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2 Publications visible to you, out of a total of 2
Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae.

Abstract (Expand)

Systems metabolomics, the identification and quantification of cellular metabolites and their integration with genomics and proteomics data, promises valuable functional insights into cellular biology. … However, technical constraints, sample complexity issues and the lack of suitable complementary quantitative data sets prevented accomplishing such studies in the past. Here, we present an integrative metabolomics study of the genome-reduced bacterium Mycoplasma pneumoniae. We experimentally analysed its metabolome using a cross-platform approach. We explain intracellular metabolite homeostasis by quantitatively integrating our results with the cellular inventory of proteins, DNA and other macromolecules, as well as with available building blocks from the growth medium. We calculated in vivo catalytic parameters of glycolytic enzymes, making use of measured reaction velocities, as well as enzyme and metabolite pool sizes. A quantitative, inter-species comparison of absolute and relative metabolite abundances indicated that metabolic pathways are regulated as functional units, thereby simplifying adaptive responses. Our analysis demonstrates the potential for new scientific insight by integrating different types of large-scale experimental data from a single biological source.

Authors: T. Maier, J. Marcos, J. A. Wodke, B. Paetzold, M. Liebeke, R. Gutierrez-Gallego, L. Serrano

Date Published: 20th Apr 2013

Publication Type: Not specified

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

Abstract (Expand)

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.

Authors: J. A. Wodke, J. Puchalka, M. Lluch-Senar, J. Marcos, E. Yus, M. Godinho, R. Gutierrez-Gallego, V. A. dos Santos, L. Serrano, E. Klipp, T. Maier

Date Published: 4th Apr 2013

Publication Type: Not specified

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