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Article

Transcriptomic Response of Larix kaempferi to Infection Stress from Bursaphelenchus xylophilus

by
Debin Li
1,2,3,
Weitao Wang
2,3,4,
Yijing Wang
1,2,3,
Hao Wu
2,3,5,
Jiaqing Wang
1,2,3 and
Shengwei Jiang
2,3,6,*
1
College of Life Engineering, Shenyang Institute of Technology, Fushun 113122, China
2
Key Laboratory of Nation Forestry and Grassland Administration on Northeast Area Forest and Glass Dangerous Pest Management and Control, Fushun 113122, China
3
Liaoning Provincial Key Laboratory of Dangerous Forest Pest Management Control and Diseases, Fushun 113122, China
4
Liaoning Provincial Forestry Investigation, Planning and Monitoring Institute China, Shenyang 110122, China
5
Liaoning Forestry Pest Control and Quarantine Station, Shenyang 110001, China
6
Liaoning Vocational College of Ecological Engineering, Shenyang 110101, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(12), 1858; https://doi.org/10.3390/f16121858
Submission received: 30 October 2025 / Revised: 10 December 2025 / Accepted: 11 December 2025 / Published: 15 December 2025
(This article belongs to the Section Forest Health)
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Abstract

The pine wood nematode (PWN) Bursaphelenchus xylophilus is a highly destructive forest quarantine pest and causal agent of pine wilt disease. The molecular response mechanism of Larix kaempferi (Japanese larch) to B. xylophilus infection remains unclear. This study aims to reveal the dynamic patterns of its defense response and screen key genes through time series transcriptomics. We found larch trees can proactively adjust their defense strategies to deal with the invasion of B. xylophilus. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, plant hormone signal transduction, MAPK signal pathway, and genes related to phenylpropane biosynthesis were more important. Through weighted gene coexpression network analysis (WGCNA), we identified two core modules that were rich in terpenoids, genes related to phenylpropane metabolism and cell wall strengthening, hormone signaling and defense regulation, and cytoskeleton and transport. Ultimately, we identified 20 core genes that were associated with several resistance-related processes, including the biosynthesis of resistance metabolites, post-translational regulation of protein homeostasis and defense signals, and transcriptional and translational reprogramming of gene expression. This study systematically depicted for the first time the continuous transcriptional regulatory network of L. kaempferi in response to pine wood nematodes. The key genes discovered provide important targets for subsequent functional verification and resistance breeding.

1. Introduction

Pine wilt disease is the most dangerous and destructive disease in forest ecosystems worldwide, caused by pine wood nematode (Bursaphelenchus xylophilus; PWN). It can pose a huge threat to the local ecological environment and cause significant economic and ecological losses to the country [1,2,3,4]. Now, there have been reports of B. xylophilus in countries such as the United States, Canada, Mexico, and China [4,5,6,7]. In 1905, a severe forest disease affecting pine species was first reported in Nagasaki, Japan [8,9]. With the development of the economy and the flow of goods, B. xylophilus is increasing in infected areas and the number of host plant species infected by B. xylophilus is also increasing. In 1982, the pine wood nematode was first discovered in Nanjing, China, and it subsequently spread to the surrounding areas [5]. Now, the disease has invaded Northeast China, where winter temperatures are usually lower than −20 °C. The main host species, vector insects, and ecosystems of B. xylophilus in Northeast China are completely different from those in other parts of China, which makes it more difficult to control the spread of B. xylophilus [10,11].
Larix kaempferi is one of the representative tree species of the coniferous forest in North China. Larix kaempferi, which is native to Japan, has longer wood fibers and stronger paper strength and tensile strength. The texture is tall and straight, the material is tough and corrosion-resistant, and it has the good mechanical properties of wood [12]. Larix kaempferi exhibits a relatively fast growth rate and strong adaptability within the genus Larix. It is usually used as an important economic and afforestation timber species. At the same time, Larix kaempferi also has a high ecological function, which can improve soil, conserve water, maintain soil and water, and regulate the climate environment [13]. It can be widely used in building and paper-making materials, urban greening, and returning farmland to forest. In 2018, Yu Haiying et al. found B. xylophilus in L. kaempferi and Larix olgensis in Fushun, Liaoning Province, and determined that B. xylophilus infected L. kaempferi under natural conditions in China [14]; this was the first report on the natural infection of pine wood nematodes in China. In 2019, Pan Long et al. found L. kaempferi carrying pine wood nematodes in Enshi Prefecture, Hubei Province, which further confirmed that pine wood nematodes can infect Japanese larch [15]. According to the special investigation conducted by our laboratory and relevant local staff, large-scale infection of L. kaempferi has not been found, and the probability of a L. kaempferi infection with B. xylophilus disease under field conditions is extremely low. Through artificial inoculation of B. xylophilus on Pinus koraiensis, Pinus tabulaeformis, and Larix kaempferi seedlings, it was found that the resistance of L. kaempferi to B. xylophilus was stronger than that of P. koraiensis and P. tabulaeformis, and the incidence time was longer and the infection rate was lower at the same time. Therefore, it is considered to be resistant, although this resistance is relative [16]. The reason is still unknown and needs further study.
Although numerous hypotheses have been proposed regarding the pathogenic mechanisms of Bursaphelenchus xylophilus, the infection process in host plants remains poorly understood. Upon pathogen invasion, plants activate multilayered defense signaling pathways mediated by cell surface/intracellular immune receptors, involving coordinated actions of mitogen-activated protein kinase (MAPK) cascades, transcription factor regulation, and hormone signaling networks [17,18,19,20]. Concurrently, secondary metabolic pathways are initiated to synthesize anti-B. xylophilus compounds such as flavonoids, terpenoids, lignin, and related enzyme systems [21,22,23,24]. Transcriptome sequencing has emerged as a pivotal tool for deciphering gene expression dynamics under pathogen stress, enabling the elucidation of molecular resistance mechanisms. For instance, transcriptomic analysis of Pinus thunbergii revealed that B. xylophilus infection significantly up-regulates genes associated with the flavonoid, lignin, and terpenoid biosynthesis pathways, confirming the central role of these secondary metabolites in resistance [25]. Similarly, studies on P. massoniana and P. thunbergii demonstrated that B. xylophilus infection differentially activates the salicylic acid (SA) and jasmonic acid (JA) signaling pathways, while up-regulating defense-related genes encoding mannose/glucose-specific lectins, HSP70 family proteins, and antimicrobial peptides [26,27,28,29]. Time-series transcriptome profiling by Yibo et al. further identified enriched KEGG pathways in B. xylophilus-inoculated P. massoniana, including plant hormone signal transduction, flavonoid biosynthesis, amino sugar/nucleotide sugar metabolism, and MAPK signaling, highlighting the synergistic engagement of multidimensional defense networks [30]. Existing studies corroborate that oxidative stress regulatory genes (e.g., superoxide dismutase), key flavonoid biosynthesis enzymes (e.g., phenylalanine ammonia-lyase (PAL)) [31,32], terpenoid metabolism-related genes (e.g., terpene synthases) [33], and pathogenesis-related proteins (e.g., chitinases, β-1,3-glucanases) [34,35] are integral to plant defense against B. xylophilus. These findings underscore the intricate interplay between the secondary metabolism and immune signaling in shaping host–pathogen interactions. However, these studies mostly focused on the comparison of susceptible or resistant tree species, or only focused on a few time points, lacking systematic and continuous transcriptome dynamic monitoring of L. kaempferi during the whole infection process.
In order to fill this knowledge gap, this study aims to systematically analyze the dynamic changes in the transcriptome of L. kaempferi at different time points after pine wood nematode infection. We intend to continuously sample the L. kaempferi seedlings after inoculation with pine wood nematodes and use RNA-Seq technology to construct their time series transcription profiles. Through bioinformatics analysis, we will reveal the differentially expressed genes (DEGs) at different infection stages and their enriched biological pathways. Weighted gene co-expression network analysis (WGCNA) was used to mine gene co-expression modules that were highly related to infection. Finally, the key candidate genes that play a central role in defense response were screened from the dynamic gene network. This study is expected to deepen the understanding of the molecular mechanism of the interaction between L. kaempferi and B. xylophilus and provide a theoretical basis for revealing the internal causes of its partial tolerance. At the same time, the key genes screened can provide valuable genetic resources and targets for the subsequent cultivation of new disease-resistant forest varieties by genetic engineering.

