New & Noteworthy

Yeast’s Skynet Against Salt

June 27, 2017


Skynet

Like Skynet, PI3,5P2 signaling is a rapid defense response. from Wikipedia.

 In the Terminator franchise, the U.S. creates an artificial intelligence (AI)-based defense system called Skynet to, among other things, react more quickly to threats than any general or politician could. What starts out as an interesting idea almost dooms mankind to extinction once Skynet becomes conscious and decides to eliminate its greatest threat—humans. 

Our friend Saccharomyces cerevisiae has its own version of Skynet for when it is “attacked” by too many salt ions. No, the system isn’t conscious and it does not threaten this yeast’s very existence but like Skynet, it is designed to react more quickly than more conventional systems based on gene regulation. It basically buys yeast enough time to allow the cells to more stably adapt to their new high salt environment. 

Within 1 -5 minutes of being plunked down into high salt, yeast activates Hog1p, a key MAP kinase. The activated Hog1p heads into the nucleus and within 30-60 minutes, it tweaks the expression of a bunch of genes so the yeast can now better deal with its new environment.

This is a lot of time to be languishing in high salt. Luckily, yeast’s “whole hog” approach to high salt is not limited to just Hog1p. According to a recent study by Jin and coworkers in the Journal of Cell Biology, there is another, faster reaction to the high salt. And at least in these experiments, it is critical for yeast’s survival when it is assaulted by too much salt.  

This rapid response involves a signaling lipid found in the vacuole called phosphatidylinositol 3,5-bisphosphate, or PI3,5P2. The amount of this lipid goes way up within just five minutes of the high salt shock.

Sarah Connor

One way to give Sarah Connor an easier life might have been to make Skynet’s control of defense transient. from cdn.movieweb.com.

PI3,5P2 is synthesized in yeast by a single enzyme, Fab1p. It stands to reason that if PI3,5P2 is critical to yeast survival in high salt, then deleting FAB1 should affect yeast’s ability to deal with all of those extra ions in its environment. This is just what Jin and coworkers found.

They compared the viability of wild type, hog1Δ, and fab1Δ strains under normal conditions and after a four hour exposure to 0.9M NaCl (high salt). Under low salt conditions, the fab1Δ strain was less viable than the other two. Around 30% of the fab1Δ yeast were dead.

At high salt, less than 10% of the wild type yeast and around 30% of the hog1Δ yeast were dead after four hours. This compares to the greater than 80% dead fab1Δ yeast.

The next steps in the study were to identify how the high salt increases the amount PI3,5P2. They reasoned that they needed something fast and that kinases just might fit the bill, so they started looking for strains that dealt poorly with high salt in the “…knockout haploid yeast mutant collection of 103 nonessential protein kinases.” They found a likely candidate in Pho85p and further work showed that its partner cyclin Pho80p was also involved.

Both the pho85Δ and pho80Δ strains had enlarged vacuoles (a common phenotype in yeast that cannot make PI3,5P2). More importantly, both strains could not make PI3,5P2 either under normal or high salt conditions and were also less viable than wild type under high salt conditions.

Additional experiments provided strong evidence that Pho85p phosphorylated Fab1p and that this phosphorylated Fab1p was important for synthesizing PI3,5P2 under high salt conditions. The final experiments confirmed that something similar happens in mammalian cells.

Jin and coworkers showed that the Pho80p-Pho85p equivalent in mammalian cells, CDK5-p35, phosphorylates the Fab1p equivalent, PIKfyve, in vitro. They also showed that CDK5-p35 is important for mouse fibroblasts to make more PI3,5P2 when exposed to high salt.

These studies suggest that yeast and probably mammals have at least two systems for dealing with high salt. The first is a rapid increase in PI3,5P2 that protects the cells from the environmental insult which gives the cells time to set up the second system—a longer term, more stable adaptation.

If only the folks in the Terminator world were as smart as yeast and had made Skynet a transient system set up to protect the U.S. while humans had time to respond in a more stable way. Think how much easier Sarah Connor’s life would have been! 

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

Making the Best of a Sticky Situation

June 5, 2017


Lemonade stand

Like turning lemons into lemonade, Hope and coworkers turned gummy yeast into a useful strain. from Pixabay.

 Back in 1915, writer Elbert Hubbard coined the phrase, “When life gives you lemons make lemonade.” (His actual quote was “He picked up the lemons that Fate had sent him and started a lemonade-stand.”)

The idea of course is to take something bad and make it into something good. Like, if your research gives you terribly weak glue, invent Post-It notes.

