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  • Our study shows that the gut microbiota in both genetic


    Our study shows that the gut microbiota in both genetic and simple obesity share similar structural and functional features of dysbiosis, such as 1) higher production of toxins with known potential to induce metabolic deteriorations such as TMAO and indoxyl sulfate; 2) higher abundance of genomes that encode genes for producing these toxic co-metabolites; 3) higher abundance of pathways for biosynthesis of bacterial src inhibitors such as endotoxin. Our previous study in mice showed that diet is the major force shaping the gut microbiota. High fat diet can override the impact of host genetic mutation to gut microbiota (Zhang et al., 2010). The genetic mutation of PWS patients may have changed their dietary pattern via the hyperphagia. This over-eating behavior similar to SO volunteers may be the reason why these two cohorts share similar gut microbiota. Several structural patterns of the gut microbial community have been suggested to be associated with obesity, such as a high Firmicutes/Bacteroidetes ratio and low gene richness, but the specific relevant members of the gut microbiota and their functional interactions that contribute to obesity development and associated metabolic deteriorations remain elusive (Ley et al., 2005; Le Chatelier et al., 2013). Two or three enterotypes can be delineated from our 16S rRNA gene sequencing or metagenomic data. Short-term 10days intervention did not change host enterotypes (Wu et al., 2011). Our dietary intervention changed the enterotypes of some volunteers but the enterotype delineation and shifts showed no correlation with any of the host phenotype changes. Our volunteers were not clustered into low gene count and high gene count groups. Different from previous report (Cotillard et al., 2013), the diversity and gene richness of their gut microbiota were significantly reduced after the intervention. The pre-intervention gut microbiota had higher diversity of toxin-producing and potentially pathogenic bacteria. The post-intervention gut microbiota were dominated by Bifidobacterium spp. These changes may have contributed to the overall reduction of diversity and gene richness after the intervention. It is thus important to move from general diversity/gene richness to functionally relevant genomes/genes for understanding the contribution of gut microbiota to host health phenotypes. Accumulating evidence shows that important functions of the gut microbiota may be species or even strain-specific, yet many studies in metagenomics are conducted at genus or higher taxonomic levels due to the methodological limitation of assembling individual bacterial genomes directly from metagenomic data (Zhao, 2013). The recently developed “canopy-based” algorithm segregates individual genes into co-abundance gene groups based on the fact that the abundances of two genes contained on the same genomic DNA molecule will highly correlate with each other across complex metagenomic samples (Nielsen et al., 2014). With sufficient sequencing depth, reads in a CAG can be assembled into a high quality draft genome, which allowed us to perform genome-specific analysis of microbiota changes induced by the dietary intervention. Like species in macro-ecosystems such as the rain forest (Ellison et al., 2005), bacterial species in the human gut may also survive, adapt, and decline as interdependent functional groups (guilds) responding to environmental perturbations (Claesson et al., 2012; Ellison et al., 2005; Zhou et al., 2013). Co-abundance analysis can help identify such groups but so far most studies did this analysis at the genus level (Claesson et al., 2012). In our study, all the 161 prevalent bacterial genomes were clustered into 18 Gene Interaction Groups (GIGs). The Commensurate changes of the GIGs with the overall structural changes of the gut microbiota based on 16S rRNA or CAGs data suggest that these GIGs may work as functional groups within the ecosystem. Members of the same GIG can thrive or decline together as a guild but they can come from very different taxonomic background, suggesting that we should study functional interactions of gut bacteria at the individual strain/genome level. Studies on the mechanisms of how bacteria form a GIG would lead to new insights on ecological interactions among prevalent members of the gut microbiota. More importantly, group-level abundance of some GIGs showed positive or negative correlations with host disease/health phenotypes. For example, the Bifidobacterium-dominating GIG3 was negatively associated with 12 disease phenotypes. Their interactions with the host for phenotype development in health and disease warrant further exploration.