2. Materials and Methods

2.1. Plant Materials and Treatments

The test plants were 2-year-old L. kaempferi seedlings from a single full-sib family, obtained from the Dagujia Forest Farm in Fushun City, Liaoning Province. In April 2023, they were transplanted to the laboratory (in an outdoor nursery, brown soil, 41°51′00.04″ N, 123°43′27.62″ E). After 2 months of growth adaptation, L. kaempferi seedlings with consistent growth and good growth were selected for the experiment. PWN (collected in FuShun, Liaoning Province, China, 41°56′16.3″ N, 124°13′6.5″ E) was cultured on Botrytis cinerea barley culture medium (Barley/water is 1:1, inoculated with Botrytis cinerea after sterilization, Botrytis cinerea is a type of fungus that can serve as food for the B. xylophilus) at 25 °C in the dark. After about 2 weeks of incubation, the culture medium of B.cinerea with B. xylophilus was chopped and placed in a Berman funnel with double-layer filter paper. The material was immersed in sterile water and stood for 24 h. A suspension containing nematodes was collected from a hose at the lower end of the funnel and rinse washed at least 3 times with sterile water. Finally, the required concentration was adjusted (2000 nematodes/200 μL, female/male/larva 4:1:8) and placed in a refrigerator at 4 °C.
B. xylophilus was inoculated on L. kaempferi seedlings by skin grafting. We selected the inoculation site and cut it open with a sterile blade. When cutting the bark, we were careful not to peel too deeply and avoided causing the bark to fall off. Evenly inject 200 μL of the nematode suspension into the cut. The sealing film is made in a funnel shape to wrap the cut and the cotton ball, ensuring that the nematode liquid does not leak. B. xylophilus were inoculated into L. kaempferi seedlings (200 μL per plant) as the treatment group and isovolumes of ddH2O were inoculated into L. kaempferi seedlings as the control check (CK) group. The meteorological conditions during the experiment were as follows: the daily average temperature was 26.0 °C, the minimum temperature was 18.7 °C, and the maximum temperature was 33.4 °C. The average humidity was 77.32 and the average rainfall was 7 mm.
Samples were taken at 6 h, 1 d, 7 d, 15 d, 25 d, 30 d, and 35 d after the inoculation of the pine wood nematode. Sterile water control samples were taken at each time point. A total of 3 plants were selected as 3 biological replicates for each sampling to establish different biological libraries. A total of 6 seedlings (3 in the treatment group and 3 in the control group) were taken at one time point, and a total of 42 seedlings were taken at seven time points. With the inoculation site as reference, the cambium was scraped with a knife, 2 cm above and below the inoculation site to avoid the wound, and then placed into a 2 mL centrifuge tube; we covered the tube cover and put it into liquid nitrogen for freezing, and then stored it in a refrigerator at −80 °C and sent it to the transcriptome for sequencing.

2.2. RNA Extraction, cDNA Synthesis, Library Preparation, and Sequencing

We extracted the total RNA from each sample, according to the instructions of the kit (Tiangen, Beijing, China, DP441). A total of 42 cDNA libraries were constructed. After passing the quality inspection, the RNA was sequenced on the DNBSEQ-T7 in Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).

2.3. Data Filtering and De Novo Assembly

After obtaining the raw reads, quality control was performed using Fastp (version 0.23.1) [36] software to remove adapter sequences, sequences with uncertain base ratios, and low-quality sequences, resulting in high-quality clean reads that could be used for subsequent analysis. Using HISAT2 software [37], we mapped the clean reads of each library to the Japanese Larch reference genome v1.0 (BioProject No. PRJCA008850) [13] library. The String Tie software [38] was used to analyze these mapped result files and output the count value and TPM (transcripts per million) value of each gene.

2.4. Analysis of Differentially Expressed Genes (DEGs) and Functional Annotation

Differentially expressed Unigenes were screened using DESeq2 (p-value < 0.01, |fold change (FC)|≥ 2) in the Omicshare tool at https://www.omicshare.com/tools/, URL (accessed on 12 June 2024).
All protein sequences of the Japanese Larch genome were annotated with GO and KEGG using EggNOG-mapper v2 [39] as the enrichment background. Enrihment analysis was performed using Omicshare (p-value < 0.05) (http://www.omicshare.com, URL accessed on 15 June 2024).

2.5. Hierarchical Clustering Analysis

Trend analysis can classify genes with similar change characteristic patterns within a changing trend. We used the OmicShare online tool (https://www.omicshare.com/tools/, URL accessed on 15 June 2024) for analysis. p-value < 0.05 was considered statistically significant. The number of trends is chosen to be 20.

2.6. Gene Co-Expression Network Analysis

This study conducted WGCNA on DEGs using TBtools v2.100 [40]. We determined the soft threshold power based on the principle of a scale-free network and constructed a gene clustering tree based on the correlation of gene expression levels. The minimum number of genes in the module was set to 30, and modules with similar expression patterns were merged based on a similarity threshold of 0.75 for the module feature genes.
We analyzed the relationship between the sample and each module, drew a heatmap, and screened the modules that significantly correlated with the sample through correlation. The absolute value of a correlation that was close to 1 indicated a stronger correlation.

2.7. PPI Analysis and Key Gene Screening

We imported the genes in the module into the STRING database (https://string-db.org/, URL accessed on 12 July 2024). Protein interaction analysis was conducted to reveal the interaction relationships between genes in the module. The results were visualized using Cytoscape 3.9.1 [41] software to present the interactions between genes.

2.8. Real-Time Quantitative PCR Analysis

The RNA samples were reverse-transcribed using M-MuLV first strand cDNA synthesis kit (Shanghai Shenggong). The primers were designed using TBtools-II v2.376 software, with EF1 as the internal reference gene (Supplementary Table S1). RT-qPCR verification was performed using the Bole CFX connect system. All RT-qPCR tests were repeated 3 times.

3. Results

3.1. Symptoms of L. kaempferi

In this experiment, we found that the disease development of L. kaempferi after inoculation with B. xylophilus can be divided into four typical stages: L. kaempferi seedlings had no obvious symptoms from one day after inoculation. The needle leaves near the inoculation point were dehydrated and wilted, which was in the early stage of infection (10 days after inoculation). In the middle stage of infection (24 days after inoculation), half of the needles faded green and turned pale, but did not turn yellow. At the late stage of infection (30 days after inoculation), most of the needles became yellow and drooping, and easily fell off. At the end of infection (34 days after inoculation), all needles became red, curved, and drooping and easily fell off, and the whole plant withered (Figure 1).

3.2. RNA-Seq Data Evaluation

A total of 42 L. kaempferi samples from the treatment group and the control group were subjected to transcriptome sequencing analysis. Each sample obtained Q30-based percentages greater than 91.72% and GC percentages ranging from 41.46 to 47.47%, indicating that the RNA-Seq data were of high quality for further analysis (Supplementary Table S2). Subsequently, 2,665,801,972 clean reads were mapped on the L. kaempferi reference genome v1.0, and the uni-transcripts were annotated as known genes. A total of 55,816 genes with an average TPM of >1 in at least one treatment were considered to be expressed genes.
The results in Figure 2 showed that 3047 genes showed differential expression at 6 h, of which 1653 genes were up-regulated and 1394 genes were down-regulated. At 1 d, 7225 genes showed differential expression, of which 3554 genes were up-regulated and 3671 genes were down-regulated. At 7 d, 6395 genes showed differential expression, of which 3263 genes were up-regulated and 3132 genes were down-regulated. At 15 d, 5832 genes showed differential expression, of which 2528 genes were up-regulated and 3304 genes were down-regulated. At 25 d, 5001 genes showed differential expression, of which 1987 genes were up-regulated and 3014 genes were down-regulated. At 30 d, 3220 genes showed differential expression, of which 1660 genes were up-regulated and 1560 genes were down-regulated. At 35 d, 3592 genes showed differential expression, of which 2209 genes were up-regulated and 1383 genes were down-regulated.
RT-qPCR expression levels of target genes (Supplementary Table S2) showed trends comparable to RNA-seq data. Variation at certain time points likely resulted from biological and technical variability, including different RNA batches, normalization methods, and sensitivity differences between RT-qPCR and RNA-seq.
Through multi-stage KEGG pathway analysis of pathogen-infected plants (6 h to 35 d post-infection), we delineated a phased defense–metabolism interplay culminating in systemic collapse. Early response (6 h–7 d): asymptomatic but defense-activated. Among the up-regulated genes, the MAPK signaling pathway (ko04016) was significantly up-regulated at 1 day and 7 days, indicating early signal transduction activation. The plant hormone signal transduction (ko04075) was up-regulated at 1 and 7 days, suggesting that hormones such as jasmonic acid and ethylene are involved in defense. Protein processing (ko04141) and ribosome biosynthesis (ko03008) in the endoplasmic reticulum were up-regulated at 6 h, indicating that the cells were trying to maintain protein homeostasis. Phenylpropanoid biosynthesis (ko00940) and terpenoid skeleton biosynthesis (ko00900) were up-regulated at 1 day and 7 days, involving the synthesis of antibacterial compounds. Among the down-regulated genes, photosynthesis inhibition, photosynthesis (ko00195), and photosynthesis-antenna protein (ko00196) were down-regulated at 6 h, 1 day, and 7 days, indicating a shift in resources from growth to defense. In the down-regulation of secondary metabolite synthesis, some secondary metabolite biosynthesis pathways (ko01110) were down-regulated at 6 h, which may reflect early metabolic disorders. This indicates that at the initial stage of pathogen infection, although no phenotypic symptoms have occurred, plants have rapidly started defense preparation, including strengthening protein synthesis and folding capabilities, and activating key immune signal transduction pathways (Supplementary Tables S3–S8).
Pre-susceptibility (15 days): Needle leaves wilted and defenses were strengthened. Among the up-regulated genes, α-linolenic acid metabolism (ko00592) and glutathione metabolism (ko00480) were continuously up-regulated, indicating an oxidative stress response and jasmonic acid signal activation. Secondary metabolite biosynthesis (ko01110) and phenylpropanoid biosynthesis (ko00940) were significantly enriched, supporting the accumulation of defensive compounds. Increased glycolysis/gluconeogenesis (ko00010) and carbon metabolism (ko01200) may provide energy for defense. Among the down-regulated genes, autophagy (ko04136) was down-regulated, which may inhibit cell death to delay symptoms. Inositol phosphate metabolism (ko00562) is down-regulated, affecting signal transduction and cell homeostasis (Supplementary Tables S9 and S10).
Mid-stage of infection (25 days): Half of the needles were chlorotic, metabolic recombination. Linoleic acid metabolism (ko00591) and α-linolenic acid metabolism (ko00592) were up-regulated, involving membrane lipid repair and signal molecule generation. Ubiquitin-mediated proteolysis (ko04120) was up-regulated, indicating that the damaged protein clearance mechanism was activated. Amino acid sugar and nucleotide sugar metabolism (ko00520) were up-regulated, supporting cell wall strengthening. Photosynthesis-related pathways (ko00195, ko00196) and ribosomes (ko03010) were significantly down-regulated, indicating impaired photosynthetic capacity and protein synthesis. Carbon fixation (ko00710) and glucose metabolism pathways were down-regulated, reflecting a shortage of carbon sources (Supplementary Tables S11 and S12).
Late stage (30–35 days): Needles yellowed to death, metabolic collapse. Glutathione metabolism (ko00480) and selenium compound metabolism (ko00450) were continuously up-regulated to cope with oxidative damage. Secondary metabolite biosynthesis (ko01110) and phenylpropanoid biosynthesis (ko00940) were still active and may have served as the last line of defense. Cysteine and methionine metabolism (ko00270) were up-regulated, involving antioxidant and ethylene synthesis. Photosynthesis (ko00195) and photosynthesis-antenna protein (ko00196) were significantly down-regulated at 30 days and 35 days, indicating that photosynthesis was completely inhibited. Carbon fixation (ko00710), carbon metabolism (ko01200), and starch sucrose metabolism (ko00500) were down-regulated, indicating that the energy metabolism collapsed and the central metabolism was disordered. Vitamin B6 metabolism (ko00750) and riboflavin metabolism (ko00740) were down-regulated, affecting the basic functions of cells (Supplementary Tables S13–S16).

