The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

Noronha, Alberto; Modamio, Jennifer; Jarosz, Yohan; Guerard, Elisabeth; Sompairac, Nicolas; Preciat, German; Daníelsdóttir, Anna Dröfn; Krecke, Max; Merten, Diane; Haraldsdóttir, Hulda S.; Heinken, Almut; Heirendt, Laurent; Magnúsdóttir, Stefanía; Ravcheev, Dmitry A.; Sahoo, Swagatika; Gawron, Piotr; Friscioni, Lucia; García, B. González; Prendergast, Mabel; Puente, Alberto; Rodrigues, M; Roy, Akansha; Rouquaya, Mouss; Wiltgen, Luca; Žagare, Alise; John, Elisabeth; Krueger, Maren; Kuperstein, Inna; Zinovyev, Andreï; Schneider, Reinhard; Fleming, Ronan M. T.; Thiele, Ines

doi:10.1093/nar/gky992

Cited by 295 publications

(326 citation statements)

References 38 publications

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“…Thus, we were limited to study metabolic effects that are driven by microbiota composition alone and not by additional factors such as diet or total bacterial biomass. To use a moderately realistic set of import constraints for the community models, we modeled all individuals as consuming an average Western diet (32). Import fluxes for external metabolites were based on a reported set of fluxes for an average Western diet (31,33).…”

Section: Resultsmentioning

confidence: 99%

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

2020

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Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.

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Section: Resultsmentioning

confidence: 99%

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

2020

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“…Key metabolic discoveries by the early biochemists are the discovery and characterization of glycolysis by Pasteur, Embden, Meyerhof, and Parnas; the discovery of enzymes; the mapping of metabolic pathways; and discoveries linked to biochemical genetics [1,2]. Our current knowledge of human metabolism is summarized in genome-scale metabolic reconstructions such as the Virtual Metabolic Human database, containing 17730 reactions and 5180 metabolites [3]. Metabolites are the molecules that change or react in metabolic reactions of a living being.…”

Section: Introductionmentioning

confidence: 99%

Metabolite Concentration Changes in Humans After a Bout of Exercise: a Systematic Review of Exercise Metabolomics Studies

et al. 2020

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Background: Exercise changes the concentrations of many metabolites, which are small molecules (< 1.5 kDa) metabolized by the reactions of human metabolism. In recent years, especially mass spectrometry-based metabolomics methods have allowed researchers to measure up to hundreds of metabolites in a single sample in a non-biased fashion. To summarize human exercise metabolomics studies to date, we conducted a systematic review that reports the results of experiments that found metabolite concentrations changes after a bout of human endurance or resistance exercise.Methods: We carried out a systematic review following PRISMA guidelines and searched for human metabolomics studies that report metabolite concentrations before and within 24 h after endurance or resistance exercise in blood, urine, or sweat. We then displayed metabolites that significantly changed their concentration in at least two experiments. Results: Twenty-seven studies and 57 experiments matched our search criteria and were analyzed. Within these studies, 196 metabolites changed their concentration significantly within 24 h after exercise in at least two experiments. Human biofluids contain mainly unphosphorylated metabolites as the phosphorylation of metabolites such as ATP, glycolytic intermediates, or nucleotides traps these metabolites within cells. Lactate, pyruvate, TCA cycle intermediates, fatty acids, acylcarnitines, and ketone bodies all typically increase after exercise, whereas bile acids decrease. In contrast, the concentrations of proteinogenic and non-proteinogenic amino acids change in different directions.Conclusion: Across different exercise modes and in different subjects, exercise often consistently changes the average concentrations of metabolites that belong to energy metabolism and other branches of metabolism. This dataset is a useful resource for those that wish to study human exercise metabolism.

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“…To further facilitate research connecting the microbiome to the human host, the virtual metabolic human database (VMH) was developed [103]. The database used five resources: Human metabolism (Recon3D human metabolic reconstructions) [104], ReconMaps (seven human metabolic maps of six organelles and one accounting for all of them including the reactions occurring in extracellular space and cytosol) [94], Gut microbiome (818 manually curated GENREs of 667 species), Disease (255 mendelian diseases) and Nutrition (food database and diet database).…”

Section: Modeling Methodologiesmentioning

confidence: 99%

Computational Modeling of the Human Microbiome

Chowdhury

Fong

2020

Microorganisms

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The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

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The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

Cited by 295 publications

References 38 publications

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

Metabolite Concentration Changes in Humans After a Bout of Exercise: a Systematic Review of Exercise Metabolomics Studies

Computational Modeling of the Human Microbiome

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