Mauleon, R. P. | bioRxiv Science

bioRxiv2022

Identifying Novel Candidate Defense Genes Against Rice Blast By Disease-Resistance Transcriptome Analysis

A blast-resistance rice mutant, GR978, generated by gamma-irradiation of indica cultivar IR64 was used to characterize the disease resistance transcriptome of rice to gain a better understanding of genes or chromosomal regions contributing to broad-spectrum disease resistance. GR978 was selected from the IR64 mutant collection at IRRI. To facilitate phenotypic characterization of the collection, a set of controlled vocabularies (CV) documenting mutant phenotypes in [~]3,700 entries was developed. In collaboration with the Tos17 rice mutant group at National Institute of Agrobiological Sciences, Japan, a merged CV set with 91 descriptions that map onto public ontology databases (PO, TO, OBO) is implemented in the IR64 mutant database. To better characterize the disease resistance transcriptome of rice, gene expression data from a blast resistant cultivar, SHZ-2, was incorporated in the analysis. Disease resistance transcriptome parameters, including differentially expressed genes (DEGs), regions of correlated gene expression (RCEs), and associations between DEGs and RCEs were determined statistically within and between genotypes using MAANOVA, correlation, and fixed ratio analysis. Twelve DEGs were found within the inferred physical location of the recessive gene locus on a [~]3.8MB region of chromosome 12 defined by genetic analysis of GR978. Highly expressed DEGs ([≥] 2fold difference) in GR978 or SHZ-2 and in common between the two, are mostly defense-response related, suggesting that most of the DEGs participate in causing the resistance phenotype. Comparing RCEs between SHZ-2 and GR978 showed that most RCEs between genotypes did not overlap. However, an 8-gene RCE in chromosome 11 was in common between SHZ2 and GR978. Gene annotations and GO enrichment analysis showed a high association with resistance response. This region has no DEGs nor is it associated with known blast resistance QTLs. Association analyses between RCEs and DEGs show that there was no enrichment of DEGs in the RCEs within a genotype and across genotypes as well. Association analysis of blast-resistance QTL (Bl-QTLs) regions (assembled from published literature; data courtesy of R. Wisser, pers comm., Cornell University) with DEGs and RCEs showed that while Bl- QTLs are not significantly associated with DEGs, they are associated with genotype-specific RCEs; GR978- RCEs are enriched within Bl-QTLs. The analysis suggested that examining patterns of correlated gene expression patterns in a chromosomal context (rather than the expression levels of individual genes) can yield additional insights into the causal relationship between gene expression and phenotype. Based on these results, we put forward a hypothesis that QTLs with small or moderate effects are represented by genomic regions in which the genes show correlated expression. It implies that gene expression within such a region is regulated by a common mechanism, and that coordinated expression of the region contributes to phenotypic effects. This hypothesis is testable by co segregation analysis of the expression patterns in well-characterized backcross and recombinant inbred lines.

plant biology↗

bioRxiv2018

Rice Galaxy: an open resource for plant science

Background\n\nRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.\n\nFindings\n\nWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.\n\nConclusions\n\nRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

bioinformatics↗