Gut Dysbiosis and Adaptive Immune Response in Diet-induced Obesity vs. Systemic Inflammation - PubMed (original) (raw)
Gut Dysbiosis and Adaptive Immune Response in Diet-induced Obesity vs. Systemic Inflammation
Jana Pindjakova et al. Front Microbiol. 2017.
Abstract
A mutual interplay exists between adaptive immune system and gut microbiota. Altered gut microbial ecosystems are associated with the metabolic syndrome, occurring in most obese individuals. However, it is unknown why 10-25% of obese individuals are metabolically healthy, while normal weight individuals can develop inflammation and atherosclerosis. We modeled these specific metabolic conditions in mice fed with a chow diet, an obesogenic but not inflammatory diet-mimicking healthy obesity, or Paigen diet-mimicking inflammation in the lean subjects. We analyzed a range of markers and cytokines in the aorta, heart, abdominal fat, liver and spleen, and metagenomics analyses were performed on stool samples. T lymphocytes infiltration was found in the aorta and in the liver upon both diets, however a significant increase in CD4+ and CD8+ cells was found only in the heart of Paigen-fed animals, paralleled by increased expression of IL-1, IL-4, IL-6, IL-17, and IFN-γ. Bacteroidia, Deltaproteobacteria, and Verrucomicrobia dominated in mice fed Paigen diet, while Gammaproteobacteria, Delataproteobacteria, and Erysipelotrichia were more abundant in obese mice. Mice reproducing human metabolic exceptions displayed gut microbiota phylogenetically distinct from normal diet-fed mice, and correlated with specific adaptive immune responses. Diet composition thus has a pervasive role in co-regulating adaptive immunity and the diversity of microbiota.
Keywords: adaptive immune system; gut microbiota; inflammation; obesity.
Figures
Figure 1
(A) Pie chart representing diet compositions in terms of fat, carbohydrates, choline, cholesterol, proteins. (B) Body weight of C57/BL6 mice fed for 20 weeks with a normal chow diet (ND), high fat diet (HD), or Paigen diet (PD). (C) Representative pictures from hematoxylin and eosin staining of liver sections (upper panels) and aorta sections (lower panels) in C57/BL6 mice fed with ND, HD, or PD. (D) Steatosis, lobular inflammation, and ballooning were scored semi quantitatively (0–4). *p < 0.05; **p < 0.01; ***p < 0.001 vs. ND.
Figure 2
Metabolic parameters of C57BL/6 mice fed a standard normal chow (ND) or a high fat diet (HD) or a Paigen diet (PD) for 15 weeks. (A) fasting glucose; (B) fasting insulin; (C) serum triglycerides; (D) serum cholesterol. N = 4. *p < 0.05, **p < 0.01, ***p < 0.001 vs. ND; #p < 0.05, ##p < 0.01 vs. HD.
Figure 3
(A) Cell suspension obtained from tissues were surface-stained for T lymphocyte markers with antibody combination CD45, CD4, and CD8 (A) and gated for CD45+ CD4+ T lymphocytes and CD45+ CD8+ T lymphocytes. (B) For myeloid cell subsets, the cell suspensions were surface-stained with antibody against CD45, CD11b, CD11c, Ly6G, and F4/80 (B) and gated for CD45+ CD11b+ Ly6G+ neutrophils, CD45+ CD11b+ CD11c+ dendritic cells, CD45+ CD11b+ CD11c- F4/80+ macrophages.
Figure 4
Immune cells profiling in the tissues of Normal diet (ND)-, High fat diet (HD)-, and Paigen diet (PD)-fed mice. Single cell suspensions were prepared from solid tissues (aorta, heart, and liver) and processed for FACS analyses. Combination of surface markers for T-lymphocytes was CD45, CD4, and CD8 and myeloid cells were stained for CD45, CD11b, CD11c, F4/80, and Ly6G. (A) Frequency of total myeloid cells and cells positive gated for CD45, CD11b, CD11c, F4/80, and Ly6G in the spleen. (B) Frequency of lymphocytes in the aorta and in the liver, of gated cells. (C) Frequency of cells gated for CD4 and CD8 in the aorta and in the heart. N = 3.4. *p < 0.05; **p < 0.01.
Figure 5
Cytokine gene expression in the tissues of Normal diet (ND)-, High fat diet (HD)-, and Paigen diet (PD)-fed mice. Single cell suspensions were prepared from solid tissues (aorta, heart, and abdominal fat), and used for total RNA extraction and for qPCR. Relative quantification of IL-17A, IFN-γ, IL-4, TGF-β, IL-1α, IL-12, and IL-6 mRNA levels were performed using the comparative CT method with normalization to GAPDH; results were expressed as fold difference relative to a relevant control sample. (A) IL-17 mRNA levels in the aorta, heart and adipose tissue. (B) IFN-γ and IL-4 mRNA levels in the aorta. (C) IL-1α, IFN-γ, and IL-4 mRNA levels in the heart. (D) IL-6 and IL-12 mRNA levels in the adipose tissue. N = 3–4. *p < 0.05; **p < 0.01.
Figure 6
(A) Alpha diversity using Shannon index of the fecal microbiota for each groups. (B) Relative abundance of major Phylum (Bacteroidetes and Firmicutes) for each group. (C) Relative abundance of most significant species, using RDP v11.4 databank in fecal samples of ND, HD, or PD mice. Graphs are displayed as mean ± SEM. **p < 0.01; ***p < 0.001, One-Way Anova followed by Kruskal–Wallis test.
Figure 7
Gut microbiota profiling in Normal diet (ND)-, High fat diet (HD)-, and Paigen diet (PD)-fed mice. (A) Multi Dimentional Scaling (MSD) of Unifrac distances of the fecal microbiota for each groups. (B) Hierarchical clustering of Unifrac distances of the fecal microbiota for each groups. (C,D) Relative abundance of Phylum and Family, respectively for each fecal sample. (E) Cladogram representing taxa enriched in fecal samples of ND, HD, or PD mice detected by the LEfSe tool. (F) Relative abundance of most significant taxa in fecal samples of ND, HD, or PD mice. Graphs are displayed as mean ± SEM.
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