An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism
Year: 2021
Type: article
Abstract: Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.
Authors: Dawson D. Payne, Alina Renz, Laura J. Dunphy, Taylor Lewis, Andreas Dräger, Jason A. Papin
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
Funders: National Science Foundation, Foundation for the National Institutes of Health, U.S. Department of Health and Human Services, Deutsches Zentrum für Infektionsforschung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases
Awards: G1251911596, G160184881, G2036822895, REU Site: Multi-Scale Systems Bioengineering and Biomedical Data Sciences, EXC 2124: Kontrolle von Mikroorganismen zur Bekämpfung von Infektionen (CMFI), G5226254531, G5974744882, Multi-scale model of microbial phenotype modulation by mucins, G9000268398, Interdisciplinary Training in Systems & Biomolecular Data Science
OpenAlex ID: W3203505575
