Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

Belcour, Arnaud; Frioux, Clémence; Aïte, Méziane; Bretaudeau, Anthony; Siegel, Anne

doi:10.1101/803056

Cited by 4 publications

(7 citation statements)

References 58 publications

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“…The reconstruction of genome-scale metabolic networks and models (GSMNs) using Metagenome-Assembled Genomes (MAGs) or the inference of functional categories to single genes allows to precisely predict the metabolic capabilities of the gut microbiome ( Frioux et al, 2020 ). A good example of such tools is the Metage2Metabo algorithm developed by Belcour et al (2019) that enables the analysis of metabolic pathways at the ecosystem level using both reference genomes and MAGs. As keystone species carry essential metabolic functions in the gut ecosystem, this tool was used to detect putative keystone bacteria.…”

Section: Bioinformatic Tools To Identify Keystone Species and Functions Of The Gut Microbiomementioning

confidence: 99%

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

Tudela

Claus²,

Saleh

2021

Front. Cell Dev. Biol.

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The community of the diverse microorganisms residing in the gastrointestinal tract, known as the gut microbiota, is exceedingly being studied for its impact on health and disease. This community plays a major role in nutrient metabolism, maintenance of the intestinal epithelial barrier but also in local and systemic immunomodulation. A dysbiosis of the gut microbiota, characterized by an unbalanced microbial ecology, often leads to a loss of essential functions that may be associated with proinflammatory conditions. Specifically, some key microbes that are depleted in dysbiotic ecosystems, called keystone species, carry unique functions that are essential for the balance of the microbiota. In this review, we discuss current understanding of reported keystone species and their proposed functions in health. We also elaborate on current and future bioinformatics tools needed to identify missing functions in the gut carried by keystone species. We propose that the identification of such keystone species functions is a major step for the understanding of microbiome dynamics in disease and toward the development of microbiome-based therapeutics.

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Section: Bioinformatic Tools To Identify Keystone Species and Functions Of The Gut Microbiomementioning

confidence: 99%

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

Tudela

Claus²,

Saleh

2021

Front. Cell Dev. Biol.

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“…Therefore, Prokka v1.14.6, 73 was used to annotate the 119 unannotated genomes as a preliminary step. Pathway predictions were then performed for all 336 genomes with mpwt v0.5.3 multiprocessing tool, 74 for the PathoLogic pipeline of Pathway Tools 23.0. 75 Pathways for ethanol and short chain fatty acid (acetate, butyrate, propionate) production, bile acid metabolism, and choline degradation to trimethylamine were identified from MetaCyc pathway classifications (see ref.…”

Section: Pathway Inference For Taxa Associated With Flimentioning

confidence: 99%

Links between gut microbiome composition and fatty liver disease in a large population sample

Ruuskanen

Åberg

Männistö

et al. 2020

Preprint

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Background. Fatty liver disease is the most common liver disease in the world. It is characterized by a build-up of excess fat in the liver that can lead to cirrhosis and liver failure. The link between fatty liver disease and gut microbiome has been known for at least 80 years. However, this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample in Finland. We based our models on biometric covariates and phylogenetically transformed gut microbiome compositions from shallow metagenome sequencing. Results. Our classification models were able to discriminate between individuals with a high FLI (≥ 60, indicates likely liver steatosis) and low FLI (< 60) in our validation set, consisting of 30% of the data not used in model training, with an average AUC of 0.75. In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Pathway analysis of representative genomes of the FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of e.g., ethanol fermentation pathways. Conclusions. Through modeling the fatty liver index, our results provide with high resolution associations between gut microbiota composition and fatty liver in a large representative population cohort. Our results lend further support to the role of endogenous ethanol producers in the development of fatty liver.

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“…It can be used on a workstation or on a cluster using Docker or Singularity. M2M’s source code is available on github.com/AuReMe/metage2metabo , ( Belcour and Frioux, 2020 ; copy archived at swh:1:rev:2cab4c79acd814eb177a370602c07599a93bc947 ) and the package is available though the Python Package Index at pypi.org/project/Metage2Metabo/ . A detailed documentation is available on metage2metabo.readthedocs.io .…”

Section: Methodsmentioning

confidence: 99%

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species

Belcour

Frioux

Aïte

et al. 2020

eLife

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To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

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Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

Cited by 4 publications

References 58 publications

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

Links between gut microbiome composition and fatty liver disease in a large population sample

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species

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