Introduction

A. alternata are major pathogens that cause severe crop diseases, such as tobacco brown spot disease, which is characterized by brown lesions on the leaves. These lesions severely impair photosynthetic capacity, leading to a significant decline in crop yields1,2,3. A. alternata infections are among the most destructive leaf spot diseases, affecting crops across various agricultural systems globally. A. alternata fungi invade plant leaves, causing wilting, necrosis, and premature leaf senescence, which severely compromise plant health and reduce crop productivity4. In addition to direct tissue damage caused by the fungi, Alternaria species produce toxic metabolites that worsen the damage by inducing oxidative stress, which accelerates leaf senescence and further impairs plant health1,5. As a result,, A. alternata infections lead to significant yield losses and deterioration in crop quality, reducing the market value of agricultural products6. Effective management of Alternaria diseases is crucial for maintaining both high crop yields and product quality.

The plant immune system is sophisticated, with pattern recognition receptors (PRRs) playing a central role in defense against pathogens7. This immune system is organized into two major layers: pattern-triggered immunity (PTI) and effector-triggered immunity (ETI), both of which work in tandem to protect plants from various pathogens8. PTI is activated by PRRs recognizing pathogen-associated molecular patterns (PAMPs), while ETI involves nucleotide-binding leucine-rich repeat (NLR) receptors, which provide a more specific, often stronger, immune response to effector molecules from pathogens9. Research has also highlighted the critical role of plant hormones, such as auxin, abscisic acid (ABA), ethylene (ET), and jasmonic acid (JA), in modulating immune responses and the balance between defense and growth10. In addition, non-coding RNAs and transcription factors play pivotal roles in regulating immune responses9,11. Together, these findings suggest that the plant immune system is an intricate network of signaling pathways and regulatory mechanisms that involve a variety of molecular players.

Leucine-rich repeat (LRR) proteins are essential components of plant immunity, primarily acting as immune receptors that detect pathogens. These proteins are crucial for pathogen recognition and the initiation of immune signaling pathways12,13. LRR proteins contribute to disease resistance by activating immune responses, directly recognizing pathogen effector proteins, and modulating plant immunity through positive and negative regulatory mechanisms14,15. Furthermore, LRR proteins contribute to resistance against insect herbivory, showing their multifunctionality in defense16. The evolutionary dynamics of LRR proteins have led to the discovery of new disease resistance genes, which are vital for enhancing plant resilience against pathogens17. Thus, LRR proteins represent a promising target for improving crop resistance to diseases such as A. alternata infections.

In previous studies18, we identified NaLRR-RK4, a leucine-rich repeat receptor-like kinase (LRR-RK), which is significantly upregulated in tobacco leaves upon infection with A. alternata. Silencing NaLRR-RK4 using virus-induced gene silencing (VIGS) or RNA interference significantly increased the susceptibility of tobacco plants to A. alternata, suggesting that NaLRR-RK4 plays an essential role in plant resistance. Furthermore, NaLRR-RK4 regulates the expression of key defense-related genes, including NaERF109 and NaDEF19, which are involved in tobacco’s defense against A. alternata. These findings underscore NaLRR-RK4 as a critical resistance gene in the plant’s immune arsenal against A. alternata.

Nicotiana attenuata, a wild tobacco native to the Great Basin Desert of the western United States, has been widely recognized as an ideal model for ecological, chemical, and molecular studies of plant defense19. Its well-annotated genome, ease of genetic manipulation, and established protocols for gene silencing make it a powerful system for dissecting molecular responses to biotic stress20. These advantages make N. attenuata particularly well suited for exploring gene functions in plant–pathogen interactions.

Building on this foundation, this study aims to investigate the molecular mechanisms underlying the disease resistance conferred by NaLRR-RK4 in Nicotiana attenuata. By silencing NaLRR-RK4 in plant models and conducting comprehensive transcriptomic analysis, we expect to elucidate the gene’s role in resistant to Alternaria infection. The insights gained from this research will enhance our understanding of NaLRR-RK4’s contribution to plant immunity, paving the way for the development of crops with improved resistance to Alternaria and other pathogens. Ultimately, this work will provide valuable knowledge to improve crop resilience and sustainability in the face of ongoing plant disease threats.

