This process, known as introgression, happens when related species mate to form hybrids. Stretches of DNA that are transferred in this way are recognizable because gene order is preserved, but all the genes they contain are highly diverged.

The researchers found 141 of these regions containing 401 genes. Many showed similarity to S. paradoxus, which is known to hybridize with S. cerevisiae, but others apparently came from unknown, as yet un-sequenced Saccharomyces species. In a couple of cases that the authors looked at closely, the introgressed genes had slightly different functions from their native S. cerevisiae counterparts.

Another notable finding by Strope and colleagues concerned some genes that exist in multiple copies. The ENA genes, encoding an ATP-dependent sodium pump, are present in 3 copies in S288C (ENA1ENA2, and ENA5), while the CUP1-1 and CUP1-2 genes, encoding metallothionein that binds to copper and mediates copper resistance, are present in 10-15 copies.

To get perspective on a whole species, you need to look at lots of different examples. Image by Sue Clark via Flickr

The sequence coverage in these regions relative to their flanking regions allowed the researchers to see exactly how many repeats are present in each strain. All had between 1-14 copies of ENA genes and 1-18 copies of CUP genes. Interestingly, the strains of clinical origin had significantly higher copy numbers of CUP genes than the non-clinical strains, suggesting that copper resistance is an important trait for virulence.

So, instead of being confined to the S288C genome, S. cerevisiae researchers can now get a much fuller idea of the range of genetic and phenotypic variation within the species. The strains (available at the Fungal Genetic Stock Center), along with their genome sequences (available in GenBank), are an amazing resource for classical and quantitative genetics and comparative genomics.

Unlike those aliens, we won’t end up thinking of yeast as a mostly bald dog with a mohawk. No, we will have a fuller picture of S. cerevisiae strains in all their glory.

A few technical details

In selecting the strains to sequence, Strope and colleagues chose from a wide variety of yeast cultures isolated from the environment and from hospital patients with opportunistic S. cerevisiae infections. But they faced a problem: many of the cultures had irregular numbers of chromosomes or genome rearrangements, which would complicate both interpretation of the sequence data and any future genetic analysis.

To avoid this problem, the researchers selected only strains that were able to sporulate and produce four viable spores—showing that their genomes weren’t messed up. They also wanted strains with no auxotrophies (nutritional requirements), since these can negatively affect growth and complicate the comparison of phenotypes. In some cases, they corrected specific mutations in the strains to increase their fitness.

They ended up with 93 homozygous diploid strains to sequence. Producing paired-end reads of 101 bp, they generated genome assemblies that had 22- to 650-fold coverage per strain.

Because the sequence reads were relatively short, they didn’t provide enough information to assemble the sequence across repetitive regions. So Strope and colleagues used a genetic method to determine gene order. They crossed haploid derivatives of the strains to the reference strain S288C; if their genomes were not colinear with that of S288C, then some of the resulting spores would be inviable.

This analysis showed that 79 of the strains had chromosomes colinear to those of S288C, and allowed assembly of their genomes across multicopy sequences. The remaining strains had chromosomal translocations relative to S288C. Twelve of these carried the same reciprocal translocation between chromosomes 8 and 16.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: strains , Saccharomyces cerevisiae , genome

If it Swims Like a Duck…

May 06, 2015


It may swim like a duck, but this beast is obviously not a duck. Just like the glycine patch of Pxr1 looks like an interaction region when it isn’t. Image by Yotujonoo via Creative Commons

Back in the 1980’s some U.S. politicians were proposing to raise money by something they called “revenue enhancements”. Richard Darman, the budget director at the time, correctly pointed out that a revenue enhancement really is just a tax increase by another name. 

To make his point, he used the expression, “If it looks like a duck and quacks like a duck, it’s a duck.” In other words, just because politicians call it something else, if a revenue enhancement does everything a tax increase does then it really is just a tax increase.

This same reasoning is often used in biology. If two regions of a protein look the same (are homologous) and the proteins do similar things, then the two similar regions do the same thing. Except, of course, when they don’t.

