The majority of cohort-specific studies associating gut microbiota with obesity are often contradictory; thus, the replicability of the signature remains questionable. Moreover, the species that drive obesity-associated functional shifts and their replicability remain unexplored. Thus, we aimed to address these questions by analyzing gut microbial metagenome sequencing data to develop an in-depth understanding of obese host-gut microbiota interactions using 3329 samples (Obese, n = 1494; Control, n = 1835) from 17 different countries, including both 16S rRNA gene and metagenomic sequence data. Fecal metagenomic data from diverse geographical locations were curated, profiled, and pooled using a machine learning-based approach to identify robust global signatures of obesity. Furthermore, gut microbial species and pathways were systematically integrated through the genomic content of the species to identify contributors to obesity-associated functional shifts. The community structure of the obese gut microbiome was evaluated, and a reproducible depletion of diversity was observed in the obese compared to the lean gut. From this, we infer that the loss of diversity in the obese gut is responsible for perturbations in the healthy microbial functional repertoire. We identified 25 highly predictive species and 37 pathway associations as signatures of obesity, which were validated with remarkably high accuracy (AUC, Species: 0.85, and pathway: 0.80) with an independent validation dataset. We observed a reduction in short-chain fatty acid (SCFA) producers (several Alistipes species, Odoribacter splanchnicus, etc.) and depletion of promoters of gut barrier integrity (Akkermansia muciniphila and Bifidobacterium longum) in obese guts. Our analysis underlines SCFAs and purine/pyrimidine biosynthesis, carbohydrate metabolism pathways in control individuals, and amino acid, enzyme cofactor, and peptidoglycan biosynthesis pathway enrichment in obese individuals. We also mapped the contributors to important obesity-associated functional shifts and observed that these are both dataset-specific and shared across the datasets. In summary, a comprehensive analysis of diverse datasets unveils species specifically contributing to functional shifts and consistent gut microbial patterns associated to obesity.

This review highlights recent advances made using small molecules that promote changes in the fate of cells.

Diabetes is a concerning health malady worldwide. Islet or pancreas transplantation is the only long-term treatment available; however, the scarcity of transplantable tissues hampers this approach. Therefore, new cell sources and differentiation approaches are required. Apart from the genetic- and small molecule-based approaches, exosomes could induce cellular differentiation by means of their cargo, including miRNA. We developed a chemical-based protocol to differentiate mouse embryonic fibroblasts (MEFs) into β-like cells and employed mouse insulinoma (MIN6)-derived exosomes in the presence or absence of specific small molecules to encourage their differentiation into β-like cells. The differentiated β-like cells were functional and expressed pancreatic genes such as Pdx1, Nkx6.1, and insulin 1 and 2. We found that the exosome plus small molecule combination differentiated the MEFs most efficiently. Using miRNA-sequencing, we identified miR-127 and miR-709, and found that individually and in combination, the miRNAs differentiated MEFs into β-like cells similar to the exosome treatment. We also confirmed that exocrine cells can be differentiated into β-like cells by exosomes and the exosome-identified miRNAs. A new differentiation approach based on the use of exosome-identified miRNAs could help people afflicted with diabetes

Summary: Cohort-specific 16S rRNA sequence-based studies associating gut microbiota with obesity are often marred with contradictory findings regarding community structure and composition leading to “reproducibility crisis” of the signals. Moreover, taxonomic drivers of the obesity-linked gut microbial functional imbalances and their replicability also remains unexplored which should be useful for in-depth understanding of obese host-gut microbiota interaction and, strategizing therapeutics. We addressed these questions through unbiased meta-analysis and further machine-learning validation of 692 curated fecal whole metagenomic sequence datasets from diverse geographical locations. Further, obesity-linked pathway shifts were traced back to their specific drivers by integrating the species and pathway profiles through genomic content of the species. We found reproducible depletion of diversity in obese gut microbiome without any pattern in Firmicutes/Bacteroidetes ratio. Additionally, we also identified obesity-linked robust and reproducible gut microbial species and pathway features. Contributors of these pathway features identified as both dataset-specific and shared across the datasets.