Biochemical And Genetic Bases Of Maize Kernel Metabolome

Plants produce numerous structurally and physiological function diverse metabolites, which play essential roles in growth, development and stress responses. In addition, they provide essential resources for human survival. Maize is the world’s largest food and feed crops, which play an important role in the world economy and agricultural production. Here we present a comprehensive study of maize metabolism, combining genomics, genetics and expression profiling methodologies to dissect the genetic basis and natural variation of metabolic diversity in maize kernel.Application of a widely targeted metabolomics method based on liquid chromatography-mass spectrometry(LC-MS), the MS2 spectral tag(MS2T) library can be effectively constructed. In the maize kernel study, a total number of 983(almost) non-redundant metabolite features include 184 identified/annotated metabolites were obtained through a stepwise multiple ion monitoring-enhanced product ions(MIM-EPI) method. Integrating the data based on the MS2 T library and other available scheduled multiple reaction monitoring(sMRM) information, we measured 702 maize genotypes planted at multiple locations which containing three association panel and two RIL populations. To elucidate the natural variation and biochemical base of maize kernel metabolome, we analysed 983 metabolite features in 368 diverse maize inbreds through a further metabolic genome-wide association study(mGWAS). A total number of 1459 significant locus-trait associations(P ≤1.8×10-6) were identified across three environments. In the present study, 1197 unique candidate genes corresponding to 1459 significant locus-trait were annotated and 58.5% of the identified loci were supported by expression QTLs(eQTLs). To further validate the mGWAS results, we performed a linkage mapping analysis in two RIL populations(BB, B73/By804; ZY, Zong3/Yu87-1), which provided 1876 metabolic quantitative trait loci(mQTL). Some(14.7%) of the loci identified by GWAS were validated through linkage mapping.In the gene function study of maize kernel, a total number of 50 candidate genes associated with 19 phenolamides, 39 flavonoids and 8 tryptophan metabolite were obtained. Various methodologies like re-sequencing, expression QTLs and candidate gene association analysis were used to identify potential causal variants involved in metabolic traits. Five candidate genes were validated and two of these genes(PHT and CCoAOMT1) were further validated by mutant and transgenic analysis. Reconstructed of metabolic network is the other point of our research after gene function validation. Combining chemical construction analysis and genetic confirmation by mutant and transgenic, we reconstructed the pathway of phenolamides and flavonoids in maize kernel.Metabolic complement that considered as on kind of measured phenotypes in the study may correlated with some complex traits. A total number of 42 metabolite features significantly associated(P ≤ 0.05) with 100-kernel weight(HKW) were found by general step-wise regression analysis. In the present study, 26 metabolite feature detected in E2 can explain 72.6% of the phenotypic variance. Metabolite features associated with kernel with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.