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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.

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


Authors

Nicole Pearcy

Marco Garavaglia

Thomas Millat

James P. Gilbert

Yoseb Song

Hassan Hartman

Craig Woods

Claudio Tomi-Andrino

Rajesh Reddy Bommareddy

Byung Kwan Cho

David A. Fell

Mark Poolman

JOHN KING JOHN.KING@NOTTINGHAM.AC.UK
Professor of Theoretical Mechanics



Contributors

Costas D. Maranas
Editor

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

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