Materials and methods

Plant materials

Both transgenic and non-transgenic Nicotiana attenuata plants were cultivated under identical controlled conditions. Due to the dormancy and poor germination capacity of wild Nicotiana attenuata seeds under standard conditions, a specific germination treatment was necessary. Seeds were placed in 2 ml sterile centrifuge tubes, soaked in 1.5 ml sterile water containing 0.03 g DCCS and 15 μl of 0.5% Tween 20 for surface sterilization for 5–8 min. After removing the sterilant, seeds were rinsed with sterile water more than five times, followed by immersion in 1.5 ml smoke solution supplemented with 15 μl of 0.1 M GA₃ for 55–65 min. The seeds were then rinsed again and evenly sown on GB5 medium using sterilized forceps. All procedures were conducted under a laminar flow hood. Seeds were germinated in a growth chamber set to 25–28 °C with a 16 h light / 8 h dark cycle for 10–14 days. Seedlings were transplanted into 12-well plant culture trays (diameter ~ 3 cm per well), and later into 12 cm-diameter disposable nursery bags once the leaves reached the edge of the wells. Plants were cultivated until the rosette stage before being used in further experiments.

Seeds of a second inbred line of Nicotiana attenuata were used as the wild-type (WT) genotype. To generate stable transgenic NaLRR-RK4 RNA interference (RNAi) lines, a 225 bp fragment of the NaLRR-RK4 coding sequence (GenBank accession no.: XM_01937432.3), previously validated in VIGS experiments as an effective silencing region, was amplified by PCR using primers Z123-F and Z124-R. The fragment was digested with Sac I and Xho I and ligated into the pRESC8 vector (pre-digested with the same enzymes) using T4 DNA ligase. After transformation into Escherichia coli, positive monoclonal colonies were verified by sequencing using primer Z133-F. Correct constructs were then digested with Pst I and BstE II and ligated with the corresponding antisense fragment to form the hairpin structure. Clones were further confirmed by sequencing with primer Z134-R. Verified RNAi constructs were introduced into Agrobacterium tumefaciens strain LBA4404 via electroporation for subsequent transformation of N. attenuata21.

Genetic transformation was performed following the protocol described by Han et al.22. Transgenic seedlings were screened on MS medium supplemented with 30 mg/L hygromycin. T1 progeny were selected based on hygromycin resistance, and individuals showing a 3:1 segregation ratio were considered to carry single T-DNA insertions. Resistant seedlings were grown to maturity for T2 seed collection. Homozygous T2 lines were identified by uniform hygromycin resistance across all germinated seedlings within a Petri dish. These homozygous T2 RNAi lines were maintained and used in subsequent experiments.

Transcript levels of NaLRR-RK4 in RNAi lines were confirmed to be significantly reduced under both control and pathogen-inoculated conditions using RT-qPCR (see Fig. 2b). Among multiple independent transformants, one stable homozygous RNAi line exhibiting consistent silencing efficiency and phenotypic response was selected for transcriptomic and physiological analyses. Complementation lines were not included in this study and are considered for future investigations.

Fungal material and incubation

Seed germination and plant growth were performed according to established protocols23,24. A. alternata isolate CN174, a tobacco-specific pathotype, was generously provided by Dr. Zhenyuan Xia (Yunnan Academy of Tobacco Agricultural Science). The fungus was cultured on potato dextrose agar (PDA) plates at 28 °C for 8–12 days prior to inoculation. For accurate and reproducible pathogenicity assessments, precise control of both humidity and temperature is essential. Therefore, greenhouse inoculation of whole plants was deemed unsuitable. Instead, a detached leaf assay, as described by Williams, et al.25, was employed to assess plant resistance. Since spore suspensions did not consistently induce reliable disease symptoms, and there were concerns about the loss of fungal virulence during spore preparation26, agar plugs (3 mm in diameter) from actively growing fungal cultures were used for all inoculations. Briefly, transition leaves (leaf 0) were excised and inoculated with four 3-mm PDA plugs containing actively growing A. alternata per leaf. The inoculated leaves were placed in transparent 12 × 12 cm square Petri dishes, maintained at 100% humidity, and incubated at 25 °C with a 16 h light/8 h dark photoperiod. Symptom development was monitored daily.

Experimental design

The experiment included four treatments, as follows:

RNAi_in: NaLRR-RK4-silenced plants with inoculation. RNAi_un: NaLRR-RK4-silenced plants without inoculation. WT_in: WT plants with A. alternata inoculation. WT_un: WT plants without A. alternata inoculation.

