On the processing of metabolic information through metabolite–gene communication networks: An approach for modelling causality

Szymański, Jędrzej; Bielecka, Monika; Carrari, Fernando; Fernie, Alisdair R.; Hoefgen, Rainer; Nikiforova, Victoria J.

doi:10.1016/j.phytochem.2007.04.017

Cited by 9 publications

(12 citation statements)

References 62 publications

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“…This disproportion between the number of negative and positive correlations is a common feature of correlation networks and has also been observed e.g. in case of transcriptome data and other highly clustered datasets [51], [52]. The reason for the overrepresentation of positive correlations must remain speculative, however it is self-explanatory that clusters of commonly co-regulated genes or metabolites will enrich the pool of positive correlations independently of general increase or decrease.…”

Section: Resultsmentioning

confidence: 72%

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

et al. 2009

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BackgroundBiological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network.Methodology/Principal FindingsStarting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response.Conclusions/SignificanceNetwork-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.

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Section: Resultsmentioning

confidence: 72%

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

et al. 2009

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“…So far, in plants, besides sugar sensing (Rolland et al, 2006), there is scarce evidence of such metabolite control. In complex cases, the metabolitetranscript correlation may also result from coregulation of the elements under study by a third element (Szymanski et al, 2007). These individual correlations are embedded in a global network that constitutes a functional dynamic system.…”

Section: Metabolite and Regulatory Gene Network Reveal Regulatory Humentioning

confidence: 99%

“…The correlation network highlighted in this work, however, does not imply causal directionality. Indeed, in the context of early fruit development, it was difficult to identify the initial "exciter" and the "effect" (Szymanski et al, 2007) of the network response as described, respectively, for hyposulfur stress response (exciter = the sulfur molecule itself; Nikiforova et al, 2005) or the last metabolite to accumulate in the ripening tomato fruit (effect = isoprenoid accumulation; . Although our biological system is close to the ripening tomato model, it is focused on an intermediary developmental phase, between fruit set and fruit ripening, with transitory metabolite accumulation impeding the clear definition of exciters and/or effects.…”

Section: Metabolite and Regulatory Gene Network Reveal Regulatory Humentioning

confidence: 99%

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

et al. 2009

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Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

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“…As such, it can identify directionality in relationships (Szymanski et al, 2007;Jansen et al, 2009;Kliebenstein, 2009), a property that is often missed in correlation studies where no information about cause and consequence can be obtained. Immortal populations also allow destructive analyses of different tissues, developmental stages, and experimental settings, revealing information on spatial, temporal, and conditional regulation.…”

Section: Generation and Use Of Experimental Populationsmentioning

confidence: 99%

Metabolic Networks: How to Identify Key Components in the Regulation of Metabolism and Growth

2009

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Plants display enormous diversity in their metabolism. Although the biosynthesis and function of the myriad plant metabolites have been studied for decades, we have little understanding of the interactions between metabolites, metabolite signaling, interactions with development, and the role of metabolism in genotype-to-phenotype relationships. Technologies for the analysis of metabolites have made tremendous progress in recent years, both in terms of the number of metabolites that are identified and of throughput. Recent developments allow the construction of metabolic networks and study of the role of these networks in plant growth and development. In this review, we discuss what types of information can be obtained from measurements of metabolites and what requirements they have with respect to the comprehensiveness of coverage and the precision of identification and quantification, and we outline procedures that can be implemented to validate the measurements. We then discuss what sorts of perturbations can be used to disturb metabolic networks, including environmental and physiological treatments, chemicals, reverse genetics, and the use of natural genetic diversity.Plants are the most consummate and sophisticated chemical system in the world. They use light energy to convert CO 2 into carbohydrates in their leaves. They absorb nutrients like nitrate, phosphate, and sulfate via their roots and convert them to amino acids and nucleotides, using light energy in the leaves in the day and energy derived from respiration in leaves in the dark and in nonphotosynthetic tissues. Carbohydrates, amino acids, and nucleotides are then transported to growing tissues, where they are converted into macromolecular cellular components like proteins, nucleic acids, cell walls, pigments, and lipids. Plants also synthesize tens of thousands of secondary metabolites, including phenylpropanoids and flavonoids, terpenoids, glucosinolates, and alkaloids. These have important roles in cellular function, in signaling, and in adaptation to abiotic and biotic stress. Their unique synthetic ability is the result of a highly complex and sophisticated metabolic apparatus. Its complexity and flexibility were already appreciated by the 1980s, as biochemical studies of cellular compartmentation revealed that many basic pathways like glycolysis, the oxidative pentose phosphate pathway, and organic acid metabolism were present in more than one compartment (Lunn, 2007), and the diversity of plant secondary metabolites was unveiled. It was further underlined as genome sequencing uncovered families of genes for enzymes in central metabolism, including starch and Suc synthesis and degradation, glycolysis, many reactions of the tricarboxylic acid cycle and nucleotide degradation, and huge families of genes that encode enzymes that introduce modifications into basic metabolite structures, like the alcohol dehydrogenases, 2-oxoglutarate dioxygenases, acyl transferases, UDP glucosyltransferases, O-methyl transferases, nitrilases, cytochrome P50s, myr...

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On the processing of metabolic information through metabolite–gene communication networks: An approach for modelling causality

Cited by 9 publications

References 62 publications

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

Metabolic Networks: How to Identify Key Components in the Regulation of Metabolism and Growth

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