Exogenous gibberellin promotes lateral branch development in stumping Pinus yunnanensis by regulating endogenous hormones and TCP genes

Abstract

While plant stumping can break apical dominance, promote lateral branching, and thereby increase yield, a significant technical challenge remains in further enhancing the coppicing regrowth capacity after stumping. This study used one-year-old Pinus yunnanensis Franch seedlings as subjects. Through exogenous application of gibberellin 3 (GA₃) and Paclobutrazol (PAC), it investigated the regulatory role of gibberellin (GA) in sprouting and identified TCP genes related to branching within the GA pathway. Results indicate that exogenous hormone treatments altered endogenous hormone levels in P. yunnanensis seedlings. Exogenous GA₃ treatment significantly increased endogenous IPA, ABA, GA₃, GA₁, SA, and SAG content. Correlation analysis with sprout number revealed a positive relationship between endogenous hormones and sprout number, indicating that exogenous GA₃ significantly influenced sprout number. After hormone application, the cumulative number of sprouts increased with seedling age across all treatments. Therefore, to further investigate whether changes in sprout number were caused by gene alterations in the GA signaling pathway, this study analyzed the GA signaling pathway and its associated transcription factors. Fifteen TCP family members were screened from the P. yunnanensis transcriptome, phylogenetically classified into Class I and Class II subfamilies. Conserved motif sites within the Class II family co-occurred, exhibiting high conservation. Genes from the Class II subfamily were cloned, and their differential expression across various tissues was analyzed via real-time quantitative PCR. Expression patterns under different hormone treatments were also examined. Our results reveal that PyTCP4, PyTCP6, and PyTCP20 are significantly upregulated by PAC (a GA synthesis inhibitor) and show strong negative correlations with sprouts number, while GA₃ treatment partially alleviates this effect. These findings provide direct evidence that TCP transcription factors act as repressors of shoot branching, linking their hormone-responsive expression to the regulation of sprouts formation in P. yunnanensis. These results indicate that TCP transcription factors play a crucial role in shoot and root emergence, with their expression influenced by exogenous hormones, thereby affecting the growth and development of emerging shoots and roots. This provides a reference and basis for analyzing the expression patterns and functions of TCP genes in P. yunnanensis, facilitating further research into their functions.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09353-5.

Introduction

Gibberellin (GA), a vital phytohormone, regulates numerous processes during plant growth and development [1, 2]. To date, more than 130 GA compounds have been identified, yet only a few such as GA₁, GA₃, GA₄, and GA₇ are biologically active [3]. GAs strongly promotes stem elongation and internode growth, whereas PAC inhibits GA biosynthesis by blocking the oxidation of kaurene to kaurenic acid, thereby reducing plant height and promoting branching [4–6]. In Pisum sativum L, GA inhibits lateral shoot growth by modulating sensitivity to strigolactones (SL) [7]. Transcriptome-revealed plant hormones and nutrients are key factors influencing branching in ornamental kale. Stumping treatment and exogenous hormone application revealed that auxin and SLs inhibited lateral bud outgrowth, respectively [8]. In Arabidopsis thaliana L., GA-insensitive mutants show weakened apical dominance and increased axillary bud formation, while overexpression of GA20ox elevates GA levels in leaf axils and suppresses axillary meristem initiation and lateral branching [9, 10]. Conversely, overexpression of the GA catabolic gene GA2ox increases tiller or branch number in Oryza sativa L [11], Solanum lycopersicum [12], and Populus [13]. However, the effects of GA are highly species‑dependent: exogenous GA₃ promotes axillary bud outgrowth in Citrus reticulata [14], Prunus avium [15], and Jatropha curcas L [16], but not in Malus domestica [17]. In J. curcas, GA₃ acts synergistically with cytokinins (CTKs) to promote bud growth, and CTKs can further regulate GA metabolism [18]. Collectively, existing research reveals that GA regulates plant branching in a complex, species-specific manner with unresolved molecular mechanisms, while TCP transcription factors integrate GA and other hormone signals to act as pivotal nodes in the GA-dependent regulatory pathway controlling lateral branch formation.

The TCP family is a class of transcription factors unique to plants, and its members contain a TCP domain, a specialized basic helix-loop-helix (bHLH) superfamily domain consisting of approximately 59 amino acids [19, 20]. This domain originates from TB1 in Zea mays, CYC in Antirrhinum majus, and PCF1/PCF2 in O. sativa, hence the designation of the TCP gene family as an acronym of these three gene groups. [21, 22]. During plant growth and development, TCP transcription factor genes exert critical functions and modulate lateral branching development via tight crosstalk with hormone signaling pathways [23]. The TCP transcription factors BRC1 have been confirmed to locally repress shoot branching in A. thaliana [24]. As a core TCP family member [25], BRC1 modulates lateral branch growth by interacting with multiple hormones including GA, auxin, cytokinin and SL [9]. Furthermore, exogenous SL upregulates PsBRC1 expression whereas cytokinin suppresses it, demonstrating that PsBRC1 cooperates with SL and cytokinin to jointly modulate axillary meristem activity [26]. This study demonstrates that the pea orthologous gene PsBRC1 is highly expressed in axillary buds and is indispensable for strigolactone (SL)-mediated repression of lateral branching [27]. BRC1 interacts with HB21, HB40 and HB53 to form a protein complex that activates the ABA biosynthetic gene NCED3, thereby directly regulating lateral branch formation [28]. The rice TCP gene TIG1 modulates tiller development by regulating the auxin-responsive gene SAUR39 within the auxin signaling pathway [29]. These findings indicate that TCP transcription factors serve as central regulators of plant lateral branching by integrating GA and multiple hormone signals, whereas such regulatory mechanisms remain uncharacterized in the ecologically important species P. yunnanensis.

Pinus yunnanensis possesses outstanding tolerance to drought and infertile soils. Its deep, robust root system and needles with thick cuticles enable it to survive drought stress effectively. After afforestation, it not only stabilizes slopes and retains soil moisture but also improves site conditions through litter decomposition, rendering it a native pioneer tree species for ecological restoration of barren mountains in Southwest China [30]. This tree species boasts remarkable economic, social as well as ecological benefits [31]. Economically, its timber is widely used for construction, furniture, and paper pulp; its resin provides raw materials for rosin and turpentine production; and its edible pine nuts generate income for local communities. Socially, afforestation with this species creates rural employment in nursery management, planting, and harvesting, while also stabilizing slopes and reducing natural disaster risks (e.g., landslides) that threaten villages and farmland. Ecologically, as a pioneer species it rapidly colonizes degraded barren mountains, improves soil structure and fertility through litter accumulation, prevents soil erosion, sequesters carbon, and facilitates the subsequent regeneration of other native understory plants, thereby enhancing overall biodiversity recovery.

