Thallium(I) exposure perturbs the gut microbiota and metabolic profile as well as the regional immune function of C57BL/6 J mice
Li, Dong (2022), Thallium(I) exposure perturbs the gut microbiota and metabolic profile as well as the regional immune function of C57BL/6 J mice, Dryad, Dataset, https://doi.org/10.5061/dryad.8931zcrrt
Intestinal microbes regulate the development of diseases induced by environmental exposure. Thallium (Tl) is a highly toxic heavy metal, and its toxicity is rarely discussed in relation to gut microbes. Herein, we showed that Tl(I) exposure (10 ppm for two weeks) affected the alpha diversity of bacteria in the ileum, colon, and feces, but had little effect on the beta diversity of bacteria through 16S rRNA sequencing. LEfSe analysis revealed that Tl(I) exposure changed the abundance of intestinal microbiota along the digestive tract. Cecum metabolomic detection and analysis showed that Tl(I) exposure altered the abundance and composition of metabolites. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that Tl(I) exposure impaired amino acid, lipid, purine metabolism, and G protein-coupled receptor signalling pathways. A consistency test revealed a strong correlation, and a Pearson’s correlation analysis showed an extensive interaction, between microorganisms and metabolites. Analysis of the intestinal immunity revealed that Tl(I) exposure suppressed the immune responses, which also had regional differences. These results identify the perturbation of the intestinal microenvironment by Tl exposure and provide a new explanation for Tl toxicity.
Mice in the experimental group were exposed to 10 ppm TINO3 for two weeks. Cecum content (50 mg) metabolite extract was used to perform metabolomics analysis using UHPLC-QTOF-MS/MS (Waters, USA). Waters Acquity UPLC HSS T3 (1.8 µm, 2.1 mm × 100 mm) was used to separate the components of the metabolite extract. One microliter of metabolites solution was injected and eluted with a mobile phase comprising solvent A (0.1% formic acid) and B (0.1% formic acid-acetonitrile) at a flow rate of 400 μL/min according to the following process: solvent B was maintained at 2% for 0.25 min; subsequently, the concentration of solvent B was linearly increased from 2% to 98% over 10 min and maintained for 3 min; finally, the concentration of solvent B was decreased to 2% within 0.1 min, and maintained for 1.9 min. The mass spectrometry conditions were as follows: capillary voltage, 2000 V (positive ion mode) or -1500V (negative ion mode); cone voltage, 30 V; ion source temperature, 150 °C; desolventizing gas temperature, 500 °C; cone gas flow rate, 50 L/h; and desolventizing gas flow rate, 800 L/h. Full scans were acquired in a scan range of 50–1200 m/z with a scan frequency of 0.2 s, and data were collected via MassLynx (v. 4.2, Waters) and processed by Progenesis QI software such as peak extraction and peak alignment, and identification based on the METLIN database. At the same time, theoretical fragment identification was performed. The mass deviations were all within 100 ppm.
Fundamental Research Funds of China West Normal University, Award: 416927