Dissecting the energy metabolism in <i>Mycoplasma pneumoniae</i> through genome‐scale metabolic modeling

Wodke, Judith A. H.; Puchałka, Jacek; Lluch‐Senar, María; Marcos, Josep; Yus, Eva; Godinho, Miguel; Gutiérrez-Gallego, Ricardo; Santos, Vítor A. P. Martins dos; Serrano, Luís; Klipp, Edda; Maier, Tobias

doi:10.1038/msb.2013.6

Cited by 77 publications

(137 citation statements)

References 83 publications

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“…The covered organisms include three archaea (Feist et al 2006;Gonzalez et al 2010;Satish Kumar et al 2011), five prokaryotes Nogales et al 2008;Zhang et al 2009;Orth et al 2011;Wodke et al 2013), and five eukaryotes (Duarte et al 2007;Andersen et al 2008;Quek and Nielsen 2008;Manichaikul et al 2009;Arnold and Nikoloski 2014).…”

Section: Analyzed Metabolic Networkmentioning

confidence: 99%

Control of fluxes in metabolic networks

et al. 2016

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Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.[Supplemental material is available for this article.]Understanding how cellular systems are controlled on a genomescale is a central issue in biology and medicine. Metabolic networks are at the center of systems biology approaches to unraveling cellular control, because metabolism carries the life-sustaining cellular functions shaping the molecular phenotype (Sweetlove and Ratcliffe 2011). The steady-state principle and physico-chemical constraints (e.g., mass balance and thermodynamics) have been employed to reduce the number of considered network states, facilitating the prediction of genotype-phenotype relationships and intervention strategies for biotechnological or medical purposes (McCloskey et al. 2013). In particular, flux balance analysis and variations thereof have been successfully applied to the metabolic networks of unicellular organisms to predict their metabolic and cellular phenotypes (Varma and Palsson 1994). Yet, those approaches are biased (Lewis et al. 2012) because they restrict the flux space to an a priori specified reference state by assuming a cellular objective to be optimized by the organism (Schuetz et al. 2007). While optimization of biomass yield has proven useful for unicellular organisms, identification of a suitable objective for multicellular organisms remains a nontrivial endeavor (Sweetlove and Ratcliffe 2011). Other approaches, e.g., elementary flux modes (Schuster and Schuster 1993) and extreme pathways analyses (Schilling et al. 2000), do not assume a cellular objective and hence are unbiased. However, despite extensive studies and recent advances (Terzer and Stelling 2008), these unbiased approaches are limited to rather small networks due to their intrinsic computational complexity. We still lack an unbiased computational approach for systematically studying the control of large-scale metabolic networks.Here, we develop such an approach by employing the flux coupling between reactio...

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Section: Analyzed Metabolic Networkmentioning

confidence: 99%

Control of fluxes in metabolic networks

et al. 2016

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“…Particularly well represented are microbes colonizing the human body (16). GENREs have been applied successfully to the elucidation of biochemical and metabolic properties of a variety of microorganisms, including the Fe(III) reducer Geobacter sulfurreducens (17), the photosynthetic alga Chlamydomonas reinhardtii (18), and the human pathogen Mycoplasma pneumoniae (19).…”

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confidence: 99%

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

et al. 2014

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eThe human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

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“…Metabolic modeling can reveal additional features of the adaptation to the intracellular milieu. Wodke et al (2013) quantitatively analyzed the intermediary and energetic metabolism of M. pneumoniae using a genome-wide, constraintbased metabolic model (iJW145, in silico model including 145 genes), combined with experimental validations and systematic searches of literature. A remarkable result was the observation that M. pneumoniae invests most of its energy (i.e., ATP) in cell homeostasis rather than in growth, coherently with its adaptive strategy to parasitic intracellular life.…”