Long-term human disturbance and environmental stress have degraded P. yunnanensis stand quality, productivity and germplasm resources. Stumping promotes sprouting and high-quality cutting propagation by breaking apical dominance, while maintaining superior maternal traits and shortening the seedling cycle. Although GA facilitates axillary bud outgrowth, the effects of exogenous GA₃ and the associated molecular mechanisms on sprouting in stumped P. yunnanensis remain unclear, particularly regarding the regulatory role of TCP transcription factors. Therefore, we treated stumped seedlings with GA₃ to explore GA-mediated sprouting physiology and molecular regulation for germplasm improvement and seedling cultivation. As an ecologically dominant native tree in southwest China, P. yunnanensis sprouting regulation remains poorly understood under GA₃ and PAC treatments, restricting targeted seedling regulation techniques. This study analyzed dynamic phytohormone responses during sprouting to clarify GA-related regulatory patterns and provide a theoretical basis for P. yunnanensis physiological research and cultivation.

Materials and methods

Research material

Seeds were collected from the “Midu P. yunnanensis Franch Asexual Seed Plantation,” a national P. yunnanensis seed base in Midu County, Dali Prefecture, Yunnan Province, China [32, 33]. They were sterilized by soaking in a 0.5% potassium permanganate solution for 30 min, rinsed with running water, and soaked in warm water at 50 °C for 24 h [34]. The soil and seedling trays were sterilized with potassium permanganate solution. The seeds were sown [35]. After germination, a layer of pine needles was spread on the surface of the seedling trays, a sunshade was installed, and the trays were covered with film. Regular management measures, such as watering and weeding, were carried out. When the P. yunnanensis seedlings were uniform, after hardening off, seedlings with good growth status were selected for single transplantation into the nursery pots. During the nursery period, the soil was a mixture of humus and red soil at a 3:1 ratio [36].

Seedling stumping and exogenous hormone treatment

In March 2024, after one year of seedling cultivation, P. yunnanensis seedlings with uniform growth status were selected. A single-factor experimental design was adopted with three treatments. Each treatment had three replicates, totaling nine plots; each plot contained 68 seedlings, with a total of 612 plants. Based on previous studies, the seedlings were stubbed at a uniform height of 5 cm, and wound healing cream was applied to the cuts immediately after stubbing to prevent water evaporation and microbial contamination. Before stumping, the seedlings were comprehensively surveyed to record the number of existing sprouts, which were marked with ties to distinguish them from the new sprouts that would emerge later. Cumulative sprout number was determined via serial observations of the same individual plants from March to December. Each seedling was labeled individually, and sprout numbers were recorded non-destructively at each time point without pooling or destructive sampling. The same individuals were monitored continuously to ensure accurate cumulative counting.

The prepared solutions were applied via foliar spraying, immediately after stumping. The experiment was conducted on sunny mornings after the dew had evaporated to avoid affecting the spraying effect. The spraying volume was controlled to ensure that the needles of the P. yunnanensis seedlings were thoroughly wetted, with droplets forming at the needle tips. Treatment 1 (T₁) was sprayed with water as a control, Treatment 2 (T₂) with a 300 mg/L GA₃ aqueous solution, and Treatment 3 (T₃) with a 500 mg/L aqueous solution of the gibberellin biosynthesis inhibitor, PAC.

Sampling and assay of endogenous hormones

For all assays, including endogenous hormone quantification, RNA extraction, and qPCR analysis, P. yunnanensis seedlings were sampled at 0 h, 4 h, 8 h, 12 h, 1 d, 2 d, 3 d, 5 d, 7 d, and 14 d after hormone spraying. During sampling, stem segments 2 cm below the stumping site were excised immediately, placed into pre-labeled cryopreservation tubes, and frozen in liquid nitrogen on-site. Samples were subsequently stored at −80 °C until use [37, 38]. For each treatment group, nine seedlings were randomly selected at each time point, and their tissues were pooled to form one biological replicate; three independent biological replicates were collected per time point. Latex gloves and masks were worn throughout sampling to avoid contamination and ensure the dryness of sample surfaces.

Based on preliminary experiments and transcriptomic data, samples collected at 4 h, 1 d, and 5 d after stumping were selected for endogenous hormone quantification. Endogenous hormones were quantified using UPLC-MS/MS (AB Sciex ExionLC coupled with 6500 + Qtrap mass spectrometer) in MRM mode. Stable isotope-labeled internal standards IAA‑D5 (indole-3-acetic acid‑d5) and ABA‑D6 (abscisic acid‑d6) were spiked during sample extraction to correct matrix effects and procedural losses. The limits of detection (LOD) for target hormones ranged from 0.05 ng/mL to 25 ng/mL based on the standard curves. Method recoveries were calibrated using the internal standards to ensure accuracy. The abbreviations of endogenous hormones used in this study are defined in Table S1 (List of abbreviations for endogenous hormones).

Transcriptome sequencing analysis

Research materials

Whole one-year-old seedlings of P. yunnanensis were collected from the nursery of Southwest Forestry University and used as experimental materials. For tissue-specific qPCR analysis, only untreated seedlings (0 h, i.e., no hormone or stumping treatment) were sampled. For other assays, including total RNA extraction, transcriptome sequencing, and hormone quantification, samples were collected at 0 h, 4 h, 1 d, and 5 d after hormone spraying and stumping.

At each time point, seedlings were rinsed with running water to remove surface impurities, disinfected with sterile water, and gently blotted dry with filter paper. Tissues were immediately placed into 50 mL centrifuge tubes, frozen in liquid nitrogen on-site, and stored at −80 °C until use. For all assays, three independent biological replicates were collected per time point. Each biological replicate consisted of tissues pooled from nine randomly selected seedlings to minimize individual variation. Total RNA was extracted from these samples for RNA-seq and qPCR analysis. Clean reads were mapped to the Pinus tabuliformis reference transcriptome from the Chinese Pine Information Resource (CPIR, conifers.cn). The transcriptome assembly contained a total of 690 transcripts, with an average length of 1455.5 bp and an N50 length of 2070.93 bp, demonstrating high quality and completeness for subsequent gene expression analysis. All raw RNA-seq data were deposited in the SRA under Bioproject PRJNA1406671 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1406671/). All quality assessment images are included as Supplementary Figures (S1, S2, S3).

