Automated CRISPR-Based Iterative Genome Engineering for Rapid Strain Improvement 

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Reducing engineering cycle times for rapid strain improvement

Microbial strain improvement projects – such as protein or metabolic engineering – require many rounds of genome editing. The overall project timeline depends on the speed that you can move through the Design-Generate-Test-Learn (DGTL) cycle. The Onyx® Iterative Genome Engineering workflow streamlines the steps required to get from one genome engineering round to the next and allows you to move through the DGTL cycle much faster. To demonstrate what you can do with this workflow, we designed two applications: 1) heterologous protein engineering in E. coli and 2) improving the production of an industrially important recombinant enzyme in S. cerevisiae. In both cases, the project took less than 6 months to complete.

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Engineering novel protein variants in E. coli

For this application, we engineered the heterologous green fluorescent protein (GFP) in the E. coli genome with a library of 723 targeted edits against known mutational hotspots. The first round of editing and screening identified variants with altered fluorescence characteristics, one of which showed a shift in the absorption-emission spectrum towards the yellow fluorescent protein (YFP). This strain was used to stack the same library of edits in the second round of genome engineering. As a result, two novel YFP variants were identified with significantly improved fluorescence.

GFP iterative edit
Discovery of two novel yellow fluorescent protein (YFP) variants through two cycles of Iterative Genome Engineering
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Improving recombinant protein production in S. cerevisiae with diverse genome-wide edits

The second application used a whole-genome approach to improve production of heterologous cellobiohydrolase I (CBH1) enzyme in S. cerevisiae CEN​.PK strain. We designed diverse libraries with >14,000 edits to target both known and unknown genes across many functional classes. After the first round of screening, we identified close to 100 hits producing up to 1.6‑fold improvement across different pathways, including transcriptional regulators, stress response, protein degradation, trafficking and glycosylation, and other genes.

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Recombining edits from different libraries for further improvement

To see if we can achieve further improvement by combining edits from different libraries, we took two top hits from the first round of screening and stacked a genome-wide knockout library on top of them. Additionally, we took the best performing strain from round 1 and stacked the remaining ~100 hits on top of it. These libraries showed up to 2.4‑fold improvement, and the best performing variant from round 2 was combined with the first-round hits for the third cycle of Iterative Genome Engineering. The final strain showed a ~3‑fold improvement in plate-based screening and a 4‑fold increase in CBH1 activity when tested in shake flasks. 

CBH1 iterative edit
Fold-improvement in CBH1 activity over 3 cycles of Iterative Genome Engineering (plate-based screening)

Learn more about what you can do with Iterative Genome Engineering by downloading the app note or watching the webinar recording. 

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