iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations

BackgroundBacillus subtilis is an organism of interest because of its extensive industrial applications, its similarity to pathogenic organisms, and its role as the model organism for Gram-positive, sporulating bacteria. In this work, we introduce a new genome-scale metabolic model of B. subtilis 168 called iBsu1103. This new model is based on the annotated B. subtilis 168 genome generated by the SEED, one of the most up-to-date and accurate annotations of B. subtilis 168 available.ResultsThe iBsu1103 model includes 1,437 reactions associated with 1,103 genes, making it the most complete model of B. subtilis available. The model also includes Gibbs free energy change (ΔrG'°) values for 1,403 (97%) of the model reactions estimated by using the group contribution method. These data were used with an improved reaction reversibility prediction method to identify 653 (45%) irreversible reactions in the model. The model was validated against an experimental dataset consisting of 1,500 distinct conditions and was optimized by using an improved model optimization method to increase model accuracy from 89.7% to 93.1%.ConclusionsBasing the iBsu1103 model on the annotations generated by the SEED significantly improved the model completeness and accuracy compared with the most recent previously published model. The enhanced accuracy of the iBsu1103 model also demonstrates the efficacy of the improved reaction directionality prediction method in accurately identifying irreversible reactions in the B. subtilis metabolism. The proposed improved model optimization methodology was also demonstrated to be effective in minimally adjusting model content to improve model accuracy.

[1]  M. Itaya,et al.  Cloning, Sequencing, and Disruption of theBacillus subtilis psd Gene Coding for Phosphatidylserine Decarboxylase , 1998, Journal of bacteriology.

[2]  B. Palsson,et al.  Metabolic Capabilities of Escherichia coli II. Optimal Growth Patterns , 1993 .

[3]  U. Sauer,et al.  Stoichiometric growth model for riboflavin-producing Bacillus subtilis. , 2001, Biotechnology and bioengineering.

[4]  P. Noirot,et al.  A new mutation delivery system for genome‐scale approaches in Bacillus subtilis , 2002, Molecular microbiology.

[5]  H. Qian,et al.  Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism. , 2005, American journal of physiology. Endocrinology and metabolism.

[6]  Matthew D. Jankowski,et al.  Group contribution method for thermodynamic analysis of complex metabolic networks. , 2008, Biophysical journal.

[7]  Matthias Heinemann,et al.  Systematic assignment of thermodynamic constraints in metabolic network models , 2006, BMC Bioinformatics.

[8]  Susumu Goto,et al.  The KEGG databases at GenomeNet , 2002, Nucleic Acids Res..

[9]  J. Edwards,et al.  Robustness Analysis of the Escherichiacoli Metabolic Network , 2000, Biotechnology progress.

[10]  Jochen Förster,et al.  Modeling Lactococcus lactis using a genome-scale flux model , 2005, BMC Microbiology.

[11]  I. Grossmann,et al.  Recursive MILP model for finding all the alternate optima in LP models for metabolic networks , 2000 .

[12]  Vinay Satish Kumar,et al.  GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions , 2009, PLoS Comput. Biol..

[13]  B. Palsson,et al.  Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation , 2005, BMC Microbiology.

[14]  Bernhard O. Palsson,et al.  Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets , 2007 .

[15]  M. Mavrovouniotis.  Estimation of standard Gibbs energy changes of biotransformations. , 1991, The Journal of biological chemistry.

[16]  Rick L. Stevens,et al.  The RAST Server: Rapid Annotations using Subsystems Technology , 2008, BMC Genomics.

[17]  B. Palsson,et al.  Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data* , 2007, Journal of Biological Chemistry.

[18]  Rick L. Stevens,et al.  The SEED: a peer-to-peer environment for genome annotation , 2004, CACM.

[19]  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.

[20]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[21]  S. Ehrlich,et al.  Essential Bacillus subtilis genes , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Matthew D. Jankowski,et al.  Genome-scale thermodynamic analysis of Escherichia coli metabolism. , 2006, Biophysical journal.

[23]  U. Sauer,et al.  Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism , 2005, Nature Genetics.

[24]  B. Palsson,et al.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data , 2001, Nature Biotechnology.

[25]  T. Jeffries,et al.  Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae. , 2004, Metabolic engineering.

[26]  R. Losick,et al.  Bacillus subtilis and Its Closest Relatives , 2002 .

[27]  Markus J. Herrgård,et al.  Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. , 2004, Genome research.

[28]  V. Hatzimanikatis,et al.  Thermodynamics-based metabolic flux analysis. , 2007, Biophysical journal.

[29]  B. Bochner.  Global phenotypic characterization of bacteria , 2008, FEMS microbiology reviews.

[30]  S. Kanaya,et al.  Enhanced Recombinant Protein Productivity by Genome Reduction in Bacillus subtilis , 2008, DNA research : an international journal for rapid publication of reports on genes and genomes.

[31]  S. Panke,et al.  Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data , 2006, Molecular systems biology.

[32]  M. Mavrovouniotis.  Group contributions for estimating standard gibbs energies of formation of biochemical compounds in aqueous solution , 1990, Biotechnology and bioengineering.

[33]  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.

[34]  E. Papoutsakis,et al.  Equations and calculations of product yields and preferred pathways for butanediol and mixed‐acid fermentations , 1985, Biotechnology and bioengineering.

[35]  Adam M. Feist,et al.  Modeling methanogenesis with a genome‐scale metabolic reconstruction of Methanosarcina barkeri , 2006 .

[36]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[37]  U. Sauer,et al.  Physiology and metabolic fluxes of wild-type and riboflavin-producing Bacillus subtilis , 1996, Applied and environmental microbiology.

[38]  Vinay Satish Kumar,et al.  A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189 , 2009, PLoS Comput. Biol..

[39]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[40]  Vincent Fromion,et al.  Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis , 2008, BMC Systems Biology.

[41]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[42]  M. Tomita,et al.  Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. , 2003, Journal of proteome research.

[43]  Vinay Satish Kumar,et al.  Optimization based automated curation of metabolic reconstructions , 2007, BMC Bioinformatics.

[44]  A. Burgard,et al.  Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. , 2001, Biotechnology and bioengineering.

[45]  John Gould,et al.  Toward the automated generation of genome-scale metabolic networks in the SEED , 2007, BMC Bioinformatics.

[46]  Jan Maarten van Dijl,et al.  Towards the development of Bacillus subtilis as a cell factory for membrane proteins and protein complexes , 2008, Microbial cell factories.

[47]  R. Losick,et al.  Bacillus Subtilis and Its Closest Relatives: From Genes to Cells , 2001 .