A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications

Pearcy, Nicole; Garavaglia, Marco; Millat, Thomas; Gilbert, James P.; Song, Yoseb; Hartman, Hassan; Woods, Craig; Tomi-Andrino, Claudio; Bommareddy, Rajesh Reddy; Cho, Byung Kwan; Fell, David A.; Poolman, Mark; King, John R.; Winzer, Klaus; Twycross, Jamie; Minton, Nigel P.

Abstract

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model

Citation

Pearcy, N., Garavaglia, M., Millat, T., Gilbert, J. P., Song, Y., Hartman, H., …Minton, N. P. (2022). A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications. PLoS Computational Biology, 18(5), Article e1010106. https://doi.org/10.1371/journal.pcbi.1010106

• Journal Article Type: Article
• Acceptance Date: Apr 14, 2022
• Online Publication Date: May 23, 2022
• Publication Date: May 1, 2022
• Deposit Date: Jun 13, 2022
• Publicly Available Date: Jun 13, 2022
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science (PLoS)
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 5
• Article Number: e1010106
• DOI: https://doi.org/10.1371/journal.pcbi.1010106
• Keywords: Computational Theory and Mathematics; Cellular and Molecular Neuroscience; Genetics; Molecular Biology; Ecology; Modeling and Simulation; Ecology, Evolution, Behavior and Systematics
• Public URL: https://nottingham-repository.worktribe.com/output/8226845
• Publisher URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010106