Disease resistance assessment

To evaluate the disease resistance of plants, leaves from WT and RNAi lines were inoculated with A. alternata and incubated for 3 days. At 3 dpi, lesion diameters on the leaf surface were measured using a vernier calipers. The severity of lesions caused by A. alternata infection was used as an indicator of the plant’s resistance level.

Transcriptomic sample collection

Leaf samples were collected from the same positions at 4 days post-inoculation (dpi), and immediately flash-frozen in liquid nitrogen before being transported to the laboratory for analysis. Total RNA was extracted from frozen samples using TRIzol™ reagent (Invitrogen Life Technologies, USA), following the manufacturer’s instructions. RNA degradation and contamination were assessed using 1% agarose gel electrophoresis. RNA purity was evaluated using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), and RNA concentration was quantified using the Qubit® RNA Assay Kit (Life Technologies, CA, USA) in a Qubit® 2.0 Fluorometer. RNA integrity was assessed using the RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100 system.

Library preparation

A total of 1 µg of RNA per sample was used to prepare the RNA sequencing libraries. Libraries were constructed using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) according to the manufacturer’s guidelines. First-strand cDNA synthesis was performed using a random hexamer primer and M-MuLV reverse transcriptase (RNase H-). Second-strand cDNA synthesis was conducted using DNA Polymerase I and RNase H27. The cDNA fragments (250–300 bp) were size-selected using the AMPure XP system (Beckman Coulter, Beverly, USA). To enhance library quality, 3 µl of USER Enzyme (NEB, USA) was incubated with size-selected, adapter-ligated cDNA at 37 °C for 15 min, followed by 5 min at 95 °C before PCR amplification. PCR amplification was performed with Phusion High-Fidelity DNA polymerase, universal PCR primers, and index primers. The PCR products were purified using the AMPure XP system, and the library quality was assessed using an Agilent Bioanalyzer 2100 system28.

Data quality control, processing, and functional annotation analysis

Fastp v0.19.4 was used to filter raw sequencing data, removing reads with adapter sequences, reads with > 10% ambiguous bases (N), and low-quality reads (Q ≤ 20) where > 50% of the bases were of low quality29. Transcriptome assembly was performed using Trinity (v2.11.0), and relevant transcripts were regrouped into gene clusters using Corset (https://github.com/trinityrnaseq/trinityrnaseq)30. Coding regions within the transcript sequences were predicted using TransDecoder (https://github.com/TransDecoder/TransDecoder/wiki)29. Gene functional annotations were performed using the Diamond or HMMER tools with the following databases: Nr, Swiss-Prot, Trembl, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO). Gene expression levels were estimated using RNA-Seq by Expectation Maximization (RSEM), and fragments per kilobase of transcript per million mapped reads (FPKM) were calculated for each gene. Differentially expressed genes (DEGs) between treatment groups were identified using DESeq2 (v1.22.1) and edgeR, with a corrected p-value < 0.05 and |log2foldchange|≥ 1 as thresholds for significance31. Enrichment analysis was performed using the hypergeometric test for KEGG pathways and GO terms32.

Validation of RNA-seq data by RT-qPCR

Total RNA was extracted using the KKFast Plant RNApure Kit (ZP405K-2, Zoman Biotech, Beijing, China), and cDNA was synthesized using the SuperMix cDNA Synthesis Kit (AT311-03, Transgene Biotech, Beijing, China). RT-qPCR was conducted using Universal SYBR qPCR Mastermix (Q712-02, Vazyme, China) under the following conditions: initial denaturation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing/extension at 58 °C for 30 s. The relative gene expression was calculated using the 2 − ΔΔCt method33, with all reactions performed in triplicate. The sequences of primers used in this study are provided in Table S2.

Data analyses

Protein sequence analysis and phylogenetic tree construction for NaLRR-RK4 were conducted using MEGA6.0 software34. Protein domains were predicted using the SMART35. Sequence alignment and distance visualization was performed using ClustalW and Jalview tools36. In R 4.3.3, heatmap plots, CPCoA plots, and chord diagrams were calculated and generated using the “pheatmap”37,“vegan”38, and “circlize” packages39, respectively. Monadic linear regression analysis was performed using the “ggplot2” and “ggpmisc” packages40. All figures were finalized in Adobe Illustrator 2023 (Adobe Inc., CA, USA).

