Integrated multi-omics identifies plant hormone signal transduction and phenylpropanoid biosynthesis as key pathways in kiwifruit (Actinidia chinensis var. deliciosa) resistance to Botryosphaeria Dothidea infection

Abstract

Background: Soft rot, a devastating fungal disease, severely impacts kiwifruit during postharvest storage, causing substantial fruit damage and rendering the fruit inedible. Botryosphaeria dothidea (B. dothidea) is the primary pathogen causing kiwifruit soft rot, but its pathogenic mechanism remains unclear.

Results: In this study, morphological observation, transcriptome sequencing, and untargeted metabolomics were employed to analyze the changes in 'Jinkui' kiwifruit after artificial inoculation with B. dothidea. The results showed that obvious lesions began to appear on the fruit surface at 72 h post-inoculation (hpi) with B. dothidea, and the peel rot initiated at 120 hpi. Transcriptome sequencing revealed a significantly higher number of differentially expressed genes (DEGs) at 72 and 120 hpi than at 12 and 24 hpi. KEGG enrichment analysis indicated that DEGs at 72 and 120 hpi were significantly enriched in the plant hormone signal transduction pathway. A total of 403 metabolites were identified via untargeted metabolomics, among which the number of differentially accumulated metabolites (DAMs) was highest at 72 hpi (46 DAMs). These DAMs predominantly belong to the lipid and organic acid classes and are associated with pathways such as phenylpropanoid biosynthesis and secondary metabolite biosynthesis. Co-enrichment analysis revealed significant enrichment of DEGs and DAMs in the phenylpropanoid biosynthesis pathway. The correlation between DEGs and DAMs was assessed using the O2PLS model and the Pearson correlation coefficient. Two key DEGs were identified: a MYB transcription factor (Acc32263) and a ChlH-like gene (Acc00854), both of which potentially play crucial regulatory roles in the pathogenesis of kiwifruit soft rot.

Conclusions: Significant changes in kiwifruit tissue morphology, gene expression, and metabolite profiles were observed after 72 h of B. dothidea infection. The plant hormone signal transduction and phenylpropanoid biosynthesis pathways are closely associated with the pathogenesis of kiwifruit soft rot. The MYB transcription factor (Acc32263) and the ChlH-like gene (Acc00854) are likely to be the key regulators. This study identifies the core regulatory pathways and genes involved in kiwifruit soft rot, provides new insights into the pathogenic mechanism of B. dothidea, and offers theoretical support for developing effective strategies to control kiwifruit soft rot.

Keywords: B. dothidea; Kiwifruit; Metabolome; Soft rot; Transcriptome.

Fig. 1

Phenotypic analysis of ‘Jinkui’ kiwifruit inoculated with B. dothidea. A Morphological progression of kiwifruit soft rot in wounded fruit inoculated with or without (mock) B. dothidea. Bar = 1 cm. B Histopathological observation of fruits at 120 h post-inoculation with B. dothidea. M: mycelium, invading the flesh cells. DC: damaged cells, where mycelium invasion destroyed cell structure, with the cells filled with mycelium. NC: normal cells. C Lesion diameter in infected fruits at different time points

Fig. 2

Analysis of differentially expressed genes in the transcriptome. A Differentially expressed gene statistics of the four stages. B Venn diagrams of the differentially expressed genes between the four stages. C KEGG enrichment analysis of differentially expressed genes at 72 and 120 h after treatment. C: control group inoculated with sterile PDA medium; T: treatment group inoculated with B. dothidea. The top 20 enriched KEGG pathways were listed

Fig. 3

Expression profiles of differentially expressed genes related to the plant hormone signal transduction pathway. There are eight hormones: Auxin, Cytokine, Gibberellin, Abscisic acid, Ethylene, Brassinosteroid, Jasmonic acid, and Salicylic acid. The substance in the color box on the left side of the gray vertical line is the precursor of the hormone synthesis pathway, and the gene in the box on the right side of the gray vertical line is the key regulatory gene of the signaling pathway. The heatmap shows the relative expression levels of differentially expressed genes, with low to high expression accompanied by a gradient of blue to red. The genes in the red box represent up-regulated genes, and the genes in the blue box represent down-regulated genes. The genes in the gray box indicate that some genes are up-regulated and some genes are down-regulated

Fig. 4

Analysis of differentially accumulated metabolites in the metabolome. A Metabolome classification of all detected metabolites. B Statistics of up-regulated and down-regulated differentially accumulated metabolites in the four stages. C Venn diagram analysis of differentially accumulated metabolites in the four stages. D-G Heatmap analysis of differentially accumulated metabolites with FC > 1.5 or FC < 0.67 at 12 (D), 24 (E), 72 (F), and 120 (G) hours after treatment

Fig. 5

K-means clustering analysis of differentially accumulated metabolites

Fig. 6

Expression profiles of differentially expressed genes and differentially accumulated metabolites in the phenylpropanoid biosynthesis pathway. The heatmap shows the relative expression level. The expression levels of the differentially expressed genes are represented by a blue to red gradient, and the accumulation levels of metabolites are represented by a green to red gradient. Genes are represented by boxes, and metabolites are represented by circles. The color in the box or circle represents the level of gene expression or metabolite accumulation. Red: up-regulated expression, blue: down-regulated expression, orange: partially up-regulated and partially down-regulated

Fig. 7

Correlation analysis between differentially expressed genes and differentially accumulated metabolites. A Association loading plot of the transcriptome and metabolome obtained by the O2PLS model. The top 25 genes and metabolites with the highest correlations are shown in the figure. B Network diagram of the correlation between the expression levels of differentially expressed genes and the abundances of differentially accumulated metabolites based on the Pearson correlation coefficient. The commonly differentially expressed genes and differentially accumulated metabolites identified by the integrated analysis of the O2PLS model and Pearson correlation coefficient are marked in orange