Lactobacillus rhamnosus GG induces STING-dependent IL-10 in intestinal monocytes and alleviates inflammatory colitis in mice
Wei Si, Xin Zhao, Ruitong Li, Yaopeng Li, Cui Ma, Xiaohan Zhao, Jason Bugno, Yuchang Qin, Junmin Zhang, Hongwei Liu, Liangliang Wang
Wei Si, Xin Zhao, Ruitong Li, Yaopeng Li, Cui Ma, Xiaohan Zhao, Jason Bugno, Yuchang Qin, Junmin Zhang, Hongwei Liu, Liangliang Wang
View: Text | PDF
Research Article Immunology Inflammation

Lactobacillus rhamnosus GG induces STING-dependent IL-10 in intestinal monocytes and alleviates inflammatory colitis in mice

Abstract

Preclinical and clinical observations indicate that the probiotic Lactobacillus rhamnosus GG (LGG) can modulate colonic inflammation. However, the underlying mechanisms have not been explored in depth. Here, we demonstrate that oral administration of live LGG alleviated inflammatory colitis by increasing IL-10 expression in intestinal Ly6C+ monocytes. Mechanistically, LGG induced IL-10 production via the stimulator of IFN genes (STING)/TBK1/NF-κB (RELA) signaling pathway in intestinal Ly6C+ monocytes, enhancing their immune-suppressive function. Elevated IL-10 subsequently activated IL-10 signaling in Ly6C+ monocytes, resulting in an IL-10–based autocrine regulatory loop and inhibition of proinflammatory cytokine production. Furthermore, LGG shifted the gut microbial community and its metabolic functions, leading to intestinal immune responses against colitis. Fecal microbiota transplantation from LGG-colonized mice alleviated immune checkpoint blockade–associated colitis. Our findings highlight the importance of STING signaling in IL-10–dependent antiinflammatory immunity and establish an empirical basis for developing oral administration of live LGG as an efficient and safe therapeutic strategy against inflammatory colitis.

Authors

Wei Si, Xin Zhao, Ruitong Li, Yaopeng Li, Cui Ma, Xiaohan Zhao, Jason Bugno, Yuchang Qin, Junmin Zhang, Hongwei Liu, Liangliang Wang

×

Figure 6

LGG shapes the gut microbial community and its metabolic function associated with intestinal immune responses.

Options: View larger image (or click on image) Download as PowerPoint
LGG shapes the gut microbial community and its metabolic function associ...
(A) Unconstrained principal coordinate analysis (PCoA) with Bray-Curtis distance indicates microbial community differences (variation) across treatments. The distribution of the first two most informative coordinates was performed in the corresponding marginal box plot. Each data point corresponds to a sample, which is colored according to the treatment. Ellipses represent a 95% CI around the cluster centroid. PERMANOVA detected significant differences (P < 0.05) among different treatments, and all pairwise differences were statistically significant with P values adjusted using the Benjamini and Hochberg methods. (B) Box plot of the Shannon index represents α diversity for intestinal microbiota from the indicated treatment groups. Statistical significance was assessed using ANOVA and t test. All P values were adjusted using the Benjamini and Hochberg methods. (C) Distribution of the intestinal microbiota at the phylum level. Taxonomic composition distribution was labeled with different colors; the top 9 phyla ranked by the average counts among all samples are illustrated. “Low abundance” represents the combination of the rest of the phyla. (D) Cladogram showing differential bacterial abundance between DSS (red) and LGG+DSS (green) treatment groups based on LEfSe analysis. The levels indicate, from the inner to outer rings, phylum, class, order, family, and genus. Linear discriminant analysis score for discriminative features >3. (E) Score plot of the first 3 principal components analysis calculated on fecal metabolites. Each sphere represents 1 LC-MS sample. Spheres are colored according to treatment group: the DSS group is in blue and the LGG+DSS group is in purple. DSS fecal samples and LGG+DSS fecal samples separated from each other, indicating that the metabolome profiles were different. The variance explained by each PC is reported. The 3 major principal components explained the percentage of the cumulative variance. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.