Gut microbiota-mediated tributyltin-induced metabolic disorder in rats

Abstract

Tributyltin (TBT), an environmental pollutant widely used in antifouling coatings, can cause multiple-organ toxicity and gut microbiome dysbiosis in organisms, and can even cause changes in the host metabolomic profiles. However, little is known about the underlying effects and links of TBT-induced metabolic changes and gut microbiome dysbiosis. In this study, rats were exposed to TBT at a dose of 100 μg kg^-1 body weight (BW) for 38 days, followed by multi-omics analysis, including microbiome, metabolomics, and metallomics. Results showed that TBT exposure reduced rat weight gain and decreased the serum triglyceride (TG) level. Metabolic analysis revealed that TBT fluctuated linoleic acid metabolism and glycerophospholipid metabolism in the liver; the tricarboxylic acid cycle (TCA cycle), nicotinate and nicotinamide metabolism, and arachidonic acid metabolism in serum; glycine, serine, and threonine metabolism, the one carbon pool by folate, nicotinate, and nicotinamide metabolism; and tryptophan metabolism in feces. Furthermore, TBT treatment dictated liver inflammation due to enhancing COX-2 expression by activating protein kinase R-like ER kinase (PERK) and C/EBP homologous protein (CHOP) to induce endoplasmic reticulum (ER) stress instead of stimulating arachidonic acid metabolism. Meanwhile, alteration of the intestinal flora [Acetivibrio]_ethanolgignens_group, Acetatifactor, Eisenbergiella, Lachnospiraceae_UCG-010, Enterococcus, Anaerovorax, and Bilophila under TBT exposure were found to be involved in further mediating liver inflammation, causing lipid metabolism abnormalities, such as TG, linoleic acid, and glycerophospholipids, and interfering with the energy supply process. Among these, [Acetivibrio]_ethanolgignens_group, Enterococcus, and Bilophila could be considered as potential biomarkers for TBT exposure based on receiver operator characteristic (ROC) curve analysis.

Fig. 1. TBT-induced lipid disorder and liver inflammation in rats. (A) Effect of animal weight gain. (B) The level of TG in serum. (C) Histology of abdominal adipose tissue. (D) Histology of liver tissue with H&E staining. Black arrows point to inflammatory cell infiltration. (E) Immunohistochemistry for COX-2. The images were taken under the same size magnification. Scale bar: 100 μm.

Fig. 2. TBT induced metabolic changes, ER stress, and metal disturbances. Partial least squares discrimination analysis (PLS-DA) of the metabolites in rat liver (A) and serum (B) were performed and quality control was simplified to QC. Metabolic pathways affected by TBT treatment in the liver (C) and serum (D) are presented as a bubble diagram based on KEGG metabolic pathway database. Larger bubbles and darker colors indicate that the pathways were more affected. Metabolites that had significant changes in liver (E) and serum (F) due to TBT intervention are presented in the heatmap. (G) Western blotting of protein kinase R-like ER kinase (PERK) and C/EBP homologous protein (CHOP) in the endoplasmic reticulum (ER) stress pathway. (H) The relative expression of protein PERK and CHOP. Metal elements Ca (I), Cu (J), and Fe (K) in serum were determined by ICP-MS.

Fig. 3. TBT changed the fecal microbiota composition in rats. (A) Principal coordinate analysis (PCoA) plots of the fecal microbiota on OTU level based on binary-Pearson distance metrics. (B) Community bar plot analysis. The average percent of community abundance of the phylum level in each group is presented as a bar plot. (C) Comparison and classification of gut microbiota at the genus level. Wilcoxon rank-sum test were performed between the control group and TBT group, *P < 0.05, **P < 0.01. D. KEGG functional annotation predicted from 16S rRNA sequencing. Predicted metabolic pathways were analyzed by Tax4Fun on the i-sanger platform using STAMP software. Differences between groups were determined using the Welsh's t-test. P < 0.05 was considered to be significant.

Fig. 4. Gut microbiome participate in TBT-induced metabolic changes. (A) Partial Least Squares Discrimination Analysis (PLS-DA) of the metabolites in rat feces. (B) Metabolic pathways affected by TBT treatment in feces are presented as a bubble diagram based on the KEGG metabolic pathway database. (C) Metabolites with statistical significance under TBT exposure. (D) Correlation analysis between differential intestinal bacteria and metabolites, with significant differences marked with *. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 5. Receiver operator characteristic (ROC) curve analysis of gut microbiome under TBT exposure. ROC analysis of (A) [Acetivibrio]_ethanolgignens, (B) Acetatifactor, (C) Eisenbergiella, (D) Lachnospiraceae_UCG-010, (E) Enterococcus, (F) Anaerovorax, and (G) Bilophila was performed using GraphPad Prism 7 software. The area under the curve (AUC) indicated the accuracy of the prediction and P < 0.0332 was considered statistically significant.