Extraction of RNA and cDNA reverse transcription

Total RNA was extracted from all tissues of one-year-old P. yunnanensis seedlings using the FastPure Universal Plant Total RNA Isolation Kit. After confirming that the extracted total RNA had good concentration, purity, and integrity, it was reverse transcribed into single-stranded cDNA using the HiScript III 1 st Strand cDNA Synthesis Kit (+ gDNA wiper) [39].

Tissue specificity of candidate TCP genes and expression analysis in response to different hormone treatments

This study employed one-year-old P. yunnanensis seedlings as experimental plant materials. Eight distinct tissue types, namely young leaves, old leaves, young stems, old stems, dormant buds, apical buds, germinating buds and roots, were all harvested from the same set of 30 seedlings (Fig. 1). For each tissue type, tissue aliquots taken from 10 individual seedlings were combined to constitute a single biological replicate; three independent biological replicates were prepared per tissue, which required pooling tissue samples from 10 seedlings for each replicate, with all sampled tissues originating collectively from the 30 fixed seedlings. After collection, total RNA from different tissues was extracted sequentially, as described in Sect. "Extraction of RNA and cDNA reverse transcription", and reverse-transcribed into cDNA. Real-time quantitative PCR (qPCR) analysis was performed using the Actin-4 gene as the internal reference gene.

Fig. 1.

Sampling sites of P. yunnanensis for tissue-specific analysis

Data analysis

Experimental data were analyzed using Microsoft Excel 2019 and SPSS 27.0 software for one-way analysis of variance (ANOVA), and significance was tested using Duncan's multiple range test at P = 0.05. The DPS Data Processing System was used to establish the logistic equation for ground diameter increment over growth time and to fit the seedling growth curve. Relative gene expression in qPCR was calculated using the 2^(-ΔΔCt) method. GraphPad Prism 10.0 and Origin 2021 are used for graphing, and all data were presented as mean ± Standard Error of the Mean (SEM). Phylogenetic trees were constructed using the maximum likelihood (ML) method. Bootstrap support values were calculated with 1000 replicates and are indicated at each node of the phylogenetic tree. Cumulative sprouting number and monthly sprouting increment were monitored monthly from March to December, and the full time-series data were plotted in Figure 2 to reflect the complete seasonal sprouting dynamics after stumping. For correlation analysis between endogenous hormone concentrations and sprouting quantity, this study only adopted data from June to December, as sprout counts showed almost no variation during March–May, and invariant values would reduce the reliability of correlation coefficients.

Fig. 2.

Sprout dynamics of P. yunnanensis seedlings under different treatments. a Cumulative number of sprouts per seedling over the monitoring period. b Monthly increment of sprouts per seedling. Treatments: CK (control), GA₃ treatment, PAC treatment. Note: Data are presented as means ± standard error of the mean (SEM), with n = 9 seedlings per treatment. Different lowercase letters above the data points indicate significant differences among treatments at the same time point, as determined by one-way ANOVA followed by Duncan’s multiple range test (p < 0.05)

Results

Effects of hormone treatment on the sprouting ability of P. yunnanensis Franch seedlings

The cumulative number of sprouts in P. yunnanensis seedlings under different hormone treatments evolved over time, as shown in Fig. 2a. During the measurement period, all hormone treatments exhibited a steady increase in the cumulative number of sprouts over time. The number of sprouting shoots in the GA₃ treatment was greater than that in the CK and PAC groups from 30 days after treatment (i.e., April). From September onward, it was significantly greater than that in the CK and PAC groups, with a significant difference (p < 0.05) among treatments.

As shown in Fig. 2b, the monthly increment in the number of sprouting shoots of P. yunnanensis under different hormone treatments showed multiple peaks over time. Across all treatments, the initial, dominant peak of sprouting occurred 30 days after stumping, corresponding to the March-to-April interval. This synchronous, early peak across all treatments is a conserved physiological response to the stumping stimulus, rather than a hormone-specific effect. Critically, this marked increase in both the cumulative number of sprouts and the monthly increment during March–April is driven by the interaction of two key factors: the immediate physiological response to stumping, and favorable seasonal environmental conditions. Stumping eliminates the apical dominance exerted by the main shoot apex, which constitutively suppresses axillary bud outgrowth via auxin signaling. The removal of this suppression immediately triggers a coordinated, rapid burst of axillary bud activation in the first month post-treatment, driven by the plant’s inherent wound response and resource reallocation to regenerative growth. Concurrently, the March–April period coincides with rising temperatures and increasing day length in the study area, which further promote cell division, elongation, and the mobilization of stored carbohydrates to support new growth. These favorable seasonal cues act synergistically with the stumping-induced signal to drive the observed early sprouting surge. In summary, GA₃ effectively promotes the sprouting and growth of P. yunnanensis seedlings, which is closely linked to the activation of its core GA signaling pathway.

Response of endogenous hormones to exogenous hormone levels in P. yunnanensis seedlings after stumping

Temporal dynamics of endogenous hormone content under different treatments

To characterize the dynamic changes in endogenous phytohormones during sprouting and their responses to GA₃ and PAC treatments, the temporal profiles of 24 hormones/metabolites were quantified in P. yunnanensis seedlings over a 5-day period. Cytokinin-related compounds exhibited distinct responses: IPA levels were significantly elevated in the PAC treatment compared with the CK and GA₃ groups at all three time points, peaking at 5 days (p < 0.05). By contrast, the GA₃ treatment rapidly increased IP and TZR concentrations, which reached their maximum at 5 days. Among auxin-related signals, GA₃ significantly upregulated IAA at 4 h, while PAC induced sustained high levels of DZ throughout the treatment period. For gibberellins, exogenous GA₃ triggered a sharp, transient increase in bioactive GA₁, GA₃, and GA₈ within 4 h, which then gradually declined over time. This phenomenon is a typical hormone homeostatic response: plants perceive excessive active GAs and initiate negative feedback to accelerate GA degradation and suppress GA synthesis. In contrast, these GA levels remained consistently low in the PAC treatment. The GA precursor GA₇ was significantly suppressed by GA₃ but accumulated to high levels under PAC treatment, indicating feedback inhibition of GA biosynthesis. Ethylene precursor ACC and jasmonate-related signals (JA, JA-Ile, MEJA) showed divergent patterns: ACC concentrations were highest in the PAC group at 5 days, while JA and JA-Ile peaked at 4 h in the GA₃ treatment before rapidly decreasing. SA and its conjugate SAG were strongly induced by GA₃ at 1 day, whereas CK plants showed a gradual increase in SA over time. ABA levels were highest in the GA₃ treatment at 4 h, but all three groups converged to similarly low ABA concentrations by 1 day. The auxin catabolite OPDA remained significantly higher in the GA₃ group throughout the experiment, while PAC significantly promoted the accumulation of the cytokinin-related metabolite 6-BA at later stages. Collectively, these results demonstrate that GA₃ and PAC induce highly specific, time-dependent alterations in multiple hormonal pathways, particularly cytokinin, gibberellin, and auxin signals, which may coordinate to regulate sprouting and branching development in P. yunnanensis.

