Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment

Ezzamouri, Bouchra; Rosário, Dorinês; Bidkhori, Gholamreza; Lee, Sunjae; Uhlén, Mathias; Shoaie, Saeed

doi:10.1038/s41540-022-00261-6

Cited by 16 publications

(9 citation statements)

References 62 publications

(75 reference statements)

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“…For example, Ezzamouri and collaborators used metagenomics data from a metformin study with genome-scale metabolic modelling of the key bacteria (e.g. Akkermansia municiphila, Intestinibacter bartlettii, Clostridium saudiense, Romboutsia timonensis ) to research the mechanistic role of the GM in response to metformin in a Spanish T2D cohort ( 24 ). However, the GM is a complex ecological system that involves interactions between hundreds of bacterial species.…”

Section: Introductionmentioning

confidence: 99%

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

et al. 2023

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IntroductionThe human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.MethodsHere, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).ResultsBy exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.DiscussionBased on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

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Section: Introductionmentioning

confidence: 99%

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

et al. 2023

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“…Figure 4 displays the top 10 results from the comparison between T2D and HSS. The most notable findings are functions related to glycan degradation (map00511) and sphingolipids (map00600), both of which have substantial supporting evidence for their involvement in disease status or development [11, 71, 79, 31, 76, 36, 1, 75, 26]. Besides, we further examined the results regarding Carbohydrate metabolism, a well-established association in the context of type 2 diabetes [67].…”

Section: Resultsmentioning

confidence: 99%

Fast, lightweight, and accurate metagenomic functional profiling using FracMinHash sketches

Rahman Hera,

Liu,

Wei

et al. 2023

Preprint

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MotivationFunctional profiling of metagenomic samples is essential to decipher the functional capabilities of these microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general,k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented insourmash, a publicly available software), ak-mer-sketching algorithm, to obtain functional profiles of metagenome samples. We show how pieces of thesourmashsoftware (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample.ResultsWe report that the functional profiles obtained using this pipeline demonstrate superior completeness and purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. At the same time, we also report that our functional profiling pipeline is 42-51x faster in CPU time, 10-15.8x faster in running time, and consumes up to 20% less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets.ReproducibilityThis fast and lightweight metagenomic functional profiler is freely available and can be accessed here:https://github.com/KoslickiLab/funprofiler. All scripts of the analyses we present in this manuscript can be found onGitHub.

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“…Specifically, a shared phenomenon emerged across these disorders – a heightened propensity for glutamate consumption, coupled with the concomitant production of ammonia, arginine, and proline by gut bacteria (Proffitt et al 2022). Another instance highlighting the toolbox’s prowess emerged from the work of Ezzamouri et al (Ezzamouri et al 2023). This study successfully established a compelling association between the human gut microbiome and type 2 diabetes mellitus.…”

Section: Resultsmentioning

confidence: 99%

MIGRENE: The toolbox for microbial and individualized GEMs, reactobiome and community network modelling

Bidkhori,

Shoaie

2023

Preprint

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Motivation: Understanding microbial metabolism is crucial for evaluating shifts in human host-microbiome interactions during health and disease. However, the primary hurdle in the realm of constraint-based modelling and genome-scale metabolic models (GEMs) pertaining to host-microbiome interactions lies in the efficient utilization of metagenomic data for constructing GEMs that encompass unexplored and uncharacterized genomes. Challenges persist in effectively employing metagenomic data to address individualized microbial metabolism to investigate host-microbiome interactions. Result: To tackle this issue, we have created a computational framework designed for personalized microbiome metabolism. This framework takes into account factors such as microbiome composition, metagenomic species profiles and microbial gene catalogues. Subsequently, it generates GEMs at microbial level and individualized microbiome metabolism, including reaction richness, reaction abundance, reactobiome, individualized reaction set enrichment (iRSE), and community models. Availability and implementation: The MIGRENE toolbox is freely available at our GitHub repository https://github.com/sysbiomelab/MIGRENE. Comprehensive tutorials on the GitHub were provided for each step of the workflow.

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Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment

Cited by 16 publications

References 62 publications

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

Fast, lightweight, and accurate metagenomic functional profiling using FracMinHash sketches

MIGRENE: The toolbox for microbial and individualized GEMs, reactobiome and community network modelling

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