Nitrogen (N) is an essential and often limiting nutrient to plant growth and development. Previous studies have shown that the mRNA expressions of numerous genes are regulated by nitrogen supplies; however, little is known about the expressed non-coding elements, for example long non-coding RNAs (lncRNAs) that control the response of maize (Zea mays L.) to nitrogen. LncRNAs are a class of non-coding RNAs larger than 200 bp, which have emerged as key regulators in gene expression. In this study, we surveyed the intergenic/intronic lncRNAs in maize B73 leaves at the V7 stage under conditions of N-deficiency and N-sufficiency using ribosomal RNA depletion and ultra-deep total RNA sequencing approaches. By integration with mRNA expression profiles and physiological evaluations, 7245 lncRNAs and 637 nitrogen-responsive lncRNAs were identified that exhibited unique expression patterns. Co-expression network analysis showed that the nitrogen-responsive lncRNAs were enriched mainly in one of the three co-expressed modules. The genes in the enriched module are mainly involved in NADH dehydrogenase activity, oxidative phosphorylation and the nitrogen compounds metabolic process. We identified a large number of lncRNAs in maize and illustrated their potential regulatory roles in response to N stress. The results lay the foundation for further in-depth understanding of the molecular mechanisms of lncRNAs’ role in response to nitrogen stresses.

The response of the same genes to cold stress often varies between maize and sorghum, but the set of genes with conserved patterns of regulation show greater ev

Significance: The same gene is often regulated differently in response to stress in even closely related plant species. Directly measuring stress-responsive gene expression can be financially and logistically challenging in nonmodel species. Here, we show that models trained using data on which genes respond to cold in one species can predict which genes will respond to cold in related species, even when the training and target species vary in their degree of tolerance to cold. The prediction models we used require only genomic sequence and gene models. As a result, data from well-studied model species may be used to predict which genes will respond to stress in less-studied species with sequenced genomes.

Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.

Background: Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors9 investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Results: Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (p<0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Conclusion: Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids.