Plant Molecular Biology

, Volume 94, Issue 4–5, pp 453–467 | Cite as

Large-scale transcriptome analysis reveals arabidopsis metabolic pathways are frequently influenced by different pathogens

  • Zhenhong Jiang
  • Fei He
  • Ziding ZhangEmail author


Key message

Through large-scale transcriptional data analyses, we highlighted the importance of plant metabolism in plant immunity and identified 26 metabolic pathways that were frequently influenced by the infection of 14 different pathogens.


Reprogramming of plant metabolism is a common phenomenon in plant defense responses. Currently, a large number of transcriptional profiles of infected tissues in Arabidopsis (Arabidopsis thaliana) have been deposited in public databases, which provides a great opportunity to understand the expression patterns of metabolic pathways during plant defense responses at the systems level. Here, we performed a large-scale transcriptome analysis based on 135 previously published expression samples, including 14 different pathogens, to explore the expression pattern of Arabidopsis metabolic pathways. Overall, metabolic genes are significantly changed in expression during plant defense responses. Upregulated metabolic genes are enriched on defense responses, and downregulated genes are enriched on photosynthesis, fatty acid and lipid metabolic processes. Gene set enrichment analysis (GSEA) identifies 26 frequently differentially expressed metabolic pathways (FreDE_Paths) that are differentially expressed in more than 60% of infected samples. These pathways are involved in the generation of energy, fatty acid and lipid metabolism as well as secondary metabolite biosynthesis. Clustering analysis based on the expression levels of these 26 metabolic pathways clearly distinguishes infected and control samples, further suggesting the importance of these metabolic pathways in plant defense responses. By comparing with FreDE_Paths from abiotic stresses, we find that the expression patterns of 26 FreDE_Paths from biotic stresses are more consistent across different infected samples. By investigating the expression correlation between transcriptional factors (TFs) and FreDE_Paths, we identify several notable relationships. Collectively, the current study will deepen our understanding of plant metabolism in plant immunity and provide new insights into disease-resistant crop improvement.


Biotic stresses Gene set enrichment analysis (GSEA) Defense response Large-scale transcriptional data Metabolic pathways 



We thank Dr. Yuan Zhou at Peking University for helpful discussion on this topic.


This work was supported by Beijing Natural Science Foundation (5172021) and the National Natural Science Foundation of China (31471249).

Author contributions

ZJ designed the study, performed the analyses and drafted the manuscript. ZZ and FH revised the manuscript. ZZ supervised the study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
  2. 2.Biology DepartmentBrookhaven National LaboratoryUptonUSA

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