Berberine, a major pharmacological component of the Chinese herb Coptis chinensis, which was originally used to treat bacterial diarrhea, has recently been demonstrated to be clinically effective in alleviating type 2 diabetes. In this study, we revealed that berberine effectively prevented the development of obesity and insulin resistance in high-fat diet (HFD)-fed rats, which showed decreased food intake. Increases in the levels of serum lipopolysaccharide-binding protein, monocyte chemoattractant protein-1, and leptin and decrease in the serum level of adiponectin corrected for body fat in HFD-fed rats were also significantly retarded by the co-administration of berberine at 100 mg/kg body weight. Bar-coded pyrosequencing of the V3 region of 16S rRNA genes revealed a significant reduction in the gut microbiota diversity of berberine-treated rats. UniFrac principal coordinates analysis revealed a marked shift of the gut microbiota structure in berberine-treated rats away from that of the controls. Redundancy analysis identified 268 berberine-responding operational taxonomic units (OTUs), most of which were essentially eliminated, whereas a few putative short-chain fatty acid (SCFA)-producing bacteria, including Blautia and Allobaculum, were selectively enriched, along with elevations of fecal SCFA concentrations. Partial least square regression models based on these 268 OTUs were established (Q2>0.6) for predicting the adiposity index, body weight, leptin and adiponectin corrected for body fat, indicating that these discrete phylotypes might have a close association with the host metabolic phenotypes. Taken together, our findings suggest that the prevention of obesity and insulin resistance by berberine in HFD-fed rats is at least partially mediated by structural modulation of the gut microbiota, which may help to alleviate inflammation by reducing the exogenous antigen load in the host and elevating SCFA levels in the intestine.

With the expanding availability of sequencing technologies, research previously centered on the human genome can now afford to include the study of humans’ internal ecosystem (human microbiome). Given the scale of the data involved in this metagenomic research (two orders of magnitude larger than the human genome) and their importance in relation to human health, it is crucial to guarantee (along with the appropriate data collection and taxonomy) proper tools for data analysis. We propose to adapt the approaches defined for the analysis of gene-expression microarray in order to infer information in metagenomics. In particular, we applied SAM, a broadly used tool for the identification of differentially expressed genes among different samples classes, to a reported dataset on a research model with mice of two genotypes (a high density lipoprotein knockout mouse and its wild-type counterpart). The data contain two different diets (high-fat or normal-chow) to ensure the onset of obesity, prodrome of metabolic syndromes (MS). By using 16S rRNA gene as a genomic diversity marker, we illustrate how this approach can identify bacterial populations differentially enriched among different genetic and dietary conditions of the host. This approach faithfully reproduces highly-relevant results from phylogenetic and standard statistical analyses, used to explain the role of the gut microbiome in relation to obesity. This represents a promising proof-of-principle for using functional genomic approaches in the fast growing area of metagenomics, and warrants the availability of a large body of thoroughly tested and theoretically sound methodologies to this exciting new field.