To unravel the molecular mechanisms underpinning maize (Zea mays L.) drought stress tolerance, we conducted comprehensive comparative transcriptome and physiological analyses of drought-tolerant YE8112 and drought-sensitive MO17 inbred line seedlings that had been exposed to drought treatment for seven days. Resultantly, YE8112 seedlings maintained comparatively higher leaf relative water and proline contents, greatly increased peroxidase activity, but decreased malondialdehyde content, than MO17 seedlings. Using an RNA sequencing (RNA-seq)-based approach, we identified a total of 10,612 differentially expressed genes (DEGs). From these, we mined out four critical sets of drought responsive DEGs, including 80 specific to YE8112, 5140 shared between the two lines after drought treatment (SD_TD), five DEGs of YE8112 also regulated in SD_TD, and four overlapping DEGs between the two lines. Drought-stressed YE8112 DEGs were primarily associated with nitrogen metabolism and amino-acid biosynthesis pathways, whereas MO17 DEGs were enriched in the ribosome pathway. Additionally, our physiological analyses results were consistent with the predicted RNA-seq-based findings. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis and the RNA-seq results of twenty representative DEGs were highly correlated (R2 = 98.86%). Crucially, tolerant line YE8112 drought-responsive genes were predominantly implicated in stress signal transduction; cellular redox homeostasis maintenance; MYB, NAC, WRKY, and PLATZ transcriptional factor modulated; carbohydrate synthesis and cell-wall remodeling; amino acid biosynthesis; and protein ubiquitination processes. Our findings offer insights into the molecular networks mediating maize drought stress tolerance.

Drought stress is the major abiotic factor threatening maize (Zea mays L.) yield globally. Therefore, revealing the molecular mechanisms fundamental to drought tolerance in maize becomes imperative. Herein, we conducted a comprehensive comparative analysis of two maize inbred lines contrasting in drought stress tolerance based on their physiological and proteomic responses at the seedling stage. Our observations showed that divergent stress tolerance mechanisms exist between the two inbred-lines at physiological and proteomic levels, with YE8112 being comparatively more tolerant than MO17 owing to its maintenance of higher relative leaf water and proline contents, greater increase in peroxidase (POD) activity, along with decreased level of lipid peroxidation under stressed conditions. Using an iTRAQ (isobaric tags for relative and absolute quantification)-based method, we identified a total of 721 differentially abundant proteins (DAPs). Amongst these, we fished out five essential sets of drought responsive DAPs, including 13 DAPs specific to YE8112, 107 specific DAPs shared between drought-sensitive and drought-tolerant lines after drought treatment (SD_TD), three DAPs of YE8112 also regulated in SD_TD, 84 DAPs unique to MO17, and five overlapping DAPs between the two inbred lines. The most significantly enriched DAPs in YE8112 were associated with the photosynthesis antenna proteins pathway, whilst those in MO17 were related to C5-branched dibasic acid metabolism and RNA transport pathways. The changes in protein abundance were consistent with the observed physiological characterizations of the two inbred lines. Further, quantitative real-time polymerase chain reaction (qRT-PCR) analysis results confirmed the iTRAQ sequencing data. The higher drought tolerance of YE8112 was attributed to: activation of photosynthesis proteins involved in balancing light capture and utilization; enhanced lipid-metabolism; development of abiotic and biotic cross-tolerance mechanisms; increased cellular detoxification capacity; activation of chaperones that stabilize other proteins against drought-induced denaturation; and reduced synthesis of redundant proteins to help save energy to battle drought stress. These findings provide further insights into the molecular signatures underpinning maize drought stress tolerance.

Despite recent scientific headway in deciphering maize (Zea mays L.) drought stress responses, the overall picture of key proteins and genes, pathways, and protein–protein interactions regulating maize filling-kernel drought tolerance is still fragmented. Yet, maize filling-kernel drought stress remains devastating and its study is critical for tolerance breeding. Here, through a comprehensive comparative proteomics analysis of filling-kernel proteomes of two contrasting (drought-tolerant YE8112 and drought-sensitive MO17) inbred lines, we report diverse but key molecular actors mediating drought tolerance in maize. Using isobaric tags for relative quantification approach, a total of 5175 differentially abundant proteins (DAPs) were identified from four experimental comparisons. By way of Venn diagram analysis, four critical sets of drought-responsive proteins were mined out and further analyzed by bioinformatics techniques. The YE8112-exclusive DAPs chiefly participated in pathways related to “protein processing in the endoplasmic reticulum” and “tryptophan metabolism”, whereas MO17-exclusive DAPs were involved in “starch and sucrose metabolism” and “oxidative phosphorylation” pathways. Most notably, we report that YE8112 kernels were comparatively drought tolerant to MO17 kernels attributable to their redox post translational modifications and epigenetic regulation mechanisms, elevated expression of heat shock proteins, enriched energy metabolism and secondary metabolites biosynthesis, and up-regulated expression of seed storage proteins. Further, comparative physiological analysis and quantitative real time polymerase chain reaction results substantiated the proteomics findings. Our study presents an elaborate understanding of drought-responsive proteins and metabolic pathways mediating maize filling-kernel drought tolerance, and provides important candidate genes for subsequent functional validation.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six F st peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