3.3. Gene Expression Patterns of L. kaempfer in Response to B. xylophilus Infection

Trend analysis was performed on the 20,503 DEGs screened above, which were all clustered into 20 profiles (Figure 3). Among them, four color modules were significantly enriched: namely, profile0, profile19, profile10, and profile11, with a total of 14,418 DEGs. In profile 10, 5286 differential genes showed that the gene was up-regulated from 0 h to 1 d, remained unchanged from 1 d to 7 d, was down-regulated from 7 d to 25 d, and then up-regulated after 25 d. In profile 0, 2960 differential genes showed a continuous decline with the increase in stress time. In profile 19, 1091 differential genes continued to increase with the increase in stress time. The gene trends in the profile 0 and 19 modules were clear and may contain biological pathways that were most associated with the sustained activation or sustained inhibition of disease resistance and death. The gene trends in profile 10 were up-regulated in the asymptomatic period, down-regulated after the onset of symptoms, and up-regulated after 25 days. This tends to be consistent with the phenotype. Because of continuous activation or continuous inhibition, we chose trends 0 and 19. Because the trend of genetic changes is nearly consistent with the phenotype, we chose trend 10. We selected these modules for the next step of analysis.
The sustained down-regulation of pathways in the profile 0 module reflected a systemic collapse of the critical metabolic and defense processes under prolonged biotic stress. Suppression of ribosome (ko03010) and protein processing in endoplasmic reticulum (ko04141) indicated severe translational inhibition and unresolved proteotoxic stress, impairing cellular repair and defense protein synthesis. The concurrent inhibition of photosynthesis (ko00195), carbon fixation (ko00710), and TCA cycle (ko00020) disrupted the energy production, while diminished carbon/fatty acid metabolism (ko01200/ko00071) suggested a catastrophic energy deficit and lipid reserve depletion. Notably, the down-regulation of plant–pathogen interaction (ko04626) and MAPK signaling (ko04016) highlighted compromised pathogen recognition and signal transduction, with crippling immune activation. Reduced glutathione metabolism (ko00480) exacerbated the oxidative damage, and suppressed secondary metabolite biosynthesis (alkaloids, betalains, phenylpropanoids) eliminated the antimicrobial chemical defenses. The collective failure of ABC transporters (ko02010) and nucleocytoplasmic transport (ko03013) further disrupted detoxification and stress signaling (Figure 4A). The results indicate that after inoculation with B. xylophilus, L. kaempfer’s photosynthesis, defense signaling, cell repair, enzyme system maintenance, and production of disease-resistant proteins were continuously inhibited.
The sustained up-regulation of 1091 DEGs in profile 19 during biotic stress progression highlighted a coordinated defense strategy integrating metabolic adaptation, redox regulation, and secondary metabolite synthesis. Enrichment in amino sugar/nucleotide sugar metabolism (e.g., ko00520) and cell wall-associated pathways (e.g., galactose/phosphatidylinositol signaling) suggested cell wall remodeling to impede the pathogen invasion. The concurrent activation of the glutathione metabolism (ko00480), selenocompound metabolism (ko00450), and beta-alanine metabolism (ko00410) reflected persistent efforts to counteract oxidative stress through ROS scavenging and alternative redox buffering systems. The prominence of secondary metabolite biosynthesis (flavonoids, glucosinolates, betalains) and α-linolenic acid metabolism (ko00592) underscored the sustained production of antimicrobial compounds and jasmonate-mediated defense signaling. Notably, plant hormone signal transduction (ko04075) indicated crosstalk between the salicylic acid (SA) and jasmonic acid (JA) pathways to balance localized defense and systemic responses. However, the dysregulation of the starch/sucrose metabolism (ko00500) and nitrogen metabolism (ko00910) pointed to metabolic trade-offs, where carbon/nitrogen resources were diverted from growth to sustain defense-related biosynthesis. The enrichment of endoplasmic reticulum protein processing (ko04141) further implied unresolved proteotoxic stress, potentially contributing to cellular dysfunction. Collectively, these responses delineated a transition from active defense to metabolic exhaustion, where persistent pathogen pressure overwhelmed redox homeostasis and energy reserves, culminating in systemic collapse (Figure 4B).
We performed KEGG pathway enrichment analysis on the profile 10 gene, set to elucidate its potential biological functions and metabolic pathways. The results revealed 20 significantly enriched pathways (p-value < 0.05), primarily categorized under metabolism and genetic information processing. Among the metabolic pathways, Terpenoid backbone biosynthesis (ko00900) exhibited the highest enrichment significance (p-value = 2.448 × 10−6) with an enrichment ratio of 0.263, indicating a crucial functional role for this pathway within the target gene set. Furthermore, the overarching metabolic pathways (ko01100) and biosynthesis of secondary metabolites (ko01110) were also significantly enriched, encompassing 779 and 502 genes, respectively, suggesting the central importance of these fundamental metabolic processes in our study. Regarding amino acid and nucleotide metabolism, biosynthesis of amino acids (ko01230), phenylalanine, tyrosine and tryptophan biosynthesis (ko00400), and nucleotide metabolism (ko01232) were all significantly enriched, reflecting the potential role of the target genes in generating precursors for protein synthesis. Additionally, the ribosome pathway (ko03010), the only pathway related to genetic information processing, also showed significant enrichment (p-value = 0.0010255), implying the potential regulatory significance of the translation machinery. Other notable enriched pathways included glycolysis/gluconeogenesis (ko00010), glutathione metabolism (ko0480), and flavonoid biosynthesis (ko00941). These results collectively demonstrate the broad involvement of the target gene set in diverse metabolic and biosynthetic pathways (Figure 4C).

3.4. Construction of Weighted Gene Co-Expression Network and Identification of Key Modules

The WGCNA of DEGs is divided into 13 modules, with different colors according to its expression mode (Figure 5A). Each color represents a gene block, which is made up of at least 34 genes (Figure 5B). The correlation analysis of module traits was carried out on 21 samples, and two modules reached a significant level (red and blue). The correlation between 1 day in the early stage of infection and 25 days in the middle stage of infection was the strongest (Figure 5C).

3.5. Functional Enrichment Analysis of Key Modules

GO enrichment and KEGG enrichment methods were used to annotate and enrich the core genes in these two modules. Red modules had 129 genes and GO analysis indicated that ATPase binding (GO:0051117) was the most significantly enriched. Additionally, the protein catabolic process (GO:0030163), response to organic cyclic compound (GO:0014070), protein neddylation (GO:0045116), ethylene metabolic process (GO:009692), and ethylene biosynthetic process (GO:009693) were enriched (Figure 6A). KEGG analysis indicated that ubiquitin-mediated proteolysis (ko04120) was the most significantly enriched metabolic pathway. Additionally, ribosome (ko03010), phagosome (ko04145), steroid biosynthesis (ko00100), plant hormone signal transduction (ko04075), and protein export (ko00360) were enriched (Figure 6C)
The blue module had 522 genes and GO analysis indicated that the acetyl-CoA metabolic process (GO:0006084) was the most significantly enriched Go term. In addition, the sulfur compound metabolic process (GO:0006790), acyl coenzyme A metabolic process (GO:0006637), isopentenyl diphosphate biosynthetic process (GO:0009240), and isopentenyl diphosphate metabolic process (GO:0046490) were enriched (Figure 6B). KEGG analysis indicated the metabolic pathway (ko01100) and biosynthesis of secondary metabolites (ko01110) were the most significantly enriched metabolic pathways. Additionally, the terpenoid skeleton biosynthesis (ko00900), cysteine and methionine metabolism (ko00270), ascorbic acid and arabinic acid metabolism (ko00053), and amino acid biosynthesis (ko01230) were enriched (Figure 6D).

