ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization. Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

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Genome-Scale Metabolic Reconstruction and in Silico Perturbation Analysis of the Polar Diatom Fragilariopsis cylindrus Predicts High Metabolic Robustness

Biology ◽

10.3390/biology9020030 ◽

2020 ◽

Vol 9 (2) ◽

pp. 30

Author(s):

Michel Lavoie ◽

Blanche Saint-Béat ◽

Jan Strauss ◽

Sébastien Guérin ◽

Antoine Allard ◽

...

Keyword(s):

Growth Rate ◽

Single Gene ◽

Scale Model ◽

Metabolic Reconstruction ◽

Model Parameters ◽

Alternative Pathways ◽

A Genome ◽

Fragilariopsis Cylindrus ◽

Metabolic Robustness ◽

Genome Scale

Diatoms are major primary producers in polar environments where they can actively grow under extremely variable conditions. Integrative modeling using a genome-scale model (GSM) is a powerful approach to decipher the complex interactions between components of diatom metabolism and can provide insights into metabolic mechanisms underlying their evolutionary success in polar ecosystems. We developed the first GSM for a polar diatom, Fragilariopsis cylindrus, which enabled us to study its metabolic robustness using sensitivity analysis. We find that the predicted growth rate was robust to changes in all model parameters (i.e., cell biochemical composition) except the carbon uptake rate. Constraints on total cellular carbon buffer the effect of changes in the input parameters on reaction fluxes and growth rate. We also show that single reaction deletion of 20% to 32% of active (nonzero flux) reactions and single gene deletion of 44% to 55% of genes associated with active reactions affected the growth rate, as well as the production fluxes of total protein, lipid, carbohydrate, DNA, RNA, and pigments by less than 1%, which was due to the activation of compensatory reactions (e.g., analogous enzymes and alternative pathways) with more highly connected metabolites involved in the reactions that were robust to deletion. Interestingly, including highly divergent alleles unique for F. cylindrus increased its metabolic robustness to cellular perturbations even more. Overall, our results underscore the high robustness of metabolism in F. cylindrus, a feature that likely helps to maintain cell homeostasis under polar conditions.

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Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Genetics ◽

10.1093/genetics/159.4.1765 ◽

2001 ◽

Vol 159 (4) ◽

pp. 1765-1778

Author(s):

Gregory J Budziszewski ◽

Sharon Potter Lewis ◽

Lyn Wegrich Glover ◽

Jennifer Reineke ◽

Gary Jones ◽

...

Keyword(s):

Large Scale ◽

Protein Translocation ◽

Gene Families ◽

Mutant Phenotype ◽

Lethal Mutant ◽

A Genome ◽

Genes Encoding ◽

High Level ◽

Mutant Lines ◽

Genome Scale

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

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Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

Journal of Bacteriology ◽

10.1128/jb.01583-07 ◽

2008 ◽

Vol 190 (8) ◽

pp. 2790-2803 ◽

Cited By ~ 175

Author(s):

Matthew A. Oberhardt ◽

Jacek Puchałka ◽

Kimberly E. Fryer ◽

Vítor A. P. Martins dos Santos ◽

Jason A. Papin

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Opportunistic Pathogen ◽

Scale Model ◽

Modeling Framework ◽

Major Life ◽

Metabolic Versatility ◽

A Genome ◽

Scale Properties ◽

Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽

10.1099/mic.0.076091-0 ◽

2014 ◽

Vol 160 (6) ◽

pp. 1252-1266 ◽

Cited By ~ 28

Author(s):

Hassan B. Hartman ◽

David A. Fell ◽

Sergio Rossell ◽

Peter Ruhdal Jensen ◽

Martin J. Woodward ◽

...

Keyword(s):

Salmonella Enterica ◽

Energy Demand ◽

Drug Targets ◽

Minimal Medium ◽

Model Organism ◽

Metabolic Model ◽

Scale Model ◽

Glucose Minimal Medium ◽

A Genome ◽

Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

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A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

mSystems ◽

10.1128/msystems.00165-16 ◽

2017 ◽

Vol 2 (2) ◽

Cited By ~ 3

Author(s):

Keith Dufault-Thompson ◽

Huahua Jian ◽

Ruixue Cheng ◽

Jiefu Li ◽

Fengping Wang ◽

...

Keyword(s):

Energy Conservation ◽

Deep Sea ◽

Carbon Sources ◽

Scale Model ◽

Anaerobic Conditions ◽

A Genome ◽

Group 2 ◽

Genome Scale ◽

Genome Scale Model ◽

Group 1

ABSTRACT The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

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Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

Biotechnology and Bioengineering ◽

10.1002/bit.26598 ◽

2018 ◽

Vol 115 (7) ◽

pp. 1815-1828 ◽

Cited By ~ 6

Author(s):

Valeria Razmilic ◽

Jean F. Castro ◽

Barbara Andrews ◽

Juan A. Asenjo

Keyword(s):

Model Prediction ◽

Metabolic Networks ◽

Scale Model ◽

Specialized Metabolites ◽

A Genome ◽

Genome Scale ◽

Genome Scale Model

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Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

10.1101/2021.02.18.431926 ◽

2021 ◽

Author(s):

Christopher E. Lawson ◽

Aniela B. Mundinger ◽

Hanna Koch ◽

Tyler B. Jacobson ◽

Coty A. Weathersby ◽

...

Keyword(s):

Wastewater Treatment ◽

Metabolic Networks ◽

Metabolic Model ◽

Scale Model ◽

Nitrifying Bacteria ◽

Metabolomic Analysis ◽

A Genome ◽

Nitrite Oxidizing Bacteria ◽

Genome Scale ◽

Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

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A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

PLoS Computational Biology ◽

10.1371/journal.pcbi.1000285 ◽

2009 ◽

Vol 5 (2) ◽

pp. e1000285 ◽

Cited By ~ 94

Author(s):

Patrick F. Suthers ◽

Madhukar S. Dasika ◽

Vinay Satish Kumar ◽

Gennady Denisov ◽

John I. Glass ◽

...

Keyword(s):

Mycoplasma Genitalium ◽

Metabolic Reconstruction ◽

A Genome ◽

Genome Scale

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Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection

Journal of Bacteriology ◽

10.1128/jb.00900-10 ◽

2010 ◽

Vol 192 (20) ◽

pp. 5534-5548 ◽

Cited By ~ 69

Author(s):

Matthew A. Oberhardt ◽

Joanna B. Goldberg ◽

Michael Hogardt ◽

Jason A. Papin

Keyword(s):

Cystic Fibrosis ◽

Disease Progression ◽

Time Course ◽

Lung Infection ◽

System Level ◽

Metabolic Reconstruction ◽

Metabolic Network Analysis ◽

A Genome ◽

Future Drug ◽

Genome Scale

ABSTRACT System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of P seudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

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