Episode Summary: Millions of people die every year from chronic diseases. Traditional drug discovery has failed in identifying solutions to many of these persistent health challenges. Functional genomics is offering a way forward by identifying gene networks and enabling the development of drugs with very specific targets. But, rather than just relying on gene targets within humans, Linda and her company, Fauna Bio, are casting a wider net across the animal kingdom. Extreme adaptation is common across many mammals, giving us an incredible pool of potential targets to go after. Whereas a single heart attack can kill a person, certain animals not only survive 25 heart attacks a year but also go on to thrive, living 2x longer than other mammals their size. By identifying and understanding the gene networks underlying these extreme adaptations, Fauna can identify novel targets across 415 different species, map them to human genes, and develop drugs that exploit our natural protective physiological mechanisms.About the Guest Linda is the Co-Founder and CTO at Fauna Bio, a biotechnology company leveraging the science of hibernation to improve healthcare for humans. She earned an MPhil in Computational Biology from the University of Cambridge and got her Ph.D. in Genetics and Genomics from Harvard University. She previously held positions at the Broad Institute and Stanford University studying comparative mammalian genomics and human disease genetics.Key Takeaways Many mammals have evolved complex adaptations that enable them to survive in extreme environments or withstand physiological events that humans cannot.At Fauna Bio, Linda Goodman and her team are working to better understand the biological networks that underlie these adaptations, in hopes of developing therapeutics inspired by the adaptations of the animal kingdom.Impact Drawing on a completely new source of knowledge about the defense mechanisms of living organisms, Fauna Bio goes beyond the limitations of traditional drug development and looks for better, more effective drugs based on natural defense mechanisms.Company: Fauna Bio

Episode SummaryThe expression of genes in our genome to produce proteins and non-coding RNAs, the building blocks of life, is critical to enable life and human biology. So, the ability to predict how much of a gene is expressed based on that gene’s regulatory DNA, or promoter sequence, would help us both understand gene expression, regulation, and evolution, and would also help us design new, synthetic genes for better cell therapies, gene therapies, and other genomic medicines in bioengineering.However, the process by which gene transcription is regulated is incredibly complex; thus, prediction transcriptional regulation has been an open problem in the field for over half a century. In his work, Eeshit used neural networks to predict the levels of gene expression based on promoter sequences. Then, he reverse engineered the model to design specific sequences that can elicit desired expression levels. Eeshit’s work developing a sequence-to-expression oracle also provided a framework to model and test theories of gene evolution.About the GuestEeshit earned his double major in CS & Engineering and Biological Sciences & Engineering from the Indian Institute of Technology in Kanpur. During his PhD at MIT, working on Dr. Aviv Regev’s team, he published 4 papers in Nature-family journals, including 2 on the cover and 1 on the cover as first and corresponding author. Eeshit’s work is in Cell, Nature Biotechnology, Nature Medicine, Nature Communications, and beyond.Key Takeawayscis-regulatory elements like promoters interact with transcription factors in the cell to regulate gene expression.Variation in cis-regulatory elements drives phenotypic variation and influences organismal fitness.Modeling the relationship between promoter sequences and their function – in this case, the expression levels they induce – is important to better understand regulatory evolution and also enable the engineering of regulatory sequences with specific functions with applications across therapeutics and cell-based biomanufacturing.By cloning 50 million sequences into a yellow fluorescent protein (YFP) expression vector in S. cerevisiae and measuring the YFP levels they induced, Eeshit generated a rich dataset to map yeast promoter sequence to expression levels.Next, Eeshit trained neural network models, including convolutional neural networks and Transformers, to predict expression from sequence with high accuracy.Eeshit then “reverse-engineered” these convolutional models to create genetic algorithms that designed sequences which could induce desired expression levels.Finally, Eeshit’s sequence-to-expression oracle allowed for the computational evaluation of regulatory evolution across different evolutionary scenarios, including genetic drift, stabilizing selection, and directional selection.ImpactEeshit’s work developing a sequence-to-expression oracle provided a framework to model and test theories of gene evolution.This framework can help us both understand gene expression, regulation, and evolution, and design new, synthetic genes for better cell therapies, gene therapies, and other genomic medicines in bioengineering.Paper: The evolution, evolvability and engineering of gene regulatory DNA 

In this special episode, Seth is joined by Ash Trotman-Grant to demystify spinning out from academia. They discuss the Spinout Playbook, offering tips for entrepreneurial academics. They address challenges in negotiating technology transfer, equity conversations, and fairness concerns. They emphasize the importance of involving the Principal Investigator early on and highlight the significance of assignments in tech transfer. They explore recreating technology outside of the university, discuss leverage in negotiations, and share future plans for the playbook.

Episode SummaryChimeric antigen receptors, or CARs, repurpose the build-in targeting and homing signals of our immune system to direct T cells to find and eliminate cancers. Although CAR-T cells have transformed the care of liquid tumors in the circulating blood, like B cell leukemia and lymphoma, CAR-T therapy has shown limited efficacy against solid tumors. To unlock the full potential of CAR-T therapies, better receptor designs are needed. Unfortunately, the space of potential designs is too large to check one by one. To design better CARs, Dan and his co-author Camillia Azimi developed CAR Pooling, an approach to multiplex CAR designs by testing many at once with different immune costimulatory domains. They select the CARs that exhibit the best anti-tumor response and develop novel CARs that endow the T cells with better anti-tumor properties. Their methods and designs may help us develop therapies for refractory, treatment-resistant cancers, and may enable CAR-T cells to cure infectious diseases, autoimmunity, and beyond.About the AuthorDuring his PhD in George Church’s lab at Harvard Medical School, Dan studied interactions between bacterial transcription and translation, built and measured libraries of tunable synthetic biosensors, and constructed a new version of the E. coli genome capable of incorporating new synthetic amino acids into its proteins. He also built a high-throughput microbial genome design and analysis software platform called Millstone.As a Jane Coffin Childs Postdoctoral Fellow at UCSF, Dan is currently applying these high-throughput synthetic approaches to engineer T cells for the treatment of cancer and autoimmune disease. He is also working in the Bluestone, Roybal, and Marson labs.Key TakeawaysBy genetically engineering the chimeric antigen receptor (CAR), T cells can be programmed to target new proteins that are markers of cancer, infectious diseases, and other important disorders.However, to realize this vision, more powerful CARs with better designs are needed - current CAR-T therapies have their restraints, including limited performance against solid tumors and lack of persistence and long-term efficacy in patients.An important part of the CAR response is “costimulation,” which is mediated by the 4-1BB or CD28 intracellular domains in all CARs currently in the clinic. Better designs of costimulatory domains could unlock the next-generation of CAR-T therapies.Since there are so many possibilities for costimulatory domain designs, it’s difficult to test them all in the lab.Based on his experience in the Church Lab, Dan has developed tools to “multiplex” biological experiments; that is, to test multiple biological hypotheses in the same experiment and increase the screening power.Dan and his co-author Camillia Azimi developed “CAR Pooling”, a multiplexed approach to test many CAR designs at once.Using CAR Pooling, Dan tested 40 CARs with different costimulatory domains in pooled assays and identified several novel cosignaling domains from the TNF receptor family that enhance persistence or cytotoxicity over FDA-approved CARs.To characterize the different CARs, Dan also used RNA-sequencing.ImpactThe CAR Pooling approach may enable new, potent CAR-T therapies that can change the game for solid tumors and other cancers that are currently tough to treat.Highly multiplexed approaches like CAR Pooling will allow us to build highly complex, programmable systems and design the future of cell engineering beyond CAR-T.In addition to new therapeutics, high-throughput studies will allow us to understand the “design rules” of synthetic receptors and improve our understanding of basic immunology.Paper: Pooled screening of CAR T cells identifies diverse immune signaling domains for next-generation immunotherapies 

Episode Summary: DNA is an ideal molecule for storing information in our genomes because it’s stable, programmable, and well understood. The same qualities make DNA a great building block or construction material for nanoscale biomolecular structures that have nothing to do with our genome, like molecular scaffolds created by folding DNA into 2D and 3D shapes. This technology is known as DNA origami.However, the practical applications of DNA origami are limited by spontaneous growth and poor reaction yields. Anastasia developed a method that uses crisscross DNA polymerization of single-stranded DNA slats or DNA origami tiles to assemble DNA structures in a seed-dependent manner. This work may be useful to produce ultrasensitive, next-generation diagnostics or in programmable biofabrication at the multi-micron scale.Search Keywords: fifty years, bio, translation, ayush noori, ashton trotman grant, dna origami, dna, monomers, anastasia ershova, structures, diagnostics, proteins, micron scale, nucleation, biology, nanoscaleEpisode Notes:About the Guest Anastasia is a PhD candidate at Harvard University, currently working on DNA nanotechnology in William Shih's lab at the Wyss Institute and Dana-Farber Cancer Institute.She received her bachelor’s degree in Natural Sciences from Cambridge University.During her PhD at Harvard, she co-founded the Molecular Programming Interest Group, an international community of students in the molecular programming, DNA computing and related fields.Impact DNA Origami will provide us with a plethora of new information on biology and physics.By manipulating that data on the nanoscale, we can get answers to a lot of questions in the future.Quick diagnostics can enable people all over the world to quickly get diagnosis-related answers and seek targeted treatment.PapersRobust nucleation control via crisscross polymerization of highly coordinated DNA slatsMulti-micron crisscross structures from combinatorially assembled DNA-origami slats