Results

Phylogenetic and structural analysis of NaLRR-RK4 and its evolutionary relationship with NtLRR-RK4

To better understand the evolutionary relationship between NaLRR-RK4 and LRR-RK4 in other plants, the amino acid sequences of NtLRR-RK4 from Arabidopsis and five Nicotiana specieswere analyzed using MEGA6.0 software. Phylogenetic analysis revealed that NtLRR-RK4 (XP_016495409.1) grouped closely with NaLRR-RK4 (XP_019229868.1) from wild tobacco in the same branch (Fig. 1a), suggesting a close evolutionary relationship.The protein structure of NaLRR-RK4 was further predicted using the SMART software (Fig. 1b). Multiple sequence alignment was performed using ClustalW and visualized with Jalview. The structural prediction and alignment results revealed that the LRR-RK4 protein contains two highly conserved LRR domains and one LRR-6 domain (Figure S1). Specifically, the predicted LRR domains are indicated by blue rectangles, and the LRR-6 domain is marked with a red rectangle in Figure S1, corresponding precisely to the structural domain predictions. Notably, more than 50 amino acid differences were identified between NaLRR-RK4 and its homolog in cultivated tobacco, NtLRR-RK4, which may contribute to their differing responses to pathogen infection. These findings provide important insights into the potential functional mechanisms of LRR-RK4 in disease resistance.

Fig. 1
figure 1

Phylogenetic and Structural Analysis of NaLRR-RK4. Panel (a) Phylogenetic tree shows the evolutionary relationship of LRR-RKs proteins. Panel (b) presents the predicted protein structure of NaLRR-RK4, highlighting its domain.

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Evaluation of the NaLRR-RK4 Gene in Nicotiana attenuata upon Alternaria alternata infection

Transcriptome analysis of Nicotiana attenuata leaves infected by A. alternata identified a receptor-like kinase gene, NaLRR-RK4 (NCBI accession number: XM_019374323.1) (Fig. 2a). In WT tobacco leaves, the relative expression level of NaLRR-RK4 was significantly higher in infected treatments compared to uninfected controls at both 1 and 3 days post-A. alternata infection, demonstrating that A. alternata can induce the expression of the NaLRR-RK4 gene in wild tobacco."

Fig. 2
figure 2

Expression analysis of NaLRR-RK4 and disease phenotype in tobacco under Alternaria alternata treatment (Mean ± SD). (a) Relative expression levels of NaLRR-RK4 in tobacco leaves at 1 and 3 days post-A. alternata inoculation, based on transcriptome data. (b) RT-qPCR analysis of NaLRR-RK4 expression in WT and RNAi plants under both inoculated and non-inoculated conditions at 1 and 3 days. (c) Lesion diameters in WT and RNAi plants at 3 days after A. alternata inoculation. Here, RNAi_un represents silenced plants without A.alternata inoculation, RNAi_in represents silenced plants with A.alternata inoculation, WT_un represents wild-type plants without A.alternata inoculation, and WT_in represents wild-type plants with A.alternata inoculation. The red asterisks (“**” for P < 0.01 and "***" for P < 0.001) indicate statistically significant differences among treatments, as determined by Tukey’s test.

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After A. alternata inoculation, the transcript levels of NaLRR-RK4 in RNAi plants were significantly reduced compared to WT plants, showing decreases of 74.63% and 77.89% at 1 and 3 days post-inoculation, respectively (Fig. 2b). Meanwhile, at 3 days post-inoculation, RNAi plants developed necrotic lesions that were 54.48% larger than those observed in WT plants (Fig. 2c). These results indicate that NaLRR-RK4 was successfully silenced in RNAi lines and validate their use for subsequent experiments. Moreover, they suggest that NaLRR-RK4 plays an important role in the defense response against A. alternata in cultivated tobacco.

Transcriptional differences among treatments

The RNA sequencing data exhibited high quality, with an average Q30 value of 93.3%, indicating reliable base calling. The GC content ranged from 43.55% to 44.59%, and the percentage of valid bases was between 91.7% and 95.36%, confirming the reliability of the sequencing results for subsequent analysis (Table S3). Comparative analyses among different treatments are detailed in Tables S4–S7.

