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Article

Investigation of Pine Wilt Disease in Chongqing: From Field Occurrence and Genetic Diversity to Endophytic Microbial Composition and Functional Analysis

by
Haorong Yang
1,
Lan Jiang
1,
Xu Hu
1,
Shan Chen
1,
Fan Jia
1,2,
Guanhua Ma
1,
Kuo Huang
3,
Ziqin Bai
4,
Yang Zheng
5,* and
Guokang Chen
1,*
1
Chongqing Key Laboratory of Plant Disease Biology, College of Plant Protection, Southwest University, Chongqing 400716, China
2
Agriculture Technology Extension Center of Wanrong County, Wanrong 044200, China
3
College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
4
Fruit Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550000, China
5
Institute of Vegetable and Flower Research, Chongqing Academy of Agricultural Sciences, Chongqing 400055, China
*
Authors to whom correspondence should be addressed.
Plants 2026, 15(5), 775; https://doi.org/10.3390/plants15050775
Submission received: 23 January 2026 / Revised: 18 February 2026 / Accepted: 22 February 2026 / Published: 3 March 2026
(This article belongs to the Special Issue Integrating Plant Immunity and Ecological Strategies for Sustainable Disease Management)
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Abstract

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, is a destructive forest disease leading to rapid mortality. Although Chongqing is a major epidemic region in China, the population genetic structure of B. xylophilus and the ecological interactions among nematode occurrence, blue stain formation, and microbial community dynamics remain insufficiently clear. This study systematically surveyed nematode incidence and performed morphological and molecular identification, revealing strong correlations between nematode presence, blue stain, and insect infestation (p < 0.0001). Within Monochamus alternatus, nematodes were mainly distributed in the abdomen and thorax (p < 0.0001). High-throughput sequencing showed significantly higher fungal (e.g., Leptographium) and bacterial (e.g., Burkholderia-Caballeronia-Paraburkholderia) diversity in diseased than healthy pinewood, indicating pronounced microbial shifts during disease progression. mtCOI-based genetic analyses of 162 nematodes from 11 populations revealed five haplotypes, with Hap1 shared across all populations. AMOVA indicated that over 80% of genetic variation occurred within populations, and neutrality and mismatch analyses suggested recent expansion in some populations (Beibei, Jiangbei, Rongchang). These findings clarify nematode epidemiology, microbial shifts, and genetic characteristics in Chongqing, providing a scientific basis for precise sampling, rapid detection, and integrated management of PWD, and suggest that microbial community changes may contribute to rapid pine decline.

1. Introduction

Pine wood (Pinus spp.) is a widely utilized industrial resource valued for its application in construction, furniture manufacturing, pulp production, and bio-based materials. However, the productivity and quality of pine-derived industrial products are severely threatened by pine wilt disease (PWD), caused by the invasive pinewood nematode (Bursaphelenchus xylophilus) [1]. As a major pathogen of coniferous trees, B. xylophilus infects economically important species such as Pinus massoniana and P. elliottii, leading to extensive wood degradation and significant economic losses in the timber and forest product industries [2,3]. Since its first appearance in Nanjing, China, in 1982 [4], PWD has spread rapidly and devastated over 50,000 m3 of pine wood across the country, particularly in key timber-producing regions such as Chongqing. Chongqing is a key outbreak area for PWD. According to the Chongqing Forestry Bureau, PWD was first introduced into Chongqing in 2001, causing substantial damage to pine forest resources. The epidemic remains serious, with the number of affected areas, epidemic points, and epidemic area in Chongqing ranking 10th, 9th, and 8th in China, respectively, highlighting the need for continued monitoring and management [5].
The nematode parasitizes host xylem tissues [6,7,8], feeding on parenchyma cells, disrupting water transport [9,10,11], and causing internal discoloration (blue staining) [12,13], while facilitating secondary infection through insect vectors such as Monochamus alternatus [14,15,16,17]. Endophytic microbial communities can enhance host defense but may shift roles during disease progression, contributing to wood decay [18]. These processes not only accelerate disease transmission [19], but also degrade wood quality and industrial value [20,21]. High-throughput sequencing of bacterial 16S rRNA and fungal ITS regions enables detection of unculturable microbes and characterization of microbial community shifts associated with nematode infection, providing insights into host–pathogen–microbe interactions.
Although numerous studies have explored the pathogenic mechanisms, population genetics, and molecular detection of B. xylophilus [22,23,24,25], studies on the genetic diversity, population structure, and historical dynamics of B. xylophilus, as well as its interactions with pine endophytes, are limited. Mitochondrial COI markers are widely used for population and evolutionary studies due to maternal inheritance, high mutation rate, and ease of amplification, and have been applied to B. xylophilus identification and population research.
To address these gaps, this study conducted a systematic survey of B. xylophilus occurrence across 11 districts in Chongqing, analyzed correlations with blue stain and insect infestation, and characterized endophytic microbial communities of healthy and diseased pinewood using high-throughput sequencing of 16S rRNA and fungal ITS regions [26,27]. In parallel, mitochondrial COI gene markers were used to assess the genetic diversity and population structure of B. xylophilus, providing critical insights into its evolutionary dynamics and regional dispersal. This work aims to establish a molecular and ecological foundation for improving detection, monitoring, and integrated management strategies to mitigate PDW and safeguard pine-derived industrial resources.

