Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality    

Abstract

Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solanum tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits.The variation observed in phenotypic trait values in plants is often of quantitative nature, and it remains challenging to unravel the genetic basis of these traits. Quantitative trait locus (QTL) mapping is currently the most commonly used approach to dissect the genetic factors underlying complex traits. The goal of QTL mapping is to identify genomic regions associated with a specific complex phenotype by statistical analysis of the associations between genetic markers and phenotypic variation (Doerge, 2002). Recently, advances in highthroughput analysis and analytical detection methods have facilitated more integrated approaches to measure global phenotypic variation at the molecular level. Metabolite profiling is a rapidly evolving technology that has significantly increased the possibilities of performing high-throughput analysis of hundreds to thousands of compounds in a range of plants, including complex crop species. Metabolite composition is of great importance in crop plants, as a number of important traits such as biotic and abiotic stress resistance, postharvest processing, and nutritional value are largely dependent on the metabolic content (Fernie and Schauer, 2009).In potato (Solanum tuberosum) breeding, metabolomic studies have progressively increased in importance, as many potato tuber traits such as content and quality of starch, chipping quality, flesh color, taste, and glycoalkaloid content have been shown to be linked to a wide range of metabolites (Coffin et al., 1987;Dobson et al., 2008). As a result, tuber quality can be assessed by assaying a range of metabolites. Gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) has been shown to be useful for the rapid and highly sensitive detection of a large fraction of plant metabolites covering the central pathways of primary metabolism (Roessner et al., 2000;Lisec et al., 2006). In potato, untargeted metab...

HsfA2 Controls the Activity of Developmentally and Stress-Regulated Heat Stress Protection Mechanisms in Tomato Male Reproductive Tissues

“…Anther powder (10-45 mg) was subsequently weighed and transferred into a 1.5-mL Eppendorf tube. The extraction of primary metabolites was performed at room temperature as previously described by Carreno-Quintero et al (2012). Seven hundred microliters of 100% methanol together with 300 mL of distilled water were added to each anther samples.…”

Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

“…Such approaches have been very successful in addressing genomic regions responsible for glucosinolate accumulation in Arabidopsis and related species (10, 14-16). Compared with modern sequencing and proteomics technologies, the profiling of entire plant metabolomes is, however, technically unfeasible with the existing analytical platforms, and, as a consequence, the analysis of metabolite natural variation has frequently been biased to secondary metabolite classes, for which a priori knowledge exists regarding their biological function, or to well-mapped parts of primary metabolism associated with energy and growth processes (17)(18)(19).Another critical aspect for exploiting natural variation in metabolism lies in the identification of unknown metabolites that exhibit significant associations with a phenotype of interest. Nontargeted approaches for rapidly collecting repertoires of tandem mass…”

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

“…GC-MS and LC-MS are sensitive, robust, and reproducible techniques for the comprehensive metabolomic profiling of a wide range of metabolites (Tohge and Fernie, 2009). They facilitated study of the coordination of carbon and nitrogen metabolism in plants (Stitt and Fernie, 2003;Rubin et al, 2009;Pracharoenwattana et al, 2010;Kusano et al, 2011a;Amiour et al, 2012) and studies to gain a better understanding of regulatory networks involved in genetic perturbation (Roessner et al, 2001;Weckwerth et al, 2004;Tohge et al, 2005), to characterize diurnal/circadian behaviors (Urbanczyk-Wochniak et al, 2005;Gibon et al, 2006;Fukushima et al, 2009a;Espinoza et al, 2010;Hoffman et al, 2010), to assess altered regulatory responses to various abiotic stresses (Kaplan et al, 2004;Urano et al, 2009;Caldana et al, 2011;Kusano et al, 2011b;Maruyama et al, 2014;Nakabayashi et al, 2014), and to identify comprehensive metabolite quantitative trait loci (Morreel et al, 2006;Schauer et al, 2006;Carreno-Quintero et al, 2012;Matsuda et al, 2012).…”