This probably isn’t what Conan O’Brien had in mind when he changed the famous expression a bit to say, “If it looks like a duck and quacks like a duck, it’s a little person dressed as a duck,” but as is often the case with Team Coco, he was right in both biology and life. Not everything that looks and quacks like a duck is a duck, and not every homologous region in proteins that do similar things does the same thing.

Conan’s point is borne out in a new study out in GENETICS where Banerjee and coworkers show that even though the yeast Prp43 RNA helicase shares glycine patches with three of the proteins with which it interacts, this doesn’t mean the glycine patches are used the same way in each case. They may all look and act like ducks, but they are not all ducks! 

Glycine patches are short, glycine-rich protein motifs that are thought to help proteins recognize other proteins or RNAs. Two of the proteins that the researchers looked at, Spp382 and Sqs1, have glycine patches that are only subtly different from that of Prp43. In both of these, the glycine patch is important for interacting with Prp43, but that isn’t its only role. The patches really are ducks in this case, just different kinds of ducks—maybe a mallard and a mandarin duck.

In the case of the third protein, Pxr1, the glycine patch seems to have a completely different (albeit important) role. In this case, it really is a little person in a duck costume!

Prp43 is involved in two different kinds of RNA processing in the yeast cell—pre-mRNA splicing and rRNA maturation. It is one of the few proteins shared between the two complexes involved in each process.

Previous work had shown that different factors in each complex are important for bringing Prp43 to each party. For rRNA maturation, Sqs1 and Pxr1 are the critical players, while for pre-mRNA splicing, Spp382 is key. Since all four proteins share little else beyond a shared weakly conserved, 45-50 amino acid glycine-rich patch, one idea was that all of these proteins use the patch to interact with one another. As is true of much in life, the real answer is a bit more complicated than that.

The first set of experiments was to determine how well Prp43 interacts with each of the other glycine patches, using yeast two-hybrid assays. With full length proteins, the authors found that Spp382 interacted most strongly with Prp43, Pxr1 was the weakest, and Sqs1 was intermediate. They got a similar order of interaction when using just the glycine patches of each of these three proteins, with one small difference: the Pxr1 glycine patch did no better than the empty vector control.

This last result suggested that the glycine patch of Pxr1 was insufficient on its own to interact with Prp43. This was confirmed when they found no difference in the interaction of full length Pxr1 and Pxr1 deleted for the glycine patch.

The Pxr1 glycine patch apparently plays no role in interacting with Prp43—it really isn’t a duck at all. But that doesn’t mean it is dispensable! They showed later that it is critical for snoRNA processing, an important step needed for rRNA maturation.

Mandarins and mallards look like ducks and quack like ducks…and they are ducks. Like these ducks, the glycine patches of Spp82 and Sqs1 look and act like interaction regions, and in fact they are interaction regions. Image via Wikimedia Commons

Of course, sometimes if it looks and quacks like a duck, it is indeed a duck. This was the case for Sqs1 and Spp382.

As shown by two-hybrid and glycine patch swap assays, each of these glycine patches do seem to be important for interacting with Prp43. But each patch was more than just a way for two proteins to hook up.

To show this, Banerjee and coworkers looked for chimeras of Spp382, Pxr1, and Sqs1 that could rescue the lethal phenotype of a Spp382 deletion. First off, they showed that deleting the glycine patch from Spp382 was equivalent to deleting the whole protein—it was a lethal event. And as expected, replacing the Spp382 glycine patch with the one from Pxr1 was still lethal. But the Sqs1 glycine patch was able to rescue the deletion strain although it grew more slowly. So the Spp382 and Sqs1 glycine patches could to some extent substitute for one another.

One way to interpret the difference in growth rates is that it has to do with the fact that the glycine patch of Spp382 bound more strongly to Prp43 than did the one from Sqs1. The glycine patch from Sqs1 can’t fully rescue the Spp382 deletion strain because it is a weaker binder. But a set of mutagenesis experiments suggests that this is not the case.

