The variability of maize kernel drying: sowing date, harvest scenario and year

The extent of the reduction of maize (Zea mays L.) kernel moisture content through drying is closely related to field temperature (or accumulated temperature; AT) following maturation. In 2017 and 2018, we selected eight maize hybrids that are widely planted in Northeastern China to construct kernel drying prediction models for each hybrid based on kernel drying dynamics. In the traditional harvest scenario using the optimal sowing date (OSD), maize kernels underwent drying from 4th September to 5th October, with variation coefficients of 1.0–1.9. However, with a latest sowing date (LSD), drying occurred from 14th September to 31st October, with variation coefficients of 1.3–3.0. In the changed harvest scenario, the drying time of maize sown on the OSD condition was from 12th September to 9th November with variation coefficients of 1.3–3.0, while maize sown on the LSD had drying dates of 26th September to 28th October with variation coefficients of 1.5–3.6. In the future harvest scenario, the Fengken 139 (FK139) and Jingnongke 728 (JNK728) hybrids finished drying on 20th October and 8th November, respectively, when sown on the OSD and had variation coefficients of 2.7–2.8. Therefore, the maize kernel drying time was gradually delayed and was associated with an increased demand for AT ⩾ 0°C late in the growing season. Furthermore, we observed variation among different growing seasons likely due to differences in weather patterns, and that sowing dates impact variations in drying times to a greater extent than harvest scenarios.

Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development

Background Maize kernel filling, which is closely related to the process of double fertilization and is sensitive to a variety of environmental conditions, is an important component of maize yield determination. Silk is an important tissue of maize ears that can discriminate pollen and conduct pollination. Therefore, investigating the molecular mechanisms of kernel development and silk senescence will provide important information for improving the pollination rate to obtain high maize yields. Results In this study, transcript profiles were determined in an elite maize inbred line (KA105) to investigate the molecular mechanisms functioning in self-pollinated and unpollinated maize kernels and silks. A total of 5285 and 3225 differentially expressed transcripts (DETs) were identified between self-pollinated and unpollinated maize in a kernel group and a silk group, respectively. We found that a large number of genes involved in key steps in the biosynthesis of endosperm storage compounds were upregulated after pollination in kernels, and that abnormal development and senescence appeared in unpollinated kernels (KUP). We also identified several genes with functions in the maintenance of silk structure that were highly expressed in silk. Further investigation suggested that the expression of autophagy-related genes and senescence-related genes is prevalent in maize kernels and silks. In addition, pollination significantly altered the expression levels of senescence-related and autophagy-related genes in maize kernels and silks. Notably, we identified some specific genes and transcription factors (TFs) that are highly expressed in single tissues. Conclusions Our results provide novel insights into the potential regulatory mechanisms of self-pollinated and unpollinated maize kernels and silks.

PPR-DYW Protein EMP17 Is Required for Mitochondrial RNA Editing, Complex III Biogenesis, and Seed Development in Maize

The conversion of cytidines to uridines (C-to-U) at specific sites in mitochondrial and plastid transcripts is a post-transcriptional processing event that is important to the expression of organellar genes. Pentatricopeptide repeat (PPR) proteins are involved in this process. In this study, we report the function of a previously uncharacterized PPR-DYW protein, Empty pericarp17 (EMP17), in the C-to-U editing and kernel development in maize. EMP17 is targeted to mitochondria. The loss-function of EMP17 arrests maize kernel development, abolishes the editing at ccmFC-799 and nad2-677 sites, and reduces the editing at ccmFC-906 and -966 sites. The absence of editing causes amino acid residue changes in CcmFC-267 (Ser to Pro) and Nad2-226 (Phe to Ser), respectively. As CcmFC functions in cytochrome c (Cytc) maturation, the amount of Cytc and Cytc1 protein is drastically reduced in emp17, suggesting that the CcmFC-267 (Ser to Pro) change impairs the CcmFC function. As a result, the assembly of complex III is strikingly decreased in emp17. In contrast, the assembly of complex I appears less affected, suggesting that the Nad2-226 (Phe to Ser) change may have less impact on Nad2 function. Together, these results indicate that EMP17 is required for the C-to-U editing at several sites in mitochondrial transcripts, complex III biogenesis, and seed development in maize.

Genetic basis of maize kernel oil-related traits revealed by high-density SNP markers in a recombinant inbred line population

Background Maize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding. Results In this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis. Conclusions Our results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.

Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection

A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100–2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky–Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels.