The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Seaver, Samuel M. D.; Liu, Filipe; Zhang, Qizhi; Jeffryes, James G.; Faria, José P.; Edirisinghe, Janaka N.; Mundy, Michael B.; Chia, Nicholas; Noor, Elad; Beber, Moritz Emanuel; Best, Aaron A.; DeJongh, Matthew; Kimbrel, Jeffrey A.; D’haeseleer, Patrik; McCorkle, S.; Bolton, Jay R.; Pearson, Erik; Canon, Shane; Wood‐Charlson, Elisha M.; Cottingham, Robert W.; Arkin, Adam P.; Henry, Christopher S.

doi:10.1093/nar/gkaa1143

Cited by 42 publications

(52 citation statements)

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“…Moreover, genome-scale metabolic models (GEMs) of gut microbes have provided testable hypotheses on diet-microbe-host axis interactions in healthy vs. disease states [ 109 , 113 , 114 , 115 ]. Several tools such as Kbase [ 116 ], ModelSEED [ 117 ], COnstraint-Based Reconstruction and Analysis (COBRA) [ 118 ] and RAVEN (Reconstruction, Analysis, and Visualization of Metabolic Networks) [ 119 ], have enabled and/or aided in the reconstruction of microbial-GEMs from available genomic and metagenomic data. A detailed overview of GSMM, as applied to human gut microbiota, is reviewed elsewhere [ 109 , 110 ].…”

Section: Functional Profiling and Metabolic Modeling Of Human Gut mentioning

confidence: 99%

Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations

et al. 2021

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Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.

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Section: Functional Profiling and Metabolic Modeling Of Human Gut mentioning

confidence: 99%

Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations

et al. 2021

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“…M2M answers to the upscaling limitation of individual GSMN reconstruction with Pathway Tools by automating this task using the Mpwt wrapper. GSMNs in SBML format obtained from other platforms such as Kbase ( Arkin et al, 2018 ), ModelSEED ( Henry et al, 2010 ; Seaver et al, 2020 ), or CarveMe ( Machado et al, 2018 ), can also be used as inputs to M2M for all metabolic analyses. For instance, we used highly curated models from AGORA in the application of M2M to metagenomic datasets.…”

Section: Discussionmentioning

confidence: 99%

“… Thiele and Palsson, 2010 defined a precise protocol for their reconstruction, associating the use of automatic methods and thorough curation based on expertise, literature, and mathematical analyses. There now exists a variety of GSMN reconstruction implementations: all-in-one platforms such as Pathway Tools ( Karp et al, 2016 ), CarveMe ( Machado et al, 2018 ) or KBase that provides narratives from metagenomic datasets analysis up to GSMN reconstruction with ModelSEED ( Henry et al, 2010 ; Seaver et al, 2020 ). In addition, a variety of toolboxes ( Aite et al, 2018 ; Wang et al, 2018 ; Schellenberger et al, 2011 ), or individual tools perform targeted refinements and analyses on GSMNs ( Prigent et al, 2017 ; Thiele et al, 2014 ; Vitkin and Shlomi, 2012 ).…”

Section: Introductionmentioning

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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“…Distinct portals with their own URLs support communities focused on topics as diverse as COVID-19, rare diseases and lipid metabolism. The popular ModelSEED Biochemistry database ( 38 ), publishing here for the first time, reports a doubling of content since its first iteration. Quantitative pathway modelling requires easy access to relevant data.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2021 Nucleic Acids Research database issue and the online molecular biology database collection

Rigden

Fernández

2020

Nucleic Acids Research

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The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.

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The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Cited by 42 publications

References 0 publications

Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations

Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations

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

The 2021 Nucleic Acids Research database issue and the online molecular biology database collection

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