Mass spectrometry-based metabolite profiling reveals functional seasonal shifts in the metabolome of Zygophyllum dumosum Boiss and its relation to environmental conditions.

MAIN CONCLUSION: A multi-year study of perennial Z. dumosum shows a consistent seasonal pattern in the changes of petiole metabolism, involving mainly organic acids, polyols, phenylpropanoids, sulfate conjugates, and piperazines. GC-MS and UPLC-QTOF-MS-based metabolite profiling was performed on the petioles of the perennial desert shrub Zygophyllum dumosum Boiss (Zygophyllaceae). The petioles, which are physiologically functional throughout the year and, thus, exposed to seasonal rhythms, were collected every month for 3 years from their natural ecosystem on a southeast-facing slope. Results showed a clear multi-year pattern following seasonal successions, despite different climate conditions, i.e., rainy and drought years, throughout the research period. The metabolic pattern of change encompassed an increase in the central metabolites, including most polyols, e.g., stress-related D-pinitol, organic and sugar acids, and in the dominant specialized metabolites, which were tentatively identified as sulfate, flavonoid, and piperazine conjugates during the summer-autumn period, while significantly high levels of free amino acids were detected during the winter-spring period. In parallel, the levels of most sugars (including glucose and fructose) increased in the petioles at the flowering stage at the beginning of the spring, while most of the di- and tri-saccharides accumulated at the beginning of seed development (May-June). Analysis of the conserved seasonal metabolite pattern of change shows that metabolic events are mostly related to the stage of plant development and its interaction with the environment and less to environmental conditions per se.

The Alteration of Tomato Chloroplast Vesiculation Positively Affects Whole-Plant Source-Sink Relations and Fruit Metabolism under Stress Conditions.

Changes in climate conditions can negatively affect the productivity of crop plants. They can induce chloroplast degradation (senescence), which leads to decreased source capacity, as well as decreased whole-plant carbon/nitrogen assimilation and allocation. The importance, contribution and mechanisms of action regulating source-tissue capacity under stress conditions in tomato (Solanum lycopersicum) are not well understood. We hypothesized that delaying chloroplast degradation by altering the activity of the tomato chloroplast vesiculation (CV) under stress would lead to more efficient use of carbon and nitrogen and to higher yields. Tomato CV is upregulated under stress conditions. Specific induction of CV in leaves at the fruit development stage resulted in stress-induced senescence and negatively affected fruit yield, without any positive effects on fruit quality. Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 (CRISPR/CAS9) knockout CV plants, generated using a near-isogenic tomato line with enhanced sink capacity, exhibited stress tolerance at both the vegetative and the reproductive stages, leading to enhanced fruit quantity, quality and harvest index. Detailed metabolic and transcriptomic network analysis of sink tissue revealed that the l-glutamine and l-arginine biosynthesis pathways are associated with stress-response conditions and also identified putative novel genes involved in tomato fruit quality under stress. Our results are the first to demonstrate the feasibility of delayed stress-induced senescence as a stress-tolerance trait in a fleshy fruit crop, to highlight the involvement of the CV pathway in the regulation of source strength under stress and to identify genes and metabolic pathways involved in increased tomato sink capacity under stress conditions.

Colicin E2 expression in Escherichia coli biofilms: Induction and regulation revisited.

Colicins, bacteriocins produced by the gram-negative bacterium Escherichia coli, are tightly regulated by the DNA damage response regulatory system (SOS), and are thus triggered at times of stress. Colicins' regulation and expression profiles were primarily studied in suspended (planktonic) cultures yet, in their natural environments E. coli cells are sessile, assembled in biofilms. We hypothesized that colicin expression would differ between planktonic and biofilm E. coli cultures, even when induced by the same triggers. To test our hypothesis, we compared colicin E2 expression and SOS regulated genes in planktonic and biofilm cultures of E. coli, in response to DNA damaging agents and oxygen depletion. The results indicate that uninduced biofilms express more transcripts of the colicin operon than uninduced planktonic cells. Whole genome expression profiles confirmed that in uninduced biofilms, SOS genes are upregulated compared to planktonic cultures. However, DNA damaging agents and oxygen depletion augmented colicin expression in planktonic cells, while only marginal increase was recorded in biofilms. Our results suggest that the regulation of colicin E2 expression in E. coli biofilms considerably differ from planktonic cells, thus the induction of colicins in their host natural environment, i.e., the gastrointestinal tract, needs to be re-evaluated.

