Matthew Cohoon

University of Chicago, Chicago, Illinois, United States

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Publications (9)38.15 Total impact

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    ABSTRACT: The nonessential regions in bacterial chromosomes are ill-defined due to incomplete functional information. Here, we establish a comprehensive repertoire of the genome regions that are dispensable for growth of Bacillus subtilis in a variety of media conditions. In complex medium, we attempted deletion of 157 individual regions ranging in size from 2 to 159 kb. A total of 146 deletions were successful in complex medium, whereas the remaining regions were subdivided to identify new essential genes (4) and coessential gene sets (7). Overall, our repertoire covers ∼76% of the genome. We screened for viability of mutant strains in rich defined medium and glucose minimal media. Experimental observations were compared with predictions by the iBsu1103 model, revealing discrepancies that led to numerous model changes, including the large-scale application of model reconciliation techniques. We ultimately produced the iBsu1103V2 model and generated predictions of metabolites that could restore the growth of unviable strains. These predictions were experimentally tested and demonstrated to be correct for 27 strains, validating the refinements made to the model. The iBsu1103V2 model has improved considerably at predicting loss of viability, and many insights gained from the model revisions have been integrated into the Model SEED to improve reconstruction of other microbial models.
    Full-text · Article · Oct 2012 · Nucleic Acids Research
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    ABSTRACT: Bacillus subtilis is a gram positive, sporulating bacteria often utilized in industry as a producer of high quality enzymes and proteins [1]. The flexible growth conditions, rapid growth rate, and natural competence of B. subtilis make it an ideal candidate for scientific study and industrial use. Yet many aspects of B. subtilis are still poorly understood, and the complexity of this organism makes experimental analysis and reengineering difficult. To enhance our understanding of B. subtilis and develop a more malleable and productive strain for use in a wide variety of industrial applications, we are systematically deleting all regions of the B. subtilis chromosome that are dispensable for growth on rich defined media [2]. We have applied bioinformatics techniques [3] and experimental knowledge [4] to design a set of 157 intervals of the B. subtilis genome that are predicted to be dispensable. These intervals (which range from 2 to 130 Kb in size) include all but ~832 (20%) of the genes in the B. subtilis genome. The intervals were individually deleted at 37C in rich defined medium. 135 intervals were found to be dispensable, with the remaining 21 being essential for viability. To identify the coessential genes responsible for the 21 nonviable deletions, these intervals were iteratively subdivided into 140 smaller intervals, with new coessential and essential genes in B. subtilis ultimately being identified. All 297 experimentally implemented deletions were simulated in silico using the iBsu1103 metabolic model of B. subtilis [5]. Initially, the model incorrectly predicted the outcome of 60 (20%) interval deletions. The model was subsequently adjusted using a combination of computational analysis and experimental study to correct nearly all erroneous predictions. In several cases, the model assisted in the interval splitting process by predicting coessential gene sets. The model also predicted additional nutrients that could be added to the rich defined media to restore viability for some unviable intervals. Phenotypes for the 135 successfully implemented deletions were also studied experimentally and computationally predicting and measuring growth in minimal media, rich defined media, and LB media. Insights gained from the initial 297 interval deletions and the subsequent combination of interval deletions into a 1.5 MB knockout mutant will be discussed. 1.) Zweers JC, Barak I, Becher D et al (2008) Towards the development of Bacillus subtilis as a cell factory for membrane proteins and protein complexes. Microb Cell Fact 7:10 2.) Fabret C, Ehrlich SD, and Noirot P (2002) A new mutation delivery system for genome-scale approaches in Bacillus subtilis. Mol Microbiol 46(1):25-36 3.) Overbeek R, Disz T, and Stevens R (2004) The SEED: A peer-to-peer environment for genome annotation. Communications of the ACM 47(11):46-51 4.) Kobayashi K, Ehrlich SD, Albertini A et al (2003) Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A 100(8):4678-83 5.) Henry CS, Zinner J, Cohoon M et al (2009) iBsu1103: a new genome scale metabolic model of B. subtilis based on SEED annotations. Genome Biology 10:R69
    No preview · Conference Paper · Nov 2010
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    ABSTRACT: Background Bacillus 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. Results The 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%. Conclusions Basing 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.
    Full-text · Article · Jun 2009 · Genome biology
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    ABSTRACT: Bacillus subtilis is a gram positive bacteria often utilized in industry as a producer of high quality enzymes and proteins [1]. One of the primary challenges involved in the use of B. subtilis in industry is the extensive regulatory pathways in the cell, making the flux through the metabolic reactions of the cell extremely resistant to alteration by genetic manipulation [2]. It has already been demonstrated that removing a portion of the regulatory genes in B. subtilis results in significantly enhanced protein production by the cell [3]. Now, we are endeavoring to produce a minimal strain of the B. subtilis genome. This minimal strain will lack every dispensable alternative metabolic pathway and every dispensable regulatory gene, making the strain much more amenable to alteration for industrial use. B. subtilis was selected as the platform organism for the construction of this minimal strain because the natural competence of B. subtilis allows for rapid knockouts of single genes and intervals of genes [4]. Additionally, the extensive information available about B. subtilis will allow for a systematic and planned approach to be used during the construction of the minimal strain. To facilitate the development of our minimal organism, we have constructed a new genome-scale metabolic model of B. subtilis based on the annotations available in the SEED subsystems-based annotation environment [5] and supplemented by data included in two previously developed B. subtilis models [6, 7]. The new model includes elements of the B. subtilis regulation when necessary to properly predict the effect of gene knockouts. The thermodynamic properties of the model reactions were estimated to predict the reversibility of model reactions [8]. The new model also includes numerous genes and pathways not found in any previous models. The new model was validated against a variety of experimental observations including biolog phenotyping array results [7], gene essentiality data [9], 60 published gene interval knockout experiments [3], and over 300 new gene-interval knockout experiments. When model predictions did not match experimental observations, a variety of methods were developed and applied to improve the model accuracy. As a result of these efforts and the inclusion of features from currently published models, this is the most complete and accurate metabolic model of B. subtilis constructed to date. We will discuss the methods used to assemble and correct this new genome-scale model; we will compare the accuracy and content of the new model with previously published models; and we will discuss the application of the model to the design of a systematic and optimized strategy for combining gene interval knockouts to produce a minimal strain of B. subtilis. 