Correlation analysis between endogenous hormone levels and sprout number

Based on the temporal dynamics of endogenous hormones (Fig. 3), only those hormones exhibiting consistent and significant responses to both GA₃ and PAC treatments across the sampling times were selected for correlation analysis. These included cytokinins (IPA, IP, TZR), gibberellins (GA₁, GA₃, GA₇), auxins (IAA, ICA, OPDA), abscisic acid (ABA), jasmonates (JA, JA-Ile, MEJA), and salicylates (SA, SAG). At the 1-day (1d) time point, these hormones showed the most stable and treatment-specific changes, which allowed us to capture the core hormonal crosstalk network underlying GA-induced branching regulation.

Fig. 3.

Changes in endogenous hormone contents in P. yunnanensis seedlings under different treatments over time. Hormone levels were measured at 4 h, 1 d, and 5 d after treatment. CK, control; GA₃ treatment; PAC treatment. Note: Data are presented as the mean ± standard error of the mean (SEM) (n = 3 biological replicates). Different lowercase letters indicate significant differences among different time points within the same treatment, as determined by one-way ANOVA followed by Duncan’s multiple range test (p < 0.05)

A correlation analysis between endogenous hormone content and the number of sprouting shoots was performed, and the results are presented in Fig. 4. In the GA₃ treatment group (Fig. 4B), the content of endogenous hormones generally exhibited a positive correlation with the number of sprouting shoots, particularly during the spring and summer months (March to September). Specifically, GA₃ content showed a significant positive correlation with sprouting shoot number in April. GA₁ content was positively correlated with sprouting shoot number in June and July, while ABA content displayed highly significant positive correlations (P < 0.01) with sprouting shoot number in May and August. GA₇ and SAG also exhibited strong positive correlations with sprouting shoot number in September and May, respectively. In contrast, the correlation was weaker in the CK group (Fig. 4A), and negative correlations were observed in the PAC group (Fig. 4C), especially between December sprouting shoots and most endogenous hormones. Collectively, these results indicate that exogenous GA₃ treatment promotes an increase in branch number by reshaping the hormonal crosstalk, with GA₃, GA₁, and ABA acting as key regulators, primarily during the critical spring and summer growth periods.

Fig. 4.

Correlation analysis between endogenous hormone content and the number of sprouts. A CK treatment group; B GA₃ treatment group; C PAC treatment group. Note: SN represents sprout number. Significant correlations (p ≤ 0.05) are marked with asterisks (*)

Hormone sprout co-expression regulatory networks reveal GA mediated sprout regulation in P. yunnanensis

As shown in Fig. 5, different treatments significantly reshaped the co-expression regulatory network between hormones and sprout numbers. The CK group (A) exhibits a balanced baseline regulatory pattern, with extensive but moderately positive/negative correlations between hormones and sprout numbers (SN) across months, reflecting the fundamental homeostatic regulation of endogenous hormones. In the GA₃ treatment group (B), the network connection density increases significantly. Core hormones such as GA₃, IPA, and IAA form a tightly positively correlated module and maintain strong positive correlations with sprout number nodes across all months, indicating that exogenous GA₃ enhances the synergistic effects among hormones and continuously promotes sprout occurrence. In the PAC treatment group (C), the network connections become notably sparse; positive correlations between hormones and sprout numbers are substantially reduced while the proportion of negative correlations increases, suggesting that the promoting effects of core hormones on sprouting are weakened. This demonstrates that PAC attenuates sprout occurrence by inhibiting the synergistic regulation involving GA₃ related hormones. The structural differences among the three networks visually reveal the central role of GA in regulating P. yunnanensis sprouting, providing network level evidence for understanding its molecular mechanism.

Fig. 5.

Correlation network analysis between endogenous hormone contents and monthly sprout numbers (SN) in P. yunnanensis under different treatments. A Control (CK) group; B GA₃ treatment group; C PAC treatment group. Note: Each node represents an endogenous hormone or monthly sprout number (SN, labeled by month). Edges represent Pearson correlation coefficients (r) between pairs of variables, with only statistically significant correlations (|r|> 0.6, p < 0.05, two-tailed test) visualized. Red edges indicate positive correlations (r > 0), while blue edges indicate negative correlations (r < 0). Edge thickness is proportional to the absolute value of the correlation coefficient (|r|), where thicker edges correspond to stronger correlations. The color bar in each panel explicitly maps the correlation coefficient value to the edge color gradient

Analysis of GA signaling and metabolic pathways

Exogenous hormone application after stumping disrupts endogenous hormone homeostasis, thereby altering sprout number. Therefore, to elucidate the molecular events underlying this process, this study sought to profile the expression of DEGs in hormonal signaling pathways during subsequent growth and development, and to map these DEGs to their corresponding pathways (Fig. 6). GA can break axillary bud dormancy and is a key factor promoting lateral branch germination in plants. A total of eight DEGs were expressed during GA biosynthesis and metabolism. The GA biosynthesis gene GA20ox3 was upregulated in the GA₃ treatment group, whereas it was first downregulated and then upregulated in the PAC treatment group. The GA inactivation gene GA2ox1 was downregulated in both GA₃ and PAC treatment groups. GID1, a GA receptor in the signaling pathway, plays an important role. GIDIC-2 exhibited an expression trend of upregulation, followed by downregulation, and then upregulation in all treatment groups, whereas GIDIC-1 showed a trend of downregulation, followed by upregulation, and finally downregulation. In the GA₃ treatment group, GAI1-3 was downregulated, and both GAI1-1 and GAI1-2 displayed a trend of downregulation, followed by upregulation. All three GAI1 genes were downregulated in the PAC treatment group. From the changes in hormone content after treatment, it can be seen that after exogenous hormone application, the GA₃ content in both the GA₃ and PAC treatment groups showed consistent change trends. Still, the change in GA₃ content in the GA₃ treatment group was highly significant. The GA₇ content in both the GA₃ and PAC treatment groups also showed consistent change trends, with the change in the PAC treatment group being more substantial; however, compared with the GA₃ content, the change in GA₇ content was not significant. To elucidate its mechanistic role in the sprouting and growth of P. yunnanensis seedlings, we performed a further analysis of transcription factors. In addition, we further performed regression analysis on gibberellin‑related genes and endogenous hormones, with detailed information presented in Table S2.