The effect of circular soil biosolarization treatment on the physiology, metabolomics, and microbiome of tomato plants under certain abiotic stresses.

Soil biosolarization (SBS) is an alternative technique for soil pest control to standard techniques such as soil fumigation and soil solarization (SS). By using both solar heating and fermentation of organic amendments, faster and more effective control of soilborne pathogens can be achieved. A circular economy may be created by using the residues of a given crop as organic amendments to biosolarize fields that produce that crop, which is termed circular soil biosolarization (CSBS). In this study, CSBS was employed by biosolarizing soil with amended tomato pomace (TP) residues and examining its impact on tomato cropping under conditions of abiotic stresses, specifically high salinity and nitrogen deficiency. The results showed that in the absence of abiotic stress, CSBS can benefit plant physiological performance, growth and yield relative to SS. Moreover, CSBS significantly mitigated the impacts of abiotic stress conditions. The results also showed that CSBS impacted the soil microbiome and plant metabolome. Mycoplana and Kaistobacter genera were found to be positively correlated with benefits to tomato plants health under abiotic stress conditions. Conversely, the relative abundance of the orders RB41, MND1, and the family Ellin6075 and were negatively correlated with tomato plants health. Moreover, several metabolites were significantly affected in plants grown in SS- and CSBS-treated soils under abiotic stress conditions. The metabolite xylonic acid isomer was found to be significantly negatively correlated with tomato plants health performance across all treatments. These findings improve understanding of the interactions between CSBS, soil ecology, and crop physiology under abiotic stress conditions.

NMR-based leaf metabolic profiling of V. planifolia and three endemic Vanilla species from the Peruvian Amazon.

The fruit of Vanilla planifolia is broadly preferred by the agroindustry and gourmet markets due to its refined flavor and aroma. Peruvian Vanilla has been proposed as a possible source for genetic improvement of existing Vanilla cultivars, but, little has been done to facilitate comprehensive studies of these and other Vanilla. Here, a nuclear magnetic resonance (NMR) metabolomic platform was developed to profile for the first time the leaves - organ known to accumulate vanillin putative precursors - of V. planifolia and those of Peruvian V. pompona, V. palmarum, and V. ribeiroi, with the aim to determine metabolic differences among them. Analysis of the NMR spectra allowed the identification of thirty-six metabolites, twenty-five of which were quantified. One-way ANOVA and post-hoc Tukey test revealed that these metabolites changed significantly among species, whilst multivariate-analyses allowed the identification of malic and homocitric acids, together with two vanillin precursors, as relevant metabolic markers for species differentiation.

Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel.

BACKGROUND: Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds-namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. RESULTS: Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson's |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson's |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson's |r|≥ 0.84. CONCLUSIONS: Our analysis corrected the previously stated Pearson's correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.

Morphological and metabolic profiling of a tropical-adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor.

Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.

Correlation-based network analysis combined with machine learning techniques highlight the role of the GABA shunt in Brachypodium sylvaticum freezing tolerance.

Perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 2022, contributing significantly to carbon and nitrogen sequestration. However, perennial grasses productivity can be limited by severe freezing conditions in some geographical areas, although these risks could decrease with the advance of climate warming, the possibility of unpredictable early cold events cannot be discarded. We conducted a study on the model perennial grass Brachypodium sylvaticum to investigate the molecular mechanisms that contribute to cold and freezing adaption. The study was performed on two different B. sylvaticum accessions, Ain1 and Osl1, typical to warm and cold climates, respectively. Both accessions were grown under controlled conditions with subsequent cold acclimation followed by freezing stress. For each treatment a set of morphological parameters, transcription, metabolite, and lipid profiles were measured. State-of-the-art algorithms were employed to analyze cross-component relationships. Phenotypic analysis revealed higher adaption of Osl1 to freezing stress. Our analysis highlighted the differential regulation of the TCA cycle and the GABA shunt between Ain1 and Osl1. Osl1 adapted to freezing stress by repressing the GABA shunt activity, avoiding the detrimental reduction in fatty acid biosynthesis and the concomitant detrimental effects on membrane integrity.