Or as Hope and coworkers show in a new study in GENETICS, when your yeast experiment gets gummed up because the yeast evolves a sticky trait, do additional experiments to learn about the evolution of that complex trait. And “invent” a way to make the yeast less likely to flocculate, or stick together.

This new “invention” will be very useful for anyone trying to evolve yeast to create new products or to study how evolution works. It also showed that for this trait, under these conditions with this strain, there was one major way to get to flocculence—up-regulating the FLO1 gene. This meant they could greatly reduce the risk of this trait popping up by simply deleting FLO1.

Studying evolution in yeast often involves using a chemostat, an automated way to keep the yeast growing through repeated dilutions with fresh media. Scientists use this method to study how yeast evolves under varying conditions over hundreds of generations.

An unfortunate side effect of this method is that it also tends to select for yeast that stick together. These yeast are diluted away less, often meaning they become more common over the generations.

In this study, Hope and coworkers ran 96 chemostats under three different conditions for 300 generations and found that in 34.7% of the cultures, the yeast ended up aggregated. This was even though they used a strain of S288c in which the FLO8 gene was mutated. This strain flocculates less often than wild type!

These authors picked the 23 most aggregated cultures to study in more detail. They found that 2 out of 23 strains aggregated because of a mother/daughter separation defect. The rest were more run-of-the-mill flocculent strains. 

They next used whole genome sequencing to try to identify which genes when mutated caused flocculence in the strains. They saw no FLO8 revertants.

The two strains with a mother/daughter separation defect both had mutations in the ACE2 gene, which encodes an important transcription factor for septation as well as other processes.

Flos

By CBS Television (eBay item photo front photo back) [Public domain], via Wikimedia Commons. Progressive insurance facebook flo advertising failsBy woodleywonderworks, via Flickr
No, these Flos don’t cause flocculence, FLO1 does.

FLO1-related mutations dominated the other 21. They found a couple of different Ty insertions in the promoter region of FLO1 in 12 of the strains and mutations in TUP1 in 5 more. Tup1p is a general repressor known to repress FLO1, so it looks like up-regulating FLO1 leads to flocculent cultures. Other candidate mutations were found in FLO9 and ROX3.

They wanted to try to identify the responsible gene(s) in strains where there was no obvious candidate gene and also to confirm that the genes they identified really caused the trait, so they next did backcrosses between each mutant strain and a wild type strain.

The backcrosses identified two other ways to get flocculence— by mutating either CSE2 or MIT1. The researchers also confirmed that the mutations they found, including these two new ones, were probably the main cause of the flocculence in each individual strain.  This trait co-segregated with the appropriate mutation in a 2:2 pattern for 20/21 strains as is predicted for a single causal mutation.

The results are even more FLO1-heavy than they appear. Further studies showed that ROX3, CSE2, and MIT1 all require a functional FLO1 to see their effects.

So FLO1 appears to be the main route to flocculence in this strain. And the next set of experiments confirmed this.

Hope and coworkers ran 32 wild type and 32 FLO1 knockout strains in separate chemostats for 250 generations and found that 8 wild-type strains flocculated while only 1 FLO1 knockout strain did. Knocking out FLO1 seems to make for a more well-behaved yeast (at least in terms of evolving a flocculent trait in a chemostat).

And the strain can probably be improved upon even more. For example, researchers may want to limit Ty mobility as this was a major way that FLO1 was up-regulated, increase the copy number of key repressors or link those repressors to essential genes. Another possibility is to also mutate FLO9 as its up-regulation was the cause of that one aggregated strain in the FLO1 knockout experiment.

Researchers no longer need to “settle” (subtle, huh?) for big parts of their experiments being hampered by gummy yeast. Hope and coworkers have created a strain that is less likely to flocculate by simply knocking out FLO1. Not as ubiquitously useful as a Post-It note but a potential Godsend for scientists using yeast to understand evolution.

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

Creating an Ethanol-Making ‘Super Bowl’ Championship Team

May 24, 2017


New England v. Miami NFL

Tom Brady makes a team better by making the players around him play better. The same thing can be said for a mutant RPB7 gene that makes other genes work together better as an ethanol-making team. By Paul Keleher [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons.

There are a few ways to turn a failing sports team around. One is to tailor individual training to make each player better. Now, the team is better overall because of the changes each player makes.

Another way to improve a team is to change a player in a key position who makes everyone better. A classic example of this is the American football team, the New England Patriots.  

On September 23, 2001, Drew Bledsoe, then the starting star quarterback of the New England Patriots, took a savage hit from New York Jets linebacker Mo Lewis. The Patriots replaced Bledsoe with his backup, Tom Brady, and some might argue, the team (whom Brady led to their first Super Bowl win that year) and the NFL, has not been the same since.