3.6. Identification of Hub Genes by Integrating Protein–Protein Interaction Network

We constructed a protein interaction network (PPI) to further mine the core module genes at the blue and red modules. In the blue module and red module network, 20 hub genes were identified by CytoHubba plug-in, using the MCC algorithm in Cytoscape v3.9.1 software. In the blue module, there were AACT1 (Lk35318), CSY4 (Lk03083), HMGS (Lk02366), MVD2 (Lk15012), IPP1 (Lk32659), MK (Lk10581), FPS2 (Lk21752), PED1 (Lk39948), CYP73A5 (Lk38153), and CCR1-2 (Lk42132) (Figure 7A). In the red module, there were UBQ10 (Lk24990), ACT11 (Lk13216), ACT7 (Lk36671), RUB1 (Lk31575), F24B9.25 (Lk27298), ARP1 (Lk24642), RPS27AA (Lk37870), HTR2 (Lk36934), RPL8C (Lk30630), and SCE1 (Lk11759) (Figure 7C). In addition to CytoHubba, we also used mediocentrality to screen hub genes. In the blue module, the top 19 core genes were identified, and in the red module, the top 34 core genes were identified. According to the mediocentrality scores of the genes, AACT1 and CSY4 were the core genes of the blue module (Figure 7B) and ARP1 (Lk24642) was the core gene of the red module (Figure 7D).

4. Discussion

Now, research on B. xylophilus has become more diversified, with researchers using traditional pathological methods, single omics analysis, and multi omics combined analysis to explore the pathogenesis of B. xylophilus, such as proteomics [42], metabonomics [43], and transcriptomics [44]. With the development and popularity of omics technology, researchers can comprehensively and quickly study the molecular regulatory mechanisms of host plants and B. xylophilus. In terms of transcriptome, researchers mainly focus on the nematodes themselves [45,46], and there is relatively little research on the transcriptional changes in the host pine tree [47]. In the study of transcriptional changes in host larch, most of the research objects are pine plants, and there are few studies on non-pine genera such as larch. At present, research on the response mechanism and disease resistance of L. kaempferi to B. xylophilus is limited. Therefore, this study utilized transcriptomic analysis to elucidate the changes in transcription levels at seven time points after infection with B. xylophilus in L. kaempferi.

4.1. The Overall Dynamic Response and Defense Strategy Transformation of Host Transcriptome

In order to analyze the systemic response of L. kaempferi to pine wood nematode, we first analyzed the temporal variation pattern of its transcriptome as a whole. Compared with the control group, the differentially expressed genes of 3047, 7225, 6395, 5832, 5001, 3220, and 3592 were identified at 6 h, 1 d, 7 d, 15 d, 25 d, 30 d and 35 d, respectively. Li Shuo performed transcriptome sequencing analysis on the stem segments of Pinus tabulaeformis at 0 d, 2 d, 5 d, and 8 d after inoculation with B. xylophilus. It was found that the number of DEGs of P. tabulaeformis after inoculation with B. xylophilus also increased with the extension of infection time [48]. Wei Yongcheng (2016) found that the number of DEGs in P. massoniana increased with the extension of infection time within 15 days after inoculation of B. xylophilus by transcriptome sequencing analysis of P. massoniana stems at 1 d, 15 d, and 30 d after inoculation of B. xylophilus [49]. In this study, it was found that the number of up-regulated genes was higher than that of down-regulated genes at the early stage of B. xylophilus infection, and then the number of down-regulated genes was significantly higher than that of up-regulated genes, which indicated that L. kaempferi was more responsive to B. xylophilus infection through the positive regulation of genes at the early stage of infection.

4.2. Phased Activation of Defense Pathways and Molecular Mechanisms

Based on the above content, we further found that the host defense response showed obvious temporal stage characteristics. Many studies have shown that the genetic material of pine trees will change over time after B. xylophilus infection. In this study, photosynthesis-related pathways were continuously down-regulated throughout the course of the disease, indicating that larch shifted resources from growth to defense, which also explained the phenomenon of needle yellowing and shedding in the later stage of the disease—this may be a strategy for plants to actively reduce their energy consumption and concentrate resources to resist pathogens. The study of Wei et al. also showed that photosynthesis-related genes were significantly down-regulated [43]. Studies have shown that primary metabolism plays a crucial role in plant–environment interactions and biotic stress defense responses, and it is a source of signal molecules in response to biotic stress [26,50,51]. The results of this study showed that the larch responded quickly to pathogenic signals through the MAPK signaling pathway and protein-processing pathway in the early stage of infection; in the middle stage, secondary metabolic pathways such as phenylpropanoids and flavonoids were activated to synthesize disease-resistant substances; in the later stage, it relied on the glutathione and sulfur metabolism to alleviate oxidative damage. In the middle and late stages of infection, lipid-related pathways, such as α-linolenic acid metabolism and linoleic acid metabolism, were significantly enriched. These pathways are not only involved in membrane lipid remodeling, but may also mediate the defense signal transmission through the jasmonic acid pathway, which plays an important role in larch immunity. The down-regulation of basic genetic information processing pathways such as ribosomes and spliceosomes at the late stage of infection, combined with the death phenotype of the whole plant, suggests that cells may enter a programmed death state to limit the spread of pathogens, which is an extreme host defense strategy.

4.3. Identification and Functional Analysis of Core Defense Gene Network

In this study, by integrating transcriptome WGCNA and protein interaction network analysis, two key modules and 20 core genes were successfully identified from the transcriptional regulatory network of L. kaempferi in response to B. xylophilus infection, including AACT1, CSY4, HMGS, MVD2, IPP1, MK, FPS2, PED1, CYP73A5, CCR1-2, RUB1, RPS27AA, SCE1, UBQ10, ACT11, ACT7, F24B9.25, HTR2, ARP1, and RPL8C. These genes are mainly enriched in key pathways such as terpenoid skeleton biosynthesis, phenylpropanoid biosynthesis, ubiquitination system, and cytoskeleton and protein synthesis. They may play a central role in plant immune responses through synergy.

