What is the primary purpose and rationale of this study? Type 2 diabetes mellites (T2DM) is a health burden with a rise in epidemic prevalence worldwide1. T2D is characterized by increased blood glucose levels (hyperglycemia)2. Metformin is the most-prescribed medication to treat patients with T2DM due to its glucose-lowering effects3. Metformin propagates insulin sensitivity by mainly reducing hepatic glucose production (through an activation of the hepatic AMP-activated protein kinase protein)4. The most common side effect of metformin is gastrointestinal discomfort, including diarrhea, nausea, flatulence and bloating5. There is growing evidence in animal and human studies suggesting that the gut microbiome is another target involved in the anti-diabetic effects of metformin6,7,8,9. Recent investigations documented the therapeutic benefit of orally-administrated metformin compared to intravenously-administrated metformin in T2D patients, suggesting the beneficial contribution of the gut microbiota10. Metformin alters the gut microbiome by enhancing Escherichia sp, Akkermansia muciniphila and Subdoligranuum variable; reducing Intestinibacter bartletti and increasing the levels of short-chain fatty acids such as butyrate and propionate7,8. This could indicate the anti-obesity property of metformin by modulating the gut microbiome and its metabolites. However, the precise mechanisms are unclear.

Understanding the role of bacterial-derived gut metabolites can provide a platform to elucidate interactions between microbe-microbe, microbe-diet and drugs. The gut microbiota is an attractive target for therapeutic intervention and using nutrition may help to promote drug efficiency and reduce gastrointestinal side effects11. To elucidate these interactions individual and systems-level analysis is needed12,13,14. Hence, systems biology approaches could be applied to reveal these associations between the abundances of different microbes and the molecular mechanisms underlying metformin treatment on a metabolic level15. Genome-scale metabolic models (GEMS) have been used to gain a detailed understanding of microbial metabolic changes in various environments. Previous studies used GEMs to understand the metabolic interactions between microbes and host- microbes16,17,18.

Wu et al. 2017 collected fecal samples from treatment naïve individuals that received 1,700 mg/d metformin treatment (22 patients) for 4 months and generated shotgun metagenomics data to determine the species abundances7. In the present study we re-analyzed this metagenomics data with an updated gut microbial gene catalog and metagenome species profile19. We carried out further analysis by investigating carbohydrate-active enzymes of the significantly altered species. This analysis showed that species that are decreased after 4 months of metformin treatment have an increased number of annotated mucins and host glycan degradation in comparison to the significantly increased species. In addition, we performed constraint-based analysis using GEMS integrating different diets to predict the phenotype of the drug metformin on the human gut microbiota. These diet-microbiota interactions can help us understand how to increase drug efficiency or mimic drug effects in the gut microbiome of patients with a dysbiosis to an improved phenotype.