Quarterback Tom Brady, along with head coach Bill Belichick, makes whomever the New England Patriots bring in better. Wide receivers, tight ends, and running backs can be replaced in the lineup without the team missing a beat. He just makes the players around him better than they might be on another team.

In a new study, Qiu and Jiang take a “Patriots” approach to ethanol production in the yeast Saccharomyces cerevisiae. Rather than improving individual genes on their own, these authors instead decided to “bring in” a new version of RPB7, a gene that encodes a key subunit of RNA polymerase II, the molecular machine responsible for making messenger RNA (mRNA).

They hoped that changing this pivotal transcriptional player would cause lots of other genes to do “better” so that “team” yeast would make a lot more ethanol.  Their hopes were realized in their Tom Brady equivalent—a mutant they called M1. Yeast bearing this mutant RPB7 gene became the Super Bowl champs of ethanol production.

One of the keys to increasing ethanol production in yeast is to find strains that are more tolerant of high levels of ethanol. The more ethanol they can withstand, the more they can make.

These authors used error prone PCR mutagenesis of the RPB7 gene to find their game-changing mutant. They then took their library of ~108 clones and cultured them in increasing amounts of ethanol, selecting for more ethanol-resistant strains.

After 3-5 rounds of subculture, they plated the cells onto media containing ethanol. Around 30 colonies were picked and sequenced with the best mutant being the one with two mutations—Y25N and A76T. They named this mutant M1.

This mutant grew a bit better than the parental strain background, S288C, in the absence of ethanol, but where M1 really shined was when ethanol was around. It grew around twice as fast in 8% ethanol and could grow at 10%, a concentration that completely inhibited the parental strain from growing.

Being able to withstand high levels of ethanol is important, but it isn’t all that yeast have to deal with. There are multiple other stressors around when you are swimming in 20 proof media.

For example, yeast can suffer from high levels of reactive oxygen species (ROS). M1 not only tolerated 3.5 mM hydrogen peroxide, a proxy for ROS, better than the parental strain, but it also had around 37% of ROS levels inside cells than that of the parental strain. M1 can deal with high levels of ethanol and ROS.

The authors then tested how this mutant dealt with other potential fermentation problems. For example, acetate, a fermentation byproduct, and high levels of NaCl both inhibit yeast growth. M1 tolerated 80 mM acetic acid and 1.5 M NaCl better than the parental strain did.

drunk Gingy

A couple of mutations in the RPB7 gene makes yeast able to tolerate alcohol way better than this guy. By jerome Chua [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via flickr.

M1 appeared to be a champion mutant for making ethanol, and the fermentation studies bore this out.

Under a wide variety of conditions, M1 outperformed the parental strain in terms of growth rate, cell mass, and amount of ethanol made. For example, after 54 hours, yeast containing the M1 mutation of RPB7 managed to make 122.85 g/L of ethanol, 96.58% of the theoretical yield. This is a 40% increase over the control strain. Quite the ethanol producer!

Finally, Qiu and Jiang used microarray analysis of the parental and M1 strains at high levels of ethanol to discover the genes that M1 affected. They found 369 out of a total of 6256 genes behaved differently between the two strains. Of the 369, 144 were up-regulated and 225 were down-regulated.  

I don’t have time to go over all the genes they found but a great many of them make sense. As the authors write, “…a significant set of genes are associated with energy metabolism, including glycolysis, alcoholic fermentation, hexose transport, and NAD+ synthesis.”  M1 seems fine-tuned for making ethanol.

A mutant subunit in RNA polymerase II has made yeast better at making high levels of ethanol, most likely by affecting many key genes at once. It is a fascinating way to quickly affect a whole suite of genes involved in a process. In the ethanol-making Super Bowl, we have a new champion yeast strain, M1.

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

Mass Production in Yeast

May 11, 2017


Workers on first moving Ford assembly line

This approach changed everything when it came to manufacturing in factories. Perhaps the ideas in this new study will change things for manufacturing in cells.Image from Wikimedia Commons

After Henry Ford invented the moving assembly line, manufacturing was never the same. With it, his workers were able to push out a car every 2 ½ hours instead of the 12 it used to take. (Another website said it was reduced to 90 min!)  The technology quickly spread to every factory.

Now of course, an assembly line is only as fast as its slowest worker. If someone is taking extra time to bolt down that part, then everyone downstream will have to go slower too, resulting in fewer cars being made.

But, you also can’t go too fast. If you do, someone can get injured, shutting down the whole line. (Or the worker has to eat all the candy to keep up, like Lucy.)

