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Genome-wide transcriptome analysis between WT and OsGRP3 overexpressing (or knock-down) rice

ABSTRACT: Genome-wide transcriptome analysis between WT and OsGRP3 overexpressing (or knock-down) rice

PROVIDER: PRJNA635586 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11862818_1.fastq.gz	Fastqsanger.gz
	SRR11862818_2.fastq.gz	Fastqsanger.gz
	SRR11862819_1.fastq.gz	Fastqsanger.gz
	SRR11862819_2.fastq.gz	Fastqsanger.gz
	SRR11862820_1.fastq.gz	Fastqsanger.gz

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Project description:Purpose: The goals of this study are to find putative target transcript regulated by OsGRP3 by comparing rice transcriptome between wild-type and OsGRP3 overexpressing (or Knock-down) plants. Methods: Total RNAs were extracted from the leaf tissues of OsGRP3OE, OsGRP3KD, and non-transgenic (NT) plants using the RNeasy plant mini kit (Qiagen, USA) according to the manufacturer’s instruction. cDNA libraries were prepared using the TruSeq RNA Sample Prep kit (v2) (Macrogen, Korea). Single-end sequences were obtained using IRGSP (v 1.0) and raw sequence reads were trimmed to remove adaptor sequence, and those with a quality lower than Q20 were removed using the Trimmomatic 0.32 software (Bolger et al., 2014). To map the reads to reference genome, all reads were assembled with annotated genes from the Rap-DB database [http://rapdb.dna.affrc.go.jp; IRGSP (v 1.0)] using TopHat software (https://ccb.jhu.edu/software/tophat/index.shtml). After mapping reads to a reference genome, differentially expressed genes (DEGs) were selected using a cut-off change of at least 2-fold change and Student’s t-test (P < 0.05). The selected DEGs were grouped by hierarchical clustering analysis (Complete Linkage). Results: A cutoff change of at least two-fold was used to isolate up- and down-regulated genes by OsGRP3. The analysis revealed that OsGRP3OX plants contained 195 up-regulated and 190 down-regulated genes compared with WT plants. Among the 195 up-regulated transcripts, 45 genes belonged to the gene ontology (GO) category of response to abiotic and/or biotic stress and defense. The majority of these genes were categorized into defense- and stress-related genes and interestingly many PR genes were up-regulated. This high frequency of defense- and stress-related genes affected by OsGRP3 overexpression suggests that OsGRP3 is closely associated with biotic and abiotic stress responses. We then isolated genes showing opposite expression patterns between OsGRP3OX and OsGRP3KD plants as candidate genes directly regulated by OsGRP3. Through these criteria, we finally isolated 15 genes as target candidates in the OsGRP3-mediated drought tolerance pathway. Interestingly, six genes showing a positive correlation with the expression level of OsGRP3 were associated with plant stress responses. The list included four PR genes, LATE EMBRYOGENESIS ABUNDANT PROTEIN 17 (LEA17), and EXOCYST SUBUNIT EXO70 FAMILY PROTEIN FX1 (FX1). On the other hand, eight genes showing negative correlation with OsGRP3 expression level included PEROXIDASE 1 (POX11), METALLOTHIONEIN 1d (MT1d), CAROTENOID 9, 10-CLEAVAGE DIOXYGENASE 1 (CCD1), 4,5-DOPA DIOXYGENASE EXTRADIOL-LIKE PROTEIN (DOPA), LEUCINE-RICH REPEAT DOMAIN CONTAINING PROTEIN (LRR), LIPOXYGENASE (LOX) and S-ADENOSYLMETHOININE SYNTHETASE 1 (SAM1) that were mainly associated with plant metabolic regulations. Conclusions: OsGRP3 positively affects the expression of stress-responsive genes and negatively affected the expression of metabolic pathway genes.

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Genome-wide transcriptome analysis between WT and OsGRP3 overexpressing (or knock-down) rice

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