A Genetic Algorithm to Optimize Weighted Gene Co-Expression Network Analysis.

Weighted gene co-expression network analysis (WGCNA) is a widely used software tool that is used to establish relationships between phenotypic traits and gene expression data. It generates gene modules and then correlates their first principal component to phenotypic traits, proposing a functional relationship expressed by the correlation coefficient. However, gene modules often contain thousands of genes of different functional backgrounds. Here, we developed a stochastic optimization algorithm, known as genetic algorithm (GA), optimizing the trait to gene module relationship by gradually increasing the correlation between the trait and a subset of genes of the gene module. We exemplified the GA on a Japanese plum hormone profile and an RNA-seq dataset. The correlation between the subset of module genes and the trait increased, whereas the number of correlated genes became sufficiently small, allowing for their individual assessment. Gene ontology (GO) term enrichment analysis of the gene sets identified by the GA showed an increase in specificity of the GO terms associated with fruit hormone balance as compared with the GO enrichment analysis of the gene modules generated by WGCNA and other methods.

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

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 ß-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the ß-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.

Ethylene Response of Plum ACC Synthase 1 (ACS1) Promoter is Mediated through the Binding Site of Abscisic Acid Insensitive 5 (ABI5).

The enzyme 1-amino-cyclopropane-1-carboxylic acid synthase (ACS) participates in the ethylene biosynthesis pathways and it is tightly regulated transcriptionally and post-translationally. Notwithstanding its major role in climacteric fruit ripening, the transcriptional regulation of ACS during ripening is not fully understood. We studied fruit ripening in two Japanese plum cultivars, the climacteric Santa Rosa (SR) and its non-climacteric bud sport mutant, Sweet Miriam (SM). As the two cultivars show considerable difference in ACS expression, they provide a good system for the study of the transcriptional regulation of the gene. To investigate the differential transcriptional regulation of ACS1 genes in the SR and SM, their promoter region, which showed only minor sequence differences, was isolated and used to identify the binding of transcription factors interacting with specific ACS1 cis-acting elements. Three transcription factors (TFs), abscisic acid-insensitive 5 (ABI5), GLABRA 2 (GL2), and TCP2, showed specific binding to the ACS1 promoter. Synthetic DNA fragments containing multiple cis-acting elements of these TFs fused to ß-glucuronidase (GUS), showed the ABI5 binding site mediated ethylene and abscisic acid (ABA) responses of the promoter. While TCP2 and GL2 showed constant and similar expression levels in SM and SR fruit during ripening, ABI5 expression in SM fruits was lower than in SR fruits during advanced fruit ripening states. Overall, the work demonstrates the complex transcriptional regulation of ACS1.

Hormone balance in a climacteric plum fruit and its non-climacteric bud mutant during ripening.

Hormone balance plays a crucial role in the control of fruit ripening. We characterized and compared hormone balance in two Japanese plum cultivars (Prunus salicina Lindl.), namely Santa Rosa, a climacteric type, and Sweet Miriam, its non-climacteric bud-sport mutant. We assessed hormonal changes in gene expression associated with hormone biosynthesis, perception and signaling during ripening on-the tree and throughout postharvest storage and in response to ethylene treatments. Non-climacteric fruit displayed lower ethylene levels than climacteric fruit at all stages and lower auxin levels during the initiation of ripening on-the-tree and during most of post-harvest storage. Moreover, 1-MCP-induced ethylene decrease also resulted in low auxin contents in Santa Rosa, supporting the role of auxin in climacteric fruit ripening. The differences in auxin contents between Santa Rosa and Sweet Miriam fruit could be the consequence of different routed auxin biosynthesis pathways as indicated by the significant negative correlations between clusters of auxin metabolism-associated genes. Ethylene induced increased ABA levels throughout postharvest storage in both ripening types. Overall, ripening of Santa Rosa and Sweet Miriam fruit are characterized by distinct hormone accumulation pathways and interactions.

