Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

Abstract

Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R 2 = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R 2 >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement.

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

“…The variation of almost all PAs in this study was controlled by a few loci with large effects, which is in accordance with a handful of previous studies on specialized metabolites and distinct from those described for QTLs of primary traits (Fridman et al, 2004;Rowe et al, 2008;Schauer et al, 2008;Chan et al, 2010;Joseph et al, 2013;Alseekh et al, 2015;Wen et al, 2015), for which a large number of minor loci are usually detected. 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).…”

“…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).…”

“…Eleven rice phenolamides in this study refer to different combinations of CoA-thioesters and amine moieties. combined metabolite-based linkage and association studies have provided powerful forward-genetic strategies for dissecting the genetic and biochemical bases of metabolism in plants (Riedelsheimer et al, 2012;Gong et al, 2013;Chen et al, 2014;Wen et al, 2014Wen et al, , 2015Alseekh et al, 2015;Matsuda et al, 2015). As for the BAHD family of enzymes in rice, OsAT1 has been reported to be involved in responses to rice blight and blast (Mori et al, 2007).…”

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

“…Recently, large-scale metabolic trait QTL mapping was also performed in the same RIL population in different tissues including leaf (Wen et al, 2015). A common QTL hotspot was observed on chromosome 10 (Supplemental Fig.…”

“…With the rapid development of next-generation sequencing and high-density single-nucleotide polymorphism (SNP) genotyping technologies, linkage mapping and genome-wide association studies have been used widely to dissect the genetic architecture of agriculturally important traits in commercial maize (Buckler et al, 2009;Kump et al, 2011;Tian et al, 2011;Li et al, 2013;Wen et al, 2014Wen et al, , 2015. Many genes and variants underlying agriculturally important traits have been discovered in crops (Kesavan et al, 2013;Zuo and Li, 2013;Martinez et al, 2016).…”