New & Noteworthy

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

Meiotic Fail Safes

May 4, 2017


minuteman in silo

Launching into meiosis too soon is as dangerous (for a cell) as launching a nuclear missile. Luckily both have protocols to make sure each can only happen in the right circumstances. (Hopefully never for the nuclear missile.) Image from Wikimedia Commons.

If the movie WarGames is anything to go on, the US government does not make it easy to launch a nuclear missile. Two soldiers have to do many things simultaneously and in the right order before that missile can take flight.

This makes perfect sense as you do not want to launch a nuclear attack unless you absolutely have to. The continued existence of the human race depends on these fail safes being in place and working. The same goes for a cell that is heading into meiosis.

Meiotic fail safes are in place to ensure the survival of a cell during the dangerous, early part of meiosis, when there are lots of double-strand breaks in the DNA. These all need to be resolved before a cell is allowed to continue through meiosis to create gametes. If the cell moves on while the breaks are still there, gamete production will fail and the cells will die.

While the exact sequence of events needed to launch World War III is known (at least by a few people), the exact details of getting a cell safely through meiosis are a bit murkier. With the help of good old Saccharomyces cerevisiae, we have the broad outlines, but are still investigating the finer points.

A new study by Prugar and coworkers in GENETICS has helped clear up a bit of the murk in yeast. They have uncovered a connection between the meiosis-specific kinase Mek1p and the transcription factor Ndt80p that may explain how a cell “knows” when it is safe enough to emerge from prophase and keep progressing through Meiosis I.

Mek1p is known to be active when there are lots of these double-strand breaks around and to lose activity as these breaks are resolved. Ndt80p, on the other hand, is inactive when there are lots of these breaks and active when they are resolved. So it makes sense that their activities might be related to each other.

In this study, the authors show that once Mek1p activity falls below a certain level, it can no longer keep tamping down Ndt80p activity. Once unleashed, Ndt80p can go on to activate many genes, including the polo-like kinase CDC5 and the cyclin CLB1. This round of gene activation allows the cell to progress through meiosis.

The key to teasing this out was a set of experiments where Prugar and coworkers were able to control the activities of Mek1p and Ndt80p independent of the cell’s DNA state. It is like circumventing the set of protocols to get those missiles launched.

To independently control Ndt80p activity, they used a form of the protein that requires estradiol to be active. And they controlled the activity of Mek1p by using a mutant, mek1-as, that is sensitive to the purine analogue 1-NA-PP1. In the presence of this inhibitor, Mek1p stops working.

They looked at the targets of these two proteins to infer activity. For example, they determined if Ndt80p was active by looking for the presence of CDC5. And to see if Mek1p was active, they looked for phosphorylated Hed1p.

In the first experiment, they showed that in the absence of both estradiol and 1-NA-PP1, Hed1p stayed phosphorylated. Mek1p was constitutively active in the absence of Ndt80p even as double-strand breaks were resolved.  (They used phosphorylated Hop1p as an indirect measure of double-strand breaks.)

War Games (1983)

In the end, as yeast relies on MEK1 to prevent a meiotic disaster, humans, not computers, kept the world safe in the movie WarGames. Image from flickr

When Ndt80p was activated through the addition of estradiol, CDC5 was turned on and Hed1p lost its phosphorylation. This loss of Mek1p activity did not happen as quickly as with 1-NA-PP1.

These results suggest a negative interaction between Mek1p and Ndt80p. When Ndt80p is active, Mek1p is not and when Mek1p is active, Ndt80p is not. The resolution of the DNA breaks as indicated by the loss of phosphorylated Hop1p was not sufficient to shut off Mek1p activity. It took the activation of Ndt80p for this to happen.

Well, Ndt80p did not directly cause Mek1p’s inhibition. A second set of experiments suggested that a target of Ndt80p, CDC5, was responsible.

For this they made Cdc5p activity independent of Ndt80p induction by making it dependent on estradiol, similar to what they did with Ndt80p. Using a strain deleted for NDT80, they found that inducing Cdc5p activity was enough to eliminate Mek1p activity.