Episode Summary:Technologies like next-generation sequencing allow us to understand which RNA transcripts and proteins are expressed in biological tissues. However, it’s often equally important to understand how cells or molecules are positioned relative to one another! Whether it be a cell changing its shape, an organelle ramping up a metabolic process, or a DNA molecule traveling across the nucleus, understanding spatial context is critical. Current approaches for spatial sequencing are limited by cost, complicated equipment, sample damage, or low resolution. Recognizing this challenge, Josie and team developed Light-seq, a cheap and accessible method to combine sequencing and imaging in intact biological samples. Not only is the method inexpensive, but Light-seq can also achieve unprecedented spatial resolution by using light to add genetic barcodes to any RNA, allowing scientists to determine exactly where sequencing should occur with extreme precision. By helping researchers to understand spatial context, Light-seq-driven insights may illuminate cancer, neurodegeneration, and autoimmunity.Episode Notes:About the AuthorFollowing her lifelong passion for computer programming, Josie studied Computer Science at Caltech and worked as a software engineering intern at Google. At Caltech, a biomolecular computation course introduced her to the field of biomolecular programming. Josie was quickly excited about the intersection of computers and biology and its potential to bring about positive change in the world. She pursued this interest in her graduate studies in the Wyss Institute for Biologically Inspired Engineering at Harvard, where – as first a postdoctoral fellow, and then the Technology Development Fellow – she developed platform technologies for DNA-based imaging and sequencing assays.Key Takeaways Next-generation sequencing is a powerful technology to read the transcriptomic state of biological tissues by surveying the RNA transcripts present.However, it’s important to understand not only what is being expressed but where this expression occurs! The spatial arrangement, structure, and interactions between molecules are critical to define the functions of biological systems.By linking imaging with -omics profiling, the field of spatial biology seeks to understand molecules like RNAs in their 2D and 3D contexts.Unfortunately, currently available spatial transcriptomics methods are limited in their ability to select individual cells with complex morphologies, require expensive instrumentation or complex microfluidics setups to the tune of several $100K, and often damage the samples.Further, rare cells are often missed due to lower sequencing throughput, even though they may be critical for biological activity.Recognizing this challenge, Josie and her collaborators developed Light-seq, a new, cheap, and accessible approach for single-cell spatial indexing and sequencing of intact biological samples.Using light-controlled nucleotide crosslinking chemistry, Light-seq can correlate multi-dimensional and high-resolution cellular phenotypes – like morphology, protein markers, spatial organization) – to transcriptomic profiles across diverse sample types.In particular, using the biological equivalent of photolithography, Light-seq can add genetic barcodes to any RNA by shining light on it, allowing scientists to control exactly where sequencing should occur with extreme precision – up to the subcellular level.Light-seq can operate directly on the sample: the method does not require cellular dissociation, microfluidic separation/sorting, or custom capture substrates or pre-patterned slides.Samples used for Light-seq remain intact for downstream analysis post-sequencing.Josie evaluated Light-seq on mouse retinal sections to barcode three different cell layers and study the rare dopaminergic amacrine cells (DACs).Impact Josie created a cheap, accessible, and powerful tool for scientists to perform spatial sequencing at unprecedented resolution without requiring expensive or complicated setups.By enabling new advances in spatial biology, Light-seq has the potential to help biologists discover biomarkers for disease, measure on and off target effects of therapeutic candidates, and illuminate poorly understood biological mechanisms where understanding spatial context makes all the difference.Author: Josie KishiPaper: Light-Seq: Light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing

