Abstract

High-throughput transcriptomics technologies have been widely used to study plant transcriptional reprogramming during the process of plant defense responses, and a large quantity of gene expression data have been accumulated in public repositories. However, utilization of these data is often hampered by the lack of standard metadata annotation. In this study, we curated 2444 public pathogenesis-related gene expression samples from the model plant Arabidopsis and three major crops (maize, rice, and wheat). We organized the data into a user-friendly database termed as PlaD. Currently, PlaD contains three key features. First, it provides large-scale curated data related to plant defense responses, including gene expression and gene functional annotation data. Second, it provides the visualization of condition-specific expression profiles. Third, it allows users to search co-regulated genes under the infections of various pathogens. Using PlaD, we conducted a large-scale transcriptome analysis to explore the global landscape of gene expression in the curated data. We found that only a small fraction of genes were differentially expressed under multiple conditions, which might be explained by their tendency of having more network connections and shorter network distances in gene networks. Collectively, we hope that PlaD can serve as an important and comprehensive knowledgebase to the community of plant sciences, providing insightful clues to better understand the molecular mechanisms underlying plant immune responses. PlaD is freely available at http://systbio.cau.edu.cn/plad/index.php or http://zzdlab.com/plad/index.php.

Highlights

  • Plant diseases caused by pathogens seriously affect food security and might even threaten human health

  • When calculating the frequency of differential expression under multiple conditions, we only focused on the impacts of pathogens on plants, that is, the expression change of one gene caused by plant ecotype or genotype is not considered

  • PlaD is mainly composed of three components, and the corresponding web interfaces and usages are elaborated as follows

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Summary

Introduction

Plant diseases caused by pathogens seriously affect food security and might even threaten human health. Transcriptome data bring great opportunities and challenges to explore the molecular mechanisms of plant immunity. Dong et al employed a machine learning method to integrate transcriptional data with gene networks to study PTI and ETI in the context of network biology [10]. Jiang et al integrated transcriptional data and protein–protein interaction (PPI) network to compare plant defense responses to pathogens with different lifestyles [11]. Gene differential expression analysis remains the most popular and direct approach to process transcriptome data related to plant defense responses [12,13,14,15], and the detection of differentially expressed genes (DEGs) has become an effective way to screen plant immunity-related candidate genes

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