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. 2025 Jul 14:16:1604512.
doi: 10.3389/fpls.2025.1604512. eCollection 2025.

Transcriptomics combined with physiological analysis provides insights into the mechanism of resistance to Coleosporium bletiae in Bletilla striata

Affiliations

Transcriptomics combined with physiological analysis provides insights into the mechanism of resistance to Coleosporium bletiae in Bletilla striata

Qiao Liu et al. Front Plant Sci. .

Abstract

Introduction: Bletilla striata (Orchidaceae) is a valuable traditional Chinese medicinal plant prized for its dried rhizomes. However, its cultivation faces significant challenges from leaf rust disease caused by Coleosporium bletiae, which causes substantial yield losses.

Methods: To investigate host resistance mechanisms, we compared rust-resistant and susceptible B. striata accessions through integrated transcriptomic and physiological analyses.

Results and discussion: Phenotypic observations revealed that while both resistant and susceptible plants developed rust spores by 2 days post-inoculation (dpi), the resistant accession exhibited a significantly slower progression of spore stack formation and lesion expansion on abaxial leaf surfaces over time. Integrated transcriptomic and physiological analyses revealed that the rust-resistant material of B. striata accessions exhibited faster and stronger defense responses to pathogen infection compared to susceptible plants. These responses were characterized by significant upregulation of DEGs associated with antioxidant defense systems, secondary metabolite biosynthesis, JA, SA, and BR signaling pathways, concurrent downregulation of DEGs involved in cell wall remodeling, and calcium-mediated signaling. Furthermore, rust pathogen inoculation triggered rapid physiological responses in resistant plants, including enhanced activity of defense-related enzymes (CAT, PAL, β-1,3-glucanase, and chitinase) and early accumulation of osmolytes (soluble sugars, soluble proteins, and proline). These coordinated molecular and biochemical responses effectively restricted pathogen colonization and spread. These findings delineate the molecular basis of rust resistance in B. striata, identifying key regulatory nodes in defense pathways that could be targeted through precision breeding or genetic engineering to develop durable resistance against C. bletilla.

Keywords: Bletilla striata; physiological analysis; resistant and susceptible material; rust pathogen; transcriptomics.

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Conflict of interest statement

Authors ZL and XZ were employed by the company Guilin Sanjin Pharmaceutical Co., Ltd. Authors RQ and KH were employed by the company Guangxi Yifang Tianjiang Pharmaceutical Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Infection process of susceptible (G) and high-resistance (H) materials after inoculation of rust pathogen: (a) phenotypic changes of leaves after inoculation; (b) trends of leaves’ infected area between two materials of different days post-inoculation (dpi).
Figure 2
Figure 2
Changes of leaf physiological parameters in susceptible (G) and resistant (H) materials on different days after inoculation: Chl a (a), Chl b (b), Total chl(total chlorophyll) (c), Soluble sugar (d), Soluble protein (e), Proline (f), SOD (g), CAT (h), PAL (i), chitinase (j), and β-1,3-glucanase (k). Lowercase letters (a, b, c) represent the differences at p < 0.05.
Figure 3
Figure 3
Principal component analysis plot. Effect of pathogen infection on various biochemical parameters in plant. Different groups are indicated by different colors and symbols: GT0, GT2, GT4 and GT8 represent 0, 2, 4, and 8 dpi of susceptible material in B. striata. HT0, HT2, HT4, and HT8 represent the dpi of resistant material in B. striata. Arrows represent the contribution of each variable, with the direction and length indicating their influence on the principal components.
Figure 4
Figure 4
(a) Upset showing the shared and unique DEGs among different groups. (b) Bubble diagram of the top 10 KEGG pathway enriched by DEGs in different gene sets. GCK, G2, and G8 represent samples on 0, 2, and 8 dpi in susceptible material. HCK, H2, and H8 represent samples on 0, 2, and 8 dpi in resistant material.
Figure 5
Figure 5
Time-course analysis of dynamic gene expression changes after pathogen inoculation in the different materials of B. striata: (a) GCK, G2, and G8 represent samples on 0, 2, and 8 dpi in susceptible material; (b) HCK, H2, and H8 represent samples on 0, 2, and 8 dpi in resistant material.
Figure 6
Figure 6
Heatmap of genes from cell wall organization (a), plant hormone signal transduction (b), response to oxidative stress (c), β-1,3-D-glucan synthase activity (d), plant and pathogen interaction (e), and phenylpropane biosynthesis (f). GCK, G2, and G8 represent samples on 0, 2, and 8 dpi in susceptible material; HCK, H2, and H8 represent samples on 0, 2, and 8 dpi in resistant material. Gene co-expression regulatory networks of response to rust in B. striata (g). The colored nodes denote genes involved in different pathways, the edges stand for interactions between genes, and the gray lines prove interactions between genes.
Figure 7
Figure 7
Mechanism of resistance to rust pathogen in B. striata. The blue arrows/text indicate decreased content or downregulated genes, while the red arrows/text denote increased content or upregulated genes.

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