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. 2018 Jan 26;19(2):373.
doi: 10.3390/ijms19020373.

Transcriptome Analysis of Kiwifruit in Response to Pseudomonas syringae pv. actinidiae Infection

Tao Wang  1 , Gang Wang  2 , Zhan-Hui Jia  3 , De-Lin Pan  4 , Ji-Yu Zhang  5 , Zhong-Ren Guo  6
Affiliations

Transcriptome Analysis of Kiwifruit in Response to Pseudomonas syringae pv. actinidiae Infection

Tao Wang et al. Int J Mol Sci. .

Abstract

Kiwifruit bacterial canker caused by Pseudomonas syringae pv. actinidiae (Psa) has brought about a severe threat to the kiwifruit industry worldwide since its first outbreak in 2008. Studies on other pathovars of P. syringae are revealing the pathogenesis of these pathogens, but little about the mechanism of kiwifruit bacterial canker is known. In order to explore the species-specific interaction between Psa and kiwifruit, we analyzed the transcriptomic profile of kiwifruit infected by Psa. After 48 h, 8255 differentially expressed genes were identified, including those involved in metabolic process, secondary metabolites metabolism and plant response to stress. Genes related to biosynthesis of terpens were obviously regulated, indicating terpens may play roles in suppressing the growth of Psa. We identified 283 differentially expressed resistant genes, of which most U-box domain containing genes were obviously up regulated. Expression of genes involved in plant immunity was detected and some key genes showed differential expression. Our results suggest that Psa induced defense response of kiwifruit, including PAMP (pathogen/microbe-associated molecular patterns)-triggered immunity, effector-triggered immunity and hypersensitive response. Metabolic process was adjusted to adapt to these responses and production of secondary metabolites may be altered to suppress the growth of Psa.

Keywords: Psa; bacterial canker; kiwifruit; resistance.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Gene Ontology classification of unigenes.
Figure 2
Figure 2
Cluster of significant differentially expressed genes of the five experimental samples. The RPKM (reads per kb per million reads) values of unigenes were used for hierarchical cluster analysis. Expression level was showed by different colors, the redder the higher expression and the bluer the lower. Five treatments were set: CK, only carved; PY3, inoculated with water and sampled 48 h after inoculation; PY4, inoculated with water and sampled at 96 h; XJ3, inoculated with Psa and sampled at 48 h; XJ4, inoculated with Psa and sampled at 96 h.
Figure 3
Figure 3
GO classification of differentially expressed genes.
See this image and copyright information in PMC

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