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

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

doi:10.7554/elife.61968

Cited by 55 publications

(61 citation statements)

References 107 publications

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“…Therefore, Prokka v1.14.6, 98 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, 99 for the PathoLogic pipeline of Pathway Tools 23.0. 100 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

et al. 2021

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Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but 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 of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI (≥60, indicates likely liver steatosis) and low FLI (<60) in internal cross-region validation, consisting of 30% of the data not used in model training, with an average AUC of 0.75 and AUPRC of 0.56 (baseline at 0.30). In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Our models were also predictive of the high FLI group in a different Finnish cohort, consisting of 258 participants, with an average AUC of 0.77 and AUPRC of 0.51 (baseline at 0.21). Pathway analysis of representative genomes of the positively FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of, e.g., ethanol fermentation pathways. These results support several findings from smaller case-control studies, such as the role of endogenous ethanol producers in the development of the fatty liver.

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Section: Pathway Inference For Taxa Associated With Flimentioning

confidence: 99%

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

et al. 2021

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“…Another application of metabolic complementarity using different annotation pipeline could also be in the context of metagenome analysis, on Metagenome Assembled Genomes (MAGs) of uncultured bacteria. Belcour et al (2020) have shown that reconstructed metabolic networks from MAGs are similar to the reference genomes. Also, the stability of the producible metabolites and selected minimal symbionts was demonstrated for degraded genomes with 2% of genes randomly removed.…”

Section: Discussionmentioning

confidence: 89%

“…To be able to use this metabolic network of the host for complementarity analyses, the same version of Pathway Tools was also used to reconstruct the bacterial draft metabolic networks. For each annotated bacterial genome (i.e., 5 pipelines × 81 strains), a draft metabolic network was automatically reconstructed using Mpwt, the PathoLogic multiprocessing wrapper Python package ( Belcour et al, 2020 ) taking into account the protein description, GO terms, and complete EC numbers. The output files created by Pathway Tools were then converted to the SBML format with the Padmet software.…”

Section: Methodsmentioning

confidence: 99%

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

Karimi

Geslain

Belcour

et al. 2021

PeerJ

Self Cite

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Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

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“…Metagenomic shotgun sequencing is more expensive but has advantages compared to 16S rRNA gene sequencing 24 : it not only reveals the composition of the gut microbiome but also the functions encoded by the genes in the microbiome 25,26 . Bioinformatic tools that analyze shotgun sequencing data for different purposes-taxonomic classification of microbial composition 27 , gene abundance prediction of specialty genes such as antibiotic resistance 28,29 and virulence factors 29 , genome identification of strain-level or species-level metagenome-assembled genomes (MAGs) 30,31 and metabolic model reconstruction that translate the DNA sequences to biochemical reactions [32][33][34] -are now readily available. Some of these tools even work directly with the accession numbers of the sequencing data deposited in public repositories, which greatly facilitates analysis.…”

Section: Background and Summarymentioning

confidence: 99%

A compilation of fecal microbiome shotgun metagenomics from hospitalized patients undergoing hematopoietic cell transplantation

Yan

Liao

Taylor

et al. 2021

Preprint

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Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study how the human gut microbiome changes in response to perturbations, and how the resulting changes in the microbiome feedback on its living host. We previously compiled a large-scale longitudinal dataset of stool microbiome compositions from these patients and associated metadata1. In that dataset the microbiome analysis was limited to the taxonomic composition of the bacterial population obtained from 16S rRNA gene sequencing. Here, we augment those data with shotgun metagenomic sequences from a nested subset of 395 stool samples. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC2 to be analyzed without the users having to download or transfer the files. We provide examples that show how shotgun sequencing enriches microbiome analyses beyond the taxonomic composition such as the analysis of gene functions including virulence factors and antibiotic resistances, and the assembly of genomes from metagenomic data.

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

Cited by 55 publications

References 107 publications

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

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

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

A compilation of fecal microbiome shotgun metagenomics from hospitalized patients undergoing hematopoietic cell transplantation

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