Predicting essential metabolic genome content of niche-specific enterobacterial human pathogens during simulation of host environments

Tong Ding, Kyle A. Case, Morrine A. Omolo, Holly A. Reiland, Zachary P. Metz, Xinyu Diao, David J. Baumler

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Microorganisms have evolved to occupy certain environmental niches, and the metabolic genes essential for growth in these locations are retained in the genomes. Many microorganisms inhabit niches located in the human body, sometimes causing disease, and may retain genes essential for growth in locations such as the bloodstream and urinary tract, or growth during intracellular invasion of the hosts' macrophage cells. Strains of Escherichia coli (E. coli) and Salmonella spp. are thought to have evolved over 100 million years from a common ancestor, and now cause disease in specific niches within humans. Here we have used a genome scale metabolic model representing the pangenome of E. coli which contains all metabolic reactions encoded by genes from 16 E. coli genomes, and have simulated environmental conditions found in the human bloodstream, urinary tract, and macrophage to determine essential metabolic genes needed for growth in each location. We compared the predicted essential genes for three E. coli strains and one Salmonella strain that cause disease in each host environment, and determined that essential gene retention could be accurately predicted using this approach. This project demonstrated that simulating human body environments such as the bloodstream can successfully lead to accurate computational predictions of essential/important genes.

Original languageEnglish (US)
Article numbere0149423
JournalPloS one
Issue number2
StatePublished - Feb 2016

Bibliographical note

Publisher Copyright:
© 2016 Ding et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Dive into the research topics of 'Predicting essential metabolic genome content of niche-specific enterobacterial human pathogens during simulation of host environments'. Together they form a unique fingerprint.

Cite this