RT Journal Article
SR Electronic
T1 Automation Assisted Anaerobic Phenotyping For Metabolic Engineering
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.03.442526
DO 10.1101/2021.05.03.442526
A1 Kaushik Raj
A1 Naveen Venayak
A1 Patrick Diep
A1 Sai Akhil Golla
A1 Alexander F. Yakunin
A1 Radhakrishnan Mahadevan
YR 2021
UL http://biorxiv.org/content/early/2021/05/04/2021.05.03.442526.abstract
AB Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for a high-throughput, laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies - an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. Further, we propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding in conjunction with our phenotyping platform to serve as an effective scale-down model for bioreactor phenotypes. By integrating an in-house data-analysis pipeline, we were able to accelerate the ‘test’ phase of the design-build-test-learn cycle of metabolic engineering.Competing Interest StatementThe authors have declared no competing interest.