Iterative Genome Engineering for Rapid Improvement of Protein Activity and Production

Presented By: Eric Abbate

Speaker Biography: Throughout my academic career, I have always been passionate about how quick bacteria can adapt to difficult environments. In the context of antibiotic treatments, I focussed on “persisters”, a kind of developmental state of cells with high level of antibiotic tolerance. As one of the first to approach persistence from an evolutionary point of view, I identified the fast tuning of this ‘bet-hedging’ behaviour (populations are said to hedge their bets against potential future disasters of antibiotic encounters by producing more/less persisters). Populations under frequent treatment quickly accumulate small and unexpected mutations that reroute physiology to overcome antibiotic-induced killing in previously unknown target genes. Later we have shown that such a fast evolutionary response of antibiotic tolerance is also true in all of the ESKAPE pathogens and that it precedes and catalyses the development of genetic resistance. Currently, we are further examining how the evolution of persistence and resistance intertwines, making use of high-throughput assays, and how the evolved mechanisms are adjusted to tune the level of surviving persister cells.

Webinar: Iterative Genome Engineering for Rapid Improvement of Protein Activity and Production

Webinar Abstract: The Design–Generate–Test–Learn (DGTL) cycle represents an efficient approach to biological engineering, from single-gene to whole-genome scale. Significant improvement can be achieved in a short amount of time by recombining beneficial edits through iterative cycles of genome editing. In order to cut down development times, all aspects of the DGLT cycle must be optimized. The Onyx™ Digital Genome Engineering Platform automates the Design and Generate steps, allowing the user to create thousands of edits, identify the beneficial variants, and construct new libraries based on the improved strains – all in an integrated, seamless workflow. Here we demonstrate how Iterative Genome Engineering can be used to discover novel protein variants, optimize expression, and improve heterologous protein production in yeast and E. coli strains.

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