Enzymatic activity and gene expression related to drought stress tolerance in maize seeds and seedlings

Drought stress is a major limiting factor for the development of maize, and the identification of the expression of genes related to this stress in seeds and seedlings can be an important tool to accelerate the selection process. The expression of genes related to tolerance to water deficit in seeds and in different tissues of maize seedlings were evaluated. Four tolerant genotypes (91-T, 32-T, 91x75-T, 32x75-T) and four non-tolerant genotypes (37-NT, 57-NT, 37x57-NT and 31x37-NT) were seeded in a substrate with 10% (stress) and 70% (control) water retention capacity. The expression of 4 enzymes were evaluated: catalase (CAT), peroxidase (PO), esterase (EST), and heat-resistant protein (HRP), as well as the relative expression of 6 genes: ZmLEA3, ZmPP2C, ZmCPK11, ZmDREB2A/2.1s, ZmDBP3 and ZmAN13 were evaluated in seed, shoots and roots of seedlings submitted or not to stress. There was variation in the expression of CAT, PO, SOD, EST and HRP enzymes among the evaluated genotypes and also in the different tissues evaluated. Higher expression of the CAT and PO was observed in the shoots. There was a greater expression of the EST in the genotypes non-tolerant to water deficit. HRP was expressed only in seeds. In the aerial part of maize seedlings, classified as tolerant, higher expression of genes ZmLEA3 and ZmCPK11 was observed. There was a higher expression of the ZmAN13 and ZmDREB2A/2.1S genes in roots developed under stress conditions and a higher expression of the ZmPP2C gene in seeds of line 91-T, which is classified as tolerant to drought stress.

Inclusion of Dominance Effects in the Multivariate GBLUP Model.

New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.