The authors basically took the Spp382/Pxr1 chimera in which the Pxr1 glycine patch replaced the one from Spp382 and made a series of point mutations that slowly converted the glycine patch back to the one from Spp382. What they found was that the strength of interaction in the two-hybrid assay does not correlate with the level of rescue in the complementation assay. One interpretation is that the Spp382 glycine patch is doing more than recruiting Prp43.

Taken together, these results are a bit of a biological cautionary tale. Just because a protein region looks like another one, do not assume they are doing the same thing. Sometimes what looks and acts like a duck is just a man dressed as a duck.

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

Categories: Research Spotlight

Tags: glycine patch , Saccharomyces cerevisiae , protein-protein interactions

A Factory Without Doors

April 29, 2015


Every factory needs raw materials. Without steel, this is just a pretty factory. And without incoming xylose, a yeast cell set up to make ethanol from biomass is just a pretty cell. Image by Steve Jurvetson via Flickr

Imagine you have built a state-of-the-art factory to make a revolutionary product. The place is filled with gleaming assembly lines and you have hired the best talent in the world to run the place.

Unfortunately there was a glitch in the factory design—the builders forgot to put doors in! Now you can’t get the raw materials in to make that killer product that will change everything.

This may sound contrived or even silly, but it is sort of what is happening in attempts to use yeast to make biofuels from agricultural waste. Scientists have tweaked yeast cells to be able to turn xylose, a major sugar found in agricultural waste, into ethanol. But yeast has no transporter system for this sugar. A bit can get in through the windows, so to speak, but we need to put in a door so enough can get inside to make yeast a viable source for xylose-derived ethanol.

An important step was taken in this direction in a new study by Reznicek and coworkers. They used directed evolution to transform the Gal2 transporter of Saccharomyces cerevisiae into a better xylose transporter. And they succeeded.

After three successive rounds of mutagenesis, they transformed Gal2 from a transporter that prefers glucose into one that prefers xylose. When put in the right background, this mutant protein opens the door for getting yeast to turn agricultural waste into ethanol. Perhaps yeast can help us stave off cataclysmic climate change for just a bit longer. 

The first step was to find the right strain for assaying xylose utilization. They needed a strain lacking 8 hexose transporters, Hxt1-7 and Gal2, because these transporters can take up xylose (albeit at a very low efficiency). Deleting these genes “shuts the windows” and completely prevents the strain from utilizing xylose as a substrate (as well as impairing its ability to use glucose).

This strain was also engineered to be able to utilize xylose. It contained a xylose isomerase gene from an anaerobic fungus and also either overexpressed or lacked several S. cerevisiae genes involved in carbohydrate metabolism. With this strain in hand, the researchers were now ready to add a door to their closed off factory.  

The authors targeted amino acids 292 to 477 in Gal2. This region is thought to be critical for recognizing sugars, based on homology with other hexose transporters. They used mutagenic PCR conditions that generated an average of 4 point mutations in this region, and screened for mutants that grew better than others on plates containing 0.1% xylose.

In their initial screen they selected and replated the 80 colonies that grew best. They then chose the best 9 to analyze further. Of these 9, one mutant which they dubbed variant 1.1 grew better on xylose than a strain carrying wild-type GAL2. Variant 1.1 had a single amino acid change, L311R.

They repeated their assay using variant 1.1 as their starting source. Out of the 14,400 mutants assayed, they found four that did better than variant 1.1. These variants, dubbed 2.1-2.4, all shared the same M435T mutation.  Variant 2.1 had three additional mutations—L301R, K310R, and N314D.

These four new mutants showed better growth on 0.45% xylose, and after 62 hours, all the strains had pretty much used up the xylose in their media. Of the four, variant 2.1 appeared to be the best xylose utilizer: after 62 hours the authors could detect no xylose in the media at all. This variant also grew faster than the others in 0.1% xylose.

Reznicek and coworkers had definitely made Gal2 a better xylose transporter, but they weren’t done yet. They wanted to try to make a door that only let in the raw supplies (xylose) they wanted and not other sugars (glucose).

Up until now, the screens had been done with xylose as the sole carbon source. When they grew variant 2.1 in the presence of both 2% glucose and 2% xylose, they found that it preferentially used the glucose first. Their evolved transporter still preferred glucose over xylose!

