Abstract
Robust statistical tools such as the Skellam model and Bayesian networks can capture the count properties of transcriptome sequencing data and clusters of genes among treatments, thereby improving our knowledge of gene functions and networks. In this study, we successfully implemented a model to analyze a transcriptome dataset of Cucumis sativus and Botrytis cinerea before and after their interaction. First, 4200 differentially expressed genes (DEGs) from C. sativus were clustered into 17 distinct groups, and 670 DEGs from B. cinerea were clustered into 12 groups. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied on these DEGs to assess the interactions between C. sativus and B. cinerea. In C. sativus, more DEGs were divided into terms in the molecular function and biological process domains than into cellular components, and 277 DEGs were allocated to 19 KEGG pathways. In B. cinerea, more DEGs were divided into terms in the biological process and cellular component domains than into molecular functions, and 150 DEGs were allocated to 26 KEGG pathways. In this study, we constructed networks of genes that interact with each other to screen hub genes based on a directed graphical model known as Bayesian networks. Through a detailed GO analysis, we excavated hub genes which were biologically meaningful. These results verify that availability of Skellam model and Bayesian networks in clustering gene expression data and sorting out hub genes. These models are instrumental in increasing our knowledge of gene functions and networks in plant–pathogen interaction.
Highlights
An increasing number of virulent infectious diseases has been witnessed in the past two decades in natural populations and managed landscapes
We found that the Skellam model was capable of identifying and clustering co-expression models of genes among varied treatments
In order to understand the response of C. sativus to B. cinerea infection, Gene ontology (GO) analysis was implemented to the above differentially expressed genes (DEGs), and enrichment analysis was applied based on the hypergeometric distribution, using a false discovery rate (FDR) of < 0.05 as the cutoff
Summary
An increasing number of virulent infectious diseases has been witnessed in the past two decades in natural populations and managed landscapes. It is important to understand the molecular mechanisms underlying host– pathogen interactions in devising strategies to control diseases (Vela-Corcía et al 2019) For this purpose, many Botrytis infection mechanisms have been reported in typical plants (El Oirdi et al 2011; Hou et al 2019; Hu et al 2019; Lakkis et al 2019; Petrasch et al 2019; Tian et al 2018; Zhu et al 2019). Technological advances facilitate the collection of gene sequencing, gene expression, proteomic, and metabolomic data. The combination of these technologies can yield more information about the mechanisms of plant resistance to pathogens and pathogen–plant infection mechanisms. Genes divided into the same group may have similar features by cluster analysis, which help us explore the gene functions and networks (Eisen et al 1998; Ramoni et al 2002; Sturn et al 2002)
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