MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

Patumcharoenpol, Preecha; Nakphaichit, Massalin; Panagiotou, Gianni; Senavonge, Anchalee; Suratannon, Narissara; Vongsangnak, Wanwipa

doi:10.1371/journal.pcbi.1008487

Cited by 11 publications

(12 citation statements)

References 45 publications

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“…Additional meal-related factors including macronutrient composition and individual meal-specific responses were also found to be more predictive of PPGR than previously thought with genetic parameters found to be less influential in comparison to meal timing in determining lipidaemic response. In contrast to existing literature, the principal findings of Berry are in disagreement with previous studies [40,67,68], which largely attributes metatranscriptomic activity of the gut microbiome as the key determinant of individual postprandial glycaemic response.…”

Section: Discussioncontrasting

confidence: 93%

The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence

et al. 2021

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Postprandial hyperglycaemia is associated with increased risk of cardiovascular disease. Recent studies highlight the role of the gut microbiome in influencing postprandial glycaemic (PPG) and lipidaemic (PPL) responses. The authors of this review sought to address the question: “To what extent does individual gut microbiome diversity and composition contribute to PPG and PPL responses?”. CINAHL Plus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched from January 2010 to June 2020. Following screening, 22 studies were eligible to be included in the current review. All trials reported analysis of gut microbiome diversity and composition and PPG and/or PPL. Results were reported according to the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analysis’ (PRISMA) statement. Individual microbiota structure was found to play a key role in determining postprandial metabolic responses in adults and is attributed to a complex interplay of diet, microbiota composition, and metagenomic activity, which may be predicted by metagenomic analysis. Alterations of gut microbiota, namely relative abundance of bacterial phylum Actinobacteria and Proteobacteria, along with Enterobacteriaceae, were associated with individual variation in postprandial glycaemic response in adults. The findings of the current review present new evidence to support a personalised approach to nutritional recommendations and guidance for optimal health, management, and treatment of common metabolic disorders. In conclusion, personalised nutrition approaches based on individual microbial composition may improve postprandial regulation of glucose and lipids, providing a potential strategy to ameliorate cardiometabolic health outcomes.

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Section: Discussioncontrasting

confidence: 93%

The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence

et al. 2021

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“…On the other hand, MetGEM showed the lowest precision. One reason might be that the AGORA collections used as reference contain 818 genome-scale models from the human gut microbiome, which covered only 1470 KO terms, 983 EC numbers across 226 genera, and 690 species in total [32,34].…”

Section: Discussionmentioning

confidence: 99%

“…To overcome the lack of functional information in 16S rRNA gene profiles, tools such as Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) [27], Tax4Fun2 [28], Pangenome-based Functional Profiles (PanFP) [29], Piphilin [30], COWPI [31] and metagenome-scale models (MetGEM) [32] attempt to predict abundances of functional genes based on recorded genomic information available in the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database [33] or based on genomic models [34][35][36]. PICRUSt2 is a widely used prediction tool employing a hidden state prediction algorithm to infer functions from 16S rRNA gene phylotypes [27].…”

Section: Introductionmentioning

confidence: 99%

“…Sun et al [ 46 ] could not detect any performance differences between the tested methods, suggesting that a more comprehensive benchmark is needed to recommend tool selection guidelines and establish the limits of metagenome inference tools in human disease research. Hence, we consider the most widely used metagenome inference tools PICRUSt2 [ 27 ], PanFP [ 29 ], Tax4Fun2 [ 28 ], and MetGEM [ 32 ] in a systematic benchmark. We omit Piphillin [ 30 ] as it is only available as a web server (the command line version is in the testing stage).…”

Section: Introductionmentioning

confidence: 99%

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On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Matchado,

Rühlemann,

Reitmeier

et al. 2024

Microbial Genomics

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Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

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“…Metagenome inference was performed from bacterial ASVs frequency table with MetGEMs toolbox (Patumcharoenpol et al, 2021) using default parameters (https://github.com/yumyai/MetGEMs) and AGORA collection as reference database of genome-scale models (Magnúsdóttir et al, 2017). Gene family abundances were predicted and identified as KEGG orthologs (KO) and collapsed at level 3 of the KEGG annotations.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Metataxonomic signature of beef burger perishability depends on the meat origin prior grinding

Botta

Franciosa

Alessandria

et al. 2022

Food Research International

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MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

Cited by 11 publications

References 45 publications

The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence

The Impact of Microbial Composition on Postprandial Glycaemia and Lipidaemia: A Systematic Review of Current Evidence

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Metataxonomic signature of beef burger perishability depends on the meat origin prior grinding

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