Episode Summary:Antibodies are one of the greatest tools we have in our therapeutic arsenal and have transformed the way we treat cancer and autoimmunity. But we still largely develop these drugs using guess and check methods, massively slowing down the process. However, our own B cells are constantly making new antibodies against the pathogens and diseases we routinely suffer from, creating a gold mine of drugs floating around inside all of us. We just need to find them! Recognizing this challenge, Nima and his team at Avail Bio have leveraged their deep experience in computation and systems immunology to build a platform that massively screens the antibody repertoire of patients who have successfully cleared a disease. With it, they find ready-to-deploy antibody drugs that could treat everything from cancer to autoimmunity and even reprogram our own immune system! Search Keywords: fifty years, bio, translation, antibodies, B cells, cancer, autoimmunity, immunology, avail bio, nima emamiEpisode Notes:About the GuestNima Emami is the CEO & co-founder of Avail Bio. He received a PhD in Bioinformatics from the UCSF Cancer Center, and studied Bioengineering, Electrical Engineering and Computer Science at UC Berkeley.Key TakeawaysThe immune system contains a massive diversity of antibodies that hold clues on how to fight disease. Avail has developed a platform to discover and develop these antibodies for cancer and autoimmune disease.Companies that spin out of universities can pair with accelerators early on to both raise funding and make progress with a small amount of capital. The most challenging part of pulling IP out of a university is speed. Public universities that generate many spinouts are often overwhelmed with the amount of inventions disclosed concurrently, which lengthens the time required for tech transfer.Avail’s platform combines synbio, machine learning and genomics to both discover and validate targets, and ultimately translate those targets into drugs. Failure of clinical stage programs in cancer trials can be traced back to the failure of mouse models to faithfully recapitulate the cancer biology or the immunobiology that we see in humans.The future that Avail hopes to create is one where drugs developed using their platform will reach patients, thereby changing the drug discovery paradigm to be more data-driven.ImpactThe platform that Avail is building peers inside the human immune response to find and develop novel antibodies to cure cancer and autoimmune disease.Company: Avail Bio 

Episode SummaryImagine if every graphics design company built its own version of Photoshop in-house. That’s exactly what’s happening today in biology research. Ten-fold increases in data every two years are forcing every biology team to build out their own, in-house bioinformatics stack to store, clean, pipe, and manage the massive volumes of data generated by their experiments. All that work has to happen even before teams can analyze the results! Recognizing this obstacle to high-throughput biology research, Alfredo, Kenny and Kyle built LatchBio to bring the modern computing stack to biotech. By uniting wet lab experiments with dry lab processing, storage, and analyses, LatchBio is democratizing access to top-notch bioinformatics and empowering biologists to derive relevant insights from their data that can move our world forward. Tune in to learn more about their journey from Berkeley dropouts to entrepreneurs building no-code tools to power the biocomputing revolution.About the TeamAlfredo Andere, CEO, was born in Mexico City and raised in Guadalajara, Mexico. He majored in Computer Science and Electrical Engineering and minored in Math at UC Berkeley before dropping out to co-found LatchBio.Kyle Giffin, COO, attended UC Berkeley to study Cognitive Neuroscience and Data Science before dropping out to found LatchBio.Kenny Workman, CTO, started engaging in molecular biology research when he was 15, first at local community colleges as a lab hand and then at MIT and UC Berkeley over successive summers. Prior to co-founding LatchBio, he worked at Asimov and Serotiny as a Software and Machine Learning Engineer.Key Takeaways After hundreds of interviews with biotech leaders to discover pain points around managing data, the founders developed the LatchAI platform.Common biology analyses require piping gigabytes/terabytes of data, meaning data storage and retrieval require programming expertise.Although scientists may be experts in biological theory and wet lab experimentation, programming expertise is scarce. Biologists must rely on limited computational analysts to process and visualize their data; thus, access to bioinformaticians is a bottleneck in the scientific discovery process.On the flip side, bioinformaticians are often hampered by repetitive analysis tasks, preventing them from innovating new computational methods.Recognizing this disconnect between biologists and bioinformaticians, Alfredo, Kenny, and Kyle launched LatchBio: an end-to-end biocomputing platform to allow both wet lab and dry lab scientists to get back to what they’re trained to do - science!The team recently launched their SDK - a Python native developer toolkit - to bridge the divide between the computationally literate bioinformaticians and the no-code savvy biologists.The goal of Latch is to become the universal cloud computing platform for academic research and industry biotech.Impact The no-code platform that LatchBio is building is bringing the modern computing stack to biotech, streamlining data analysis so scientists can focus on solving the world’s biggest problems with biology.Company: LatchBio

