Recent findings have implicated the gut microbiota as a contributor of metabolic diseases through the modulation of host metabolism and inflammation. Atherosclerosis is associated with lipid accumulation and inflammation in the arterial wall, and bacteria have been suggested as a causative agent of this disease. Here we use shotgun sequencing of the gut metagenome to demonstrate that the genus Collinsella was enriched in patients with symptomatic atherosclerosis, defined as stenotic atherosclerotic plaques in the carotid artery leading to cerebrovascular events, whereas Roseburia and Eubacterium were enriched in healthy controls. Further characterization of the functional capacity of the metagenomes revealed that patient gut metagenomes were enriched in genes encoding peptidoglycan synthesis and depleted in phytoene dehydrogenase; patients also had reduced serum levels of β-carotene. Our findings suggest that the gut metagenome is associated with the inflammatory status of the host and patients with symptomatic atherosclerosis harbor characteristic changes in the gut metagenome.

Bariatric surgery is currently the most effective procedure for the treatment of obesity. Given the role of the gut microbiota in regulating host metabolism and adiposity, we investigated the long-term effects of bariatric surgery on the microbiome of patients randomized to Roux-en-Y gastric bypass or vertical banded gastroplasty and matched for weight and fat mass loss. The two surgical procedures induced similar and durable changes on the gut microbiome that were not dependent on body mass index and resulted in altered levels of fecal and circulating metabolites compared with obese controls. By colonizing germ-free mice with stools from the patients, we demonstrated that the surgically altered microbiota promoted reduced fat deposition in recipient mice. These mice also had a lower respiratory quotient, indicating decreased utilization of carbohydrates as fuel. Our results suggest that the gut microbiota may play a direct role in the reduction of adiposity observed after bariatric surgery.

Recent findings have demonstrated that the gut microbiome complements our human genome with at least 100-fold more genes. In contrast to our Homo sapiens–derived genes, the microbiome is much more plastic, and its composition changes with age and diet, among other factors. An altered gut microbiota has been associated with several diseases, including obesity and diabetes, but the mechanisms involved remain elusive. Here we discuss factors that affect the gut microbiome, how the gut microbiome may contribute to metabolic diseases, and how to study the gut microbiome. Next-generation sequencing and development of software packages have led to the development of large-scale sequencing efforts to catalog the human microbiome. Furthermore, the use of genetically engineered gnotobiotic mouse models may increase our understanding of mechanisms by which the gut microbiome modulates host metabolism. A combination of classical microbiology, sequencing, and animal experiments may provide further insights into how the gut microbiota affect host metabolism and physiology.

The human gut microbiome plays an influential role in maintaining human health and it is a potential target for prevention and treatment of disease. Genome-scale metabolic models (GEMs) can provide an increased understanding of the mechanisms behind the effects of diet, the genotype-phenotype relationship and microbial robustness. Here we reconstructed GEMs for three key species, (Bacteroidesthetaiotamicron, Eubacteriumrectale and Methanobrevibactersmithii) as relevant representatives of three main phyla in the human gut (Bacteroidetes, Firmicutes and Euryarchaeota). We simulated the interactions between these three bacteria in different combinations of gut ecosystems and compared the predictions with the experimental results obtained from colonization of germ free mice. Furthermore, we used our GEMs for analyzing the contribution of each species to the overall metabolism of the gut microbiota based on transcriptome data and demonstrated that these models can be used as a scaffold for understanding bacterial interactions in the gut.