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

Competing Endogenous RNA Networks Underlying Anatomical and Physiological Characteristics of Poplar Wood in Acclimation to Low Nitrogen Availability

Although poplar plantations are often established on nitrogen (N)-poor soil, the physiological and molecular mechanisms underlying wood properties of poplars in acclimation to low N availability remain largely unknown. To investigate wood properties of poplars in acclimation to low N, Populus � canescens saplings were exposed to either 50 (low N) or 500 (normal N) �M NH4NO3 for 2 months. Low N resulted in decreased xylem width and cell layers of the xylem (the number of cells counted along the ray parenchyma on the stem cross section), narrower lumina of vessels and fibers, greater thickness of double fiber walls (the walls between two adjacent fiber cells), more hemicellulose and lignin deposition, and reduced cellulose accumulation in poplar wood. Consistently, concentrations of gibberellins involved in cell size determination and the abundance of various metabolites including amino acids, carbohydrates and precursors for cell wall biosynthesis were decreased in low N-supplied wood. In line with these anatomical and physiological changes, a number of mRNAs, long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) were significantly differentially expressed. Competing endogenous RNA regulatory networks were identified in the wood of low N-treated poplars. Overall, these results indicate that miRNAs-lncRNAs-mRNAs networks are involved in regulating wood properties and physiological processes of poplars in acclimation to low N availability.

Combined transcription factor profiling, microarray analysis and metabolite profiling reveals the transcriptional control of metabolic shifts occurring during tomato fruit development

Maturation of fleshy fruits such as tomato (Solanum lycopersicum) is subject to tight genetic control. Here we describe the development of a quantitative real-time PCR platform that allows accurate quantification of the expression level of approximately 1000 tomato transcription factors. In addition to utilizing this novel approach, we performed cDNA microarray analysis and metabolite profiling of primary and secondary metabolites using GC-MS and LC-MS, respectively. We applied these platforms to pericarp material harvested throughout fruit development, studying both wild-type Solanum lycopersicum cv. Ailsa Craig and the hp1 mutant. This mutant is functionally deficient in the tomato homologue of the negative regulator of the light signal transduction gene DDB1 from Arabidopsis, and is furthermore characterized by dramatically increased pigment and phenolic contents. We choose this particular mutant as it had previously been shown to have dramatic alterations in the content of several important fruit metabolites but relatively little impact on other ripening phenotypes. The combined dataset was mined in order to identify metabolites that were under the control of these transcription factors, and, where possible, the respective transcriptional regulation underlying this control. The results are discussed in terms of both programmed fruit ripening and development and the transcriptional and metabolic shifts that occur in parallel during these processes.

Correlation Network Analysis reveals a sequential reorganization of metabolic and transcriptional states during germination and gene-metabolite relationships in developing seedlings of Arabidopsis

BACKGROUND

Holistic profiling and systems biology studies of nutrient availability are providing more and more insight into the mechanisms by which gene expression responds to diverse nutrients and metabolites. Less is known about the mechanisms by which gene expression is affected by endogenous metabolites, which can change dramatically during development. Multivariate statistics and correlation network analysis approaches were applied to non-targeted profiling data to investigate transcriptional and metabolic states and to identify metabolites potentially influencing gene expression during the heterotrophic to autotrophic transition of seedling establishment.

RESULTS

Microarray-based transcript profiles were obtained from extracts of Arabidopsis seeds or seedlings harvested from imbibition to eight days-old. 1H-NMR metabolite profiles were obtained for corresponding samples. Analysis of transcript data revealed high differential gene expression through seedling emergence followed by a period of less change. Differential gene expression increased gradually to day 8, and showed two days, 5 and 7, with a very high proportion of up-regulated genes, including transcription factor/signaling genes. Network cartography using spring embedding revealed two primary clusters of highly correlated metabolites, which appear to reflect temporally distinct metabolic states. Principle Component Analyses of both sets of profiling data produced a chronological spread of time points, which would be expected of a developmental series. The network cartography of the transcript data produced two distinct clusters comprising days 0 to 2 and days 3 to 8, whereas the corresponding analysis of metabolite data revealed a shift of day 2 into the day 3 to 8 group. A metabolite and transcript pair-wise correlation analysis encompassing all time points gave a set of 237 highly significant correlations. Of 129 genes correlated to sucrose, 44 of them were known to be sucrose responsive including a number of transcription factors.

CONCLUSIONS

Microarray analysis during germination and establishment revealed major transitions in transcriptional activity at time points potentially associated with developmental transitions. Network cartography using spring-embedding indicate that a shift in the state of nutritionally important metabolites precedes a major shift in the transcriptional state going from germination to seedling emergence. Pair-wise linear correlations of transcript and metabolite levels identified many genes known to be influenced by metabolites, and provided other targets to investigate metabolite regulation of gene expression during seedling establishment.

Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach

BACKGROUND

Biological 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 FINDINGS

Starting 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/SIGNIFICANCE

Network-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.

Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development

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.

Characterization of dietary protein-dependent amino acid metabolism by linking free amino acids with transcriptional profiles through analysis of correlation

This study aims to characterize diet-dependent amino acid metabolism by linking profiles of amino acids concentrations (“aminograms”) with transcript datasets through the analysis of correlation. We used a dietary model of protein restriction-to-excess, where rats were fed diets with different levels of casein (5, 10, 15, 20, 30, 50, and 70%) for 2 wk. Twenty-five different amino acids in the plasma, liver, kidney, small intestine, and muscle and 71 gene transcripts in these compartments were measured together with general physiological variables. Under low-protein diet (LPD) conditions, the plasma aminogram for EAA was similar to that of the liver and the small intestine, respectively. Under the high-protein diet (HPD), however, the plasma aminogram for EAA became like that of muscle, while that of NEAA was similar with that of both liver and muscle. To assess the impact of gene expressions in each tissue on the plasma aminograms, correlations were obtained between aminograms and transcripts in each tissue under a diet with different protein levels. Based on the correlations obtained, amino acids and transcripts were systematically connected and then a metabolite-to-gene network was constructed for either LPD or HPD condition. The networks obtained and some other metabolically meaningful relationships such as ureagenesis and serine metabolism clearly illustrated activation of either body protein breakdown with LPD or amino acid catabolism with HPD.

Metabolomics integrated with transcriptomics: assessing systems response to sulfur-deficiency stress

Sulfur-containing amino acids, cysteine and methionine synthesized in plants are essential for human and animal nutrition. That is why understanding of how inorganic sulfur is taken up by plants and built into the organic molecules in the process of sulfur assimilation is important. As complex biological systems, plants subsist as integrated molecular, organelle, cell, tissue and organ entities, being in permanent synergistic coordination. The process of sulfur uptake and assimilation is an integral part of this dense network of influences, its reconstruction may help in manipulating the bioproduction of organic sulfur-containing compounds. New high-throughput technologies allow the systems' view on the coordination of complex processes in living organisms. Among them, transcriptomics and metabolomics studies were applied to Arabidopsis plants subjected to sulfur-deficiency stress. From the integrated analysis of the obtained data, the mosaic picture of distinct sulfur stress response events and processes are starting to be assembled into the whole systems' network of sulfur assimilation. At the time trajectory of sulfur stress response, two system states can be distinguished. The first state of short-term responses is characterized by the development of enhanced lateral roots exploring the space in search for the lacking nutrient. When this physiological reaction cannot be accomplished by bringing the system back to the initial state of sulfur sufficiency, a new program is toggled aiming at saving the organismal resources for vital seed production. Here, we describe the biological reasoning in these two system states and the process of state transition between them.