Bile acid toxicity in Paneth cells contributes to gut dysbiosis induced by high-fat feeding

Abstract

High-fat feeding (HFF) leads to gut dysbiosis through unclear mechanisms. We hypothesize that bile acids secreted in response to high-fat diets (HFDs) may act on intestinal Paneth cells, leading to gut dysbiosis. We found that HFF resulted in widespread taxonomic shifts in the bacteria of the ileal mucosa, characterized by depletion of Lactobacillus and enrichment of Akkermansia muciniphila , Clostridium XIVa, Ruminococcaceae, and Lachnospiraceae, which were prevented by the bile acid binder cholestyramine. Immunohistochemistry and in situ hybridization studies showed that G protein–coupled bile acid receptor (TGR5) expressed in Paneth cells was upregulated in the rats fed HFD or normal chow supplemented with cholic acid. This was accompanied by decreased lysozyme + Paneth cells and α-defensin 5 and 6 and increased expression of XBP-1 . Pretreatment with ER stress inhibitor 4PBA or with cholestyramine prevented these changes. Ileal explants incubated with deoxycholic acid or cholic acid caused a decrease in α-defensin 5 and 6 and an increase in XBP-1 , which was prevented by TGR5 antibody or 4PBA. In conclusion, this is the first demonstration to our knowledge that TGR5 is expressed in Paneth cells. HFF resulted in increased bile acid secretion and upregulation of TGR5 expression in Paneth cells. Bile acid toxicity in Paneth cells contributes to gut dysbiosis induced by HFF.

TGR5 Expression Is Associated with Changes in the Heart and Urinary Bladder of Rats with Metabolic Syndrome

“…This finding is consistent with the hyperglycemia-induced changes in the hearts of diabetic rats [ 9 ]. Insulin resistance is exacerbated by an increase in inflammation, along with a parallel increase in the activation of TGR5 [ 35 ], which may play a protective role in obese rats. TGR5 was identified in vivo, which may be targeted by bile acids [ 5 ] in both the healthy and diseased states [ 9 ].…”

Dietary Patterns and Associated Microbiome Changes that Promote Oncogenesis

“…Dysbiosis is implicated as a bridge between changing gut microbiome composition and the incipient manifestation of extraintestinal tumors. The microbial balance shifts away from commensal bacteria in the gut, creating a favorable environment for chronic inflammation as well as the suppression of immune surveillance ( Zhou et al, 2020 ; Kovács et al, 2020 ). Intriguingly, it has been hypothesized that pathogenic bacteria, bacterial products, and metabolites escape into the systemic circulation via increased leakiness of tight junctions and contribute to promoting inflammatory pathways in other organs.…”

“…Interestingly secondary bile acid is increased in CRC and HCC patients, as well as the bacteria metabolizing the secondary bile acids ( Modica et al, 2009 ). Large amounts of secondary bile acids can reach systemic circulation via portal vein, leading to liver inflammation ( Wang et al, 1999 ; Zhou et al, 2020 ). The secondary bile acid deoxycholic acid can induce ROS mediated DNA damage and alter hepatic stellate cell phenotype resulting in IL-6 and IL-1beta production.…”

Regional Cell Atlas of Human Intestine Shapes Distinct Immune Surveillance

“…Graph clustering and partitioning cells into distinct compartments Downstream analysis included normalization (scanpy.pp.normalize_total method, target_sum=1e4), log-transformation (scanpy.pp.log1p method, default parameters), cell cycle score (scanpy.tl.score_genes_cell_cycle method, cell cycle genes defined in Tirosh et al, 2016 54 , feature regress out (scanpy.pp.regress_out method, UMI counts, percentage of mitochondrial genes and cell cycle score were considered to be the source of unwanted variability and were regressed), feature scaling (scanpy.pp.scale method, max_value=10, zero_center=False), PCA analysis (scanpy.tl.pca method, svd_solver='arpack'), batch-balanced neighbourhood graph building (scanpy.external.pp.bbknn method, n_pcs=20) 55 , leiden graph-based clustering (scanpy.tl.leiden method, resolution=1.0) 56 , and UMAP visualization (scanpy.tl.umap method) 57 performed using scanpy (version 1.7.1) 52 . Clusters were preliminarily partitioned into 6 compartments, using marker genes found in the literature in combination with differentially expressed genes (scanpy.tl.rank_gene_groups method, method='Wilcoxon test').…”

“…In brief, we processed the normalized gene-cell matrix in three steps. 1, the principal component analysis and batch correction based on BBKNN 55 were performed. 2, we estimated the diffusion map of epithelium differentiation.…”