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Season 5, Episode 1: Building the DNA Oracle with Eeshit Vaishnav

Episode Contributors: Ayush Noori, Ashton Trotman-Grant, Eeshit Vaishnav

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

Season 4, Episode 6: Cell Therapies of the Future with Dan Goodman

Episode Contributors: Michael Chavez, Alex Teng, Daniel Goodman

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