Now in some ways this wasn’t surprising, as the mutations had not really affected the part of the protein thought to be involved in recognizing sugars. They next set out to evolve Gal2 so that it would transport xylose preferentially over glucose.

This time they used a slightly different background strain for their screen. This strain, which was deleted for hxk1, hxk2, glk1, and gal1, was unable to use glucose although it could transport it.

They repeated their mutagenesis and looked for mutants that grew best in 10% glucose and 2% xylose. We would predict that any growing mutants would have to transport xylose better than glucose. And this is just what they found.

When they analyzed the mutants, they found that the key mutation in making Gal2 prefer xylose over glucose in the variant 2.1 background was T386A. Based on homology with Hxt7, this mutation happens smack dab in the middle of the sugar recognition part of the protein. Most likely this mutation compromised the ability of Gal2 to recognize glucose, as opposed to improving recognition for xylose.

These experiments represent an important but by no means final step in engineering yeast to make fuel from biomass. We are on our way to a smaller carbon footprint and perhaps a world made somewhat safer from climate change.

First, beer, wine, and bread; next, keeping coral alive and saving countless species from extinction. Nice work, yeast.

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

Categories: Research Spotlight

Tags: evolution , Saccharomyces cerevisiae , biofuel

Sharing the Health

April 22, 2015

When yeast are forced to eat a meager diet, they not only live longer themselves but they also make a mysterious chemical that helps nearby yeast live longer. If they stay away from all-they-can-eat buffets, that is… Image by Andreas Praefcke via Wikimedia Commons


A study published a few years ago made a big splash in the health news by showing that obesity is socially contagious. If one person gains weight, their friends tend to gain weight too—even if they don’t live in the same town! This works the opposite way too: thinner people are more likely to be socially connected with thinner people.

You might think this is because people tend to make friends with others of a similar size, but this doesn’t seem to be the case. The researchers concluded that there is actually a cause-and-effect relationship: we all influence the weight of our friends.

Well, S. cerevisiae cells are not so different. They may not have social lives, but since they can’t move on their own, they do tend to live together in colonies. And within these colonies, they influence each other: not in terms of weight, but in terms of the effect that calorie intake has on the length of their lives.

Turns out that like nematodes, fruit flies and even mice, living on a meager diet makes yeast live longer. And in a new study published in PLOS Biology, Mei and Brenner found that yeast cells actually share the life-extending benefits of calorie restriction with their neighbors, probably via a still-unidentified small molecule.  

Yeast are normally grown in the lab on medium containing 2% glucose. To a yeast cell, this is like an all-you-can eat buffet that goes on for its entire lifetime. Media with a glucose content of 0.5% or less represent a meager diet. But that deprivation comes with a benefit, in the form of an extended lifespan.

Mei and Brenner already had some hints from previous studies that yeast cells might excrete a substance that promoted lifespan extension. To study this systematically, they devised an experiment to test whether mother cells change the media surrounding them as they divide.

The researchers placed individual mother cells in specific spots on Petri plates containing an all-you-can-eat buffet (2% glucose), a restrictive diet (0.5% glucose), or a near-starvation diet (0.2% glucose). They watched as the cells budded, and removed each new daughter cell as it separated from the mother, counting the buds. The lifespan of a mother yeast cell, termed the replicative lifespan, is measured as the number of times she can bud during her lifetime.

After the mother cells had budded 15 times, half of them were physically moved to fresh parts of the same plate, while the other half were left in place. For the mothers on the 2% glucose plates where calories were abundant, the move didn’t change anything. The mothers that were moved had exactly the same replicative lifespan as those that stayed put.

On the plates where calories were restricted, it was a different story. The cells that stayed in place had extended lifespans, as expected under these low-calorie conditions. But the cells that were moved to new locations lost most or all of the life extension—even though calories were still restricted in their new locations. This suggested that the mother cells had secreted a “longevity factor” into the medium surrounding them, which then extended their lifespan when they got older.