Episode Summary: Evolution is happening even at the cellular scale. Whether it's a virus, a bacterial pathogen, or a cancer cell, disease-causing agents are responding to the therapies we throw at them, updating their genes and molecular pathways to resist death. As a trained microbiologist, Nick Goldner and his co-founder Chris Bulow spent their years in grad school using -omics data to overcome antibiotic resistance in bacteria which led to their first company Viosera. As they struggled with the harsh realities of the antibiotics market, they stumbled upon the connection between bacterial and cancer resistance mechanisms. With this, they started resistanceBio which combines sophisticated tumoroids, intense patient sampling, and multi-omics to mimic the evolution of real tumors and ultimately find therapies that are irresistible. Episode Notes:About the AuthorNick Goldner is co-founder and CEO of resistanceBio, a company harnessing evolution to develop therapies that defeat treatment resistant tumors.His interest in biotechnology was sparked by his own battle with treatment resistant bacteria.Nick and his friend and labmate, Chris Bulow, knew they wanted to start a company and began Viosera to fight antibiotic resistant bacteria as graduate students.Recognizing the inherent difficulty of bringing new antibiotics to market, they adapted their technology to cancer and spun-out resistanceBio.Key TakeawaysResistance is very similar in both cancer and bacteria – in response to a drug, both will change their phenotype in a way that reduces its efficacy.Traditionally, we understand cancer resistance by growing cancer lines in a dish and evolving them over long periods in a way that is very different from what happens in the body.Nick and his team developed ResCu, a method that cultures tumor cells as tumoroids that mimics how a tumor evolves during a patient's course of therapy.Combining this with multi-omics, Nick and his team can untangle how the underlying resistance mechanism evolves over time.The data that comes from this points resistanceBio toward therapies that will turn these resistances into vulnerabilities.ImpactThe drugs discovered through resistanceBio’s platform create cancer cures for people who currently have no options.The data created through ResCu generate biomarkers ensuring that the right drugs are given to the right people.With the foresight of how cancers evolve, resistanceBio could completely overcome the use of chemo and other non-targeted therapies that are hard on patients and instead have completely personalized therapies that are tailored to block all roads to resistance.Company: resistanceBio

Episode Summary: When COVID-19 hit and society decided to use mRNA vaccines for the first time, many questions remained about whether RNA itself was ready for the challenge. But three scientists at Stanford University who had barely worked with each other before the pandemic realized that RNA’s limitations were merely a design challenge and not an issue with the substrate itself. Through emails and zooms, Kathrin, Gun, and Hannah built a tool to massively test RNA designs. With it, they screened for RNA with better functionality, increasing the stability and expression of the protein they encode and ultimately creating a platform to improve these life-saving vaccines. Episode Notes:About the AuthorsHannah, Gun, and Kathrin had all been separately researching various aspects of genetics and RNA before the pandemic.When COVID hit and RNA vaccines were being built, the three realized they had newly complementary skill sets.They set aside their individual projects, leveraged their unique backgrounds, and worked in shifts to abide by social distance rules in order to solve multiple issues facing RNA as a substrate for vaccines.Key TakeawaysRNA holds great potential for therapies and vaccines as they are highly programmable, extremely flexible, and are much easier to scale than other options.But RNA is hard to deploy for vaccines because it is extremely unstable both in the body and on the shelf.Enhancing the expression and stability of RNA allows us to reduce the amount needed to give a person, increasing the number of people that can be vaccinated.The three designed PERSIST-seq to test a multitude of RNA designs in one-pot by leveraging synthetic biology and next generation sequencing.They also leveraged citizen science through a “game” called Eterna in order to optimize sequences using the collective brain power of humanity.With it in they found synonymous mutations and alterations to the untranslated regions that changed RNA folding and improved stability and translation.TranslationPERSIST-seq must still be validated in animal models to fully connect how improvements on stability and expression alter vaccine efficacy.The team is ready to leverage their approach through licensing to help RNA vaccine companies improve their designs.The design rules and method to discover them can be used to enhance any RNA therapeutic that will undoubtedly be coming through the pipeline soon.First Authors: Kathrin Leppek, Gun Woo Byeon, and Hannah Wayment-SteelePaper: Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics