Episodes
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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
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Episode Summary
The 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 Guest
Eeshit 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 Takeaways
cis-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.Impact
Eeshit’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
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Missing episodes?
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Episode Summary:
In this very special episode of Translation, Seth is joined by Ash Trotman-Grant to demystify spinning out from academia. Much of this knowledge has so far only been available to select groups of academics and PhD founders are at a disadvantage – some potentially breakthrough technologies never saw the light of day and didn’t get a chance to have a real impact. We want to bring the power of the tech transfer process back to entrepreneurial scientists.
Enter the Spinout Playbook – your complete guide to spinning out of academia. In this episode, we chat about the Playbook’s content and share useful tips for entrepreneurial academics eager to spin out their research into an impactful company. Ash shares his experience from spinning out Notch Therapeutics and, together with Seth, they offer brilliant insights into navigating the (up until now) stormy waters of the spinout process.
About the Guests
Seth is a Founding Partner at Fifty Years, a venture capital firm backing founders using technology to solve the world’s biggest problems.Ash is a Synthetic Biologist at Fifty Years and Founder of Notch Therapeutics, a stem cell spin out company from the University of Toronto.Ash & the Fifty Years team have created the Spinout Playbook, a living document that will help academic founders spin out their companies from universities and negotiate with Tech Transfer Offices – TTOs.Key Takeaways
A spinout is a company that has been developed from a university's research.The process of establishing the spinout as a new company involves multiple hurdles, like licensing patents from the tech transfer office, splitting equity among academic and full-time founders, and deciding when to leave academia.Universities take months to sign agreements and make startups unfundable by taking too much equity.The final licensing agreement may include counter-productive clauses that prevent the company from succeeding.University tech transfer offices (TTOs) refuse to negotiate directly with grad students and postdocs.For Ash, the creation of Notch Therapeutics was his first real step into the entrepreneurship world and the first encounter with the process of spinning out a company.The Spinout Playbook, the newest Fifty Years initiative, will serve as a comprehensive guide for founders and scientists wishing to spin out a company.Impact
The Spinout Playbook will help future founders and scientists better navigate the challenges of the process.Previously only available to a coterie of academics, the know-how of tech transfer will allow great science to see the light of day more easily.A transparent process can give scientists the tools and information they need to build world-changing companies, which is hard enough by itself.The Spinout Playbook
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Episode Summary
Chimeric 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 Author
During 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 Takeaways
By 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.Impact
The 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
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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, nanoscale
Episode 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.Papers
Robust 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 Author
Following 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 Kishi
Paper: Light-Seq: Light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing
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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 emami
Episode Notes:
About the Guest
Nima 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 Takeaways
The 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.
Impact
The 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
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Episode Summary
Imagine 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 Team
Alfredo 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
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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 Author
Nick 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 Takeaways
Resistance 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.Impact
The 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
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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 Authors
Hannah, 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 Takeaways
RNA 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.Translation
PERSIST-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-Steele
Paper: Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics
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Episode Summary:
COVID-19 tests have become synonymous with jamming a swab up our nose to find out whether we have an active infection. But as we progress through this pandemic, a test that tells us whether people have antibodies against the virus will be massively important to creating public health initiatives and deciding who to vaccinate next. Unfortunately, these serology tests are exceedingly tedious to perform, inhibiting their widespread use. Realizing this problem, Susanna talks us through how she utilized protein engineering to create a novel serology test that is massively easier and quicker than traditional methods. Importantly, this test can be used in resource low settings to help end the pandemic worldwide.
Episode Notes:
About the Author
Susanna’s scientist parents and love for the natural world drove her to research biology and chemistry.Susanna is most excited about adding new dimensions to biomolecules through bioconjugation to enhance their function.Key Takeaways
A serology test is used to see whether a person has antibodies against a specific pathogen.Positive serology tests can tell us whether getting the disease led to immunity, whether a vaccine worked, or whether a person is protected from new variants.This could be massively useful to help understand who is protected and who to vaccinate next to finally beat the SARS-CoV-2 pandemic.Traditional serology tests use hard to scale and overly laborious methods that hinder their adoption, especially in a low resource setting.Susanna used protein engineering and leveraged the shape of antibodies to develop an entirely new serology test.She engineered protein fusions that when simply mixed with a human sample such as serum or saliva, will generate light if antibodies against COVID-19 are present.This much easier test as well as the variety of human samples it can use as inputs make it a much more approachable option and enables its use in low-resource settings.Translation
Susanna and her colleagues are working to make this test available for field studies by making the protein easier to ship and making a handheld device that can measure the readout.Productizing this test will require more research in how to stabilize the components, incorporate controls, and most importantly, make it high-throughput.Susanna hopes to leverage this technology to help us beat the variants of SARS-CoV-2 and eventually rapidly test for other infectious diseases and autoimmunity.First Author: Susanna Elledge
Paper: Engineering luminescent biosensors for point-of-care SARS-CoV-2 antibody detection
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Episode Summary:
Brain machine interfaces untangle the complex web of neurons firing in our brains and relay the underlying meaning to a computer. These devices are being adapted to help patients regain motor control, monitor our mental well being, and may one day even make us more empathetic. State of the art methods to do this have massive trade-offs, either being high resolution yet requiring devices to be embedded in our heads or low resolution but non-invasive. Finding a key middle ground, Sumner uses advances in ultrasound to monitor the brain activity of monkeys performing specific tasks. With this data, he can not only record the brain activity associated with performing the task itself but also the intention of doing it before the subject even has a chance to move.Episode Notes:
About the Author
Sumner started his career in mechanical and aerospace engineering, performing research on haptics and mechatronics.This developed a love for how humans and computers interact, leading him to earn a PhD developing exoskeleton robots for motor learning and control.Through this, he realized that to translate these technologies, we need better methods to get information out of the brain.Key Takeaways
Ultrasound technologies are leveraged to monitor brain activity.The signal that is generated when these methods “listen” to the brain is extremely complex and entangled, akin to trying to make out a sentence from across a loud stadium.Sumner taught monkeys how to perform a task, reading the brain with ultrasound and using machine learning to decode the message.With it, they were able to read which way the monkey intended to move, when the movement would occur, which way the monkey actually moved, and whether it would move its hands or eyes.Translation
This technology has massive potential to help those suffering from motor impairment and could one day connect us all on a deeper level.To get there, the device will need to be optimized to find the best way to maximize signal-to-noise but minimize invasiveness.Additionally, advances in miniaturization, wireless connections, lowered cost of goods, and finding the right balance between AI and BMI control are needed to get this extremely new technology into the hands of everyone.First Author: Sumner Norman
Paper:
Single-trial decoding of movement intentions using functional ultrasound neuroimaging
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Episode Summary: Enzymes that break down other proteins, or proteases, could be used as a powerful therapeutic if they could specifically chew-up disease causing entities. However many proteases are non-specific, breaking any protein in their path, while the specific ones target proteins that would provide no therapeutic benefit. Travis and his colleagues developed a riff on the method known as PANCE that utilizes bacteria and bacterial viruses known as phages to evolve proteins toward a specific goal. With it, he retrains the sequence-specific protease, botulinum neurotoxin, toward new targets and away from its original ones. The novel enzymes Travis generates have the potential to not only stimulate nerve regeneration but also deliver itself to the correct cell types for a whole new type of therapy.
Episode Notes:
About the Author
Travis is a postdoc who performed this work in the lab of Professor David Liu at Harvard University. The Liu lab is famous for engineering and evolving proteins that can be utilized as massively impactful tools for overcoming diverse diseases. Travis’s teachers fostered a curiosity that created a passion for chemistry and ultimately led him to engineer new biochemistries.Key Takeaways
Proteases are enzymes that cut up other proteins.Proteases can either be non-specific, a nuke obliterating any protein in their path, or sequence-specific, a heat seeking missile only cutting very specific protein motifs.Sequence-specific proteases that target disease causing proteins would make great drugs but therapeutically useful proteases rarely exist in nature.Travis focuses on re-engineering the sequence-specific protease known as botulinum neurotoxin so that it cuts an entirely new, therapeutically relevant protein sequence.Using a method called PANCE that utilizes bacteria and bacterial viruses (phages), Travis trains botulinum neurotoxin toward cutting a new target and leaving its original target alone.Translation
Botulinum neurotoxin has a cutting domain that Travis engineered toward a therapeutically relevant target, and a targeting domain that delivers the protein toward neurons.The enzymes generated could be used to cure neural pathologies but the PANCE could also be applied to change which cell type the protease targets, creating a highly programmable therapeutic protease platform.The platform has a ton of interest from industry and Travis is continuing to work on it outside of academia so that these proteases make it to the clinic and impact patient lives.First Author: Travis Blum
Paper:
Phage-assisted evolution of botulinum neurotoxin proteases with reprogrammed specificity
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Episode Summary:
Novel drugs that boost the immune system to fight cancer have become pharma darlings in the few short years since their approval. These drugs, known as immunotherapies, have so far focused on improving T cell responses and can be used to cure a multitude of different cancer types. Yet more often than not, immunotherapies have no effect on a patient, leaving doctors guessing on whether to prescribe the drug. To find the reason why some people respond while others don’t, Kevin and his team create a huge database of sequences derived from immunotherapy-treated patients. With it, he discovers biomarkers, mutational signatures, and immune profiles that correlate to response with the hopes that one day, these measurements form a diagnostic to ensure we treat the right patients.
Episode Notes:
About the Author
Kevin is a group leader at University College London and performed this work in the lab of Charles Swanton at the Francis Crick Institute. Dr. Swanton and his group are experts in studying the genome instability and evolution of cancer.Kevin started his career as a mathematician but was always driven to apply his skills to improving medicine.Key Takeaways
Immunotherapies aim to cure cancer by “taking the breaks off” your immune system, supercharging it to attack tumors.Two immunotherapies known as checkpoint inhibitors (CPI), anti-CTLA-4 and anti-PD-1, work by enhancing T cells and have recently become blockbuster drugs for the treatment of multiple different cancer types.These immunotherapies don’t work in many patients and medicine has yet to understand why.Kevin aggregated DNA and RNA sequencing data across multiple studies to generate a dataset that contained over 1,000 CPI treated patients who did and did not benefit from treatment.With this data, Kevin discovers mutational signatures, biomarkers, and immune profiles that correlate to whether a patient will respond to treatment.Translation
Kevin finds measurable signatures of a patient’s cancer that could be used to determine whether a patient should receive CPIs.This retrospective analysis will need to be validated as a prospective study to determine whether Kevin’s findings actually predict response.More tumor data as well as information about the patient’s genetics is being brought in to improve the accuracy of this prediction.Collaborations between academics, medical centers, non-profits, and industry partners will enable the findings to make an impact on patient outcomes.First Author: Kevin Litchfield
Paper: Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition
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Episode Summary:
In a single decade, CRISPR has made a dramatic impact on literally every facet of biotechnology. This game-changing system is traditionally programmed to make cuts at very specific parts of the genome, altering the code to cure disease. But a new class of CRISPRs discovered by Leo’s colleagues don’t simply cut DNA -- they integrate entirely new genetic material at targeted locations. With it, Leo generates a new method to perform very specific and highly efficient genome engineering on bacteria and describes the multitude of ways it can generate strains that revolutize commodity molecule synthesis and medicine.
Episode Notes:
About the Author
Leo is a PhD candidate who performed this work under Professor Sam Sternberg at Columbia University in New York City. Dr. Sternburg and his team are world experts in CRISPR biology having discovered multiple new CRISPR systems, including the function of Cas9 during his time in Professor Jennifer Doudna’s lab.Leo was driven to become a synthetic biologist after being exposed to all the ways nature has engineered biology to overcome problems.Key Takeaways
A new class of CRISPRs have been discovered that don't cut DNA but instead integrate new DNA on the genome.Leo hijacks this CRISPR’s novel functionality to integrate whatever new DNA he wants into whatever location on a genome he desires. Through the tools of synthetic biology, the system generates extremely targeted integrations at high efficiency in bacterial cells.This CRISPR tool allows for integration of huge genetic payloads, iterative integrations, and integration of payloads at multiple locations in a single step, all of which create entirely new options for strain engineering.The tool can be applied to multiple bacterial species and has proven utility in engineering the microbiome in situ as well as modifying industrially sought after strains.Translation
Leo demonstrates that the system is highly effective in laboratory settings and can be optimized to overcome new challenges in new bacterial hosts.The tool is undergoing further development and optimization to do population scale engineering -- making targeted and useful modifications to bacteria in communities like those seen in our gut or in nature. Further research is needed to move this powerful integration tool into human cells as a novel method to overcome genetic disease and engineer future cell therapies.First Author: Leo Vo
Paper:
CRISPR RNA-guided integrases for high-efficiency, multiplexed bacterial genome engineering, Nature Biotechnology, 2020.
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Whether it's Multiple Sclerosis, Type 1 Diabetes, Lupus, or Crohn's Disease, autoimmunity is a rapidly growing problem that traditional pharmaceuticals have failed to completely cure. While these diseases have very different symptoms, they all have the same root cause -- the body’s immune system is attacking its own healthy organs. Lurking within ourselves are a group of T cells called regulatory T cells that have the power to suppress immune function. These cells have huge potential to be engineered and utilized as a platform to cure any autoimmune disease. Unfortunately, they easily lose their suppressive abilities and can even exacerbate autoimmunity if handled incorrectly. Looking to stabilize regulatory T cells, Jessica and her colleagues perform a CRISPR screen to map which genes are responsible for maintaining their suppressive function. Using this data, Jessica takes the first step to bring this incredibly powerful cell type to the clinic to help millions of patients suffering from a myriad of diseases.
About the Author
Jessica performed this work in the lab of Professor Alex Marson at the University of California, San Francisco. The Marson lab is renowned for their work in building and applying synthetic biology tools to understand and improve the therapeutic value of immune cells.Jessica is driven to understand and cure autoimmune diseases because her mother, her sister, and her have all been diagnosed with autoimmune diseases.Key Takeaways
Regulatory T cells can suppress immune reactions, making them an attractive therapeutic to be used to cure any autoimmune disease.These regulatory T cells do not easily maintain their suppressive function, necessitating some engineering to make sure they maintain their therapeutic value.With CRISPR, Jessica turned every gene off one-by-one in regulatory T cells to find which genes were involved in maintaining its suppressive function.Jessica found a gene, USP22, that when expressed, inhibited regulatory T cell function making it a useful target for both autoimmunity and cancer.Translation
While Jessica focused on one of the hits from the screen, there were many more that have massive potential as drug targets or as engineering steps for T cell therapies against autoimmunity.Maintaining a stable regulatory T cell is the vital first step to creating a world where all autoimmune diseases are cured using cells.First Author: Jessica Cortez
Paper:
CRISPR screen in regulatory T cells reveals modulators of Foxp3
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Engineered T cells that hunt and kill blood cancers have recently obtained three landmark FDA approvals, forever changing the way we treat this disease. Even with its massive clinical success, these cells come with life-threatening neurotoxicities. But is neurotoxicity a set feature of using T cell therapies or is our engineering accidentally targeting the brain? Utilizing advances in bioinformatics and the huge sequencing datasets available to science, Kevin uncovers similarities between a cell type in our brain and the cancer we target with engineered cells. Finding this needle in a haystack, Kevin creates a link between how we engineer these cells and the neurotoxicities we see, discovering a potential root cause of the problem and generating a rule for how to engineer around it.
About the Author
Kevin recently received his PhD from Stanford University in the labs of Professor Howard Chang and Professor Ansuman Satpathy. These labs specialize in uncovering the molecular mechanisms of disease using advanced sequencing modalities.Bridging both biology and computer science, Kevin’s background and expertise made him uniquely suited to hunt down the culprit of CAR T cell neurotoxicity.Key Takeaways
CAR T cells are excellent at killing blood cancers but are not without side-effects -- they can cause severe neurotoxicities.The receptor engineered into CAR T cells was thought to be specific to these blood cancers, ensuring the therapies don't attack healthy tissue.Kevin looked at publically available single cell sequencing data to find a small subset of brain cells hiding in plain sight that the CAR T cells could attack. In mice, engineered “blood cancer specific” T cells attack the brain, demonstrating that neurotoxicity is an off-target effect of the therapy, not a byproduct.Translation
The finding points to the potential need for different engineered receptors to be used to target these blood cancers.As CAR T cells expand to other cancers and malignancies, this process can be run to ensure we engineer cells that minimize the opportunity for damage to healthy tissue.First Author: Kevin Parker
Paper:
Single-Cell Analyses Identify Brain Mural Cells Expressing CD19 as Potential Off-Tumor Targets for CAR-T Immunotherapies
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Small molecules are a pillar of human health, making up a majority of the drugs we have in our healthcare arsenal. Many of these drugs are obtained by utilizing synthetic chemistry to modify the composition of some small molecule found in nature. Derivatives of tropane alkaloids, for example, alleviate neuromuscular disorders and are derived from a chemical found in nightshade plants. However, sourcing these plants have become exceedingly difficult as climate change, the pandemic, and geopolitics ravage the supply chain. Looking to overcome these challenges, Prashanth recapitualed the biochemical pathway that makes these tropane alkaloids in yeast. In the most complex feat of metabolic engineering to date, Prashanth can make these life-saving drugs in a bioreactor, insulated from the issues that make them expensive and in short-supply.