There were a couple of metabolites that were prime candidates for the longevity factor: nicotinic acid (NA) and nicotinamide riboside (NR). NA and NR are precursors to nicotinamide adenine dinucleotide (NAD+), a compound that acts as an essential cofactor for many important enzymes. They had already been implicated in lifespan extension because mutating genes involved in their metabolism can affect how long various creatures live.

When the scientists tried supplementing calorie-restricted cells that had been moved to fresh medium with either NA or NR, they found that supplying these metabolites could restore the longevity benefit.  This finding strengthens the idea that NAD+ metabolism is involved.

But was the longevity factor actually NA or NR? To test this, Mei and Brenner grew yeast in liquid media with the different glucose concentrations and then tested for NA and NR in the medium using liquid chromatography-mass spectrometry analysis.  They found that under all the conditions, the amount of NA secreted by the cells didn’t change and secreted NR was undetectable, suggesting that neither was the factor induced by calorie restriction.

To ask directly whether there is a diffusible longevity factor, the researchers grew cells in liquid medium containing 2% or 0.2% glucose until all the glucose was used up, then separated out the cells and freeze-dried the remaining liquid. They suspended the dried “conditioned” medium in water and spread it on plates to repeat the cell-moving assay.

Just like before, cells grown in 2% glucose had the same lifespan after being moved to a fresh spot, and the addition of resuspended conditioned medium to the plate didn’t change that. However, the starved cells grown on 0.2% glucose not only kept their lifespan extension when moved to conditioned media, but actually lived 10% longer compared to starved cells on un-conditioned media that were not moved.

When the researchers dialyzed the conditioned medium so that molecules smaller than 3.5 kDa were lost, the longevity factor was lost too. So it looks to be a small molecule, and of course they are actively pursuing its identity. Intriguingly, this would explain why other scientists have been unable to detect calorie restriction-induced lifespan extension in yeast using microfluidic technology, where immobilized yeast cells are grown with a constant exchange of growth medium. Under these conditions, a small molecule that promotes longevity would be washed away.

So, even though they don’t have Facebook friends, yeast cells influence the health of their peers. Rather than spreading the influence through social interactions as we humans do, they broadcast a chemical that is the key to long life. 

It’s tempting to think that the identity of this chemical will tell us something about human aging. But if this mysterious molecule worked in humans the same way as it does in yeast, people would still have to eat just enough food to stay alive to get the benefits. Still, perhaps the molecule can point us towards finding a treatment that will let us live longer while enjoying lots of good food. We could have our cake and eat it too!

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

Categories: Research Spotlight

Tags: NAD+ , aging , Saccharomyces cerevisiae , calorie restriction

Once You Start Looking, Shu2 Homologs Are Everywhere

April 09, 2015


Once you notice the first robin of the spring, you see them everywhere. And once you notice an important protein, you might well find it all over the evolutionary tree. Image by Jerry Friedman via Wikimedia Commons

Have you ever heard of the Baader-Meinhof phenomenon? (Don’t worry, we hadn’t either!) But you’ve surely experienced it.

The phenomenon describes the experience of suddenly seeing something everywhere, after you’ve noticed it for the first time. For those of us in Northern climates, it’s like like robins coming back in the springtime: one day, you see a single robin hopping on the grass; the next day, you look around and realize they’re all over.

Something similar happened to Godin and colleagues as they investigated the S. cerevisiae Shu2 protein. People knew about the protein and its SWIM domain but no one had looked to see just how conserved it was.

When the researchers looked for homologous proteins that contained the characteristic SWIM domain, they found homologs in everything from Archaea through primitive eukaryotes, fungi, plants, and animals. They were practically everywhere (in the evolutionary sense).

In a new paper in GENETICS, Godin and coworkers described their studies on this relatively little-studied, unsung protein. They found that it is an important player in the essential process of DNA double-strand break repair via homologous recombination, and its SWIM domain is critical to this function. Not only that, but being able to compare the SWIM domains from so many different homologs allowed them to refine its consensus sequence, identifying a previously unrecognized alanine within the domain was both highly conserved and very important.

