Genetic mapping of semi-polar metabolites in pepper fruits (Capsicum sp.): towards unravelling the molecular regulation of flavonoid quantitative trait loci

Abstract

Untargeted LCMS profiling of semi-polar metabolites followed by metabolite quantitative trait locus (mQTL) analysis was performed in ripe pepper fruits of 113 F2 plants derived from a cross between Capsicum annuum AC1979 (no. 19) and Capsicum chinense No. 4661 Selection (no. 18). The parental accessions were selected based on their variation in fruit morphological characteristics and fruit content of some target phytonutrients. Clear segregation of fruit colour and fruit metabolite profiles was observed in the F2 population. The F2 plants formed three clusters based on their metabolite profiles. Of the total of 542 metabolites, 52 could be annotated, including a range of flavonoids, such as flavone C-glycosides, flavonol O-glycosides and naringenin chalcone, as well as several phenylpropanoids, a capsaicin analogue, fatty acid derivatives and amino acid derivatives. Interval mapping revealed 279 mQTLs in total. Two mQTL hotspots were found on chromosome 9. These two chromosomal regions regulated the relative levels of 35 and 103 metabolites, respectively. Analysis also revealed an mQTL for a capsaicin analogue, located on chromosome 7. Confirmation of flavonoid mQTLs using a set of six flavonoid candidate gene markers and their corresponding expression data (expression QTLs) indicated the Ca-MYB12 transcription factor gene on chromosome 1 and the gene encoding flavone synthase (FS-2) on chromosome 6 as likely causative genes determining the variation in naringenin chalcone and flavone C-glycosides, respectively, in this population. The combination of large-scale metabolite profiling and QTL analysis provided valuable insight into the genomic regions and genes important for the production of (secondary) metabolites in pepper fruit. This will impact breeding strategies aimed at optimising the content of specific metabolites in pepper fruit.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-013-9967-0) contains supplementary material, which is available to authorized users.

QTL mapping of insect resistance components of Solanum galapagense

“…Identifying hot spots and cold spots for mQTLs in plant genomes is not uncommon (Carreno-Quintero et al 2012; Keurentjes et al 2006; Maharijaya et al 2018; Rowe et al 2008; Wahyuni et al 2014). In a Solanum pennellii introgression population, hot spots and cold spots were detected for mQTLs related to secondary metabolism in tomato fruit pericarp (Alseekh et al 2015), while in pepper fruits two closely linked mQTL hot spots were detected that regulated the relative levels of 35 and 103 secondary metabolites, respectively (Wahyuni et al 2014). In leaves of the same pepper population, three mQTL hot spots were detected, of which one overlapped with the fruit mQTL hot spots (Maharijaya et al 2018).…”

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

“…While only a handful of studies have used broad genetic crosses to identify mQTLs for a broad range of secondary metabolites (Kliebenstein et al, 2001a(Kliebenstein et al, , 2001bKeurentjes et al, 2006;Morreel et al, 2006;Khan et al, 2012;Matsuda et al, 2012;Routaboul et al, 2012;Gong et al, 2013;Wahyuni et al, 2014) and previous studies have largely been focused on Arabidopsis, considerable research has been focused on defining QTLs for volatile organic compounds from tomato fruit (Tieman et al, 2006;Mathieu et al, 2009;Mageroy et al, 2012;Rambla et al, 2014) and acyl-sugars in tomato leaf trichomes (Schilmiller et al, 2012(Schilmiller et al, , 2010. In addition, the levels of primary metabolites have been evaluated in multiple different breeding populations (Schauer et al, , 2008Do et al, 2010) as well as in association mapping panels (Sauvage et al, 2014).…”

Natural variation of plant metabolism: genetic mechanisms, interpretive caveats, evolutionary and mechanistic insights.

“…Similar approaches have been used to identify and expand pathways for unknown metabolite classes within nonmodel systems like N. attenuata, Barbarea vulgaris, and Capsicum spp. (Dalby-Brown et al, 2011;Wahyuni et al, 2014;Agerbirk et al, 2015;Li et al, 2015). While this approach is very powerful at identifying both new compound structures and new enzymes, it is solely reliant on the presence of genetic variation in the genes controlling the accumulation of these unknown compounds.…”