Fig. 6.

Hormone signal transduction pathways involved in the development of P. yunnanensis. Note: The biosynthetic, metabolic, and signal transduction pathways of GA; Arrows: Black arrows indicate direct biological activation/transduction; red cross indicates enzymatic inhibition; black triangular arrows indicate gene expression changes; curved arrows indicate feedback/regulatory loops; Colored shapes: Red triangles represent GA₃; light blue trapezoids represent GID1 receptors; yellow pentagons represent DELLA proteins; broken shapes indicate protein degradation or functional inhibition.; Heatmaps: Color intensity (green to red color scale) represents normalized gene expression levels (log₂ fold change) at different time points (0 h, 4 h, 1 d, 5 d) under GA₃ or PAC treatment, with green indicating downregulation and red indicating upregulation; Bar graphs: Show GA₃ and GA₇ hormone contents (ng/g) over time under GA₃ (red line) and PAC (green line) treatments; indicates p < 0.0001 (Student’s t test); The pathway illustrates that GA₃ treatment promotes cell elongation and plant growth by degrading DELLA repressors, while PAC (a GA biosynthesis inhibitor) suppresses this process

Analysis of sprout related transcription factor families in P. yunnanensis following hormone treatment

Based on the aforementioned changes in hormonal signals, this study conducted an in-depth study to elucidate the mediating role of transcription factors in regulating the sprouting and growth of P. yunnanensis seedlings. This study identified a total of 50 transcription factor families across the nine comparison groups (Fig. 7). At 4 h post treatment (Fig. 7A, a–c), the MYB, LBD, bHLH, ERF, WRKY, GRAS, and HD ZIP families exhibited the highest number of differentially expressed genes in the three comparison groups At one day post treatment (Fig. 7 A d–f), the MYB, LBD, ERF, bHLH, WRKY, NAC, and TCP families showed the most differentially expressed genes in the three comparison groups. At 5 days post treatment (Fig. 7 A g–i), the MYB, ERF, bHLH, LBD, MADS, TCP, WRKY, GATA, and Dof families had the highest number of differentially expressed genes in the three comparison groups. In summary, transcription factor families such as MYB, ERF, bHLH, LBD, WRKY, and TCP were predominant during the growth and development of adventitious buds in P. yunnanensis seedlings, with their expression patterns varying across different time points. This indicates that these transcription factors play crucial regulatory roles in the process.

Fig. 7.

A Transcription factor family analysis; B Heatmap of differentially expressed transcription factors in seedling sprouting development after hormone treatment. Note: A a:CK-4h_Vs_GA3-4 h; b:CK4h_Vs_PAC-4 h; c:GA3-4h_Vs_PAC-4 h; d:CK-1d_Vs_GA3-1d; e:CK-1d_Vs_PAC-1d; f:GA3-1d_Vs_PAC-1d; g:CK-5d_Vs_GA3-5d; h:CK-5d_Vs_PAC-5d; i:GA3-5d_Vs_PAC-5d. B Colors indicate the intensity of gene expression. Specifically, green indicates downregulation, and purple indicates upregulation

Additionally, following hormone treatment, 95 differentially expressed transcription factors related to adventitious bud development were identified, including MYB (19), WOX (5), LAX (2), SBP/SPL (11), LOB (2), bHLH (20), WRKY (21), and TCP (15). As shown in Fig. 7B, the expression patterns of these transcription factors primarily followed two trends: one group exhibited an initial up regulation followed by down regulation under both GA₃ and PAC treatments, while the other group showed an initial down regulation followed by up regulation under GA₃ treatment, and a down up down expression trend under PAC treatment. Under GA₃ treatment, most genes were down regulated. In summary, various transcription factors collectively coordinate sprout development, among which the TCP family, as a key player, merits in depth analysis of its expression dynamics.

Cloning analysis of candidate TCP genes

Phylogenetic analysis of P. yunnanensis TCP family members

The hidden Markov model (HMM) for the TCP conserved domain (PF03634) was obtained from the Pfam database (http://pfam.xfam.org). Using HMMER 3.0 software, TCP proteins were searched against the P. yunnanensis transcriptome with a defined E value threshold of < 10⁻⁵. After removing sequences lacking the conserved domain and redundant sequences, a total of 15 TCP proteins were ultimately identified. Their gene names and IDs are listed in Table.S3, and their amino acid sequences and protein sequences were used for subsequent analysis.

The phylogenetic results (Fig. 8A) indicated that the TCP transcription factor family of P. yunnanensis was divided into two major classes: Class I and Class II. Class I is also known as the PCF subfamily, and Class II can be further divided into CYC/TB1 and CIN subfamilies. Among them, eight P. yunnanensis TCP members were distributed in the PCF subfamily and seven in the CIN subfamily. In addition, A. thaliana TCP members were present in all subfamilies, whereas P. yunnanensis TCP members were restricted to the PCF and CIN subfamilies only. This suggests that the distribution of TCP members in P. yunnanensis differs from that in A. thaliana in terms of phylogenetic relationships.

Fig. 8.

A Phylogenetic analysis of TCP transcription factor family in P. yunnanensis. B Sequence alignment of conserved domains of TCP family proteins in P. yunnanensis. Note: Yellow, green, red, and dark blue backgrounds indicate 33%, 50%, 75%, and 100% protein identities, respectively, and helix loop helix and basic structure domains are marked with black lines

To determine the conserved structural domains of the P. yunnanensis TCP transcription factor family, multiple sequence alignment of the conserved structural domains was performed using DNAMAN 9.0 software. The results are presented in Fig. 8B. All TCP proteins contain a complete TCP structural domain, including a helix loop helix domain and a basic domain. In the basic domain, four amino acid residues were absent in class I members (PyTCP36, PyTCP37, PyTCP38, PyTCP39, PyTCP40, PyTCP41, PyTCP42, and PyTCP43) compared with class II members. Conserved characteristic sites were also identified in the helix loop helix domain. These results indicate that the P. yunnanensis TCP proteins are highly conserved.