2. Results

2.1. Survey on the Occurrence of Pinewood Nematode in Chongqing

In 2019, a total of 192 dead pine tree samples, suspected of pine wilt disease infection, were collected from multiple districts across Chongqing. Morphological identification of B. xylophilus was conducted on these samples (Figure 1). The detection rates of the pinewood nematode differed markedly among the districts. Nan’an District exhibited the highest detection rate at 88.89%, followed by Xiushan District at 83.33%, while Kaizhou City recorded the lowest rate at 68.81%. For blue-stained dead wood infected with pine wilt disease (Figure S1), Shapingba District had the highest detection rate, reaching 94.44%. However, insect infestation rates were generally low, with Kaizhou District showing the highest rate at 23.33% and Xiushan District recording the lowest at only 6.67% (Figure 2a).
In 2020, an expanded survey was conducted, collecting 618 dead pine samples from six districts in Chongqing. A comparison of national standards (GB/T 23476-2009) [28] and industry standards (LY/T 2350-2014) [29] for pine wilt disease detection revealed that the industry standard was more sensitive, capable of detecting a single nematode (Figure S2). Molecular identification was performed following the industry standard LY/T 2350-2014, “Technical Regulations for Molecular Detection and Identification of Pinewood Nematode”. Detection rates varied across districts, with Jiangjin District achieving the highest rate at 97.22%, followed by Beibei District at 93.99%. Detection rates in Shapingba, Dianjiang, Jiulongpo, and Banan Districts were 89.93%, 89.09%, 89.09%, and 71.67%, respectively.
Morphological analysis of blue-stained wood revealed significant regional differences. The highest blue-stain rate was observed in Jiulongpo District (97.69%), while Shapingba District had the lowest rate (73.02%). Pest morphology identification showed low rates across districts: 35.00% in Banan, 10.00% in Beibei, 8.00% in Jiulongpo, 16.00% in Shapingba, 1.00% in Dianjiang, and 6.00% in Jiangjin (Figure 2b).
Statistical analysis revealed strong correlations between detection methods and infection indicators. Morphological detection rates correlated significantly with blue-stain rates (Pearson coefficient = 0.99) and insect infestation rates (Pearson coefficient = 0.87) (Table S1). Similarly, molecular detection rates correlated with blue-stain rates (Pearson coefficient = 0.99) and insect infestation rates (Pearson coefficient = 0.97). Regression analysis confirmed these relationships, showing robust correlations between morphological detection and blue-stain rates (R2 = 0.98, p < 0.0001), as well as insect infestation rates (R2 = 0.75, p < 0.0001). Molecular detection showed similar trends, correlating with blue-stain rates (R2 = 0.99, p < 0.0001) and insect infestation rates (R2 = 0.95, p < 0.0001) (Figure 2c,d).

