Abstract
It has been recently demonstrated, utilizing interspecific introgression lines of tomato, generated from the cross between Solanum lycopersicum and the wild species Solanum pennellii, that the efficiency of photosynthate partitioning exerts a considerable influence on the metabolic composition of tomato fruit pericarp. In order to further evaluate the influence of source-sink interaction, metabolite composition was determined by gas chromatography-mass spectrometry in a different population. For this purpose, we used 23 introgression lines resulting from an interspecific cross between S. lycopersicum and the wild species Solanum chmielewskii under high (unpruned trusses) and low (trusses pruned to one fruit) fruit load conditions. Following this strategy, we were able to contrast the metabolite composition of fruits from plants cultivated at both fruit loads as well as to compare the network behavior of primary metabolism in the introgression line population. The study revealed that while a greater number of metabolic quantitative trait loci were observed under high fruit load (240) than under low fruit load (128) cultivations, the levels of metabolites were more highly correlated under low fruit load cultivation. Finally, an analysis of genotype × fruit load interactions indicated a greater influence of development and cultivation than genotype on fruit composition. Comparison with previously documented transcript profiles from a subset of these lines revealed that changes in metabolite levels did not correlate with changes in the levels of genes associated with their metabolism. These findings are discussed in the context of our current understanding of the genetic and environmental influence on metabolic source-sink interactions in tomato, with particular emphasis given to fruit amino acid content.
Highlights
It has been recently demonstrated, utilizing interspecific introgression lines of tomato, generated from the cross between Solanum lycopersicum and the wild species Solanum pennellii, that the efficiency of photosynthate partitioning exerts a considerable influence on the metabolic composition of tomato fruit pericarp
To investigate metabolic quantitative trait locus (QTL) apparent under two different fruit load treatments (HL, in which tomato fruits were allowed to develop naturally, and LL, where all but one fruit per truss was removed), we carried out extensive metabolic profiling of the primary metabolism of tomato fruit pericarp in a previously characterized S. chmielewskii introgression line population (Prudent et al, 2009) using an established gas chromatography-mass spectrometry-based method (Lisec et al, 2006)
We evaluated primary metabolite composition under two different fruit load conditions in a S. chmielewskii introgression line population in which the majority of the S. chmielewskii genome was substituted in the background of S. lycopersicum ‘Moneyberg’ (Prudent et al, 2009)
Summary
It has been recently demonstrated, utilizing interspecific introgression lines of tomato, generated from the cross between Solanum lycopersicum and the wild species Solanum pennellii, that the efficiency of photosynthate partitioning exerts a considerable influence on the metabolic composition of tomato fruit pericarp. In order to further evaluate the influence of source-sink interaction, metabolite composition was determined by gas chromatography-mass spectrometry in a different population For this purpose, we used 23 introgression lines resulting from an interspecific cross between S. lycopersicum and the wild species Solanum chmielewskii under high (unpruned trusses) and low (trusses pruned to one fruit) fruit load conditions. While association mappingbased approaches have begun to be advocated for this purpose (Fernie and Schauer, 2009; Sulpice et al, 2009) and examples even exist where it has been successful, such as the mapping of genes underlying provitamin A content (Harjes et al, 2008) and kernel composition (Wilson et al, 2004) in maize (Zea mays), the majority of studies to date have relied on quantitative trait locus (QTL)-based methods (Keurentjes et al, 2006; Lisec et al, 2006, 2008; Schauer et al, 2006, 2008; Rowe et al., 2008; Zanor et al, 2009a) This is true for metabolomics-scale approaches in which tens to hundreds of metabolites are measured simultaneously. The fact that dramatic alterations were observed in the amino acid content, without consistent alterations in transcript abundance of genes associated with either amino acid metabolism or protein degradation, sug-
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