New & Noteworthy

Yeast Researchers Turn up the Heat on Essential Genes

September 09, 2015

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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.

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: community resource, mutant collection, Saccharomyces cerevisiae

Look for SGD at the 27th ICYGMB!

August 30, 2015

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SGD staff will be attending the 27th International Conference on Yeast Genetics and Molecular Biology (ICYGMB), September 6-12, in Levico Terme, Italy. We will be hosting a Workshop, Posters, and an Exhibit Table. We’ll be available during the entire conference to hear your comments or suggestions about SGD and answer your questions.

Follow @yeastgenome and #Yeast2015 on Twitter for the latest research being presented at ICYGMB.

Find these SGD staff members at the conference:

Mike Cherry

Maria Costanzo

Stacia Engel

Edith Wong

Giltae Song

Workshop: “Getting More out of SGD”

Sunday, September 6, 4:00 – 6:00 PM

We’ll be discussing our curation efforts in capturing yeast-human functional complementation data, the new sequence Variant Viewer, new data in YeastMine, and more. Bring your questions and comments – we love feedback!

Posters

In addition to the Workshop, SGD staff will present three posters – please stop by and chat with us.

Poster no.	Title	Presenter	Location	Time/Day
PS7-9	Homology curation at SGD: budding yeast as a model for eukaryotic biology	Stacia Engel	Sala Belvedere	2:30-4 PM, Tuesday and Thursday, 9/8 and 9/10
PS15-24	Inferring Genome Variation Patterns in Saccharomyces cerevisiae using the Eukaryote Pan-Genome Toolset	Giltae Song	Sala Impero	2:30-4 PM, Monday and Thursday, 9/7 and 9/10
PS15-29	Integrating genome-wide datasets into the Saccharomyces Genome Database	Edith Wong	Sala Impero	2:30-4 PM, Tuesday and Thursday, 9/8 and 9/10

Exhibit Table

SGD will also have an exhibit table at the conference. Come by to take a spin on our site, learn about various features of the database, and provide us with feedback as to what we can do to improve SGD. You might even receive a prize for a good question or suggestion!

Categories: Conferences

SGD Summer 2015 Newsletter

August 28, 2015

SGD periodically sends out its newsletter to colleagues designated as contacts in SGD. This Summer 2015 newsletter is also available on the community wiki. If you would like to receive the SGD newsletter in the future please use the Colleague Submission/Update form to let us know.

Categories: Newsletter

Many Modules Make Light(er) Work

August 26, 2015

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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.

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

SGD Help: Interaction Overview and Network

August 24, 2015

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SGD includes data on many thousands of genetic and physical interactions between the genes and proteins of Saccharomyces cerevisiae, as curated by our friends at the BioGRID database. We provide two different graphical displays that help you get a very quick and intuitive overview of known interactions for a particular gene or protein.

All interactions for a gene and its product are listed on its interactions page (see an example). At the top of the page, the Interactions Overview shows at a glance how many interactions have been curated and whether they are physical or genetic. This video explains the details of the Interactions Overview diagram:

Farther down on the Interactions page, the Interaction Network is a visual representation of genetic interactions for a particular gene and the protein-protein interactions for its gene product. The network is interactive, allowing you to choose to view either genetic or physical interactions or both. Using the slider, you can set a minimum number of experiments supporting the interactions displayed. Learn how to use the interactive features of the Interaction Network by watching this brief video:

Categories: Tutorial

Tags: BioGRID, genetic interactions, protein-protein interactions

Ancient Hybridization Causes Revision of Yeast’s Calendar

August 19, 2015

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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:

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

Redesigning Life, Ethically

August 13, 2015

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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: Saccharomyces cerevisiae, synthetic biology

SGD Help Video: Genome Snapshot

August 10, 2015

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Have you ever wondered just how many genes are found in the genome of the S. cerevisiae reference strain S288C, or how well characterized they are? SGD’s Genome Snapshot gives you a graphical overview of the annotation state of the genome, updated daily. This brief video gives you a tour of the page and explains the information shown in each section.

Categories: Tutorial

Tags: Genome Snapshot, video

Private Nurses Help Birth Ribosomal Proteins

August 06, 2015

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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.

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: chaperones, ribosomal proteins, Saccharomyces cerevisiae

Another Small Victory for Lamarck

July 29, 2015

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

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