Scalable metabolic pathway analysis

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

[1]  F. Turroni,et al.  Unveiling the gut microbiota composition and functionality associated with constipation through metagenomic analyses , 2017, Scientific Reports.

[2]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

[3]  Karsten Zengler,et al.  Modelling approaches for studying the microbiome , 2019, Nature Microbiology.

[4]  B. Palsson,et al.  Elimination of thermodynamically infeasible loops in steady-state metabolic models. , 2011, Biophysical journal.

[5]  B. Palsson,et al.  Expanded Metabolic Reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an In Silico Genome-Scale Characterization of Single- and Double-Deletion Mutants , 2005, Journal of bacteriology.

[6]  Thomas Bernard,et al.  MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks , 2013, Bioinform..

[7]  A. Burgard,et al.  Minimal Reaction Sets for Escherichia coli Metabolism under Different Growth Requirements and Uptake Environments , 2001, Biotechnology progress.

[8]  Jens Nielsen,et al.  The gut microbiota modulates host amino acid and glutathione metabolism in mice , 2015 .

[9]  C. Jungreuthmayer,et al.  Toward Genome‐Scale Metabolic Pathway Analysis , 2016 .

[10]  Eugénio C. Ferreira,et al.  Random sampling of elementary flux modes in large-scale metabolic networks , 2012, Bioinform..

[11]  Ronan M. T. Fleming,et al.  Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota , 2016, Nature Biotechnology.

[12]  T. Preston,et al.  Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism , 2016, Gut microbes.

[13]  Tom M. Conrad,et al.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models , 2010, Molecular systems biology.

[14]  Philip Miller,et al.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models , 2015, Nucleic Acids Res..

[15]  Lixin Zhu,et al.  Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: A connection between endogenous alcohol and NASH , 2013, Hepatology.

[16]  Angel Rubio,et al.  Computing the shortest elementary flux modes in genome-scale metabolic networks , 2009, Bioinform..

[17]  Avlant Nilsson,et al.  Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism , 2018, Nature Biotechnology.

[18]  S. Schuster,et al.  Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. , 2009, Genome research.

[20]  Iain G. Johnston,et al.  The Essential Genome of Escherichia coli K-12 , 2017, mBio.

[21]  Adam M. Feist,et al.  The biomass objective function. , 2010, Current opinion in microbiology.

[22]  Doraiswami Ramkrishna,et al.  Sequential computation of elementary modes and minimal cut sets in genome‐scale metabolic networks using alternate integer linear programming , 2017, Bioinform..

[23]  Jens Nielsen,et al.  Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling. , 2018, Metabolic engineering.

[24]  J. Stelling,et al.  Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis , 2019, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Leen Stougie,et al.  Enumeration of minimal stoichiometric precursor sets in metabolic networks , 2016, Algorithms for Molecular Biology.

[26]  M. Sabater,et al.  Elevated circulating levels of succinate in human obesity are linked to specific gut microbiota , 2018, The ISME Journal.

[27]  Joshua A. Lerman,et al.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python , 2013, BMC Systems Biology.

[28]  Daniel C. Zielinski,et al.  A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism. , 2016, Cell systems.

[29]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.

[30]  J. Stelling,et al.  Microbial network disturbances in relapsing refractory Crohn’s disease , 2019, Nature Medicine.

[31]  Leen Stougie,et al.  Modes and cuts in metabolic networks: Complexity and algorithms , 2009, Biosyst..

[32]  Piotr Gawron,et al.  The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease , 2018, bioRxiv.

[33]  Jan Schellenberger,et al.  Use of Randomized Sampling for Analysis of Metabolic Networks* , 2009, Journal of Biological Chemistry.

[34]  Kristopher A. Hunt,et al.  Complete enumeration of elementary flux modes through scalable demand-based subnetwork definition , 2014, Bioinform..

[35]  Bernhard O. Palsson,et al.  Connecting Extracellular Metabolomic Measurements to Intracellular Flux States in Yeast , 2022 .

[36]  Francisco J. Planes,et al.  Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks , 2014, Bioinform..

[37]  S. Klamt,et al.  EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model , 2017, Scientific Reports.

[38]  Leen Stougie,et al.  Algorithms and complexity of enumerating minimal precursor sets in genome-wide metabolic networks , 2012, Bioinform..

[39]  Steffen Klamt,et al.  Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks , 2014, PLoS Comput. Biol..

[40]  Ines Thiele,et al.  Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework , 2015, Gut microbes.

[41]  Adam M. Feist,et al.  iML1515, a knowledgebase that computes Escherichia coli traits , 2017, Nature Biotechnology.

[42]  J. Stelling,et al.  Combinatorial Complexity of Pathway Analysis in Metabolic Networks , 2004, Molecular Biology Reports.

[43]  V. de Crécy-Lagard,et al.  Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes , 2015, Front. Genet..

[44]  R. Urbanczik.  Enumerating constrained elementary flux vectors of metabolic networks. , 2007, IET systems biology.

[45]  Jörg Stelling,et al.  Large-scale computation of elementary flux modes with bit pattern trees , 2008, Bioinform..

[46]  Xiaomin Su,et al.  Lactobacillus maintains healthy gut mucosa by producing L-Ornithine , 2019, Communications Biology.

[47]  U. Sauer,et al.  A Novel Metabolic Cycle Catalyzes Glucose Oxidation and Anaplerosis in Hungry Escherichia coli* , 2003, Journal of Biological Chemistry.

[48]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[49]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[50]  S. Peterson,et al.  B-Vitamin Sharing Promotes Stability of Gut Microbial Communities , 2019, Front. Microbiol..

[51]  Sebastián N. Mendoza,et al.  A systematic assessment of current genome-scale metabolic reconstruction tools , 2019, Genome Biology.

[52]  Steffen Klamt,et al.  From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints , 2017, PLoS Comput. Biol..

[53]  Frédéric Cazals,et al.  A note on the problem of reporting maximal cliques , 2008, Theor. Comput. Sci..

[54]  Zhen Xie,et al.  Pooled CRISPR interference screening enables genome-scale functional genomics study in bacteria with superior performance , 2018, Nature Communications.

[55]  F. Rousset,et al.  Genome-wide CRISPR-dCas9 screens in E. coli identify essential genes and phage host factors , 2018, bioRxiv.

[56]  H. Mori,et al.  Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection , 2006, Molecular systems biology.

[57]  Bernhard O. Palsson,et al.  Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways , 2015, PLoS Comput. Biol..

[58]  Jörg Stelling,et al.  System-Level Insights into Yeast Metabolism by Thermodynamic Analysis of Elementary Flux Modes , 2012, PLoS Comput. Biol..

[59]  Norman Pavelka,et al.  Emerging and evolving concepts in gene essentiality , 2017, Nature Reviews Genetics.

[60]  David E. Ruckerbauer,et al.  Metabolomics integrated elementary flux mode analysis in large metabolic networks , 2015, Scientific Reports.

[61]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[62]  Rajagopalan Srinivasan,et al.  An efficient graph theory based method to identify every minimal reaction set in a metabolic network , 2014, BMC Systems Biology.

[63]  Jörg Stelling,et al.  Genome-scale metabolic networks in time and space , 2018 .

[64]  Christian Jungreuthmayer,et al.  regEfmtool: Speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic , 2013, Biosyst..