Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling

• Published in: Molecular systems biology 2013, Vol.9 (1), p.653-n/a
• Main Authors: Wodke, Judith A H, Puchałka, Jacek, Lluch‐Senar, Maria, Marcos, Josep, Yus, Eva, Godinho, Miguel, Gutiérrez‐Gallego, Ricardo, dos Santos, Vitor A P Martins, Serrano, Luis, Klipp, Edda, Maier, Tobias
• Format: Article
• Language: English
• Subjects: bacillus-subtilis; biochemical networks; biomass composition; cobra toolbox extension; Computer Simulation; Constraint modelling; Deoxyribonucleic acid; Design; DNA; EMBO23; EMBO26; Energy metabolism; Energy Metabolism - genetics; Enzymes; escherichia-coli; Experiments; flux balance models; Gene Expression Regulation, Bacterial; Genes; genetic-analysis; Genome, Bacterial; Genomes; Glucose; in silico knock‐outs; invivo measurement; Mass spectrometry; Mathematical models; membrane-lipids; metabolic modeling; Metabolic networks; Metabolic Networks and Pathways - genetics; Metabolism; Metabolites; Model testing; Models, Biological; Mutagenesis; Mutation; Mycoplasma pneumonia; Mycoplasma pneumoniae; Mycoplasma pneumoniae - genetics; Mycoplasma pneumoniae - metabolism; optimal selection; Phenotypes; Proteins; reduced bacterium; Regulatory mechanisms (biology); Scientific imaging; Static models; Time dependence
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb.2013.6

Mycoplasma pneumoniae , a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae . The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms. A new genome‐scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome‐reduce human pathogen. Synopsis A new genome‐scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome‐reduce human pathogen. We established a detailed biomass composition for M. pneumoniae , thus allowing for growth simulations. Using our metabolic model, we corrected the metabolic network topology and the functional annotation of key metabolic enzymes. M. pneumoniae , unlike other laboratory‐grown bacteria, uses a high fraction of energy (up to 89%) for cellular maintenance and not for growth. Simulating different growth conditions as well as single and double mutant phenotypes, we analyzed pathway connectivity and the impact of gene deletions on the growth performance of M. pneumoniae , highlighting the limited adaptive capabilities of this minimal model organism.