4.3.1. Resistance Metabolites Synthesis Genes (Terpenoids and Phenylpropanoids)

Multiple core genes are enriched in the biosynthetic pathway of resistant metabolites, which constitutes the molecular basis of host chemical defense. AACT1, HMGS, MVD2, IPP1, FPS2, MK, and PED1 are key enzymes in the mevalonate pathway and MEP pathway, which are responsible for the supply of isoprene precursors. These precursors are the basis for the synthesis of important hormones such as sterols and gibberellins, as well as more critical defensive terpenoids. PED1 (fatty acid β-oxidation) may provide energy and carbon skeleton for these synthesis processes. IPP (isopentenyl pyrophosphate) is the central precursor for the synthesis of all terpenoids, and there are two synthetic pathways in plants: namely, the 2-methyl-D-erythritol-4-phosphate (MEP) pathway and MVA [52]. AACT1, as the first key enzyme in the MVA, is a key regulatory enzyme in the synthesis of C5 unit IPP and DMAPP in the cytoplasmic MVA pathway. Belonging to the type II thiolase family, Aact1 is a precursor of terpenoid synthesis. Participating in the production of plant protectors (such as gibberellenone) directly inhibits pathogens [53]. HMGS (Hydroxymethylglutaryl-CoA synthase), a member of the HMG-CoA synthase family, is one of the key enzymes in the MVA pathway. It catalyzes the condensation of acetoacetyl-CoA (acetoacetyl-Co A) and acetyl-CoA (acetyl-Co A) to form 3-hydroxy-3-methylglutaryl CoA (HMG-Co A), which is specific to HMG-CoA synthesis. It can regulate the synthesis of sterols and terpenoids [54]. Ginkgo GGB HMGS2 responded to Me JA, abscisic acid (ABA), and SA treatments [55]. MVD2, as a key enzyme after the PMK upstream of the MVA pathway, acts specifically on substrate (R) -5-diphosphate mevalonate (MVAPP), catalyzes the MVA pathway to produce a universal precursor of isoprene compounds such as terpenes and sterols. Isopentene pyrophosphate (IPP) supports the synthesis of antibacterial compounds [56]. Overexpression of MVD can accelerate the synthesis rate of terpenoids in plants [57]. MVD plays an important role in the biosynthesis of terpenoid compounds in Panax ginseng [58]. Xing Chaobin et al. found that there was a significant positive correlation between the expression of MVD and the saponin content in P. ginseng [59]. FPS2 (farnesyl pyrophosphate synthase) is one of the key enzymes in the mevalonate pathway and is also the first enzyme to undertake the reaction substrate to the branch points of each compound. It catalyzes the synthesis of farnesyl pyrophosphate (FPP) and provides precursors for triterpenoids (such as saponins) and sterol defense substances [60]. The FPS gene has been cloned from plants such as Arabidopsis and Clematis and its expression in plants is tissue-specific, and it also affects the content of isoprene derivatives [61,62]. IPP1 (Isopentenyl-diphosphate Delta-isomerase I, chloroplastic) belongs to the methylerythritol phosphate pathway (MEP pathway) and catalyzes the 1,3-allylic rearrangement of the homoallylic substrate isopentenyl (IPP) to its highly electrophilic allylic isomer, dimethylallyl diphosphate (DMAPP). It is a key step in the biosynthesis of terpenoids [63]. Transgenic corn showed good resistance to waterlogging, strong light, high temperature, and other adversities, and its tolerance to organic phosphine became significantly stronger [64]. Overexpression of the IPP gene can increase the production of lycopene, carotene, and carotenoid [65,66]. By constructing recombinant plasmids and transferring them into engineered Escherichia coli, Zhang et al. compared the plaque with the control group and observed color differences, indicating that IPP of salvia miltiorrhiza promoted the accumulation of lycopene [67]. AACT1, HMGS, MVD2, IPP1, and FPS2 together constitute the mevalonate (MVA) pathway, which provides precursors for terpenoids (such as phytoprotecins and sterols) and directly participates in anti-nematode chemical defense. MK is an important rate-limiting enzyme in the mevalonate pathway (MVA pathway), which is responsible for the phosphorylation of mevalonate (MVA) to mevalonate 5-phosphate (MVAP). IPP/DMAPP, produced by MK through the MVA pathway, is a precursor to phytoprotecins (such as erythralenone) and volatile terpenes (such as monoterpene limonene), and is directly involved in disease resistance. Intermediates of the MVA pathway (e.g., Farnyl pyrophosphate FPP) are indirect sources of jasmonic acid (JA) synthesis precursors (e.g., OPDA), and JA is a key signaling molecule for insect/disease resistance in plants [68]. Insufficient MK activity may lead to decreased JA signal strength and terpenoid content, weakening the systemic immune response. Woo et al. overexpressed the Artemisia annua Aa PMK gene in Escherichia coli, and successfully increased the terpene compound content in transformed strains by three times [69]. After silencing the Mi PMK gene in mango (Mangifera indica), Pathak et al. found that the contents of terpenes, such as geranyl, trans-farnesol and β-caryophyllene and their derivatives in fruits, were significantly reduced [70]. PED1 (Peroxisomal 3-ketoacyl-CoA thiolase 2) is involved in the β-oxidation process of long chain fatty acids during seed germination and seedling growth. PED1 is required for the induction of local and systemic JA biosynthesis after plant injury and may be involved in JA biosynthesis during aging. Jasmonic acid accumulates in most plants and plays an important role in coping with biological and abiotic stresses [71,72]. In this study, we found that MK and PED1 indirectly regulate the expression of downstream defense genes such as CYP73A5 through the supply of metabolic precursors. This suggests that during pathogen infection, plants may reprogram their metabolic flux by up-regulating these genes to synthesize a large number of terpenoids with direct antibacterial activity, which is a basic physical and chemical defense barrier for plants.
CYP73A5 (Trans-cinnamate 4-monooxygenase, C4H) is one of the key enzymes involved in the synthesis of various secondary metabolic substances such as flavonoids and lignin and is widely distributed and highly active in various plant tissues [73]. Millar et al. significantly increased the lignin content in tomatoes by interfering with the expression of the C4H gene in tomatoes [74]. CCR1-2 (cinnamyl-CoA reductase) is the first key enzyme in the lignin-specific pathway and is also a key enzyme in the phenylpropane metabolic pathway, participating in the later stages of lignin biosynthesis. In one of the key steps to catalyze the biosynthesis of lignin monomer, cinnamoyl-CoA is transformed into the corresponding cinnamaldehyde [75]. As an important component of the plant cell wall, lignin plays an important role in enhancing the mechanical strength and disease resistance of the plant cell wall [76]. Li et al. found that the expression of the SbCCR2-1 gene in sorghum was significantly higher under drought stress, while the expression of the SbCCR2-2 gene was significantly increased under the stress of sorghum aphids [77]. In this study, it was suggested that CYP73A5 and CCR1-2 jointly drive lignin monomer synthesis, strengthen the cell wall, and restrict the movement of nematodes during the disease of L. kaempferi. These two genes were screened out, strongly suggesting that one of the core response strategies of the module is to activate the phenylpropanoid metabolism, thereby enhancing the physical barrier (cell wall thickening) and chemical defense (antimicrobial substance synthesis).

4.3.2. Protein Homeostasis and Signal Regulation Genes (Ubiquitination and Cytoskeleton)

In addition to direct metabolic defense, fine signal regulation and protein turnover are also the core of the immune response. RUB1, RPS27AA, SCE1, and UBQ10 are closely related to the ubiquitin-proteasome system and belong to the protein turnover and regulation system. SCE1 (SUMO-conjugating enzyme), as a SUMO-binding enzyme (E2 enzyme), receives SUMO protein from E1 SUMO-activated heterodimer SAE1/SAE2, and uses E3 SUMO ligases (such as SIZ1, MMS21) to assist in catalyzing SUMO covalently to target proteins. SCE1 and SIZ1 synergistically mediate SUMO modification of transcription factor GTE3 and participate in various biological processes such as transcriptional regulation, cell cycle regulation, and immune response [78]. Two genes in the Arabidopsis genome encode the E2-binding enzymes SCE1a (SUMO-conjugating enzyme 1a) and SCE1b (SUMO-conjugating enzyme 1b) [79]. In Arabidopsis thaliana, SCE1a is highly expressed in roots, leaves, and stems, while SCE1b is relatively low, suggesting that the E2 binding enzyme of SUMO plays a role in the substrate recognition of SUMO molecules [76]. SUMO modification can enhance the stability or activity of repair proteins (such as RAD51) and affect plant immune signaling pathways (such as SA and JA signals). The ubiquitin-NEDD8-like protein (RUB1) belongs to the NEDD8 family. The activation of the SCF complex mediated by RUB1 can accelerate the degradation of the JAZ protein, release its inhibition of transcription factor MYC2, and drive the expression of terpenoid synthases and defense proteins. It plays an important role in regulating protein degradation, cell cycle and signal transduction in plants [80,81]. Plants lacking the NEDD8 E2 homologous genes AXR1 and RCE1 exhibit seedling lethality, which is characteristic of auxin signaling defects [82]. UBQ10 (Polybiquitin 10) is one of the genes encoding Ubiquitin in plants. The ubiquitin protein encoded by UBQ10 is the core component of the ubiquitin proteasome system (UPS), which can clear misfolded proteins and maintain cell homeostasis under stress. It can degrade the JA signal suppressor (JAZ), regulate the MAPK pathway, and enhance immune response. RUB1, SCE1, and UBQ10 jointly regulate JAZ protein degradation and transcription factor activity in the JA signaling pathway, enhancing the disease resistance response. Pathogen infection can cause a large amount of protein damage and signal reprogramming. The UPS is responsible for rapidly degrading misfolded proteins, removing signal proteins that are no longer needed (such as some negative regulators), and mediating defense hormone signaling pathways, such as salicylic acid. This reflects the ability of plants to ‘fine-tune’ the proteome at the post-transcriptional level to quickly adjust the immune status.
As a multifunctional cytoskeletal protein, actin is widely distributed and highly conserved, and plays a key role in cytoplasmic flow, cell morphology maintenance, cell division, organelle movement, and elongation growth [83,84]. ACT11 (AcN-11) belongs to the actin gene, which belongs to one of the actin subtypes related to reproduction [85]. ACT7 is an actin gene, a trophic actin subtype involved in hormone-induced plant cell proliferation and callus. ACT7 affects cell elongation and differentiation by influencing auxin (IAA) [86]. In this study, ACT7 and ACT11 jointly optimize the local enrichment of disease-resistant molecules and enhance the chemical defense efficiency.