Section: A Diversity Of Metabolic Modes In Cells With Reduced Genomesmentioning

confidence: 99%

“…Gene essentiality was determined in in silico knock-out experiments using Flux Balance Analysis (FBA) on metabolic models inferred from complete genomes, except for the minimal theoretical network, based on CMG (Gil et al, 2004), where Elementary Flux Mode analysis was used. From right to left, the data points correspond to E. coli , ancestral and extant S. glossinidius network , M. pneumoniae (Wodke et al, 2013), Blattabacterium (González-Domenech et al, 2012), B. aphidicola BAp (Thomas et al, 2009), B. aphidicola BCc , and the minimal theroretical metabolism (Gabaldón et al, 2007). CDS, protein coding sequences.…”

Section: A Hierarchy Of Minimal Cells Based On Metabolic Complexitymentioning

confidence: 99%

“…In other words, the number of alternative pathways able to rescue for a specific deleted enzymatic step is drastically reduced and the consequent fragility of the network could limit its usefulness as a support (or chassis) in synthetic biology projects. A ladder of small networks can be observed in Figure 1, from that of an autonomous free-living bacterium (e.g., E. coli) up to the theoretical minimalist heterotrophic network (Gabaldón et al, 2007), throughout the stages of obligate intracellular symbionts, such as M. pneumoniae (Wodke et al, 2013), B. cuenoti (González-Domenech et al, 2012), and B. aphidicola (Thomas et al, 2009;Belda et al, 2012), and the secondary endosymbiont Sodalis glossinidius and its putative evolutionary precursor . It would be of interest to model natural free-living cells with incomplete metabolisms to examine their relative position in this hierarchy.…”

Section: A Hierarchy Of Minimal Cells Based On Metabolic Complexitymentioning

confidence: 99%

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Small genomes and the difficulty to define minimal translation and metabolic machineries

Gil

Peretó

2015

Front. Ecol. Evol.

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Citation:Gil R and Peretó J (2015) Small genomes and the difficulty to define minimal translation and metabolic machineries. Front. Ecol. Evol. 3:123. doi: 10.3389/fevo.2015.00123 Small genomes and the difficulty to define minimal translation and metabolic machineries The notion of minimal life has sparked the interest of scientists in different fields, ranging from the origin-of-life research to biotechnology-oriented synthetic biology. Whether the interest is focused on the emergence of protocells out of prebiotic systems or the design of a cell chassis ready to incorporate new devices and functions, proposing minimal combinations of genes for life is not a trivial task. Using comparative genomics and biochemistry of endosymbionts (i.e., intracellular mutualistic symbionts) and intracellular parasites, we proposed a decade ago the core of a minimal gene set for a simple heterotrophic cell adapted to a chemically complex environment. In this work, we discuss the state-of-the-art of the definition of the minimal genome, based on our current knowledge about bacteria with naturally reduced genomes, including both endosymbionts and free-living cells. Any proposed minimal genome would be composed by a set of protein-coding and RNA genes involved in the flux of genetic information (from DNA to functional proteins) and a group of protein-coding sequences embodying a minimalist, stoichiometrically consistent metabolic network. Although the informational portion of the minimal genome is considered quasi-universal, previous proposals have not addressed the need of tRNA post-transcriptional modifications in order to perform their function. For this reason, we have focused on the essentiality of some enzymes involved in such modifications, in order to refine the set of informational genes. As for the metabolic aspect, an obvious difficulty is that there is no one minimal gene set for life but many, depending on the environment. Among cells with reduced genomes, we find a continuum of metabolic modes, from organic matter-dependent heterotrophy to the minimally demanding autotrophy. Both best-known cases of cells with small genomes, the endosymbionts and the ultra-small free-living bacteria, speak in favor of metabolic sharing as a strong force in genome reductive evolution. A hierarchy of minimal cells supported by different metabolic networks with a complexity inversely correlated with the chemical complexity of the environment can be postulated.

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

Abstract: 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.

Cited by 77 publications

References 83 publications

Control of fluxes in metabolic networks

Control of fluxes in metabolic networks

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Small genomes and the difficulty to define minimal translation and metabolic machineries

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