Conserved protein motif analysis and PCR amplification with verification of TCP family members in P. yunnanensis

The conserved motifs of the P. yunnanensis TCP family were identified using the online tool MEME v5.5.7. A total of 10 conserved motifs were determined among the 15 TCP family members and designated as Motif 1 to Motif 10 (Fig. 9A). The number of amino acids in these motifs ranged between 10 and 50, with Motif 1 representing the characteristic TCP domain. Figure 9A-a shows the distribution patterns of these motifs. All P. yunnanensis TCP proteins contained the conserved Motif 1, indicating the structural integrity of the TCP family in this species. Furthermore, members within the same subfamily generally shared similar motif compositions. For instance, all Class I members contained Motif 1, while Motifs 2 and 5 were unique to Class I. All Class II members contained Motif 1, with Motifs 3, 4, 6, 7, 8, and 10 being specific to this subfamily. The logo sequences of these motifs are presented in Fig. 9Aa-b. These results demonstrate that motif distribution correlates with phylogenetic classification, and differences in motif composition among members may reflect variations in protein structure. Given the aforementioned bioinformatic findings, particularly the unique motif composition of the Class II subfamily and its critical role in lateral branch development, this study selected its members for cloning to facilitate further experimental validation.

Fig. 9.

A Phylogenetic tree of TCP family proteins in P. yunnanensis. B Conserved motif analysis of TCP family proteins in P. yunnanensis; C Gel electrophoresis image of PCR amplification products for TCP genes. M: DNA Marker; Lanes 1–6: PyTCP2, PyTCP3, PyTCP4, PyTCP5, PyTCP6, and PyTCP20, respectively. D Gel electrophoresis image of colony PCR identification for TCP genes

In the plant TCP transcription factor family, the Class II subfamily plays a critical role in the growth and development of lateral branches. Furthermore, our analysis of transcription factors in Sect. "Analysis of GA signaling and metabolic pathways" revealed differential expression within the TCP family. Therefore, this study conducted cloning and bioinformatic analysis of the TCP Class II members in P. yunnanensis. During PCR amplification of the TCP transcription factor family, the agarose gel electrophoresis results were generally consistent with the transcriptome sequencing data, indicating successful amplification of the open reading frames (ORFs) of all six P. yunnanensis TCP genes (Fig. 9B). Further verification by colony PCR showed specific bands above 1000 bp for all targets (Fig. 9C), which was highly consistent with the expected results. Sequencing analysis confirmed that the sequences obtained from the bacterial cultures basically matched the corresponding sequences in the transcriptome database, thus verifying the successful cloning of all six TCP genes. In summary, this study successfully cloned six TCP Class II transcription factors from P. yunnanensis. To further investigate their biological functions, this study analyzed the expression profiles of these candidate genes across different tissues.

Tissue specific expression analysis of candidate TCP

We performed qPCR analysis to measure the expression levels of the six cloned Class II TCP transcription factors (from Sect. 3.4.2) across eight different tissues of one-year-old P. yunnanensis seedlings: young leaves, old leaves, young stems, old stems, dormant buds, terminal buds, released buds, and roots. The results are shown in Fig. 10. Class II TCP genes were expressed in all eight tissues, though relative expression levels varied. PyTCP3 exhibited the highest expression in dormant axillary buds, followed by higher expression in released buds, apical buds, and roots. This expression pattern suggests that the preferential expression of PyTCP3 in axillary buds may be closely associated with adventitious shoot formation. To further elucidate its regulatory mechanism, this study analyzed the expression changes of these TCP genes under different hormonal treatments.

Fig. 10.

Expression patterns of class II TCP family members in different tissues of P. yunnanensis. Note: Relative expression is expressed as mean ± standard deviation (SD); lowercase letters indicate differences between organizations (p < 0.05)

Expression levels of candidate TCP genes in response to different hormone treatments

To investigate how hormone signaling regulates TCP gene expression to influence sprouting development, this study further analyzed the expression patterns of six P. yunnanensis TCP genes under different hormone treatments (Fig. 11). Compared to the 0 h control, PyTCP2 and PyTCP3 exhibited an overall expression trend of initial increase followed by decrease, with significant upregulation at 4 h. These expression patterns largely aligned with previous transcriptomic data, further validating the reliability of the transcriptomic results. In summary, the six TCP genes exhibited distinct response patterns to different hormone treatments, suggesting they may participate in hormone mediated shoot development through distinct molecular pathways. This study provides important theoretical foundations for elucidating the functions of P. yunnanensis TCP transcription factors and their roles within shoot regulation networks.

Fig. 11.

Expression pattern analysis of class II TCP family members in P. yunnanensis after hormone treatment. Note: Relative expression levels are presented as mean ± standard deviation (SD). Capital letters indicate significant differences between different treatments at the same time point, whereas lowercase letters denote significant differences among different time points within the same treatment group. (p < 0.05)

Correlation analysis of TCP Gene expression and shoot number under different hormone treatments

To validate the role of TCP genes in branching regulation, this study analyzed the correlation between their expression levels and sprout number (SN) (Fig. 12). Under control conditions, PyTCP4, PyTCP6, and PyTCP20 showed moderate correlations with SN, reflecting natural seasonal variation. GA₃ treatment intensified negative correlations between these genes and SN, while PAC treatment resulted in strong, widespread negative correlations across all seasonal samples (Fig. 12). Consistent with the correlation analysis, qRT-PCR data showed that PAC significantly upregulated PyTCP4, PyTCP6, and PyTCP20 (Fig. 11), while GA₃ had a weaker or transient inductive effect. For example, PyTCP4 expression peaked at 4 h under PAC, coinciding with the strongest negative correlation with SN. These results demonstrate that the expression levels of PyTCP4, PyTCP6, and PyTCP20 are inversely correlated with branching (SN), providing direct evidence that these TCP transcription factors act as repressors of shoot branching in P. yunnanensis.

Fig. 12.

