Time-resolved multi-omics illustrates host and gut microbe interactions during Salmonella infection.

06 March 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Salmonella infection, also known as Salmonellosis, is one of the most common food-borne illnesses. Salmonella infection can trigger host defensive functions, including an inflammatory response. The provoked-host inflammatory response has a significant impact on the bacterial population in the gut. In addition, Salmonella competes with other gut microorganisms for survival and growth within the host. Compositional and functional alterations in gut bacteria occur because of the host immunological response and competition between Salmonella and the gut microbiome. Host variation and the inherent complexity of the gut microbial community make understanding commensal and pathogen interactions particularly difficult during a Salmonella infection. Here we present metabolomics and lipidomics analyses along with 16s rRNA sequence analysis, revealing a comprehensive view of the metabolic interactions between the host and the gut microbiota during Salmonella infection in a CBA/J mouse model. We found that different metabolic pathways were altered over the four investigated time points of Salmonella infection (days -2, +2, +6, and +13). Furthermore, metatranscriptomics analysis integrated with metabolomics and lipidomics analysis facilitated an understanding of the heterogeneous response of mice depending on the degree of dysbiosis.

Keywords

salmonella
multi-omics
lipidomics
metabolomics

Supplementary materials

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Supplementary Figures
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The abundance of Salmonella of 40 mice (Figure S1); Correlation analysis between 16s rRNA result and metabolomics/lipidomics result at each time point of Salmonella infection. Correlation result (Figure S2). AUTHOR
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Supplementary Tables
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Statistical results for identified metabolites/lipids (Table S1); Metabolomics/lipidomics driven pathway analysis result (Table S2); Correlation analysis between metabolomics/lipidomics and 16s rRNA analysis (Table S3); Meta-transcriptomics analysis result (Table S4)
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