Flowering time is an important factor determining yield and seed quality in maize. A change in flowering time is a strategy used to survive abiotic stresses. Among abiotic stresses, drought can increase anthesis-silking intervals (ASI), resulting in negative effects on maize yield. We have analyzed the correlation between flowering time and drought stress using RNA-seq and bioinformatics tools. Our results identified a total of 619 genes and 126 transcripts whose expression was altered by drought stress in the maize B73 leaves under short-day condition. Among drought responsive genes, we also identified 20 genes involved in flowering times. Gene Ontology (GO) enrichment analysis was used to predict the functions of the drought-responsive genes and transcripts. GO categories related to flowering time included reproduction, flower development, pollen–pistil interaction, and post-embryonic development. Transcript levels of several genes that have previously been shown to affect flowering time, such as PRR37, transcription factor HY5, and CONSTANS, were significantly altered by drought conditions. Furthermore, we also identified several drought-responsive transcripts containing C2H2 zinc finger, CCCH, and NAC domains, which are frequently involved in transcriptional regulation and may thus have potential to alter gene expression programs to change maize flowering time. Overall, our results provide a genome-wide analysis of differentially expressed genes (DEGs), novel transcripts, and isoform variants expressed during the reproductive stage of maize plants subjected to drought stress and short-day condition. Further characterization of the drought-responsive transcripts identified in this study has the potential to advance our understanding of the mechanisms that regulate flowering time under drought stress.

BackgroundThe PLAnt co-EXpression database (PLANEX) is a new internet-based database for plant gene analysis. PLANEX (http://planex.plantbioinformatics.org) contains publicly available GeneChip data obtained from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI). PLANEX is a genome-wide co-expression database, which allows for the functional identification of genes from a wide variety of experimental designs. It can be used for the characterization of genes for functional identification and analysis of a gene’s dependency among other genes. Gene co-expression databases have been developed for other species, but gene co-expression information for plants is currently limited.DescriptionWe constructed PLANEX as a list of co-expressed genes and functional annotations for Arabidopsis thaliana, Glycine max, Hordeum vulgare, Oryza sativa, Solanum lycopersicum, Triticum aestivum, Vitis vinifera and Zea mays. PLANEX reports Pearson’s correlation coefficients (PCCs; r-values) that distribute from a gene of interest for a given microarray platform set corresponding to a particular organism. To support PCCs, PLANEX performs an enrichment test of Gene Ontology terms and Cohen’s Kappa value to compare functional similarity for all genes in the co-expression database. PLANEX draws a cluster network with co-expressed genes, which is estimated using the k-mean method. To construct PLANEX, a variety of datasets were interpreted by the IBM supercomputer Advanced Interactive eXecutive (AIX) in a supercomputing center.ConclusionPLANEX provides a correlation database, a cluster network and an interpretation of enrichment test results for eight plant species. A typical co-expressed gene generates lists of co-expression data that contain hundreds of genes of interest for enrichment analysis. Also, co-expressed genes can be identified and cataloged in terms of comparative genomics by using the ‘Co-expression gene compare’ feature. This type of analysis will help interpret experimental data and determine whether there is a common term among genes of interest.

