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Get Free AccessAbstract Achieving high titer rates and yields (TRY) remains a bottleneck in the production of heterologous products through microbial systems, requiring elaborate engineering and many iterations. Reliable scaling of engineered strains is also rarely addressed in the first designs of the engineered strains. Both high TRY and scale are challenging metrics to achieve due to the inherent trade-off between cellular use of carbon towards growth vs. target metabolite production. We hypothesized that being able to strongly couple product formation with growth may lead to improvements across both metrics. In this study, we use elementary mode analysis to predict metabolic reactions that could be targeted to couple the production of indigoidine, a sustainable pigment, with the growth of the chosen host, Pseudomonas putida KT2440. We then filtered the set of 16 predicted reactions using -omics data. We implemented a total of 14 gene knockdowns using a CRISPRi method optimized for P. putida and show that the resulting engineered P. putida strain could achieve high TRY. The engineered pairing of product formation with carbon use also shifted production from stationary to exponential phase and the high TRY phenotype was maintained across scale. In one design cycle, we constructed an engineered P. putida strain that demonstrates close to 50% maximum theoretical yield (0.33 g indigoidine/g glucose consumed), reaching 25.6 g/L indigoidine and a rate of 0.22g/l/h in exponential phase. These desirable phenotypes were maintained from batch to fed-batch cultivation mode, and from 100ml shake flasks to 250 mL ambr® and 2 L bioreactors.
Deepanwita Banerjee, Thomas Eng, Andrew K. Lau, Brenda Wang, Yusuke Sasaki, Robin A. Herbert, Yan Chen, Yuzhong Liu, Jan‐Philip Prahl, Vasanth Singan, Deepti Tanjore, Christopher J. Petzold, Jay D Keasling, Aindrila Mukhopadhyay (2020). Genome-scale metabolic rewiring to achieve predictable titers rates and yield of a non-native product at scale. , DOI: https://doi.org/10.1101/2020.02.21.954792.
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Type
Preprint
Year
2020
Authors
14
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1101/2020.02.21.954792
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