Kitae Song

PLANEX: the plant co-expression database

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.

Transcriptome Analysis of Flowering Time Genes under Drought Stress in Maize Leaves

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.

Characterization of Expressed Genes Under Ozone Stress in Soybean

To identify the genes specifically or predominantly expressed in ozone-fumigated leaves of two soybean cultivars: Jinpumkong and Cheongjakong, expression levels of mRNA were investigated using differential banding patterns on agarose gel. A total of 408 bands differently expressed after ozone fumigation was identified; 153 of which were up-regulated while 225 were down-regulated. Using BLASTx, the putative functions of the expressed sequence tags were determined. The 178 ozone-regulated differentially expressed genes (DEGs) matched with the previously known genes with high significance. The putative functional classes of these DEGs were categorized by two databases: Gene Ontology and MIPS. Based on the Gene Ontology database, majority of the DEGS have molecular function related to transferase activity. Most of them are involved in the cellular and metabolic processes. Cytoplasmic part and cell part were the primary types of cellular component in the ozone-responding DEGs. Whereas findings using the MIPS database revealed the function distribution of up-regulated DEGs across all classes. Most of the ozone-regulated genes identified in this study are related to biotic and abiotic stresses. The characterized ESTs will serve as useful data to provide a better understanding of the molecular basis and transcript profiles.

Identification of Downy Mildew Resistance Gene Candidates by Positional Cloning in Maize (Zea mays subsp. mays; Poaceae)

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 wholegenome-sequenced crops or resistance to other diseases.