Comparative Transcriptome Analysis of Iron and Zinc Deficiency in Maize (Zea mays L.).

Globally, one-third of the population is affected by iron (Fe) and zinc (Zn) deficiency, which is severe in developing and underdeveloped countries where cereal-based diets predominate. The genetic biofortification approach is the most sustainable and one of the cost-effective ways to address Fe and Zn malnutrition. Maize is a major source of nutrition in sub-Saharan Africa, South Asia and Latin America. Understanding systems' biology and the identification of genes involved in Fe and Zn homeostasis facilitate the development of Fe- and Zn-enriched maize. We conducted a genome-wide transcriptome assay in maize inbred SKV616, under -Zn, -Fe and -Fe-Zn stresses. The results revealed the differential expression of several genes related to the mugineic acid pathway, metal transporters, photosynthesis, phytohormone and carbohydrate metabolism. We report here Fe and Zn deficiency-mediated changes in the transcriptome, root length, stomatal conductance, transpiration rate and reduced rate of photosynthesis. Furthermore, the presence of multiple regulatory elements and/or the co-factor nature of Fe and Zn in enzymes indicate their association with the differential expression and opposite regulation of several key gene(s). The differentially expressed candidate genes in the present investigation would help in breeding for Fe and Zn efficient and kernel Fe- and Zn-rich maize cultivars through gene editing, transgenics and molecular breeding.

Molecular characterization of 5' UTR of the lycopene epsilon cyclase (lcyE) gene among exotic and indigenous inbreds for its utilization in maize biofortification.

Maize grains are the important source of food and energy, but possess very low proA (< 2.5 µg/g) compared to target level of 15 µg/g set by HarvestPlus to alleviate VAD. Favorable allele having variation in 5' untranslated region (UTR) of lycopene epsilon cyclase (lcyE) gene enhances concentration of proA in maize. To identify the sequence variation in 5' UTR of lcyE, a set of diverse 13 inbreds of indigenous and exotic origin was characterized for allelic constitution of lcyE. Inbreds possessed wide variation in proA (1.62-23.12 µg/g) with a mean of 9.64 µg/g. The proA in CIMMYT-HarvestPlus genotypes having favorable allele of lcyE was very high (22.28 µg/g), whereas the Indian inbreds with the same allele possessed very low proA (2.48 µg/g). Eight genotypes viz., HKI161, HKI163, HKI161-PV, HKI163-PV, HKI193-1-PV, HKI193-2-PV, HP704-22 and HP704-23 revealed the presence of favorable allele, while VQL1, DMRIL47, MGU-PV-123/C6, HKI193-1 and HKI193-2 showed the presence of unfavorable allele of lcyE gene. Sequence comparison of favorable allele of Indian (HKI161 and HKI163) and exotic genotypes (HP704-22 and HP704-23) revealed seven SNPs having three transitions (SNP1 and SNP3: G to A, SNP2: C to T) and four transversions (SNP4: C to G, SNP5: T to G, SNP6: G to C and SNP7: G to T). Four SNPs (SNP1: position 446, SNP2: position 458, SNP3: position 459 and SNP4: position 483) discriminated the low- and high- proA lines having favorable allele of lcyE 5'TE. These SNPs hold significance in enrichment of proA in maize for marker development and their use in marker-assisted selection.

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize.

Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

Genomewide Expression and Functional Interactions of Genes under Drought Stress in Maize.

A genomewide transcriptome assay of two subtropical genotypes of maize was used to observe the expression of genes at seedling stage of drought stress. The number of genes expressed differentially was greater in HKI1532 (a drought tolerant genotype) than in PC3 (a drought sensitive genotype), indicating primary differences at the transcriptional level in stress tolerance. The global coexpression networks of the two genotypes differed significantly with respect to the number of modules and the coexpression pattern within the modules. A total of 174 drought-responsive genes were selected from HKI1532, and their coexpression network revealed key correlations between different adaptive pathways, each cluster of the network representing a specific biological function. Transcription factors related to ABA-dependent stomatal closure, signalling, and phosphoprotein cascades work in concert to compensate for reduced photosynthesis. Under stress, water balance was maintained by coexpression of the genes involved in osmotic adjustments and transporter proteins. Metabolism was maintained by the coexpression of genes involved in cell wall modification and protein and lipid metabolism. The interaction of genes involved in crucial biological functions during stress was identified and the results will be useful in targeting important gene interactions to understand drought tolerance in greater detail.

Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones.

Deficiency of iron and zinc causes micronutrient malnutrition or hidden hunger, which severely affects ~25% of global population. Genetic biofortification of maize has emerged as cost effective and sustainable approach in addressing malnourishment of iron and zinc deficiency. Therefore, understanding the genetic variation and stability of kernel micronutrients and grain yield of the maize inbreds is a prerequisite in breeding micronutrient-rich high yielding hybrids to alleviate micronutrient malnutrition. We report here, the genetic variability and stability of the kernel micronutrients concentration and grain yield in a set of 50 maize inbred panel selected from the national and the international centres that were raised at six different maize growing regions of India. Phenotyping of kernels using inductively coupled plasma mass spectrometry (ICP-MS) revealed considerable variability for kernel minerals concentration (iron: 18.88 to 47.65 mg kg(-1); zinc: 5.41 to 30.85 mg kg(-1); manganese: 3.30 to 17.73 mg kg(-1); copper: 0.53 to 5.48 mg kg(-1)) and grain yield (826.6 to 5413 kg ha(-1)). Significant positive correlation was observed between kernel iron and zinc within (r = 0.37 to r = 0.52, p < 0.05) and across locations (r = 0.44, p < 0.01). Variance components of the additive main effects and multiplicative interactions (AMMI) model showed significant genotype and genotype × environment interaction for kernel minerals concentration and grain yield. Most of the variation was contributed by genotype main effect for kernel iron (39.6%), manganese (41.34%) and copper (41.12%), and environment main effects for both kernel zinc (40.5%) and grain yield (37.0%). Genotype main effect plus genotype-by-environment interaction (GGE) biplot identified several mega environments for kernel minerals and grain yield. Comparison of stability parameters revealed AMMI stability value (ASV) as the better representative of the AMMI stability parameters. Dynamic stability parameter GGE distance (GGED) showed strong and positive correlation with both mean kernel concentrations and grain yield. Inbreds (CM-501, SKV-775, HUZM-185) identified from the present investigation will be useful in developing micronutrient-rich as well as stable maize hybrids without compromising grain yield.