And you want to make sure things happen in the right part of the factory. You don’t want the paint sprayer out in the open, poisoning factory workers. So, that needs to happen in a special room.

This also applies to cell processes where something complicated is built, step by enzymatic step. All the enzymes need to be at the right levels and in the right place to maximize the productivity of the whole process.

This all becomes very obvious when you try to move an enzymatic process from one beast to another. What worked perfectly before, now barely works at all.

One way to fix this is through trial and error, trying to optimize one part of the process at a time. This is incredibly time consuming!

In a new study out in Nature Communications, Awan and coworkers show one way to tweak all of the enzymatic steps involved in making penicillin at the same time in the yeast Saccharomyces cerevisiae. While this isn’t that useful for making this antibiotic (there are better ways available right now), it does show how researchers can apply the same techniques to perhaps identify and produce new antibiotics. And, it can also be applied to other unrelated enzymatic processes.

Penicillin is made in a five-step process in filamentous fungi. In the first part of the process, two enzymes create a tripeptide precursor using alpha-aminoadipic acid, cysteine, and valine, called ACV. This part of the process had been previously recapitulated in yeast, so Awan and coworkers used this as a starting point for their penicillin producing strain.

The next part of the process uses the last three enzymes and takes place in peroxisomes in filamentous fungi. These authors found that they only got penicillin when these enzymes were tagged to be sent to the peroxisome in yeast. Like a special room for spray painting cars, these enzymes need to be in the right place to make penicillin.

But this was by no stretch of the imagination an efficient penicillin-making machine. The thing managed only 90 pg/ml in the media. As Ursula from Little Mermaid might say, “Pathetic.”

Ursula from The Little Mermaid

From Tumblr

Still, it is a starting point. The next step is to get the yeast to crank out more penicillin. To do this, they used a combinatorial approach to optimize the process all at once. Well, not really all at once.

First, they set out to optimize how much of the precursor ACV the yeast made. Then, they optimized how much ACV was converted to penicillin.

Awan and coworkers created a library of low copy plasmids that had the genes for the first two enzymes, pcbAB and npgA, under the control of different pairs of promoters. One plasmid, with the pTDH3 promoter driving pcbAB expression, and the pPGK1 promoter driving npgA expression, outperformed all of the others. As measured by Liquid Chromatography-Mass Spectrometry (LCMS), the yield of ACV increased from 20 to ~280 ng/ml.

Next, the authors used this new strain as a starting point for optimizing the activity of the final three enzymes using a similar approach. They used a “…one-pot combinatorial DNA assembly using Golden Gate cloning…” to make a library of around 1000 high copy plasmids where each gene was under the control of one of ten different promoters of varying strength. Using LCMS they found strains that could make 3 ng/ml of penicillin, a significant improvement over the original 90 pg/ml.  

The 3 ng/ml of penicillin in the media should be high enough concentration to inhibit the growth of bacteria like Streptococcus pyogenes. So, they confirmed that their penicillin was active using growth inhibition assays.

After sequencing the plasmids, the authors saw that the best strains tended to have strong constitutive promoters driving one of the genes, pclA, and medium strength promoters driving another one of the genes, pcbC. They used a minION DNA sequencer to confirm that this was not the result of a biased library.

As a final step, they set out to optimize penicillin production and to increase the throughput of their assay. They created another library that swapped six different promoters that varied in strength from medium to high for each of the last three genes in the pathway, pclA, pcbC and penDE. Instead of using LCMS to screen for penicillin production, they used a 96 well plate-based assay that looked for inhibition of Streptococcus pyogenes growth for their 120 new strains.

They selected 12 of the highest performing strains and confirmed by LCMS that they made lots of penicillin. Five of the strains made more than 5 ng/ml, a more than 50-fold increase over their original strain.

As this concentration is still three orders of magnitude below what other organisms can currently do, this new yeast strain will not go into penicillin production any time soon. But this study gives us a way to quickly optimize antibiotic production using growth inhibition assays instead of the more cumbersome LCMS.

And it isn’t restricted to just antibiotic production. Similar combinatorial approaches can be used for almost any stepwise enzymatic process. Researchers can create libraries of plasmids where levels of enzyme vary and use the long reads of minION DNA sequencing technology to confirm that their results are not skewed by a biased library.

As usual, this is only possible as a simple, easy procedure because of the awesome power of yeast genetics (#APOYG). Researchers have the tools to use yeast to find new antibiotics and to manufacture them at a high rate, like inventing the car and the assembly line at the same time.

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

Next Page »