Yield quantitative trait loci from wild tomato are predominately expressed by the shoot

Plant yield is the integrated outcome of processes taking place above and below ground. To explore genetic, environmental and developmental aspects of fruit yield in tomato, we phenotyped an introgression line (IL) population derived from a cross between the cultivated tomato (Solanum lycopersicum) and a wild species (Solanum pennellii). Both homozygous and heterozygous ILs were grown in irrigated and non-irrigated fields and evaluated for six yield components. Thirteen lines displayed transgressive segregation that increased agronomic yield consistently over 2 years and defined at least 11 independent yield-improving QTL. To determine if these QTL were expressed in the shoots or the roots of the plants, we conducted field trials of reciprocally grafted ILs; out of 13 lines with an effect on yield, 10 QTL were active in the shoot and only IL8-3 showed a consistent root effect. To further examine this unusual case, we evaluated the metabolic profiles of fruits from both the homo- and heterozygous lines for IL8-3 and compared these to those obtained from the fruit of their equivalent genotypes in the root effect population. We observed that several of these metabolic QTL, like the yield QTL, were root determined; however, further studies will be required to delineate the exact mechanism mediating this effect in this specific line. The results presented here suggest that genetic variation for root traits, in comparison to that present in the shoot, represents only a minor component in the determination of tomato fruit yield. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-010-1456-9) contains supplementary material, which is available to authorized users

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

Mode of Inheritance of Primary Metabolic Traits in Tomato[W][OA]

To evaluate components of fruit metabolic composition, we have previously metabolically phenotyped tomato (Solanum lycopersicum) introgression lines containing segmental substitutions of wild species chromosome in the genetic background of a cultivated variety. Here, we studied the hereditability of the fruit metabolome by analyzing an additional year's harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs), allowing the evaluation of putative quantitative trait locus (QTL) mode of inheritance. These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number displaying the characteristics of overdominant inheritance. Comparison of the mode of inheritance of QTL revealed that several metabolite pairs displayed a similar mode of inheritance of QTL at the same chromosomal loci. Evaluation of the association between morphological and metabolic traits in the ILHs revealed that this correlation was far less prominent, due to a reduced variance in the harvest index within this population. These data are discussed in the context of genomics-assisted breeding for crop improvement, with particular focus on the exploitation of wide biodiversity

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

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

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