Metabolite profiling and genome‐wide association studies reveal response mechanisms of phosphorus deficiency in maize seedling

Abstract

SUMMARYInorganic phosphorus (Pi) is an essential element in numerous metabolic reactions and signaling pathways, but the molecular details of these pathways remain largely unknown. In this study, metabolite profiles of maize (Zea mays L.) leaves and roots were compared between six low‐Pi‐sensitive lines and six low‐Pi‐tolerant lines under Pi‐sufficient and Pi‐deficient conditions to identify pathways and genes associated with the low‐Pi stress response. Results showed that under Pi deprivation the concentrations of nucleic acids, organic acids and sugars were increased, but that the concentrations of phosphorylated metabolites, certain amino acids, lipid metabolites and nitrogenous compounds were decreased. The levels of secondary metabolites involved in plant immune reactions, including benzoxazinoids and flavonoids, were significantly different in plants grown under Pi‐deficient conditions. Among them, the 11 most stable metabolites showed significant differences under low‐ and normal‐Pi conditions based on the coefficient of variation (CV). Isoleucine and alanine were the most stable metabolites for the identification of Pi‐sensitive and Pi‐resistant maize inbred lines. With the significant correlation between morphological traits and metabolites, five low‐Pi‐responding consensus genes associated with morphological traits and simultaneously involved in metabolic pathways were mined by combining metabolites profiles and genome‐wide association study (GWAS). The consensus genes induced by Pi deficiency in maize seedlings were also validated by reverse‐transcription quantitative polymerase chain reaction (RT‐qPCR). Moreover, these genes were further validated in a recombinant inbred line (RIL) population, in which the glucose‐6‐phosphate‐1‐epimerase encoding gene mediated yield and correlated traits to phosphorus availability. Together, our results provide a framework for understanding the metabolic processes underlying Pi‐deficient responses and give multiple insights into improving the efficiency of Pi use in maize.

Transcriptomic Analysis Reveals the Molecular Adaptation of Three Major Secondary Metabolic Pathways to Multiple Macronutrient Starvation in Tea (Camellia sinensis)

“…In the N depletion group, the highest number of up-regulated TFs was in the WRKY family (9), followed by the bHLH (7), ERF (3), MYB (2), NF-Y (1), and GARP (1), and Trihelix (1) TF families. In response to P limitation, 12 TF families were markedly changed, with bHLH (29), ERF (21), WRKY (18), and MYB (13) displaying the most abundant changes. In addition to the bHLH (64), ERF (36), MYB (38), and WRKY (30) families all contained DEGs, and some specific TF families were only represented under K depletion conditions (Tables S8-S10).…”

“…At present, there is little information about the secondary metabolite biosynthesis, or the genes and pathways modulated by N, P, and K starvation. For several decades now, RNA-sequencing has been a powerful tool for analyzing potential actors under any given condition, including macronutrient limitation [18][19][20][21][22]. Here, we reported the effects of macronutrient starvation on tea through RNA-seq.…”

Genetic study and molecular breeding for high phosphorus use efficiency in maize

“…Using the differentially expressed genes in the transcriptome data of the P-tolerant line CCM454 and the P-sensitive line 31778 as a validation, a total of 259 significantly associated genes were mined, which were mainly involved in four biochemical pathways, viz., transcriptional regulation, reactive oxygen scavenging, hormone regulation and remodeling of cell wall. Luo et al [75] . used 338 inbred lines to perform GWAS analysis and found five significant peaks for morphological traits.…”

“…For example, candidate gene mining by combining high-throughput agronomic phenotypic data and correlation analysis has been performed in rice [119] . Imaging systems have also been applied to study plant roots [75] , and this automated phenotypic identification platform is likely to have broad application in crop phenotypes and genetic research. Also, statistical methods for the association of phenotypic and genotypic data are used for linkage mapping and GWAS.…”

Transcriptomic and Genome-wide Association Study Reveal Long Noncoding RNAs Responding to Nitrogen Deficiency in Maize

“…An integrated method comprising GWAS, RNA-Seq, and genomic selection combined with phenotypic data collected from different environments found 16 loci that were signi cantly associated with soybean resistance to white mold in the eld and 11 loci in the greenhouse [42]. In maize, metabolite pro ling and GWAS were combined to analyze the mechanisms of response to low-Pi stress, and validation in a recombinant inbred line population found some candidate genes related to yield [20]. We screened 40 consistently LN-responsive candidate genes by integrating RNA-Seq pro les and GWAS data.…”