An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

• Published in: BMC systems biology Vol. 5; no. 151; p. 163
• Main Authors: Charusanti, Pep, Chauhan, Sadhana, McAteer, Kathleen, Lerman, Joshua A, Hyduke, Daniel R, Motin, Vladimir L, Ansong, Charles, Adkins, Joshua N, Palsson, Bernhard O
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.10.2011; BioMed Central
• Subjects: Analysis; Bacteria; Biological markers; Causes of; Control; Genetic aspects; Genome, Bacterial; Genomics; Health aspects; Metabolic Networks and Pathways; Metabolites; Metabolomics - methods; Microbiology; Phenotype; Physiological aspects; Plague; Systems Biology - methods; Yersinia infections; Yersinia pestis; Yersinia pestis - chemistry; Yersinia pestis - genetics; Yersinia pestis - metabolism; United States
• DOI: 10.1186/1752-0509-5-163
• ISSN: 1752-0509

Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.