A community-driven global reconstruction of human metabolism

Thiele, Ines; Swainston, Neil; Fleming, Ronan M. T.; Hoppe, Andreas; Sahoo, Swagatika; Aurich, Maike Kathrin; Haraldsdóttir, Hulda S.; Mo, Monica L.; Rolfsson, Óttar; Stobbe, Miranda D.; Thorleifsson, Stefan Gretar; Ågren, Rasmus; Bölling, Christian; Bordel, Sergio; Chavali, Arvind K.; Dobson, Paul D.; Dunn, Warwick B.; Endler, Lukas; Hala, David; Hucka, Michael; Hull, Duncan; Jameson, Daniel; Jamshidi, Neema; Jónsson, Jón J.; Juty, Nick; Keating, Sarah M.; Nookaew, Intawat; Novère, Nicolas Le; Malys, Naglis; Mazein, Alexander; Papin, Jason A.; Price, Nathan D.; Selkov, Evgeni; Sigurdsson, Martin I.; Simeonidis, Evangelos; Sonnenschein, Nikolaus; Smallbone, Kieran; Sorokin, Anatoly; Beek, Hans van; Weichart, Dieter; Goryanin, Igor; Nielsen, Jens; Westerhoff, Hans V.; Kell, Douglas B.; Mendes, Pedro; Palsson, Bernhard Ø.

doi:10.1038/nbt.2488

Cited by 941 publications

(1,158 citation statements)

References 52 publications

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“…To compare the metabolic functions, we decompartmentalized the human metabolic reactions by placing all metabolites that occur in an organelle (e.g., mitochondria) compartment in the human metabolic reconstruction Recon 2 (ref. 22), into the cytosol, and removed duplicate reactions, resulting in 6,256 unique metabolic reactions. Collectively, the AGORA reconstructions account for 3,192 unique metabolic reactions, 695 of which are shared with Recon 2, including 162 (23%) exchange reactions.…”

Section: Features Of Reconstructionsmentioning

confidence: 99%

“…(a) The refinement pipeline starts with an automatic draft reconstruction generation from the online platforms Model SEED 14 and KBase (Department of Energy Systems Biology Knowledgebase (KBase), http://kbase.us), followed by the translation of reaction and metabolite identifiers to match those of the human metabolic reconstruction, Recon 2.04 (ref. 22). The reconstructions are then tested for QC/QA measures and 162 metabolic functions are curated based on available knowledge and genomic evidence.…”

Section: Metabolic Diversity Of Agora Reconstructionsmentioning

confidence: 99%

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Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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1 r e s o u r c eChanges in the composition of the human gut microbiota have been associated with the development of chronic diseases including type 2 diabetes, obesity, and colorectal cancer 1 . Gut bacterial functions, such as synthesis of amino acids and vitamins 2 , breakdown of indigestible plant polysaccharides 3 , and production of metabolites involved in energy metabolism 4 , have been linked to human health. The use of 'omics approaches to study human microbiome communities has led to the generation of enormous data sets whose interpretations require systems biology tools to shed light on the functional capacity of gut microbiomes and their interactions with the human host 5 .In order to infer the metabolic repertoire of a gut metagenome data set, researchers usually map sequenced genes or organisms onto metabolic networks derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 6 , and functional annotations from KEGG ontologies 7 . However, this approach cannot identify the contribution of each bacterial species to the metabolic repertoire of the whole gut microbiome, nor can it infer the effects of different gut microbial communities on host metabolism.A technique that can bridge this gap is constraint-based reconstruction and analysis (COBRA) 8 using genome-scale metabolic reconstructions (GENREs) of individual human gut microbes. GENREs are assembled using the genome sequence and experimental information 9 . These reconstructions form the basis for the development of condition-specific metabolic models whose functions are simulated and validated by comparison with experimental results. The models can be used to investigate genotype-phenotype relationships 10 , microbe-microbe interactions 11 , and host-microbe interactions 11 . Numerous tools can be used to automatically generate draft GENREs but such models contain errors 12 and are incomplete.Manual curation of draft reconstructions is time consuming because it involves an extensive literature review and experimental validation of metabolic functions 9 .To provide an extensive resource of GENREs for human gut microbes, we developed a comparative metabolic reconstruction method that enables any refinement to one metabolic reconstruction to be propagated to others. This accelerates reconstruction and improves model quality. We generated AGORA, which includes 773 gut microbes, comprising 205 genera and 605 species. All reconstructions were based on literature-derived experimental data and comparative genomics. The metabolic predictions of two AGORA reconstructions and their derived metabolic models were validated against experimental data. RESULTS Metabolic reconstruction pipelineWe devised a comparative metabolic reconstruction method (Fig. 1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http:/...

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Section: Features Of Reconstructionsmentioning

confidence: 99%

Section: Metabolic Diversity Of Agora Reconstructionsmentioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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“…Normally they do not include spectral data, but they are valuable in associating expected metabolites to defined species. Examples include KEGG 12 , BioCyc 13 and Recon 14 , which is a human genome-scale metabolic reconstruction.…”

Section: Identification Of Challengesmentioning

confidence: 99%

The future of metabolomics in ELIXIR

Rijswijk

Beirnaert

Caron³

et al. 2017

F1000Res

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Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.

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“…Genome-scale metabolic networks [40] are used for the stoichiometric modeling of metabolism to infer intracellular metabolic fluxes [41]. These metabolic networks are generated from transcriptomics or proteomics data.…”

Section: Model Validationmentioning

confidence: 99%

Non-targeted Tracer Fate Detection

Weindl¹,

Wegner²,

Hiller³

2015

Methods in Enzymology

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Stable isotopes have been used to trace atoms through metabolism and quantify metabolic fluxes for several decades. Only recently non-targeted stable isotope labeling approaches have emerged as a powerful tool to gain biological insights into metabolism. However, the manual detection of isotopic enrichment for a non-targeted analysis is tedious and time consuming. To overcome this limitation, the non-targeted tracer fate detection (NTFD) algorithm for the automated metabolome-wide detection of isotopic enrichment has been developed. NTFD detects and quantifies isotopic enrichment in the form of mass isotopomer distributions (MIDs) in an automated manner, providing the means to trace functional groups, determine MIDs for metabolic flux analysis, or detect tracer-derived molecules in general. Here, we describe the algorithmic background of NTFD, discuss practical considerations for the freely available NTFD software package, and present potential applications of non-targeted stable isotope labeling analysis.

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A community-driven global reconstruction of human metabolism

Cited by 941 publications

References 52 publications

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

The future of metabolomics in ELIXIR

Non-targeted Tracer Fate Detection

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