Double-strand DNA breaks (DSBs) can happen because of exposure to DNA damaging agents, but they are also formed normally during meiotic recombination, in which a nuclease actually cuts chromosomes to start the process. Homologous recombination to repair DSBs is a key part of both mitosis and meiosis.

During homologous recombination, one strand of each broken DNA end is nibbled back to form a single-stranded region. This region is then coated with a DNA-binding protein or proteins, forming filaments that are necessary for those ends to find homologous regions and for the DSB to be repaired.

Shu2 is one of the proteins that participates in the formation of these filaments in S. cerevisiae. It was known that it was part of a complex called the Shu complex, and a human homolog, SWS1, had been identified. But the exact role of Shu2 and the significance of the SWIM (SWI2/SNF2 and MuDR) zinc finger-like domain that it contains were open questions.

One of the first questions the authors asked was whether Shu2 was widely conserved across the tree of life. Genes with similar sequences had been seen in fission yeast (Schizosaccharomyces pombe) and humans, but no one had searched systematically for orthologs. They used PSI-BLAST, a variation of the Basic Local Alignment Search Tool (BLAST) algorithm that that is very good at finding distantly related proteins, to search all available sequences.

Querying with both yeast Shu2 and human SWS1, the researchers found hits all across the tree of life—both in “lower” organisms such as Archaea, protozoa, algae, oomycetes, slime molds, and fungi, and in more complex organisms like fruit flies, nematode worms, and plants. The homologous proteins that they found across all these species also had the SWIM domain, suggesting that it might be important.

The sequence similarity was all well and good, but did these putative Shu2 orthologs actually do the same job in other organisms that Shu2 does in yeast? One way to test this is to do co-evolutionary analysis. Proteins that work together are subject to the same evolutionary pressures, so they tend to evolve at similar rates. Godin and colleagues found that evolutionary rates of the members of the Shu complex in fungi and fruit fly did generally correlate with those of other proteins involved in mitosis and meiosis.

The awesome power of yeast genetics offered Godin and coworkers a way to look at the function of Shu2. They first tested the phenotype of the shu2 null mutation, and found that it decreased the efficiency of forming filaments of the Rad51 DNA-binding protein on the single-stranded DNA ends that are created at DSBs. Formation of these filaments is a necessary step in repairing the DSBs by homologous recombination.

The comparison of SWIM domains from so many different proteins highlighted one particular alanine residue. This alanine hadn’t previously been considered part of the domain’s consensus sequence, but it was conserved in all the domains.

When the researchers changed the invariant alanine residue in yeast Shu2, the mutant protein bound less strongly to its interaction partner in the Shu complex, Psy3. When they mutated the analogous residue in the human Shu2 ortholog SWS1, this also decreased its binding to its partner, SWSAP1.

Other mutations within the SWIM domain of Shu2 also affected its interactions with other members of the Shu complex, and made the mutant cells especially sensitive to the DNA-damaging agent MMS. Diploid cells with a homozygous mutation in the Shu2 SWIM domain had very poor spore viability, suggesting that the SWIM domain is important for normal meiosis.

As one more indication of the SWIM domain’s importance, Godin and colleagues took a look in the COSMIC database, which collects the sequences of mutations found in cancer cells. Sure enough, a human cancer patient carried a mutation in that invariant alanine residue of the SWIM domain in the Shu2 ortholog, SWS1.

There’s still much more to be done to figure out exactly what Shu2 and the Shu complex are doing during homologous recombination. Yeast obviously provides a wonderful experimental system, and the discovery of Shu2 orthologs in two other model organisms that also have awesomely powerful genetics and happen to be multicellular, Drosophila melanogaster and Caenorhabditis elegans, expands the experimental possibilities even further.

There’s also a lot to be learned about the SWIM domain in particular. The discoveries that it affects the binding behavior of these proteins and that it is mutated in a cancer patient show that it’s very important, but just what does it do in Shu2? It will be fascinating to find out exactly how this domain works to help cells recover from the lethal danger of broken chromosomes. And it is amazing what you can see, once you start looking. 

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

Categories: Research Spotlight

Tags: Saccharomyces cerevisiae , evolution , model organism , homologous recombination

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