The MetaCyc database of metabolic pathways and enzymes

Caspi, Ron; Billington, Richard; Fulcher, Carol A.; Keseler, Ingrid M.; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Midford, Peter E.; Ong, Quang; Ong, Wai Kit; Paley, Suzanne M.; Subhraveti, Pallavi; Karp, Peter D.

doi:10.1093/nar/gkx935

Cited by 745 publications

(586 citation statements)

References 16 publications

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“…The glucose dehydrogenase reaction (GLCDpp) was switched off as it is only active if pyrroloquinoline quinone (PQQ) is supplied to the system (see EcoCyc, Keseler et al, , entry on this enzyme). The D ‐glucose (galactose):proton symport reaction (GLCt2pp) was also considered to be off as it is functional when 2‐deoxy‐ D ‐galactose is the specific substrate (see MetaCyc, Caspi et al, , entry on this enzyme). Reactions describing the synthesis of two recombinant proteins, GFP and GI‐malE fusion protein were added to the original stoichiometry model.…”

Section: Methodsmentioning

confidence: 99%

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Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

Zeng

Yang

2019

Biotech & Bioengineering

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Escherichia coli has been the host organism most frequently investigated for efficient recombinant protein production. However, the production of a foreign protein in recombinant E. coli often leads to growth deterioration and elevated secretion of acetic acid. Such observed phenomena have been widely linked with cell stress responses and metabolic burdens originated particularly from the increased energy demand. In this study, flux balance analysis and dynamic flux balance analysis were applied to investigate the observed growth physiology of recombinant E. coli, incorporating the proteome allocation theory and an adjustable maintenance energy level (ATPM) to capture the proteomic and energetic burdens introduced by recombinant protein synthesis. Model predictions of biomass growth, substrate consumption, acetate excretion, and protein production with two different strains were in good agreement with the experimental data, indicating that the constraint on the available proteomic resource and the change in ATPM might be important contributors governing the growth physiology of recombinant strains. The modeling framework developed in this work, currently with several limitations to overcome, offers a starting point for the development of a practical, model-based tool to guide metabolic engineering decisions for boosting recombinant protein production.

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Section: Methodsmentioning

confidence: 99%

“…The D-glucose (galactose):proton symport reaction (GLCt2pp) was also considered to be off as it is functional when 2-deoxy-D-galactose is the specific substrate (see MetaCyc,Caspi et al, 2018, entry on this enzyme). Reactions describing the synthesis of two recombinant proteins, GFP and GI-malE fusion protein were added to the original stoichiometry model.…”

mentioning

confidence: 99%

Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

Zeng

Yang

2019

Biotech & Bioengineering

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“…Here we chose the Pathway Tools suite and its PathoLogic software [27] for GSMN reconstruction. It is based on the MetaCyc database [7] that covers a large taxonomic range of organisms and groups reactions into pathways that are convenient for a higher-level analysis of metabolism. Pathway Tools is distributed with a GUI that is very user-friendly for manual curation and analysis of a small number of GSMNs, but is limiting when aiming at reconstructing a large number of them.…”

Section: Discussionmentioning

confidence: 99%

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

Belcour

Frioux

Aïte

et al. 2019

Preprint

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Capturing the functional diversity of microbiotas entails identifying metabolic functions and species of interest within hundreds or thousands.Starting from genomes, a way to functionally analyse genetic information is to build metabolic networks. Yet, no method enables a functional screening of such a large number of metabolic networks nor the identification of critical species with respect to metabolic cooperation. Metage2Metabo (M2M) addresses scalability issues raised by metagenomics datasets to identify keystone, essential and alternative symbionts in large microbiotas communities with respect to individual metabolism and collective metabolic complementarity. Genome-scale metabolic networks for the community can be either provided by the user or very effi-1 ciently reconstructed from a large family of genomes thanks to a multiprocessing solution to run the Pathway Tools software. The pipeline was applied to 1,520 genomes from the gut microbiota and 913 metagenomeassembled genomes of the rumen microbiota. Reconstruction of metabolic networks and subsequent metabolic analyses were performed in a reasonable time.M2M identifies keystone, essential and alternative organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

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“…Pathways are considered as a list of reactions with enzyme names and EC numbers. Pathway definition were obtained from MetaCyc [13], KEGG [39], and ModelSEED [34]. For MetaCyc, PathwayTools [40] was used in combination with PythonCyc to obtain pathway definitions [78] (src/meta2pwy.py).…”

Section: Pathway and Sequence Databasesmentioning

confidence: 99%

“…Inconsistencies were identified by optimising the universal network model for ATP-production without any nutritional input to the model using flux balance analysis. In case of ATP-production, the flux distributions of such thermodynamically infeasible reaction cycles were investigated by cross-checking the involved reactions with literature information, the BRENDA database for enzymes [37] and the MetaCyc database [13]. Stochiometries and reversibilities of erroneous reactions were corrected accordingly.…”

Section: Biochemistry Database Curation and Construction Of Universalmentioning

confidence: 99%

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Zimmermann

Kaleta

Waschina

2020

Preprint

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Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of prior biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the statistical prediction of an organism's metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genomescale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 1, 000 bacterial organisms comprising over 10, 000 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 80% and thereby outperforming other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq (https://github.com/jotech/gapseq) is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research.

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The MetaCyc database of metabolic pathways and enzymes

Cited by 745 publications

References 16 publications

Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

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

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

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