ArticleFunctional & integrative genomics2018
Comparison of gene co-networks reveals the molecular mechanisms of the rice (Oryza sativa L.) response to Rhizoctonia solani AG1 IA infection.
Article in Functional & integrative genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Aspergillus cvjetkovicii protects against phytopathogens through interspecies chemical signalling in the phyllosphere.Nature microbiology · 2024Article
- Genome-Wide Expression Profiling of Small RNAs in Indian Strain ofJournal of fungi (Basel, Switzerland) · 2021Article
- Strategies to Manage Rice Sheath Blight: Lessons from Interactions between Rice and Rhizoctonia solani.Rice (New York, N.Y.) · 2021Review
- Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach.Frontiers in genetics · 2021Article
- Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks.Frontiers in plant science · 2021Article
- Comparison of leaf transcriptome in response to Rhizoctonia solani infection between resistant and susceptible rice cultivars.BMC genomics · 2020Article
- WGCNA Analysis of Salt-Responsive Core Transcriptome Identifies Novel Hub Genes in Rice.Genes · 2019Article
- Comparative transcriptome profiling of the response to Pyrenochaeta lycopersici in resistant tomato cultivar Mogeor and its background genotype-susceptible Moneymaker.Functional & integrative genomics · 2019Article
- Identification of putative drought-responsive genes in rice using gene co-expression analysis.Bioinformation · 2019Article
- Comparison of gene co-networks analysis provide a systems view of rice (Oryza sativa L.) response to Tilletia horrida infection.PloS one · 2018Article
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13 authors.
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Abstract
Rhizoctonia solani causes rice sheath blight, an important disease affecting the growth of rice (Oryza sativa L.). Attempts to control the disease have met with little success. Based on transcriptional profiling, we previously identified more than 11,947 common differentially expressed genes (TPM > 10) between the rice genotypes TeQing and Lemont. In the current study, we extended these findings by focusing on an analysis of gene co-expression in response to R. solani AG1 IA and identified gene modules within the networks through weighted gene co-expression network analysis (WGCNA). We compared the different genes assigned to each module and the biological interpretations of gene co-expression networks at early and later modules in the two rice genotypes to reveal differential responses to AG1 IA. Our results show that different changes occurred in the two rice genotypes and that the modules in the two groups contain a number of candidate genes possibly involved in pathogenesis, such as the VQ protein. Furthermore, these gene co-expression networks provide comprehensive transcriptional information regarding gene expression in rice in response to AG1 IA. The co-expression networks derived from our data offer ideas for follow-up experimentation that will help advance our understanding of the translational regulation of rice gene expression changes in response to AG1 IA.
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