I don’t have the space to go into the rest of the experiments in this study, but I urge you to read it if you want to learn about more of the details of the cell’s protocol for know when it is OK to progress through meiosis.

With the help of the awesome power of yeast genetics (#APOYG), Prugar and coworkers have added to our knowledge about the safeguards that are in place to keep a cell from launching into meiosis too soon. Turns out they are even more complicated than the ones that prevent accidental thermonuclear war.

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

Knock out YME1, Luke

April 20, 2017


Death Star Explosion & Millenium Falcon

Instead of an exhaust port, one of a mitochondrion’s fatal flaws may be the YME1 gene. Image from Manoel Lemos, flickr.

In the original Star Wars, Luke destroys the Death Star with a precise strike of proton torpedoes down a small thermal exhaust port. For him it was as easy as bullseyeing “womp rats in my T-16 back home.”

Luke and the rest of the Rebel Alliance learned of this engineered fatal flaw from Jyn and her friends in the prequel Rogue One. With this information the Rebel Alliance was able to keep the rebellion alive long enough to finally bring down the Empire by the end of Return of the Jedi.

It turns out that our friend Saccharomyces cerevisiae has taught us about a fatal flaw in mitochondria. Like proton torpedoes in an exhaust port, when the gene YME1 is inactivated, mitochondria become unstable. But instead of bits of Death Star raining down on nearby planets, mitochondrial DNA (mtDNA) is released into the cytoplasm.

Sometimes this mtDNA can end up in the nucleus and find its way into nuclear DNA. And if the conclusions of a new study in Genome Medicine by Srinivasainagendra and coworkers turns out to be right, this numtogenesis (as the authors call this process) can have profound consequences when it happens in people. Their data suggests that it might lead to cancer or possibly cause cancers to spread.

These researchers searched through whole genomes of colon adenocarcinoma patients and found that these cancer cells had 4.2-fold more mtDNA insertions compared to noncancerous cells from the same patient. They also found that patients with more of these insertions tended to do worse (although the sample sizes were too small to say this definitively).

Why is this happening in the cancer cells? What has caused the mitochondria to give up their DNA?

Srinivasainagendra and coworkers turned to previous work that had been done on the YME1 gene in the yeast S. cerevisiae to find one possible reason. YME1 had been shown to be an important suppressor mtDNA migration to the nucleus. Perhaps this was true in mammalian cells as well.

A search through the genomes of cancers suggested that this seemed to be the case. Around 16% of the colorectal tumors they looked at had a mutated YME1L1 gene, the human homologue of YME1. And mutated YME1L1 genes were found in other tumors as well.


If only destroying gene function was as fun.

They used CRISPR/Cas9 to directly test the effects of knocking out YME1L1 in the breast cancer cell line MCF-7. The knock out cells had a 4-fold increase in the amount mtDNA in the nuclear fraction compared to cells that still had working YME1L1.

As a final experiment, they used a yeast strain, yme1-1, in which YME1 function was inactivated, to show that the human homologue, YME1L1, could suppress the migration of mtDNA to the nucleus.

This yme1-1 strain has a TRP1 gene encoded in the mtDNA instead of the nucleus. Since the gene cannot be read by the mitochondrial transcription machinery, the only way this yeast strain can survive in the absence of tryptophan is if the TRP1 gene moves from the mitochondrion to the nucleus. 

In their experiment, with vector alone, they got around 1000 TRP+ colonies with yme1-1. When they added back yeast YME1, this number dropped to less than 50 compared to the 100 or so they got when they added the human homologue, YME1L1. So YME1L1 can suppress mtDNA migration to the nucleus.  

Given that YME1L1 was mutated in just a subset of the cancers, it is unlikely that it is the only player in the mtDNA these authors found in the nuclei of cancer cells. But it does look like it is one way this can happen.

And it would have been very hard to fish out the human gene without the critical work that had been done in yeast previously. Yeast shows us the way again. #APOYG

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

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