Emma J Chory

Evolution navigated billions of challenges to get to us to where we are today. Directed evolution compresses this to 1D axis. Imagine if you could sample 200 dimensions at once, with data to boot 📈 First @chorylab PACE preprint on our new system to tackle this: TurboPRANCE👇

@BugajLab Thank you so much Lukasz!!

@chorye Congrats to you and the lab Emma! Huge milestone🥂

Evolution navigated billions of challenges to get to us to where we are today. Directed evolution compresses this to 1D axis. Imagine if you could sample 200 dimensions at once, with data to boot 📈 First @chorylab PACE preprint on our new system to tackle this: TurboPRANCE👇

Immensely proud of this team effort. Could not have asked for a better team. This is just the beginning. Read "An autonomous system for multi-objective continuous evolution at scale", on BioRxiv now: biorxiv.org/content/10.648…

Excited to announce another preprint from the @chorye lab, this is a robotic platform for ~100x multiplexed PACE directed evolutions with user-controlled selection pressure and multiday walkaway automation.

@bffswithbiology @stefanmgolas P.S. literally everything about getting this to work was hard. For those interested in autonomous labs and training data generation, worth a close read

@bffswithbiology @stefanmgolas The team worked incredibly hard to get this out the door in time to share with grant review committees, and I am so grateful for their effort. If you have thoughts, questions, or ideas for where this platform could (or should) go next, we would love to hear from you.

@NikoMcCarty Very cool! But, TBF, PACE still generates mutations at 2bp/kb each replication cycle at 10^8-10^14 variants every 10 min.

An orthogonal polymerase for hypermutation in E. coli. Often, we want to mutate genes really quickly to make better variants. Jason Chin’s group created a molecular tool to increase E. coli mutation rates by six orders of magnitude. First, they took two genes from bacteriophage Φ29, a virus that does not naturally infect E. coli. One gene primes the cell for DNA replication (called the “terminal protein”), and the other copies the DNA (called “polymerase”). They engineered the latter protein to make *more* mistakes than it naturally would. This polymerase, as it runs along a strand of DNA, has an error rate of about 10⁻⁴ per base per generation, meaning it makes one error every 1,000 bases of DNA, every ten cell divisions. (This mutation rate is about one-million-fold higher than the natural error rate in E. coli.) Importantly, this error-prone polymerase will only copy linear DNA that has been introduced into the cell by the scientist; it doesn’t touch the host cell’s genome. But why is this needed? Can’t we just mutate genes in a test tube instead? Yes, of course! But there are two big advantages with doing hypermutations inside *living cells*: 1. Scale: A small tube contains billions of cells, each running its own mutation experiments in parallel. 2. Selection: Living cells act as a sort of “auto-selection” mechanism. The very fact that they are “alive” tells us that a mutated gene is “acceptable.” If you evolve a gene that works really well *in vitro* and then try putting it into a living cell, it might kill the cell! But if *E. coli* makes mutations itself, and continues growing, then you already know that the mutation did not kill the cell. This is valuable info. Of course, this new paper isn’t the first to do orthogonal hypermutations in living cells. Chang Liu’s laboratory, at UC Irvine, made a similar tool, called OrthoRep, many years ago. But OrthoRep only works in yeast. And the nice thing about using E. coli is that they can grow to much higher densities in tubes and, thus, you can run more evolution experiments in parallel. Last year, Peter Schultz’s group also reported a continuous hypermutation tool for E. coli, using a T7 polymerase and replisome. Their system reportedly introduced mutations ~100,000-times faster than the normal mutation rate. But the problem with their approach is that it seems to leak with the genome; it kills cells from time to time. This new tool appears to be fully orthogonal, doesn’t damage the host genome, works in E. coli, has a very high mutation rate, and only requires two genes to function (so it’s lightweight and thus easy to engineer.) We can do so many things with this. We could evolve thousands of nanobodies in parallel, screening each for binding affinity and stability and then using the data to train predictive models. Or, we could evolve entire biosynthetic pathways to optimize chemical production. Etc etc.

Neato.

Full open access paper available here: cell.com/cell-reports-m… A noninvasive estrous tracking toolkit for wild mice. Methods built for lab mice do not translate to non model rodents. This helps overcome that barrier and may enable engineering in many other species.

So proud this multi-year effort is finally out. Led by exceptional collaborator @JoannaBuchthal who put her blood, sweat & literal tears into making an impact for families impacted by Lyme bit.ly/MAT-tracking More papers soon. Learn more & support miceagainstticks.com

@AjasjaLjubetic @NikoMcCarty TBF many of these arabidopsis anecdotes predate Covid and it’s actually so commonly discussed (e.g. you can trace nearby weeds to the closest lab), that it’s actually a little hard to decouple folklore from truth in this case.

@AjasjaLjubetic @NikoMcCarty lol don’t ask a drosophila biologist. Those guys love to bury themselves in pockets. Worth noting that most genetic mods in model organisms take a serious fitness hit.

Lab leaks are extremely common, but usually benign. Last month, I went to a conference in the UK. I was talking to some plant biologists who work with Arabidopsis thaliana, a weed in the mustard family. Arabidopsis seeds are tiny, like grains of pollen, and they stick to clothing. These seeds are often engineered with GFP, for example, such that they fluoresce green. And, being so small, they inevitably get carried (accidentally) outside the lab. One plant biologist told me that that their lab group goes outside and picks all the Arabidopsis plants they can find in the areas around campus each year. They then bring these plants back into the laboratory and sequence them. Last year, half of these "wild" plants had GFP.

See it in action!

Proud of Stefan for leading this work, and so grateful to Festo for the support that made it possible. We think this is the first fully open, industry-class liquid handler built from OEM components and controlled in Python. bioRxiv w full build + repo coming soon. Read below:

chorylab.com/open-source-lh… This is just a first step in what you can implement when the hardware and control stack are designed to be extended and modified, not just the code.