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Li J, Shi X, Wang C, Li Q, Lu J, Zeng D, Xie J, Shi Y, Zhai W, Zhou Y. Genome-Wide Association Study Identifies Resistance Loci for Bacterial Blight in a Collection of Asian Temperate Japonica Rice Germplasm. Int J Mol Sci 2023; 24:ijms24108810. [PMID: 37240156 DOI: 10.3390/ijms24108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/29/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Growing resistant rice cultivars is the most effective strategy to control bacterial blight (BB), a devastating disease caused by Xanthomonas oryzae pv. oryzae (Xoo). Screening resistant germplasm and identifying resistance (R) genes are prerequisites for breeding resistant rice cultivars. We conducted a genome-wide association study (GWAS) to detect quantitative trait loci (QTL) associated with BB resistance using 359 East Asian temperate Japonica accessions inoculated with two Chinese Xoo strains (KS6-6 and GV) and one Philippine Xoo strain (PXO99A). Based on the 55K SNPs Array dataset of the 359 Japonica accessions, eight QTL were identified on rice chromosomes 1, 2, 4, 10, and 11. Four of the QTL coincided with previously reported QTL, and four were novel loci. Six R genes were localized in the qBBV-11.1, qBBV-11.2, and qBBV-11.3 loci on chromosome 11 in this Japonica collection. Haplotype analysis revealed candidate genes associated with BB resistance in each QTL. Notably, LOC_Os11g47290 in qBBV-11.3, encoding a leucine-rich repeat receptor-like kinase, was a candidate gene associated with resistance to the virulent strain GV. Knockout mutants of Nipponbare with the susceptible haplotype of LOC_Os11g47290 exhibited significantly improved BB resistance. These results will be useful for cloning BB resistance genes and breeding resistant rice cultivars.
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Affiliation(s)
- Jianmin Li
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Xiaorong Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Chunchao Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Quanlin Li
- Institute of Genetics and Developmental Biological, Chinese Academy of Sciences, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Jialing Lu
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Dan Zeng
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junping Xie
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Wenxue Zhai
- Institute of Genetics and Developmental Biological, Chinese Academy of Sciences, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Yongli Zhou
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
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Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology. Brain Res 2022; 1785:147889. [PMID: 35339428 DOI: 10.1016/j.brainres.2022.147889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/28/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022]
Abstract
Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.
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