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.
Highlights
IntroductionA co-expression network is a type of gene regulatory network, in which each node represents a gene and each edge two correlated genes based on their expression levels (Wang et al 2014a, b)
Rice sheath blight, which is caused by the soil-borne basidiomycete fungus Rhizoctonia solani, is an economicallyWenjuan Zhao contributed to this article.Electronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.Funct Integr Genomics (2018) 18:545–557 the molecular mechanism of rice sheath resistant during or after fungal entry into host tissues (Okubara et al 2014; Silva et al 2012).With the development of high-throughput techniques, omics data are providing opportunities for research into the molecular mechanisms of biological phenotypes (Kumar et al 2015)
Based on pairwise correlations between genes in common expression trends across all samples, 11,947 candidate regulatory genes were identified between TeQing and Lemont; the average transcripts per million (TPM) were higher than 10 for all 36 samples
Summary
A co-expression network is a type of gene regulatory network, in which each node represents a gene and each edge two correlated genes based on their expression levels (Wang et al 2014a, b). This network can reflect a set of gene expression correlations from a more systematic perspective, revealing how genes regulate each other and influence a phenotype (Garg et al 2017). WGCNA has been shown to identify patterns that have been previously undetected in gene-togene comparison methods (Bao et al 2017) and has become a common and useful strategy for investigating the causes of a disease or trait, as in a study of wheat resistance responses to powdery mildew (Zhang et al 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
