Background: Grapevine metabolism in response to water deficit was studied in two cultivars, Shiraz and Cabernet Sauvignon, which were shown to have different hydraulic behaviors (Hochberg et al. Physiol. Plant.147:443–453, 2012).Results: Progressive water deficit was found to effect changes in leaf water potentials accompanied by metabolic changes. In both cultivars, but more intensively in Shiraz than Cabernet Sauvignon, water deficit caused a shift to higher osmolality and lower C/N ratios, the latter of which was also reflected in marked increases in amino acids, e.g., Pro, Val, Leu, Thr and Trp, reductions of most organic acids, and changes in the phenylpropanoid pathway. PCA analysis showed that changes in primary metabolism were mostly associated with water stress, while diversification of specialized metabolism was mostly linked to the cultivars. In the phloem sap, drought was characterized by higher ABA concentration and major changes in benzoate levels coinciding with lower stomatal conductance and suberinization of vascular bundles. Enhanced suberin biosynthesis in Shiraz was reflected by the higher abundance of sap hydroxybenzoate derivatives. Correlation-based network analysis revealed that compared to Cabernet Sauvignon, Shiraz had considerably larger and highly coordinated stress-related changes, reflected in its increased metabolic network connectivity under stress. Network analysis also highlighted the structural role of major stress related metabolites, e.g., Pro, quercetin and ascorbate, which drastically altered their connectedness in the Shiraz network under water deficit.Conclusions: Taken together, the results showed thatVitis viniferacultivars possess a common metabolic response to water deficit. Central metabolism, and specifically N metabolism, plays a significant role in stress response in vine. At the cultivar level, Cabernet Sauvignon was characterized by milder metabolic perturbations, likely due to a tighter regulation of stomata upon stress induction. Network analysis was successfully implemented to characterize plant stress molecular response and to identify metabolites with a significant structural and biological role in vine stress response.

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping. Seeds represent 70% of the food source for man and livestock. However, as a result of millennia of domestication, crop plants have undergone major genetic deterioration, leading to a loss of important quality traits. Thus, the reintroduction of these quality traits is the key to the improvement of crops in modern agriculture. Seed quality traits include nutritional components, such as proteins and amino acids, and seed germination and storability, which are, in turn, inherently related to metabolism. To understand the genetic basis of seed metabolism—a strategic need in the improvement of seed crops—we studied a collection of offspring plants stemming from the cross between a domesticated tomato cultivar Solanum lycopersicum cv M82 and its distant wild relative S. pennellii. We monitored the changes in metabolism and studied the mode of regulation of the concentration of metabolites in the seeds as a result of genetic introgression, by taking advantage of state-of-the-art technologies and methods of data elaboration such as network-based analysis. We identified a number of candidate genes that may be useful in manipulations to enhance nutritional values in seeds. Finally, in an effort to study the relation among the seed, the fruit, and the mother plant, we determined potential yield-associated metabolic markers.

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.