Correlation heatmap between TCP gene expression and sprout number (SN) in P. yunnanensis under different hormone treatments. A CK treatment group; B GA₃ treatment group; C PAC treatment group. Note: SN represents sprout number. Note: SN represents sprout number. Significant correlations (p ≤ 0.05) are marked with asterisks (*)

Discussion and conclusions

Effects of exogenous hormones on endogenous hormones in P. yunnanensis flat-crop seedlings

Plant hormone metabolism and balance are crucial for regulating plant growth and development. Exogenous hormone treatments significantly affect endogenous hormone homeostasis in flat‑cut seedlings, mainly by modulating the biosynthesis and catabolism of hormones [40]. Exogenously applied GA₃ or PAC can directly trigger or suppress the synthesis of endogenous hormones, as well as regulate their metabolic pathways, thereby reshaping the balance of multiple phytohormones. Such dynamic changes in endogenous hormone levels, in turn, serve as critical signals to coordinate sprouting and branching development in seedlings (Fig. 13). Gibberellins (GAs) play fundamental roles in almost every aspect of plant growth and development, including cell elongation, leaf expansion, leaf senescence, seed germination, and leafy head formation [41]. Previous studies have shown that exogenous GA₃ application can affect lateral branch development by regulating endogenous hormone production in J. curcas [16], which provides a reference for understanding the regulatory mechanism in P. yunnanensis.

Fig. 13.

GA treatment model for P. yunnanensis after stumping

The study indicates that exogenous GA₃ treatment significantly alters the endogenous hormone levels in P. yunnanensis stump seedlings, specifically increasing the contents of IPA, ABA, GA₃, GA₁, SA, and SAG. Furthermore, exogenous GA₃ and PAC treatments were applied to P. yunnanensis flat-crop seedlings, and the changes in endogenous hormone levels were systematically analyzed. The results showed that exogenous GA₃ treatment significantly increased the contents of endogenous IPA, ABA, GA₃, GA₁, SA, and SAG in P. yunnanensis seedlings, compared with the control (CK) and PAC treatments. In contrast, PAC treatment inhibits endogenous GA biosynthesis and triggers distinct changes in endogenous hormone levels. Specifically, it caused a downward trend in GA-related endogenous hormones (GA₃, GA₁) in the early stage of treatment. Meanwhile, GA20ox3, a key gene in GA biosynthesis, showed a expression pattern of decreasing first and then increasing, which is probably attributed to the intrinsic feedback regulatory mechanism in plants [42]. Studies on tomato have shown that PAC treatment upregulates the expression of the biosynthesis-related genes SlGA20ox-3 and SlGA3ox2 [43].

In addition, correlation analysis between exogenous hormone treatments and sprout number indicated that the increased levels of endogenous IPA, ABA, GA₃, GA₁, SA, and SAG induced by exogenous GA₃ were positively correlated with the sprout number of P. yunnanensis seedlings. This confirms that exogenous GA₃ effectively promotes the growth and development of P. yunnanensis seedlings by regulating endogenous hormone levels. These changes in endogenous hormone levels are closely related to the activation of the gibberellin core signaling pathway, as exogenous hormones can regulate plant growth and development by influencing endogenous hormone levels or their response timing [44]. Furthermore, ZT, IAA and GA interact to jointly regulate branching development after stumping. ZT directly promotes branching, while IAA indirectly modulates this process by inducing GA accumulation. GA also participates directly in branching growth. Such interactions can be observed from the dynamic changes of endogenous hormone contents under exogenous treatments.

Summary of key findings

The TCP family is a family of plant-specific transcription factors that dynamically maintain and regulate the optimal conditions required for plant growth as plants grow and develop [45, 46]. This study systematically explored the regulatory mechanism of exogenous hormones on sprout growth and development in P. yunnanensis flat-crop seedlings, combining endogenous hormone detection, transcriptome analysis, and TCP transcription factor cloning and characterization. Exogenous GA₃ treatment significantly alters endogenous hormone levels in P. yunnanensis stump seedlings, specifically increasing the contents of IPA, ABA, GA₃, GA₁, SA, and SAG, which are positively correlated with sprout number, indicating that exogenous GA₃ significantly promotes sprouting. Exogenous GA₃ and PAC treatments induce differential expression of genes related to phytohormone signaling, which are involved in the signaling pathways of GA, IAA, CTK, ABA, SA, and JA, and these differential expressions regulate endogenous hormone contents and downstream signal transduction efficiency, thereby affecting sprout growth. A total of 15 TCP transcription factor family members were identified from P. yunnanensis, which were phylogenetically classified into Class I (8 members) and Class II (7 members), all containing a complete TCP domain, indicating high conservation during evolution. Tissue-specific expression analysis showed that PyTCP genes have distinct expression profiles in different tissues, and correlation analysis confirmed that PyTCP4, PyTCP6, and PyTCP20 are key of shoot branching, whose responsive expression to GA signaling directly modulates branching.

Crosstalk between GA signaling and TCP transcription factors

Gibberellin signaling and TCP transcription factors are closely involved in the regulation of plant lateral branching and sprout growth, and their crosstalk has been widely reported in various plant species. Gibberellin can break axillary bud dormancy and promote bud germination and branching [47]; for example, GA₃ promotes axillary bud branching in J. curcas [48] and hybrid poplar [13], which is consistent with the finding that exogenous GA₃ promotes sprout growth in P. yunnanensis in this study. The TCP transcription factor family, as plant-specific transcriptional regulators, plays key roles in plant growth, development, and hormone signaling, especially in lateral branching development [49]. In A. thaliana, AtTCP12 and AtTCP18 are expressed in axillary bud tissues and function in regulating meristems [50], and their homologous genes in P. yunnanensis (Class II TCP genes) may have similar functions, as indicated by their specific expression in dormant buds, terminal buds, and stems.

The crosstalk between GA signaling and TCP transcription factors is a key regulatory node in plant branching. In this study, exogenous GA₃ treatment induced differential expression of TCP genes (especially PyTCP4, PyTCP6, and PyTCP20), and these TCP genes were closely correlated with sprout number, suggesting that GA signaling regulates TCP gene expression, which in turn modulates sprout growth. This is consistent with previous studies showing that GA signaling can regulate the expression of TCP transcription factors to affect plant architecture: for example, in P. tremula × P. tremuloides, decapitation-induced downregulation of GA2ox activates axillary buds, and this process is accompanied by the regulation of TCP-related genes [47]. Additionally, the upregulation of GA20ox3 by exogenous GA₃ in this study may enhance GA biosynthesis, maintain high endogenous GA levels, and further regulate TCP gene expression, forming a GA-TCP regulatory loop that modulates sprout growth in P. yunnanensis.

