Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

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

Additional File 1: Table S1.

Normalized full metabolic dataset. Table S2. Seed weight in response to salinity for both seasons. Table S3. Putative QTLs for maturation percent. Table S4. Putative QTLs for RMC in SDF. Table S5. Putative QTLs for RMC in SDS. (XLSX 882 kb

Additional File 2:

QTL Map of co-localized metabolite and germination QTLs in SDS. (PDF 230 kb

Additional File 3:

Correlation networks of 2010 for SDF. (PDF 545 kb

Significant metabolites identified for different ILs in season I.

<p>Bar graph representation of significant metabolites identified by Dunnett's-test (p-value<0.05 – after Bonferroni correction) as applied to dry IL seeds of harvest season I in Akko, Israel, in comparison with the control M82. Each bar graph depicts a single metabolite and fold change as compared to M82. Control (M82) levels are shown in dark blue. Metabolites are categorized according to their compound class.</p

Morphological traits—metabolite correlation/significance.

<p>Correlation between metabolic data as analyzed on dry IL seeds of harvest season I in Akko, Israel and the ILs' morphological traits. The Pearson product-moment correlation was used to calculate all pairwise correlations between morphological traits and metabolites heading the rows and morphological traits and metabolites heading the columns. In the colored area, rectangles represent <i>r</i> values resulting from Pearson correlation coefficient computation (see correlation color key). In the black and white area, rectangles represent p-values respective to Pearson correlation coefficient (see Significance color key). <i>Z</i>-score transformation was employed to enable correlation computation. X and Y-axes are categorized into morphological traits and metabolites, grouped by compound classes.</p

Network measures estimated on fruit and seed datasets.

1<p>p value based on 1,000 permutations.</p><p>Individual ILs were ranked according to increasing difference in variance of the fruit dataset compared to the average variance in the seed dataset (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002612#pgen.1002612.s017" target="_blank">Table S8</a>). Network properties were calculated from a network reconstructed by using the data from the ordered list of ILs. For instance, for n = 25, the 25 ILs from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002612#pgen.1002612.s017" target="_blank">Table S8</a> were used in creation of the correlation network associated to the data from only these n = 25 ILs. Subsets comprising the first 15 to 25, 50 and 76 of the fruit ILs ranked in non-decreasing order with respect to their variance were used to construct correlation-based networks (<i>r</i>≥0.3, p≤0.01). Four network properties were calculated for each subset-based fruit network: density, degree, clustering coefficient, and diameter (value). Values represent the estimates of the respective network measures for each subset of fruit ILs. By performing the classical permutation test with 1,000 repetitions, the statistical significance of the differences in measures between the subset-based fruit networks and the seed network (first data row in the table) were measured. In each permutation, the order of each metabolite within the subset was randomized, and the newly ordered dataset was subjected to correlation analysis and network measures estimation. The difference between newly generated network property values in the seed and the fruit, upon randomization, were tested to check whether their value is at most that of the difference for the original networks. Subsequently, the total number of occurrences meeting this criterion formed the basis for the empirical p-value estimation. With the exception of the network diameter, density, degree, and clustering coefficient of the fruit IL subset networks are significantly different from the corresponding measures in the seed network.</p

mQTL map of shared significant changes over seasons I and II.

<p>GC-MS measurements were performed for consecutive seasons I and II in Akko, Israel; metabolic concentrations were measured in dry seeds. The putative mQTL and their associated metabolites are depicted in the Figure. Symbols next to putative mQTL indicate results of pairwise 2-way ANOVA for each metabolite across the seasons of matching ILs. Blue symbols refer to the single term – genotype; yellow symbols refer to the interaction term – season * genotype. Blue and yellow ticks indicate confirmed by single or interaction term, respectively; blue and yellow crosses indicate not confirmed by single or interaction term, respectively.</p

Seed-fruit metabolite network.

<p>Network visualization of metabolites as analyzed on dry IL seed and fruit metabolites of harvest season I in Akko, Israel. Metabolites are represented as nodes, and their relations, as edges. The Pearson product-moment correlation was employed to compute all pairwise correlations between metabolites across the entire set of ILs. Only significant correlations are depicted. A significance level of ≤0.01 and an <i>r</i>-value of ≥0.3 were considered to be significant. Seed metabolites are depicted as circular black-bordered nodes, fruit metabolites are depicted as diamond-shaped red-bordered nodes. Metabolites are color coded and clustered according to their compound classes. The two tissue sub-networks are separated spatially into the left region (seed network) and right region (fruit network). Positive correlations are denoted as blue edges, and negative correlations are denoted as red edges. Computations of the correlations were conducted under the R environment. Cytoscape was used to generate the graphical output of the networks.</p