To investigate transcriptional differences among the treatments, we performed heatmap and CPCoA analyses, which revealed distinct gene expression profiles across all groups (Fig. 3). The heatmap analysis (Fig. 3a) demonstrated that A. alternata inoculation caused significant changes in gene expression, with clear separations between the wild-type (WT) and RNAi plants under both inoculated and uninoculated conditions. Notably, substantial transcriptional differences were observed between WT and RNAi plants even in the absence of A. alternata. Similarly, the CPCoA analysis (Fig. 3b) confirmed that gene expression differences among the treatments were statistically significant (P < 0.001), highlighting the distinct impacts of A. alternata inoculation and gene silencing.

Fig. 3
figure 3

Transcriptional Profiling Across Treatments. Panel (a) shows a heatmap analysis of gene expression across the four treatment groups. Each sample (e.g., XX-1, XX-2, XX-3) represents an independent biological replicate from RNA sequencing. Panel (b) presents a CPCoA (Canonical Principal Coordinates Analysis) of gene expression differences among the treatments, based on transcriptome data from these biological replicates. Panel (c) and (d) present Venn diagram analyses for the upregulated and downregulated genes across the treatments, highlighting the shared and unique gene sets in each case. The treatments include RNAi and WT plants, each with or without A. alternata inoculation, as detailed in Fig. 2.

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Figure 3, panels (c) and (d), present Venn diagram analyses of the differentially expressed genes across the various treatment conditions. Panel (c) analyzes the upregulated genes from the RNAi_in_vs_WT_in and RNAi_un_vs_WT_un comparisons, highlighting the transcriptional differences between silenced and non-silenced plants under the same conditions (inoculated or uninoculated). It also compares gene expression differences within the same plants (silenced or non-silenced) between inoculated and uninoculated treatments (RNAi_un_vs_RNAi_in vs WT_un_vs_WT_in). Panel (d) examines the downregulated genes across the same comparisons, focusing on the shared and unique gene sets between silenced and non-silenced plants under the same conditions, as well as the impact of inoculation versus non-inoculation within the same plants. Specific gene lists corresponding to these comparisons can be found in Table S8-S11.

Gene ontology (GO) analysis

The Gene Ontology (GO) analysis provided valuable insights into the differential gene expression across the four comparisons (Fig. 4). In WT_un_vs_RNAi_un, which compares wild-type and silenced plants without A. alternata inoculation, significant differences were observed in pathways related to primary metabolism and cellular homeostasis. These findings highlight the baseline physiological alterations caused by gene silencing. In WT_un_vs_WT_in, which examines the response of wild-type plants to A. alternata inoculation, pathways associated with defense response, response to biotic stimulus, and signal transduction were significantly enriched, underscoring the activation of robust defense mechanisms in wild-type plants under pathogen stress. In contrast, RNAi_un_vs_RNAi_in, which assesses the response of silenced plants to A. alternata, revealed fewer enriched defense-related pathways, suggesting a compromised ability to mount an effective defense in silenced plants. Finally, in WT_in_vs_RNAi_in, comparing wild-type and silenced plants under A. alternata inoculation, pronounced differences were identified in pathways such as oxidation–reduction processes, secondary metabolism, and response to external stimuli. These results emphasize the critical role of the silenced gene in enhancing disease resistance, as silenced plants exhibited impaired defense responses compared to the robust activation of resistance pathways observed in wild-type plants.

Fig. 4
figure 4

Gene Ontology Analysis of Differential Gene Expression. The figure presents the Gene Ontology (GO) analysis of differential gene expression across four comparisons. Here, the treatment groups, including RNAi and WT plants with or without A. alternata inoculation, are detailed in Fig. 2.

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KEGG pathway enrichment analysis

To further investigate the molecular mechanisms underlying the effects of gene silencing and A. alternata inoculation, KEGG pathway enrichment analysis was performed (Fig. 5). In WT_un_vs_RNAi_un, silenced plants displayed significant alterations in metabolic and signaling pathways under uninoculated conditions. Notably, the MAPK signaling pathway and the biosynthesis of unsaturated fatty acids were enriched, indicating that gene silencing affects basal signaling and lipid biosynthesis even in the absence of pathogen stress. In WT_un_vs_WT_in, A. alternata inoculation triggered a robust defense response in wild-type plants, with significant enrichment observed in pathways such as glutathione metabolism, phenylalanine metabolism, and starch and sucrose metabolism. These pathways are critical for antioxidative responses and metabolic adjustments during pathogen attack. In RNAi_un_vs_RNAi_in, inoculated silenced plants showed moderate activation of defense-related pathways, with glutathione metabolism being the most enriched, followed by alanine, aspartate, and glutamate metabolism. However, the limited activation of phenylpropanoid biosynthesis indicated an attenuated defense response in silenced plants. The most pronounced differences were observed in WT_in_vs_RNAi_in, where wild-type plants displayed stronger pathway enrichment than silenced plants under inoculated conditions. Key pathways, including plant hormone signal transduction, biosynthesis of unsaturated fatty acids, and the MAPK signaling pathway, were highly enriched in wild-type plants. These results highlight the impaired capacity of silenced plants to activate hormonal and signaling pathways essential for defense, further emphasizing the pivotal role of the silenced gene in regulating critical metabolic and signaling pathways necessary for resistance against A. alternata.

