Nontyphoidal Salmonella strains (NTS) are among the most common foodborne enteropathogens and constitute a major cause of global morbidity and mortality, imposing a substantial burden on global health. The increasing antibiotic resistance of NTS bacteria has attracted a lot of research on understanding their modus operandi during infection. Growth in the gut lumen is a critical phase of the NTS infection. This might offer opportunities for intervention. However, the metabolic richness of the gut lumen environment and the inherent complexity and robustness of the metabolism of NTS bacteria call for modeling approaches to guide research efforts. In this study, we reconstructed a thermodynamically constrained and context-specific genome-scale metabolic model (GEM) for S. Typhimurium SL1344, a model strain well-studied in infection research. We combined sequence annotation, optimization methods and in vitro and in vivo experimental data. We used GEM to explore the nutritional requirements, the growth limiting metabolic genes, and the metabolic pathway usage of NTS bacteria in a rich environment simulating the murine gut. This work provides insight and hypotheses on the biochemical capabilities and requirements of SL1344 beyond the knowledge acquired through conventional sequence annotation and can inform future research aimed at better understanding NTS metabolism and identifying potential targets for infection prevention.

MOTIVATION: DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization, and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills. RESULTS: Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups. The tool is seamlessly integrated with an intuitive web app for interactive data exploration via the STRING and KEGG databases to accelerate scientific discovery. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in Python. The source code is freely available (https://github.com/MicrobiologyETHZ/mbarq) and the web app can be accessed at: https://microbiomics.io/tools/mbarq-app.