Inulin has been used as a prebiotic to alleviate glucose and lipid metabolism disorders in mice and humans by modulating the gut microbiota. However, the mechanism underlying the alleviation of metabolic disorders by inulin through interactions between the gut microbiota and host cells is unclear. We use ob/ob mice as a model to study the effect of inulin on the cecal microbiota by 16S rRNA gene amplicon sequencing and its interaction with host cells by transcriptomics. The inulin-supplemented diet improved glucose and lipid metabolism disorder parameters in ob/ob mice, alleviating fat accumulation and glucose intolerance. The α diversity of gut microbial community of ob/ob mice was reduced after inulin treatment, while the β diversity tended to return to the level of wild type mice. Interestingly, Prevotellaceae UCG 001 (family Prevotellaceae) was obviously enriched after inulin treatment. A comparative analysis of the gene expression profile showed that the cecal transcriptome was changed in leptin gene deficiency mice, whereas the inulin-supplemented diet partially reversed the changes in leptin gene-related signaling pathways, especially AMPK signaling pathway, where the levels of gene expression became comparable to those in wild type mice. Further analysis indicated that Prevotellaceae UCG 001 was positively correlated with the AMPK signaling pathway, which was negatively correlated with markers of glycolipid metabolism disorders. Our results suggest that the inulin-supplemented diet alleviates glucose and lipid metabolism disorders by partially restoring leptin related pathways mediated by gut microbiota.

Accumulated evidence supports the beneficial role of inulin in alleviating metabolic dysfunction-associated fatty liver disease (MAFLD) by modulating gut microbiota. However, the underlying mechanisms are not fully understood. Here we used high-fat diet (HFD)-induced laying hen model of MAFLD to investigate the effect of inulin on ameliorating MAFLD and found that the inulin-enriched Megamonas genus was inversely correlated with hepatic steatosis-related parameters. Oral administration of a newly isolated commensal bacterium by culturomics, M. funiformis CML154, to HFD-fed hens and mice ameliorated MAFLD, changed liver gene expression profiles, and increased intestinal propionate concentration. Further evidence demonstrated that the anti-MAFLD effect of M. funiformis CML154 is attributed to propionate-mediated activation of the APN-AMPK-PPARα signaling pathway, thereby inhibiting fatty acid de novo synthesis and promoting β-oxidation. These findings establish the causal relationships among inulin, M. funiformis, and MAFLD, and suggest that M. funiformis CML154 is a probiotic candidate for preventative or therapeutic intervention of MAFLD.

Gut microbial reference genomes and gene catalogs are necessary for understanding the chicken gut microbiome. Here, we assembled 12,339 microbial genomes and constructed a gene catalog consisting of ~16.6 million genes by integrating 799 public chicken gut microbiome samples from ten countries. We found that 893 and 38 metagenome-assembled genomes (MAGs) in our dataset were putative novel species and genera, respectively. In the chicken gut,Lactobacillus aviariusandLactobacillus crispatuswere the most common lactic acid bacteria, and glycoside hydrolases were the most abundant carbohydrate-active enzymes (CAZymes). Antibiotic resistome profiling results indicated that Chinese chicken samples harbored a higher relative abundance but less diversity of antimicrobial resistance genes (ARGs) than European samples. We also proposed the effects of geography and host species on the gut resistome. Our study provides the largest integrated metagenomic dataset from the chicken gut to date and demonstrates its value in exploring chicken gut microbial genes.

Revealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high‐throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment‐specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage‐specific microbes in different gut segments, and three microbial occurrence patterns including “colonization,” “disappearance,” and “core” were defined. The microbial co‐occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time‐course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.

Poultry farms and LPMs are a reservoir of antimicrobial resistant bacteria and resistance genes from feces. The LPM is an important interface between humans, farm animals, and environments in a typical urban environment, and it is considered a reservoir for ARGs and viruses. However, the antibiotic resistomes shared between chicken farms and LPMs, and that of LPM workers and people who have no contact with the LPMs remains unknown. We characterized the resistome and bacterial microbiome of farm chickens and LPMs and LPM workers and control subjects. The mobile ARGs identified in chickens and the distribution of the mcr-family genes in publicly bacterial genomes and chicken gut metagenomes was analyzed, respectively. In addition, the prevalence of mcr-1 in LPMs following the ban on colistin-positive additives in China was explored. By profiling the microbiomes and resistomes in chicken farms, LPMs, LPM workers, and LPM environments, we found that the bacterial community composition and resistomes were significantly different between the farms and the LPMs, and the LPM samples possessed more diversified ARGs (59 types) than the farms. Some mobile ARGs, such as mcr-1 and tet(X3), identified in chicken farms, LPMs, LPM workers, and LPM environments were also harbored by human clinical pathogens. Moreover, we found that the resistomes were significantly different between the LPM workers and those who have no contact with the LPMs, and more diversified ARGs (188 types) were observed in the LPM workers. It is also worth noting that mcr-10 was identified in both human (5.2%, 96/1,859) and chicken (1.5%, 14/910) gut microbiomes. Although mcr-1 prevalence decreased significantly in the LPMs across the eight provinces in China, from 190/333 (57.1%) samples in September 2016-March 2017 to 208/544 (38.2%) samples in August 2018-May 2019, it is widespread and continuous in the LPMs. Live poultry trade has a significant effect on the diversity of ARGs in LPM workers, chickens, and environments in China, driven by human selection with the live poultry trade. Our findings highlight the live poultry trade as ARG disseminators into LPMs, which serve as an interface of LPM environments even LPM workers, and that could urge Government to have better control of LPMs in China. Further studies on the factors that promote antibiotic resistance exchange between LPM environments, human commensals, and pathogens, are warranted.