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, Cho, Byung-Kwan, Fell, David A., Poolman, Mark, King, John R., Winzer, Klaus, Twycross, Jamie and Minton, Nigel 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). e1010106. ISSN 1553-734X

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Official URL: https://doi.org/10.1371/journal.pcbi.1010106

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 accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Item Type: Article
Additional Information: Funding information: This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC); grant number BB/L013940/1) and the Engineering and Physical Sciences Research Council (EPSRC) under the same grant number.
Subjects: C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: John Coen
Date Deposited: 30 May 2022 13:11
Last Modified: 24 Jun 2022 08:15
URI: http://nrl.northumbria.ac.uk/id/eprint/49216

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