Drought remains the primary abiotic constraint to maize (Zea mays L.) productivity globally. Maize drought response involves several regulatory quantitative traits and complex gene networks. Therefore, precise location of drought-related quantitative trait loci (QTL) is imperative for drought tolerance breeding. Despite numerous studies identifying several drought-related maize QTLs, some QTL from particular genetic backgrounds showed smaller effects or could not be identified at all in different backgrounds, affected by marker sets, experimental design, mapping populations and statistical methods. Herein, therefore; using 457 published maize QTLs conferring for 18 traits, we have performed meta-analysis of data from various experiments to obtain meta-QTL (MQTL), integrate these fruitful QTL and to mine candidate genes related to drought. Resultantly, 24 MQTL with confidence interval (CI) < 5 cm were identified to be hot regions. Additionally, 47 drought related gene loci were observed and several candidate genes of the hot MQTL were reorganized by bioinformatics techniques. Thirteen gene (sod4, taf1, rps1, nthr3, oc13, bas, apx1, asn4, pck2, nac1, gst2, ao1 and kch4) loci of hot MQTL regions were homologous to their corresponding gene sequences from the PlantGDB database (http://www.plantgdb.org/search/). Further, we used a comparative genomics approach to identify the homologous regions of MQTL in rice (Oryza sativa Japonica) database (http://www.gramene.org) and observed that drought-related rice gene ATG6 was homologous to maize candidate genes GRMZM2G027857_T01 and GRMZM2G027857_T02. Conclusively, our identified MQTLs with narrowed CI could be useful for marker-assisted selection and the candidate genes harnessed for maize drought tolerance breeding.

Background During maize early kernel development, the dramatic transcriptional reprogramming determines the rate of developmental progression, and phytohormone plays critical role in these important processes. To investigate the phytohormone levels and transcriptome reprogramming profiles during maize early kernel development, two maize inbreds with similar genetic background but different mature kernel sizes (ILa and ILb) were used. Results The levels of indole-3-acetic acid (IAA) were increased continuously in maize kernels from 5 days after pollination (DAP) to 10 DAP. ILa had smaller mature kernels than ILb, and ILa kernels had significantly lower IAA levels and significantly higher SA levels than ILb at 10 DAP. The different phytohormone profiles correlated with different transcriptional reprogramming in the two kernels. The global transcriptomes in ILa and ILb kernels were strikingly different at 5 DAP, and their differences peaked at 8 DAP. Functional analysis showed that the biggest transcriptome difference between the two kernels is those response to biotic and abiotic stresses. Further analyses indicated that the start of dramatic transcriptional reprogramming and the onset of significantly enriched functional categories, especially the “plant hormone signal transduction” and “starch and sucrose metabolism”, was earlier in ILa than in ILb, whereas more significant enrichment of those functional categories occurred at later stage of kernel development in ILb. Conclusions These results indicate that later onset of the significantly enriched functional categories, coincide with their stronger activities at a later developmental stage and higher IAA level, are necessary for young kernels to undergo longer mitotic activity and finally develop a larger kernel size. The different onset times and complex interactions of the important functional categories, especially phytohormone signal, and carbohydrate metabolism, form the most important molecular regulators mediating maize early kernel development. Electronic supplementary material The online version of this article (10.1186/s12870-019-1808-9) contains supplementary material, which is available to authorized users.

Like other important cereal crop in modern agricultural production, maize is also threatened by drought. And the drought stress during maize filling stage will directly affect the quality (protein or oil concentration) and also the weight of grain. Therefore, different from previous studies focusing on inbred lines and pot experiment at seedling stage, current study selected filling stage of the adult plant and planting maize in the experimental field. Two hybrids cultivars with different drought tolerant were used for drought and water treatment respectively. We performed transcriptome sequencing analysis of 4 groups, 12 samples, and obtained 651.08 million raw reads. Then the data were further processed by mapping to a reference genome, GO annotation, enrichment analysis and so on. Among them we focus on the different change trends of water treatment and drought treatment, and the different responses of two drought-tolerant cultivars to drought treatment. Through the analysis, several transcripts which encode nitrogen metabolic, protein phosphorylation, MYB,AP2/ERF, HB transcriptional factor, O-glycosyl hydrolases and organic acid metabolic process were implicated with maize drought stress. Our data will offer insights of the identification of genes involved in maize drought stress tolerance, which provides a theoretical basis for maize drought resistance breeding.