Fig. 5
figure 5

KEGG Pathway Enrichment Analysis Across Treatments. Here, the treatment groups, including RNAi and WT plants with or without A. alternata inoculation, are detailed in Fig. 2.

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Key genes in the MAPK signaling pathway

The MAPK signaling pathway was notably enriched across all treatments and plays a central role in plant defense (Fig. 6). Among the key genes identified, XM_019395064.1, encoding WRKY33, receives signals from FLS2/BAK1 and regulates callose synthesis, a critical defense mechanism. XM_019382542.1 encodes pathogenesis-related protein 1, which is similarly activated via FLS2/BAK1 and influences late-stage defense responses. XM_019380334.1, activated by H₂O₂, encodes serine/threonine-protein kinase OX1 and is involved in H₂O₂ production and cell death during pathogen response. Another gene, XM_019383898.1, encodes an ethylene-responsive transcription factor, highlighting the role of ethylene signaling in defense. XM_019375063.1, encoding basic endochitinase B, also contributes to defense mechanisms. Abscisic acid regulates XM_019370714.1 (serine/threonine-protein kinase SRK2) and XM_019370001.1 (catalase), which are critical for stress adaptation and tolerance. Calmodulin genes XM_019369231.1 and XM_019368837.1 influence XM_019390269.1 (respiratory burst oxidase), which interacts with XM_019380334.1 to maintain reactive oxygen species homeostasis. Collectively, these genes demonstrated higher expression levels in wild-type plants, particularly under A. alternata inoculation, further corroborating their role in enhancing disease resistance. The qRT-PCR results (Fig. 6b) validated the transcriptome data, showing a strong linear correlation between FPKM and qRT-PCR values (y = -4.59 + 0.482x, R2 = 0.90), with specific values detailed in Table S12, confirming the reliability of the transcriptome analysis.

Fig. 6
figure 6

Transcriptional Profiling Across Treatments. Panel (a) illustrates the enrichment of key genes involved in the MAPK signaling pathway across all treatments, with the heatmap displaying gene expression across treatments, arranged from left to right as WT_un, RNAi_un, WT_in, and RNAi_in. Panel (b) displays the correlation between RNA-seq and RT-qPCR data, validating the transcriptomic results. Here, the treatment groups, including RNAi and WT plants with or without A. alternata inoculation, are detailed in Fig. 2.

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Discussion

This study investigates the role of the NaLRR-RK4 gene in wild tobacco’s defense against A. alternata, with a focus on the gene expression differences between silenced and wild-type plants. Our findings demonstrate that NaLRR-RK4 significantly contributes to the enhanced resistance of wild tobacco to A. alternata. Wild-type plants exhibited superior disease resistance, whereas silencing NaLRR-RK4 led to increased susceptibility, underscoring the gene’s pivotal role in mediating plant immune responses. Although we did not perform control experiments using N. attenuata plants transformed with the empty vector pRESC4, the comparison between RNAi-silenced and wild-type plants alone provides a robust basis for assessing the functional impact of NaLRR-RK441.

Phylogenetic analysis revealed that NaLRR-RK4 in wild tobacco clusters closely with its counterpart, NtLRR-RK4, in cultivated tobacco, indicating a conserved evolutionary relationship between the two species. Notably, over 50 amino acid variations were identified between the wild and cultivated tobacco varieties. These structural differences likely contribute to the observed discrepancies in disease resistance. The variations may impact key functional domains of the protein, including the leucine-rich repeat (LRR) domains, which are crucial for pathogen recognition and the initiation of defense signaling pathways42,43.

