In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 5 ( 2022-5-23), p. e1010106-
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 i CN1361), 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 i CN1361 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 i CN1361 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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010106
DOI:
10.1371/journal.pcbi.1010106.g001
DOI:
10.1371/journal.pcbi.1010106.g002
DOI:
10.1371/journal.pcbi.1010106.g003
DOI:
10.1371/journal.pcbi.1010106.g004
DOI:
10.1371/journal.pcbi.1010106.g005
DOI:
10.1371/journal.pcbi.1010106.t001
DOI:
10.1371/journal.pcbi.1010106.t002
DOI:
10.1371/journal.pcbi.1010106.t003
DOI:
10.1371/journal.pcbi.1010106.s001
DOI:
10.1371/journal.pcbi.1010106.s002
DOI:
10.1371/journal.pcbi.1010106.s003
DOI:
10.1371/journal.pcbi.1010106.s004
DOI:
10.1371/journal.pcbi.1010106.s005
DOI:
10.1371/journal.pcbi.1010106.s006
DOI:
10.1371/journal.pcbi.1010106.s007
DOI:
10.1371/journal.pcbi.1010106.r001
DOI:
10.1371/journal.pcbi.1010106.r002
DOI:
10.1371/journal.pcbi.1010106.r003
DOI:
10.1371/journal.pcbi.1010106.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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