Draft Genome Sequence of the Nitrogen-Fixing Endophyte Azoarcus communis SWub3.

Here we report a draft genome sequence of Azoarcus communis SWub3, a nitrogen-fixing bacterium isolated from root tissues of Kallar grass in Pakistan.

NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems.

The berry of Physalis peruviana L. (Solanaceae) represents an important socio-economical commodity for Latin America. The absence of a clear phenotype renders it difficult to trace its place of origin. In this study, Cape gooseberries from eight different regions within the Peruvian Andes were profiled for their metabolism implementing a NMR platform. Twenty-four compounds could be unequivocally identified and sixteen quantified. One-way ANOVA and post-hoc Tukey test revealed that all of the quantified metabolites changed significantly among regions: Bambamarca I showed the most accumulated significant differences. The coefficient of variation demonstrated high phenotypic plasticity for amino acids, while sugars displayed low phenotypic plasticity. Correlation analysis highlighted the closely coordinated behavior of the amino acid profile. Finally, PLS-DA revealed a clear separation among the regions based on their metabolic profiles, accentuating the discriminatory capacity of NMR in establishing significant phytochemical differences between producing regions of the fruit of P. peruviana L.

The common transcriptional subnetworks of the grape berry skin in the late stages of ripening.

BACKGROUND: Wine grapes are important economically in many countries around the world. Defining the optimum time for grape harvest is a major challenge to the grower and winemaker. Berry skins are an important source of flavor, color and other quality traits in the ripening stage. Senescent-like processes such as chloroplast disorganization and cell death characterize the late ripening stage. RESULTS: To better understand the molecular and physiological processes involved in the late stages of berry ripening, RNA-seq analysis of the skins of seven wine grape cultivars (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay, Sauvignon Blanc and Semillon) was performed. RNA-seq analysis identified approximately 2000 common differentially expressed genes for all seven cultivars across four different berry sugar levels (20 to 26 °Brix). Network analyses, both a posteriori (standard) and a priori (gene co-expression network analysis), were used to elucidate transcriptional subnetworks and hub genes associated with traits in the berry skins of the late stages of berry ripening. These independent approaches revealed genes involved in photosynthesis, catabolism, and nucleotide metabolism. The transcript abundance of most photosynthetic genes declined with increasing sugar levels in the berries. The transcript abundance of other processes increased such as nucleic acid metabolism, chromosome organization and lipid catabolism. Weighted gene co-expression network analysis (WGCNA) identified 64 gene modules that were organized into 12 subnetworks of three modules or more and six higher order gene subnetworks. Some gene subnetworks were highly correlated with sugar levels and some subnetworks were highly enriched in the chloroplast and nucleus. The petal R package was utilized independently to construct a true small-world and scale-free complex gene co-expression network model. A subnetwork of 216 genes with the highest connectivity was elucidated, consistent with the module results from WGCNA. Hub genes in these subnetworks were identified including numerous members of the core circadian clock, RNA splicing, proteolysis and chromosome organization. An integrated model was constructed linking light sensing with alternative splicing, chromosome remodeling and the circadian clock. CONCLUSIONS: A common set of differentially expressed genes and gene subnetworks from seven different cultivars were examined in the skin of the late stages of grapevine berry ripening. A densely connected gene subnetwork was elucidated involving a complex interaction of berry senescent processes (autophagy), catabolism, the circadian clock, RNA splicing, proteolysis and epigenetic regulation. Hypotheses were induced from these data sets involving sugar accumulation, light, autophagy, epigenetic regulation, and fruit development. This work provides a better understanding of berry development and the transcriptional processes involved in the late stages of ripening.

