ArticlePloS one2016
Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- Expanding the search for small-molecule antibacterials by multidimensional profiling.Nature chemical biology · 2022Review
- Panomics to decode virulence and fitness in Gram-negative bacteria.Frontiers in cellular and infection microbiology · 2022Article
- The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes.Nucleic acids research · 2021Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Article
- Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens.Frontiers in cell and developmental biology · 2020Review
- Review
- Biomedical applications of genome-scale metabolic network reconstructions of human pathogens.Current opinion in biotechnology · 2018Review
- Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes.PloS one · 2018Article
- Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.BMC evolutionary biology · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.