Translation - Novel Translational Therapeutics With Linda Goodman

Novel Translational Therapeutics With Linda Goodman

06/01/23 • 50 min

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 Summary:

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

Previous Episode

Building the DNA Oracle with 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