4.3.3. Gene Expression Regulation Genes (Ribosomes and Histones)

Ribosomes are organelles that translate information contained in mRNA molecules for protein synthesis and are found in the cytoplasm, mitochondria, and chloroplasts of higher plants. The ribosomes of eukaryotes are called 80S ribosomes, which are composed of 60S large subunits and 40S small subunits [87]. ARP1 (60S ribosomal protein L3-1), a core component of the large Ribosomal subunit (60S), belongs to the highly conserved uL3 family (universal Ribosomal Protein L3). RPL8C (60S ribosomal protein L8-3) belongs to the universal ribosomal protein uL2 family. RPS27AA (Ubiquitin-40S ribosomal protein S27a-1) belongs to 40S small subunits. Guo et al. have studied CspA, a cryogenic anti-gene, and found that this gene can interact with ribosomal protein in 60S, revealing that ribosomal protein plays a key role in the cryogenic signal transduction pathway during cold adaptation [88]. Kim et al. cloned three ribosomal protein genes, Gm RPS13, Gm RPS6 and Gm RPL37, from soybeans under the condition of low temperature induction and proved that the ribosomal proteins encoded by these three genes could improve the tolerance of soybeans to low temperature [89]. Under the stress of B. xylophilus, ARP1, RPL8C, and RPS27AA of L. kaempferi rapidly synthesized disease-resistant proteins through selective translation reprogramming.
Both HTR2 (Histone H3.2) and F24B9.25 (Histone H4) are core components of nucleosomes that play a central role in transcriptional regulation, DNA repair, DNA replication, and chromosome stability. Studies have shown that histones are essential for fungal development and secondary metabolite gene silencing [90]. Histones H3K9me3 and H3K27me3 regulate fungal alkaloid biosynthesis in fungal endophyt–plant symbiosis [91]. Wei et al. showed that the modification of histone H3K27me3 was related to the salt tolerance and drought tolerance of soybeans [92], and the expression level of histone coding gene in maize increased significantly at the later stage of infection by Fusarium graminearum [93].

4.3.4. Core Metabolic Node Gene (CSY4)

In addition, a gene at the intersection of the primary metabolism and secondary defense metabolism is also crucial. CSY4 (Citrate synthase 4) mitochondria belong to the citrate synthase family. Citrate synthase is one of the key rate-limiting enzymes in citric acid synthesis in the tricarboxylic acid cycle. Its role is to catalyze the condensation of acetyl-CoA and oxaloacetic acid to produce citric acid, so the activity of citrate synthase has an important influence on the rate of the entire metabolic pathway. It was found that the introduction of exogenous gene CS into a rice genome by the Agrobacterium-mediated method improved the low phosphorus sensitivity of rice [94]. After low phosphorus stress, the expression of the Co CS gene in the root system of Camellia oleifera was induced by low phosphorus, and the expression level showed a trend of first increasing and then decreasing [95]. Overexpression of the MxCS3 and MdCS genes in Arabidopsis led to increased plant fresh weight, CS activity, chlorophyll and citric acid content, and improved iron tolerance [96,97].

5. Conclusions

This study systematically revealed the molecular defense mechanism of L. kaempferi in response to pine wood nematode infection. It not only identified that key pathways such as phenylpropanoids, glutathione, and lipid metabolism played a central role in the process of disease resistance, but also analyzed its dynamic changes over time: it focused on signal transduction and protein treatment in the early stage, strengthened secondary metabolism in the middle stage, and turned to lipid and sulfur metabolism in the later stage to maintain resistance. At the same time, we screened several core hub genes, including AACT1 and CYP73A5. These findings deepen the understanding of plant–pathogen interactions, provide excellent candidate targets for subsequent functional verification by transgenic or metabolomics methods, and are expected to be applied to the development of molecular markers for disease resistance breeding and the creation of new plant immune inducers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16121858/s1, Table S1: RT-qPCR primers used in this study; Table S2: Quality statistics of filtered reads; Table S3: KEGG enrichment results for up-regulated genes at 6 h (p < 0.05 and top 20 pathway); Table S4: KEGG enrichment results for down-regulated genes at 6 h (p < 0.05 and top 20 pathway); Table S5: KEGG enrichment results for up-regulated genes at 1 d (p < 0.05 and top 20 pathway); Table S6: KEGG enrichment results for down-regulated genes at 1 d (p < 0.05 and top 20 pathway); Table S7: KEGG enrichment results for up-regulated genes at 7 d (p < 0.05 and top 20 pathway); Table S8: KEGG enrichment results for down-regulated genes at 7 d (p < 0.05 and top 20 pathway); Table S9: KEGG enrichment results for up-regulated genes at 15 d (p < 0.05 and top 20 pathway); Table S10: KEGG enrichment results for down-regulated genes at 15 d (p < 0.05 and top 20 pathway); Table S11: KEGG enrichment results for up-regulated genes at 25 d (p < 0.05 and top 20 pathway); Table S12: KEGG enrichment results for down-regulated genes at 25 d (p < 0.05 and top 20 pathway); Table S13: KEGG enrichment results for up-regulated genes at 30 d (p < 0.05 and top 20 pathway); Table S14: KEGG enrichment results for down-regulated genes at 30 d (p < 0.05 and top 20 pathway); Table S15: KEGG enrichment results for up-regulated genes at 35 d (p < 0.05 and top 20 pathway); Table S16: KEGG enrichment results for down-regulated genes at 35 d (p < 0.05 and top 20 pathway); Figure S1: 10 genes were validated by real-time fluorescent quantitative PCR (RT-qPCR) for RNA sequencing (RNA-SEQ) results.

