Project description:Background & Aims: Non-alcoholic fatty liver disease (NALFLD)-associated changes in gut microbiota are important drivers of disease progression toward fibrosis. Therefore, reversing microbiota alterations could ameliorate NAFLD progression. Oat beta-glucan, a non-digestible polysaccharides, has shown promising therapeutic effects on hyperlipidemia associated with NAFLD, but its impact on gut microbiota and most importantly NAFLD fibrosis remains unknown. Methods: We performed detailed metabolic phenotyping including body composition, glucose tolerance, and lipid metabolism as well as comprehensive characterization of the gut-liver axis in a western-style diet (WSD)-induced model of NAFLD and assessed the effect of a beta-glucan intervention on early and advanced liver disease. Gut microbiota was modulated using broad-spectrum antibiotic (Abx) treatment. Results: Oat beta-glucan supplementation did not affect WSD-induced body weight gain, glucose intolerance, and the metabolic phenotype remained largely unaffected. Interestingly, oat beta-glucan dampened NAFLD inflammation, associated with significantly reduced monocyte-derived macrophages (MoMFs) infiltration, fibroinflammatory gene expression, and strongly reduced fibrosis development. Mechanistically, this protective effect was not mediated by changes in bile acid composition or signaling, but was dependent on gut microbiota and was lost upon Abx treatment. Specifically, oat beta-glucan partially reversed unfavorable changes in gut microbiota, resulting in an expansion of protective taxa, including Ruminococcus, and Lactobacillus followed by reduced translocation of TLR ligands. Conclusions: Our findings identify oat beta-glucan as a highly efficacious food supplement that dampens inflammation and fibrosis development in diet-induced NAFLD. These results, along with its favorable dietary profile, suggest that it may be a cost-effective and well-tolerated approach to preventing NAFLD progression and should be assessed in clinical studies.

Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.

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