1. Zweers, J.C., et al., Towards the development of Bacillus subtilis as a cell factory for membrane proteins and protein complexes. Microb Cell Fact, 2008. 7: p. 10. 2. Fischer, E. and U. Sauer, Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet, 2005. 37(6): p. 636-40. 3. Morimoto, T., et al., Enhanced Recombinant Protein Productivity by Genome Reduction in Bacillus subtilis. DNA Res, 2008. 15(2): p. 73-81. 4. Fabret, C., S.D. Ehrlich, and P. Noirot, A new mutation delivery system for genome-scale approaches in Bacillus subtilis. Mol Microbiol, 2002. 46(1): p. 25-36. 5. Aziz, R.K., et al., The RAST Server: rapid annotations using subsystems technology. BMC Genomics, 2008. 9: p. 75. 6. Goelzer, A., et al., Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMC Syst Biol, 2008. 2: p. 20. 7. Oh, Y.K., et al., Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem, 2007. 282(39): p. 28791-9. 8. Jankowski, M.D., et al., Group contribution method for thermodynamic analysis of complex metabolic networks. Biophysical Journal, 2008. Accepted. 9. Kobayashi, K., et al., Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A, 2003. 100(8): p. 4678-83.
    No preview · Conference Paper · Nov 2008
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    ABSTRACT: Bacillus subtilis is a gram positive bacteria often utilized in industry as a producer of high quality enzymes and proteins [1]. One of the primary challenges involved in the use of B. subtilis in industry is the extensive regulatory pathways in the cell, making the flux through the metabolic reactions of the cell extremely resistant to alteration by genetic manipulation [2]. It has already been demonstrated that removing a portion of the regulatory genes in B. subtilis results in significantly enhanced protein production by the cell [3]. Now, we are endeavoring to produce a minimal strain of the B. subtilis genome. This minimal strain will lack every dispensable alternative metabolic pathway and every dispensable regulatory gene, making the strain much more amenable to alteration for industrial use. B. subtilis was selected as the platform organism for the construction of this minimal strain because the natural competence of B. subtilis allows for rapid knockouts of single genes and intervals of genes [4]. Additionally, the extensive information available about B. subtilis will allow for a systematic and planned approach to be used during the construction of the minimal strain. To facilitate the development of our minimal organism, we have constructed a new genome-scale metabolic model of B. subtilis based on the annotations available in the SEED subsystems-based annotation environment [5] and supplemented by data included in two previously developed B. subtilis models [6, 7]. The new model includes elements of the B. subtilis regulation when necessary to properly predict the effect of gene knockouts. The thermodynamic properties of the model reactions were estimated to predict the reversibility of model reactions [8]. The new model also includes numerous genes and pathways not found in any previous models. The new model was validated against a variety of experimental observations including biolog phenotyping array results [7], gene essentiality data [9], 60 published gene interval knockout experiments [3], and over 300 new gene-interval knockout experiments. When model predictions did not match experimental observations, a variety of methods were developed and applied to improve the model accuracy. As a result of these efforts and the inclusion of features from currently published models, this is the most complete and accurate metabolic model of B. subtilis constructed to date. In this poster, we will present the methods used to assemble and correct this new genome-scale model; we will compare the accuracy and content of the new model with previously published models; and we will describe the application of the model to the design of a systemic and optimized strategy for combining gene interval knockouts to produce a minimal strain of B. subtilis. 1. Zweers, J.C., et al., Towards the development of Bacillus subtilis as a cell factory for membrane proteins and protein complexes. Microb Cell Fact, 2008. 7: p. 10. 2. Fischer, E. and U. Sauer, Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet, 2005. 37(6): p. 636-40. 3. Morimoto, T., et al., Enhanced Recombinant Protein Productivity by Genome Reduction in Bacillus subtilis. DNA Res, 2008. 15(2): p. 73-81. 4. Fabret, C., S.D. Ehrlich, and P. Noirot, A new mutation delivery system for genome-scale approaches in Bacillus subtilis. Mol Microbiol, 2002. 46(1): p. 25-36. 5. Aziz, R.K., et al., The RAST Server: rapid annotations using subsystems technology. BMC Genomics, 2008. 9: p. 75. 6. Goelzer, A., et al., Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMC Syst Biol, 2008. 2: p. 20. 7. Oh, Y.K., et al., Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem, 2007. 282(39): p. 28791-9. 8. Jankowski, M.D., et al., Group contribution method for thermodynamic analysis of complex metabolic networks. Biophysical Journal, 2008. Accepted. 9. Kobayashi, K., et al., Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A, 2003. 100(8): p. 4678-83.
    No preview · Conference Paper · Nov 2008
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    ABSTRACT: The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of ∼50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development.
    Full-text · Article · Feb 2007 · Nucleic Acids Research
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    ABSTRACT: The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.
    Full-text · Article · Feb 2005 · Nucleic Acids Research
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    ABSTRACT: The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.
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  • C.S. Henry · J.F. Zinner · M.P. Cohoon · R.L. Stevens

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