About the Author
Prashanth is a graduate student at Stanford University and published this work in the lab of Professor Christina Smolke. Christina and her team are world experts in metabolic engineering and broke multiple records in generating yeast that perform complex biosynthesis.Prashanth’s love of science was fostered by his teacher who encouraged him to combine his fascination with biology and his unique perspective on chemistry.Key Takeaways
Drugs are often sourced from natural sources like plants that have extremely precarious supply chains.The same biosynthetic pathways that makes the drug in plants can be recapitulated in yeast so that the small molecule can be brewed anywhere.Moving this biosynthetic pathway from one organism to another is not easy and still requires a ton of novel biology to be discovered in order to succeed.Here, Prashant had to hunt for new enzymes, cut-out wasted chemical reactions, and engineer ways to move the molecule and proteins to the specific parts of the cell.Translation
Scaling these microbes to make them economically viable first requires maximizing the amount of drug that each yeast can make.Directed evolution of useful enzymes, importing new molecular transporters, and optimizing growth conditions will be used to spin-out this microbe.The strain will be licensed through Stanford to pharmaceutical companies.First Author: Prashanth Srinivasan
Paper:
Biosynthesis of medicinal tropane alkaloids in yeast
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Bacteria are rapidly evolving ways to resist antibiotics, causing minor infections to become life-threatening events. Compounding the problem, new antibiotics have been incredibly challenging to develop and pharma is economically disincentivized to invest in finding them. James Martin and his colleagues Joseph Sheehan and Benjamin Bratton took on this challenge, developing an extremely potent antibiotic that targets multiple different classes of bacteria. James tells the story of identifying this antibiotic, understanding its potential, and pinpointing how its structure begets its function. Describing the state-of-the art CRISPR screens, proteomics, and machine learning methods they used, James calls for a new era of antibiotic discovery to meet the impending wave of superbugs.
About the Author
James Martin performed this work as a graduate student in Professor Zemer Getai’s lab at Princeton University.James’s optimism and drive to understand a problem from all angles led him and his colleagues to develop one of the most potent antibiotics ever found.Key Takeaways
Our arsenal of antibiotics will soon be worthless, as bacteria evolve ways to get around their killing effects.Adding new antibiotics to this arsenal has been slow because they are challenging to discover and they have poor return on investment.Synergizing a number of new biological tools available like high throughput microscopy, CRISPR, and machine learning, new antibiotics can be developed and understood faster than ever before.Applying this fresh take on antibiotic discovery, a novel drug is found that targets a wide-variety of bacteria and is difficult to evolve resistance to.Translation
Moving this extremely potent compound to the clinic will require some smart biochemistry to make it a better drug.The research of James and his colleagues demonstrates a paradigm shift in how antibiotic discovery pipelines are performed to more easily and rapidly find these new drugs.First Authors: James Martin, Benjamin Bratton, Joseph Sheehan
Paper: A Dual-Mechanism Antibiotic Kills Gram-Negative Bacteria and Avoids Drug Resistance
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Hundreds of iterations of immune cells that are engineered to kill cancer have already been designed. Corina reached outside of this box to use the same synthetic biology principles to engineer T cells to attack senescent cells, a cell type that contributes to diseases of aging. Corina walks us through how her engineered T cells know the difference between a diseased cell and healthy tissue, how she stumbled upon the chimeric antigen receptor that made this possible, and how these new T cells are being moved from academia to the clinic.
About the Author
Corina is a physician scientist who performed this work under Professor Scott Lowe at Memorial Sloan Kettering in New York City. Dr. Lowe and his team are world experts in dissecting how functional changes in a cell make them go from healthy to cancerous.Corina became fascinated with translation biotechnology after seeing her mother survive a life threatening disease using a new therapy in a clinical trial.Key Takeaways
T cells are the part of the immune system that have the ability to target and kill other cells in the body in a way similar to drug sniffing dogs.Using the hottest tool in synthetic immunology, the chimeric antigen receptor (CAR), T cells can be engineered to target and attack almost anything we like.A major hurdle to engineering these cells is finding something to target that is overrepresented in disease cells and virtually absent in healthy cells.When targeted to senescent cells, these T cells can kill precancerous cells and reverse diseases related to aging and poor diet.Translation
Corina’s research contains excellent demonstrations of these cells working in preclinical models -- mice that mimic human diseases.To move to human trials, Corina must update the therapy to attack human versions of the cells and begin to work toward understanding its safety and efficacy.Corina believes the best people to take on this challenge are the researchers who have intimate knowledge of the method and who care deeply about the disease it could cure.First Author: Corina Amor Vegas
Paper: Senolytic CAR T cells reverse senescence-associated pathologies. Nature, 2020.
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If you’re an author of an upcoming paper in bio or know of any interesting papers dropping soon and want to hear from the authors, drop us an email at translation [AT] fifty [DOT] vc.
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