Gene Ontology (GO) analysis of transcriptomic data from both silenced and wild-type plants further emphasizes the critical role of NaLRR-RK4 in plant defense mechanisms. In wild-type plants, genes associated with biotic stress responses, including pathogen-induced defense pathways, were significantly enriched. In contrast, these pathways were less activated in silenced plants, reinforcing the importance of NaLRR-RK4 in initiating the plant’s defense response to A. alternata infection. Silenced plants exhibited a compromised activation of key pathways related to oxidative stress44,45, secondary metabolism46,47, and other essential defense processes, which are vital for maintaining plant health under pathogen stress.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that silencing NaLRR-RK4 resulted in a weakened response in crucial signaling pathways, such as MAPK signaling and the regulation of reactive oxygen species (ROS). In contrast, wild-type plants exhibited robust activation of these pathways upon pathogen attack, contributing to their enhanced defense response. The MAPK signaling pathway, a core component of plant immune responses, was notably more active in wild-type plants, suggesting that NaLRR-RK4 acts as a key regulator of MAPK signaling48,49. This pathway coordinates various defense mechanisms, including the regulation of pathogenesis-related (PR) genes and stress-related enzymes50,51.

The MAPK signaling pathway emerged as a central regulator in the defense response to A. alternata. Key genes involved in stress perception and defense activation exhibited significantly higher expression in wild-type tobacco compared to silenced plants. The enrichment of this pathway across all treatments highlights its critical role in plant immunity, where it integrates signals from various receptors and stress molecules to mediate a unified defense response52,53.

The MAPK pathway involves several key genes crucial for plant defense. XM_019395064.1 encodes WRKY33, a transcription factor activated by the FLS2/BAK1 receptor complex, which regulates callose synthesis to strengthen cell walls against pathogens54,55. Elevated WRKY33 expression in wild-type plants highlights its role in early pathogen recognition and defense activation56. Similarly, XM_019382542.1 encodes PR1, a marker of systemic acquired resistance that mediates late-stage defenses, with its upregulation in wild-type plants emphasizing its immune significance, particularly against A. alternata57,58.

XM_019380334.1 encodes OX1, a serine/threonine-protein kinase activated by H₂O₂, which regulates ROS production, oxidative stress, and programmed cell death during infections59,60. XM_019383898.1 encodes an ethylene-responsive transcription factor, reflecting ethylene’s critical role in stress regulation and MAPK-mediated defense against biotic stresses61,62. Additionally, XM_019375063.1 encodes basic endochitinase B, which degrades fungal chitin, reinforcing plant resistance to pathogens63,64.

Abscisic acid (ABA) also integrates into defense responses, as seen with XM_019370714.1 (serine/threonine-protein kinase SRK2) and XM_019370001.1 (catalase), whose higher expression in wild-type plants supports stress adaptation65. Calcium signaling genes, including XM_019369231.1 and XM_019368837.1, modulate the activity of RBOH (XM_019390269.1), which generates ROS for pathogen defense66,67. The interplay between RBOH and OX1 highlights the complex regulation of ROS homeostasis in plant immunity68,69.

In future studies, we aim to explore the regulatory effect of the NaLRR-RK4 gene on resistance to A. alternata at different time points and doses, particularly in its interaction with other important immune pathways. Further investigation of the relationship between NaLRR-RK4 and MAPK signaling pathways may reveal a more complex immune network. Additionally, the resistance of NaLRR-RK4 to other pathogenic microorganisms can be further explored through transgenic plants or gene-editing technology, evaluating its performance under various pathological conditions. Simultaneously, we will strengthen research on the relationship between NaLRR-RK4 and plant growth, development, and environmental adaptability, aiming to clarify its multifunctional role in the natural environment.

Conclusion

This transcriptomic study highlights the essential role of NaLRR-RK4 in mediating plant immune responses against Alternaria. By comparing gene expression profiles between wild-type (WT) and NaLRR-RK4-silenced (RNAi) treated plants under A. alternata inoculation and non-inoculated conditions, we found that NaLRR-RK4 significantly enhances defense mechanisms through the activation of the MAPK signaling pathway. In WT plants, key MAPK-related genes, including WRKY33 and PR1, as well as ethylene- and ABA-responsive genes, were robustly upregulated in response to A. alternata infection. These genes are intrinsic components of the MAPK pathway, highlighting the pathway’s central role in regulating plant defense. RNAi-treated plants, which showed reduced activation of these genes, further underscore the critical involvement of NaLRR-RK4 in mediating defense against A. alternata.