Roles of the MYB94/FUSED LEAVES1 (ZmFDL1) and GLOSSY2 (ZmGL2) genes in cuticle biosynthesis and potential impacts on Fusarium verticillioides growth on maize silks.

Maize silks, the stigmatic portions of the female flowers, have an important role in reproductive development. Silks also provide entry points for pathogens into host tissues since fungal hyphae move along the surface of the silks to reach the site of infection, i.e., the developing kernel. The outer extracellular surface of the silk is covered by a protective hydrophobic cuticle, comprised of a complex array of long-chain hydrocarbons and small amounts of very long chain fatty acids and fatty alcohols. This work illustrates that two previously characterized cuticle-related genes separately exert roles on maize silk cuticle deposition and function. ZmMYB94/FUSED LEAVES 1 (ZmFDL1) MYB transcription factor is a key regulator of cuticle deposition in maize seedlings. The ZmGLOSSY2 (ZmGL2) gene, a putative member of the BAHD superfamily of acyltransferases with close sequence similarity to the Arabidopsis AtCER2 gene, is involved in the elongation of the fatty acid chains that serve as precursors of the waxes on young leaves. In silks, lack of ZmFDL1 action generates a decrease in the accumulation of a wide number of compounds, including alkanes and alkenes of 20 carbons or greater and affects the expression of cuticle-related genes. These results suggest that ZmFDL1 retains a regulatory role in silks, which might be exerted across the entire wax biosynthesis pathway. Separately, a comparison between gl2-ref and wild-type silks reveals differences in the abundance of specific cuticular wax constituents, particularly those of longer unsaturated carbon chain lengths. The inferred role of ZmGL2 is to control the chain lengths of unsaturated hydrocarbons. The treatment of maize silks with Fusarium verticillioides conidia suspension results in altered transcript levels of ZmFDL1 and ZmGL2 genes. In addition, an increase in fungal growth was observed on gl2-ref mutant silks 72 hours after Fusarium infection. These findings suggest that the silk cuticle plays an active role in the response to F. verticillioides infection.

Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

KEY MESSAGE: Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman's rank correlation coefficient of 0.852 between wet laboratory and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR spectra data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.

Genetic control of kernel compositional variation in a maize diversity panel.

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.