Author Contributions

D.L.: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing—original draft, Supervision, Funding acquisition. W.W.: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing—review and editing. H.W.: Validation, Writing—review and editing. Y.W.: Validation, Writing—review and editing. J.W.: Validation, Writing—review and editing. S.J.: Writing—review and editing, Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Joint Program of Science and Technology Plan of Liaoning Province (Department of Science and Technology of Liaoning Province, grant number 2024JH2/102600211), Natural Science Foundation of Liaoning Province—Doctoral project (Department of Science and Technology of Liaoning Province, grant number 2023-BS-212), and Shenyang Institute of Technology—PhD start-up project (Shenyang Institute of Technology, grant number BS202401).

Data Availability Statement

The datasets used in this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors gratefully acknowledge technical support from Niu Shihui of Beijing Forestry University. We extend our particular thanks to Shihui Niu, Zhipeng Li, Chuan Tian, Hongna Chen, and Yuezhen Yu for their valuable assistance and contributions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. External symptoms of L. kaempferi inoculated with B. xylophilus after different times: (A) 1 d after inoculation; (B) 10 d after inoculation; (C) 24 d after inoculation; (D) 30 d after vaccination; and (E) 34 d after inoculation.
Figure 1. External symptoms of L. kaempferi inoculated with B. xylophilus after different times: (A) 1 d after inoculation; (B) 10 d after inoculation; (C) 24 d after inoculation; (D) 30 d after vaccination; and (E) 34 d after inoculation.
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Figure 2. Number of DEGs in each pair. Red represents up-regulated genes, blue represents down-regulated genes, CK represents the control group, and T represents the treatment group. CK0 represents the control group at 6 h, CK1 represents the control group at 1 d, and so on. T0 represents the treatment group at 6 h, T1 represents the treatment group at 1 d, and so on. CK0VST0 represents the differential gene between the treatment group and the control at 6 h.
Figure 2. Number of DEGs in each pair. Red represents up-regulated genes, blue represents down-regulated genes, CK represents the control group, and T represents the treatment group. CK0 represents the control group at 6 h, CK1 represents the control group at 1 d, and so on. T0 represents the treatment group at 6 h, T1 represents the treatment group at 1 d, and so on. CK0VST0 represents the differential gene between the treatment group and the control at 6 h.
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Figure 3. Gene expression trends of DEGs. The number on the upper left of the box denoted different trend profiles. The number on the bottom of the box denoted gene numbers enriched in this profile, and different color boxes indicated significant expression patterns (p < 0.05). X-axis: represents the time series. Y-axis: represents the relative level of gene expression.
Figure 3. Gene expression trends of DEGs. The number on the upper left of the box denoted different trend profiles. The number on the bottom of the box denoted gene numbers enriched in this profile, and different color boxes indicated significant expression patterns (p < 0.05). X-axis: represents the time series. Y-axis: represents the relative level of gene expression.
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Figure 4. KEGG enrichment analysis of color modules. (A) Profile 0, (B) profile 19, and (C) profile 10. The Y-axis lists the enriched KEGG pathways. The X-axis represents the enrichment factor (rich factor, i.e., the ratio of DEGs annotated in a pathway to all genes annotated in that pathway). The size of each bubble corresponds to the number of DEGs mapped to the respective pathway. The color gradient from blue to red indicates the statistical significance of the enrichment, with red being the most significant.
Figure 4. KEGG enrichment analysis of color modules. (A) Profile 0, (B) profile 19, and (C) profile 10. The Y-axis lists the enriched KEGG pathways. The X-axis represents the enrichment factor (rich factor, i.e., the ratio of DEGs annotated in a pathway to all genes annotated in that pathway). The size of each bubble corresponds to the number of DEGs mapped to the respective pathway. The color gradient from blue to red indicates the statistical significance of the enrichment, with red being the most significant.
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Figure 5. Weighted gene co-expression network analysis (WGCNA) in B. xylophilus stress. (A) Cluster dendrogram and module colors of DEGs. (B) The number of genes in each module. (C) Correlation between gene modules and phenotypes at different time stages.
Figure 5. Weighted gene co-expression network analysis (WGCNA) in B. xylophilus stress. (A) Cluster dendrogram and module colors of DEGs. (B) The number of genes in each module. (C) Correlation between gene modules and phenotypes at different time stages.
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Figure 6. Gene enrichment analysis of core modules at different module. (A,B) GO enrichment analysis of the red and blue module. (C,D) KEGG enrichment analysis of the red and blue module. From the outside to the inside, the first circle represents the top 20 enrichment pathways, and the number outside the circle is the coordinate ruler of the number of genes. The second circle represents the number and Q value of the background genes in this pathway, and the more genes, the longer the bar. The third circle represents the number of the DEGs in this pathway. The fourth circle represents the value of the rich factor in each pathway.
Figure 6. Gene enrichment analysis of core modules at different module. (A,B) GO enrichment analysis of the red and blue module. (C,D) KEGG enrichment analysis of the red and blue module. From the outside to the inside, the first circle represents the top 20 enrichment pathways, and the number outside the circle is the coordinate ruler of the number of genes. The second circle represents the number and Q value of the background genes in this pathway, and the more genes, the longer the bar. The third circle represents the number of the DEGs in this pathway. The fourth circle represents the value of the rich factor in each pathway.
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Figure 7. Visualization of the protein–protein interaction (PPI) network and expression patterns of hub genes. (A) The key genes screened, according to MCC, in the blue module. (B) The key genes screened, according to betweenness, in the blue module. (C) The key genes screened, according to MCC, in the red module. (D) The key genes screened, according to betweenness, in the red module. The size of the circle indicates how significant the interaction relationship is. When there are more interaction lines between a node and other nodes, the node is larger. The darker the color, the more significant the color is.
Figure 7. Visualization of the protein–protein interaction (PPI) network and expression patterns of hub genes. (A) The key genes screened, according to MCC, in the blue module. (B) The key genes screened, according to betweenness, in the blue module. (C) The key genes screened, according to MCC, in the red module. (D) The key genes screened, according to betweenness, in the red module. The size of the circle indicates how significant the interaction relationship is. When there are more interaction lines between a node and other nodes, the node is larger. The darker the color, the more significant the color is.
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Li, D.; Wang, W.; Wang, Y.; Wu, H.; Wang, J.; Jiang, S. Transcriptomic Response of Larix kaempferi to Infection Stress from Bursaphelenchus xylophilus. Forests 2025, 16, 1858. https://doi.org/10.3390/f16121858

AMA Style

Li D, Wang W, Wang Y, Wu H, Wang J, Jiang S. Transcriptomic Response of Larix kaempferi to Infection Stress from Bursaphelenchus xylophilus. Forests. 2025; 16(12):1858. https://doi.org/10.3390/f16121858

Chicago/Turabian Style

Li, Debin, Weitao Wang, Yijing Wang, Hao Wu, Jiaqing Wang, and Shengwei Jiang. 2025. "Transcriptomic Response of Larix kaempferi to Infection Stress from Bursaphelenchus xylophilus" Forests 16, no. 12: 1858. https://doi.org/10.3390/f16121858

APA Style

Li, D., Wang, W., Wang, Y., Wu, H., Wang, J., & Jiang, S. (2025). Transcriptomic Response of Larix kaempferi to Infection Stress from Bursaphelenchus xylophilus. Forests, 16(12), 1858. https://doi.org/10.3390/f16121858

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