Downy mildew (DM) is a major disease of maize that causes significant yield loss in subtropical and tropical regions around the world. A variety of DM strains have been reported, and the resistance to them is polygenically controlled. In this study, we analyzed the quantitative trait loci (QTLs) involved in resistance to Peronosclerospora sorghi (sorghum DM), P. maydis (Java DM), and Sclerophthora macrospora (crazy top DM) using a recombinant inbred line (RIL) from a cross between B73 (susceptible) and Ki11 (resistant), and the candidate genes for P. sorghi, P. maydis, and S. macrospora resistance were discovered. The linkage map was constructed with 234 simple sequence repeat (SSR) and restriction fragment length polymorphism (RFLP) markers, which was identified seven QTLs (chromosomes 2, 3, 6, and 9) for three DM strains. The major QTL, located on chromosome 2, consists of 12.95% of phenotypic variation explained (PVE) and a logarithm of odds (LOD) score of 14.12. Sixty-two candidate genes for P. sorghi, P. maydis, and S. macrospora resistance were obtained between the flanked markers in the QTL regions. The relative expression level of candidate genes was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) using resistant (CML228, Ki3, and Ki11) and susceptible (B73 and CML270) genotypes. For the 62 candidate genes, 15 genes were upregulated in resistant genotypes. Among these, three (GRMZM2G028643, GRMZM2G128315, and GRMZM2G330907) and AC210003.2_FG004 were annotated as leucine-rich repeat (LRR) and peroxidase (POX) genes, respectively. These candidate genes in the QTL regions provide valuable information for further studies related to P. sorghi, P. maydis, and S. macrospora resistance.Genes 2020, 11, 191 2 of 20 is spread by oospores that survive in the soil [7] and can be spread through infected seeds or from plant-to-plant by airborne conidia. Because of the systemic nature of DM, susceptible lines usually die when infected in the seedling emergence stage, and when plants are infected during later growth stages, they cannot develop the maize ear despite having survived. At least six pathogens that cause DM infection of maize in Asia have been reported, including sorghum DM (P. sorghi (Weston & Uppal)), Philippine DM (P. philippinensis (Weston) Shaw), Java DM (P. maydis (Raciborski)), sugarcane DM (P. sacchari (Miyabe) Shirai and Hara), brown stripe DM (Sclerophthora rayssiae var. zeae), crazy top DM (S. macrospora), and Rajasthan DM (P. heteropogoni) [2,6,[8][9][10][11][12][13][14][15]. DM is widespread in tropical regions, although its origin is conjectural, and because of the diversity of the DM pathogens and their systemic nature, the development of resistant varieties is needed. Moreover, a renewed emphasis on cost-effectiveness and environmental safety that has brought about the application of DM management by the development of resistant varieties. According to studies on the interaction of maize and the pathogens, resistance to DM is polygenically controlled [7,13,[16][17][18]...

Premise of the study:Positional cloning in combination with phenotyping is a general approach to identify disease-resistance gene candidates in plants; however, it requires several time-consuming steps including population or fine mapping. Therefore, in the present study, we suggest a new combined strategy to improve the identification of disease-resistance gene candidates.Methods and Results:Downy mildew (DM)–resistant maize was selected from five cultivars using a spreader row technique. Positional cloning and bioinformatics tools were used to identify the DM-resistance quantitative trait locus marker (bnlg1702) and 47 protein-coding gene annotations. Eventually, five DM-resistance gene candidates, including bZIP34, Bak1, and Ppr, were identified by quantitative reverse-transcription PCR (RT-PCR) without fine mapping of the bnlg1702 locus.Conclusions:The combined protocol with the spreader row technique, quantitative trait locus positional cloning, and quantitative RT-PCR was effective for identifying DM-resistance candidate genes. This cloning approach may be applied to other whole-genome-sequenced crops or resistance to other diseases.

This study was conducted to evaluate maize downy mildew resistance using spreader row technique in Cambodia. A total of forty maize lines were used in this experiment. Seven Korean varieties and seven breeding lines showed high infection rates (80~100%) and highly susceptible (HS) to downy mildew disease in both spring and fall. Also most of nested association mapping (NAM) parent lines were highly susceptible (HS). Meanwhile three inbred lines, Ki3, Ki11, and CML228, showed highly resistant (HR) or resistant (R) in spring and moderately resistant (MR) in fall. These three lines were already known as resistant inbred lines against downy mildew disease. It appears that spreader row technique was suitable for selection of maize downy mildew resistance in Cambodia. The incidence of downy mildew was influenced by weather conditions, especially relative humidity and temperature. Among several inoculation methods to screen for downy mildew resistance, this spreader row technique is effectively and easily used in the field of Southeast Asia.