Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain

Abstract

SummaryMetabolomics is becoming an increasingly important tool in plant genomics to decipher the function of genes controlling biochemical pathways responsible for trait variation. Although theoretical models can integrate genes and metabolites for trait variation, biological networks require validation using appropriate experimental genetic systems. In this study, we applied an untargeted metabolite analysis to mature grain of wheat homoeologous group 3 ditelosomic lines, selected compounds that showed significant variation between wheat lines Chinese Spring and at least one ditelosomic line, tracked the genes encoding enzymes of their biochemical pathway using the wheat genome survey sequence and determined the genetic components underlying metabolite variation. A total of 412 analytes were resolved in the wheat grain metabolome, and principal component analysis indicated significant differences in metabolite profiles between Chinese Spring and each ditelosomic lines. The grain metabolome identified 55 compounds positively matched against a mass spectral library where the majority showed significant differences between Chinese Spring and at least one ditelosomic line. Trehalose and branched-chain amino acids were selected for detailed investigation, and it was expected that if genes encoding enzymes directly related to their biochemical pathways were located on homoeologous group 3 chromosomes, then corresponding ditelosomic lines would have a significant reduction in metabolites compared with Chinese Spring. Although a proportion showed a reduction, some lines showed significant increases in metabolites, indicating that genes directly and indirectly involved in biosynthetic pathways likely regulate the metabolome. Therefore, this study demonstrated that wheat aneuploid lines are suitable experimental genetic system to validate metabolomics-genomics networks.

Untargeted metabolomics of neuronal cell culture: A model system for the toxicity testing of insecticide chemical exposure

“…The process of statistical analysis was repeated as described previously for untargeted metabolomics by GC–MS (Francki, Hayton, Gummer, Rawlinson, & Trengove, ), and a brief outline follows. For all multivariate and univariate statistical analyses, the peak areas ( X ) were first transformed using the equation log 10 ( X + 1).…”

“…The process of statistical analysis was repeated as described previously for untargeted metabolomics by GC-MS (Francki, Hayton, Gummer, Rawlinson, & Trengove, 2016), and a brief outline follows.…”

Omics Approaches for Engineering Wheat Production under Abiotic Stresses

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Omics Approaches for Engineering Wheat Production under Abiotic Stresses

“…Aneuploidy genetic lines of wheat proved to be effective experimental design to assess genetic networks as they are helpful in identifying those genes which control phenotypes [111,112]. Michaletti et al [113] concluded that the study of metabolomics and proteomics reveal drought stress responses of leaf tissue from spring wheat and provide a better framework for understanding the mechanisms that command plant cell response to drought stress with knowledge of molecules that can be used for crop improvement programs.…”