New & Noteworthy

The Gift for the Man Who Has Everything

July 08, 2015

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

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 a–b–c–d–e–f–g 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

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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, protein localization, Saccharomyces cerevisiae

The Sounds of Silencing

June 17, 2015

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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.

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

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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.

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.

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

The Failed Hook Up: A Sitcom Starring S. cerevisiae

May 27, 2015

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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.

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

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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 (ENA1, ENA2, 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.

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: genome, Saccharomyces cerevisiae, strains

If it Swims Like a Duck…

May 06, 2015

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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.

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, protein-protein interactions, Saccharomyces cerevisiae

A Factory Without Doors

April 29, 2015

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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: biofuel, evolution, Saccharomyces cerevisiae

Sharing the Health

April 22, 2015

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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: aging, calorie restriction, NAD+, Saccharomyces cerevisiae

Once You Start Looking, Shu2 Homologs Are Everywhere

April 09, 2015

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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: evolution, homologous recombination, model organism, Saccharomyces cerevisiae

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