Species-specific and conserved regulatory mechanisms

The regulatory mechanisms of GA signaling and TCP transcription factors in plant sprout growth show both conservation across species and species-specific characteristics. In terms of conserved mechanisms. The TCP transcription factor family in P. yunnanensis is classified into two subfamilies (Class I and Class II), which is consistent with the classification in A. thaliana, Z. mays, O. sativa, and S. lycopersicum [51], indicating that the TCP family is relatively conserved during plant evolution and may have similar biological functions. All 15 PyTCP members contain a complete TCP domain, which is consistent with TCP members in A. thaliana [50], reflecting the conservation of the core functional domain of TCP transcription factors.

The regulatory roles of GA in axillary bud dormancy and branching are conserved among different plant species. GA can break axillary bud dormancy and promote branching in J. curcas [48], hybrid poplar [13], and P. yunnanensis. In addition, downregulating GA2ox genes to sustain the level of active GAs acts as a conserved mechanism for axillary bud activation [47].

The multiple sequence alignment of PyTCP transcription factors showed that Class I has four fewer amino acid residues than Class II, which is consistent with Masson pine [52] but may differ from A. thaliana and other species, reflecting species-specific differences in TCP sequence characteristics. The tissue-specific expression profiles of PyTCP genes are species-specific: for example, PyTCP2 is highly expressed in young and old leaves, PyTCP3 in dormant axillary buds, and PyTCP20 in terminal buds and young stems, which is different from the expression patterns of TCP genes in A. thaliana [50] and other species, suggesting that PyTCP genes have evolved specific functions adapted to P. yunnanensis growth and development. The response of endogenous hormones to exogenous GA₃ treatment in P. yunnanensis is species-specific: exogenous GA₃ significantly increased the contents of IPA, ABA, SA, and SAG in P. yunnanensis, and these hormones were positively correlated with sprout number, which may be a unique regulatory characteristic of P. yunnanensis compared with other tree species such as Japanese larch and Elaeagnus bockii Diels [53].

Compared with previous studies on herbaceous and broad-leaved plants, this work innovatively revealed the overall response characteristics of the phytohormone network in P. yunnanensis seedlings during sprouting. We found that exogenous GA₃ and PAC triggered distinct temporal alterations in GAs, CTKs, auxins, JAs and SA, and confirmed the existence of GA feedback regulation in this woody pine species. These findings supplement the theoretical system of hormonal regulation of branching in gymnosperms. Considering the important ecological and economic value of P. yunnanensis, the dynamic hormone profiles established in our study also lay a foundation for further exploring the molecular regulatory mechanism of sprouting and branching.

P. yunnanensis suffers severe germplasm degradation, and the GA‑TCP branching regulatory module established in model angiosperms cannot be directly applied to this gymnosperm, making it urgent to clarify the hormone regulation mechanism of its stumping sprouting. This study confirmed that exogenous GA₃ and PAC reshaped endogenous hormone balance by altering hormone signaling gene expression to regulate sprout growth. We identified 15 PyTCP members and screened three key genes whose GA‑dependent expression controls branch development. These findings initially construct the GA‑TCP regulatory pathway governing sprouting in conifers, supplement gymnosperm TCP functional data, and supply theoretical support and core gene resources for artificial sprouting regulation and seedling cultivation of P. yunnanensis.

Study limitations

Despite the comprehensive exploration of the regulatory mechanism of exogenous GA₃ on sprout growth in P. yunnanensis, this study still has some limitations that need to be addressed in future research. The study only analyzed the changes in endogenous hormone levels and gene expression at the transcriptional level; the post-transcriptional modification of key genes and the translation level of related proteins were not detected, which may affect the in-depth understanding of the GA-TCP regulatory network. The functional verification of key genes was not conducted; genetic transformation experiments are needed to further confirm their regulatory roles in P. yunnanensis sprout growth. The study only focused on GA₃ and PAC treatments; the effects of other exogenous hormones and their combinations on P. yunnanensis sprout growth and the corresponding regulatory mechanisms were not explored, which limits the comprehensiveness of understanding hormone-mediated sprout regulation. The study did not explore the interaction between TCP transcription factors and other signaling molecules in the GA signaling pathway, which may be an important part of the complex regulatory network of P. yunnanensis sprout growth.

Conclusions

This study indicates that exogenous GA₃ and PAC treatments induce differential expression of phytohormone signaling-related genes, which further modulate endogenous hormone contents and downstream signal transduction, thereby affecting sprout growth. A total of 15 TCP family members were identified in P. yunnanensis and classified into Class I and Class II subfamilies with high sequence conservation. Tissue‑specific expression and correlation analyses confirmed that PyTCP4, PyTCP6, and PyTCP20 act as key of shoot branching. Their expression responses to GA signaling directly modulate branch development, highlighting the critical roles of TCP transcription factors in regulating sprout emergence and outgrowth.

The results suggest that TCP transcription factors may play a key role in sprout formation in P. yunnanensis, with their expression influenced by exogenous hormones, thereby regulating sprout growth and development. This study provides a foundation for further exploration of the expression patterns and functions of TCP genes in P. yunnanensis, contributing to a deeper understanding of their role in the GA-mediated sprout regulatory network. Future research should address the limitations of this study, such as conducting functional verification of key genes and exploring the interaction between TCP transcription factors and other signaling molecules, to further clarify the molecular mechanism of GA-TCP crosstalk in P. yunnanensis sprout growth.

Author’s contributions

Meiling Zhu: Writing – original draft, investigation, review & editing, Conceptualization. Sili Cheng: Investigation, Formal analysis, Data curation. Zhaoliu Hu: Investigation, Formal analysis, Data curation. Shuangcheng Bai: Investigation. Yulan Xu: Investigation, review & editing. Nianhui Cai: Writing – review & editing, supervision, Funding acquisition, Conceptualization.

Funding

We thank reviewers and editors for their valuable comments on our manuscript. This research was supported by National Natural Science Foundation of China (32360381); the Yunnan Fundamental Research Projects (202401BD070001-023). The Yunnan Provincial First-Class Discipline Construction Fund for Forestry at Southwest Forestry University (LXXK-2025D01), and Yunnan Graduate Tutor Team Building Project (2024–61). Southwest Forestry University Graduate Student Scientific Research and Innovation Fund Project (YJS-KCJJ-2025-18).

Data availability

The datasets generated and/or analysed during the current study are available in the NCBI Sequence Read Archive (SRA) repository, under the accession number PRJNA1406671. The persistent web link to the dataset is: https://www.ncbi.nlm.nih.gov/sra/PRJNA1406671/.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have given their full consent for the publication of this research work.

Competing interests

The authors declare no competing interests.