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

Abstract

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.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis

“…The heritability values obtained in our analysis resemble those in previous studies of metabolic traits in Arabidopsis, tomato (Solanum lycopersicum), and wheat (Triticum aestivum) (Kliebenstein et al, 2001a;Keurentjes et al, 2006;Toubiana et al, 2012;Alseekh et al, 2015;Matros et al, 2017). Broad-sense heritability (H 2 ) for biomass, structural components, metabolites, and several enzyme activities (aINV, nINV, PEPC, AGP, UGP, NRVs, and GDH) was moderate to high (20-74%), with marker-based h 2 (Kruijer et al, 2015) very close to H 2 values.…”

Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato

“…populations with many previous examples being published in Arabidopsis, potato, and maize (Rowe et al, 2008;Lisec et al, 2011;Carreno-Quintero et al, 2013), as well as for a range of different traits and tissue types in this very tomato population (Schauer et al, , 2008Toubiana et al, 2012). However, this study revealed several novel insights concerning the interrelation of traits from primary and secondary metabolism with yieldassociated traits.…”

Evolutionarily Distinct BAHD N-acyltransferases are Responsible for Natural Variation of Aromatic Amine Conjugates in Rice

“…Tissue-specific accumulation of metabolites, representing one of the main contributions to metabolite diversity, is of great interest to plant scientists (Schilmiller et al, 2010;Chan et al, 2011;Watanabe et al, 2013). Metabolic diversity between different tissues might arise from differences in spatial-temporal expression of genes (Kim et al, 2012;Thatcher et al, 2014) and in some cases by duplicate genes that display spatially or temporally distinct expression patterns (Toubiana et al, 2012;Matsuba et al, 2013). By comparing genetically controlled natural variation of PAs in different tissues at high resolution, we have shown that although coordinated genetic control across various tissues can be observed for some PAs, the majority of the loci were obtained in a tissue-specific manner (Supplemental Table 4), suggesting distinct regulation underlying the metabolic readout between tissues.…”

“…Much of the enormous diversity of specialized metabolites in plants comes from modifications or decorations of core structures by different tailing reactions such as acylation (Bontpart et al, 2015), glycosylation (Bowles et al, 2005), methylation (Lam et al, 2007), and so on (Schwab, 2003). In addition, many studies have revealed that plant specialized metabolites accumulate with tissue specificity (Schilmiller et al, 2010;McDowell et al, 2011;Kim et al, 2012;Toubiana et al, 2012;Watanabe et al, 2013;Dong et al, 2015;Wen et al, 2015).…”