Bet-hedging in bacteriocin producing Escherichia coli populations: the single cell perspective.

Production of public goods in biological systems is often a collaborative effort that may be detrimental to the producers. It is therefore sustainable only if a small fraction of the population shoulders the cost while the majority reap the benefits. We modelled this scenario using Escherichia coli populations producing colicins, an antibiotic that kills producer cells' close relatives. Colicin expression is a costly trait, and it has been proposed that only a small fraction of the population actively expresses the antibiotic. Colicinogenic populations were followed at the single-cell level using time-lapse microscopy, and showed two distinct, albeit dynamic, subpopulations: the majority silenced colicin expression, while a small fraction of elongated, slow-growing cells formed colicin-expressing hotspots, placing a significant burden on expressers. Moreover, monitoring lineages of individual colicinogenic cells showed stochastic switching between expressers and non-expressers. Hence, colicin expressers may be engaged in risk-reducing strategies-or bet-hedging-as they balance the cost of colicin production with the need to repel competitors. To test the bet-hedging strategy in colicin-mediated interactions, competitions between colicin-sensitive and producer cells were simulated using a numerical model, demonstrating a finely balanced expression range that is essential to sustaining the colicinogenic population.

Genome-wide transcription profiling of aerobic and anaerobic Escherichia coli biofilm and planktonic cultures.

Many studies have described the response of the facultative anaerobe, Escherichia coli, to anaerobic conditions, yet they all investigated free-living (planktonic) cells because attempts to cultivate anaerobic E. coli biofilm were mostly unsuccessful. We challenged these findings and cultivated E. coli strain MG1655 biofilm under both aerobic and anaerobic conditions, characterizing the mature biofilm architecture and global gene expression profile. We used RNA sequencing technology to compare stationary phase planktonic cells with mature biofilm, cultured with and without oxygen. Our results suggest that gene expression patterns significantly differ between biofilm and planktonic cultures cultivated under the same oxygenic conditions. The anaerobic E. coli biofilms were slow growing and patchy compared to aerobic biofilms, yet some features were unchanged like the production of extracellular polymeric substances. A closer inspection of the mRNA data revealed that essential cell processes were attenuated in anaerobic biofilms, including protein synthesis, information transfer, cell structure, regulation and transport. Our results suggest that lack of oxygen imposes severe stress on mature biofilms thus limiting the cells' activity. We further propose that E. coli does not favor growing in anaerobic biofilms and when forced to do so, the cells prevail by attenuating their activity in order to survive.

Environmental and genetic effects on tomato seed metabolic balance and its association with germination vigor.

BACKGROUND: The metabolite content of a seed and its ability to germinate are determined by genetic makeup and environmental effects during development. The interaction between genetics, environment and seed metabolism and germination was studied in 72 tomato homozygous introgression lines (IL) derived from Solanum pennelli and S. esculentum M82 cultivar. Plants were grown in the field under saline and fresh water irrigation during two consecutive seasons, and collected seeds were subjected to morphological analysis, gas chromatograph-mass spectrometry (GC-MS) metabolic profiling and germination tests. RESULTS: Seed weight was under tight genetic regulation, but it was not related to germination vigor. Salinity significantly reduced seed number but had little influence on seed metabolites, affecting only 1% of the statistical comparisons. The metabolites negatively correlated to germination were simple sugars and most amino acids, while positive correlations were found for several organic acids and the N metabolites urea and dopamine. Germination tests identified putative loci for improved germination as compared to M82 and in response to salinity, which were also characterized by defined metabolic changes in the seed. CONCLUSIONS: An integrative analysis of the metabolite and germination data revealed metabolite levels unambiguously associated with germination percentage and rate, mostly conserved in the different tested seed development environments. Such consistent relations suggest the potential for developing a method of germination vigor prediction by metabolic profiling, as well as add to our understanding of the importance of primary metabolic processes in germination.

Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism.

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.

Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays.

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H(2) tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H(2) scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2' is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.