2.2. Genetic Diversity and Population Dynamics of Pinewood Nematode in Chongqing

2.2.1. mtCOI Haplotypes and Genetic Diversity

Between July and August 2020, M. alternatus samples were collected from 31 sites across 11 districts in Chongqing, resulting in 162 trap-based beetle samples used for genetic diversity analysis (Table S2). Mitochondrial COI gene fragments were sequenced using SeqMan 11.0 and MEGA 7.0 software, yielding 162 sequences of 433 bp after trimming. The sequences comprised 323 conserved sites (C), 110 variable sites (V), 4 parsimony-informative sites (Pi), and 106 singleton sites (S). Nucleotide composition was biased, with T = 43.0%, C = 10.0%, A = 29.2%, and G = 16.8%, resulting in A + T = 73.2% and G + C = 26.8%. Haplotype analysis using DNASP v6 software identified five haplotypes (Hap1–Hap5) across the 11 districts (Table 1). Hap1, the dominant haplotype, was shared by all populations, representing 93.20% of the total sequences. The Beibei (BB) and Jiangbei (JB) populations displayed the highest haplotype richness, with three haplotypes each. In contrast, other districts, including Jiulongpo (JLP), Yubei (YB), Tongliang (TL), Dadukou (DDK), Banan (BN), Gaoxin (GX), and Hechuan (HC), exhibited only a single haplotype. Unique haplotypes were identified in specific regions: Hap2 in Shapingba, Hap3 and Hap4 in Beibei, and Hap5 in Rongchang (Figure S4).
The overall haplotype diversity index (Hd) was 0.129, and the nucleotide diversity index (Pi) was 0.00215. Among districts, Jiangbei exhibited the highest Hd (0.6), followed by Shapingba (0.371), Rongchang (0.286), and Beibei (0.127). The remaining populations had the lowest diversity (Hd = 0, Pi = 0). Beibei had the highest Pi (0.01485), while Jiangbei, Rongchang, and Shapingba followed with Pi values of 0.00231, 0.00137, and 0.00097, respectively (Table 1).
Neutrality test results indicated significant negative values for the total population (Tajima’s D = −2.78962; Fu and Li’s D = −10.31880; p < 0.001), suggesting recent population expansion or selective pressure. Beibei showed similarly significant negative values (Tajima’s D = −2.81586, p < 0.01; Fu and Li’s D = −5.49276, p < 0.001). Other populations did not yield significant results in neutrality tests (p > 0.05).
The haplotype mediation network revealed connections between haplotypes through single and multiple mutations. Hap1 was the most widely shared haplotype, while Hap2, Hap3, Hap4, and Hap5 were regionally restricted, indicating localized evolutionary divergence (Figure S3).

2.2.2. Genetic Distances and Population Differentiation

Genetic distance analysis using MEGA7.0 showed that the 11 geographical populations exhibited pairwise genetic distances ranging from 0.00051 to 0.00600 (Table 2, lower left). Among these populations, the Beibei (BB), Rongchang (RC), and Shapingba (SPB) populations exhibited genetic distances with other populations, while the remaining populations displayed a genetic distance of 0. The farthest genetic distance was observed between the Beibei (BB) and Shapingba (SPB) populations (0.00600), whereas the Rongchang (RC) population exhibited the closest genetic distance to other populations (0.00051). Phylogenetic analysis using the UPGMA method in MEGA7.0, based on the mtCOI gene sequence, clustered Tongliang, Yubei, Jiulongpo, Jiangbei, Hechuan, Gaoxin, Dadukou, and Banan populations into one branch, while Rongchang, Shapingba, and Beibei populations formed distinct branches (Figure 3). Genetic differentiation analysis, performed using Arlequin v3.5, revealed differentiation indices ranging from −0.08772 to 0.24731. Significant genetic differentiation was detected in the Shapingba (SPB), Beibei (BB), and Jiulongpo (JLP) populations, while the remaining populations did not exhibit significant differentiation (p > 0.05).

2.2.3. Population Dynamics and Historical Expansion

Population historical dynamics of the pinewood nematode were analyzed using DnaSP v6 software, integrating neutrality tests (Tajima’s D and Fu and Li’s D) and mismatch distribution analysis. The overall population (Table 2) exhibited significantly negative neutrality test values (Tajima’s D = –2.78962; and Fu and Li’s D = –10.31880; p < 0.001) and a unimodal mismatch distribution (Figure 4a–e), indicating that the total population has recently undergone expansion. At the population level, Beibei showed significantly negative neutrality values, strongly suggesting recent expansion, while Jiangbei and Rongchang populations also displayed negative neutrality values, though not significant, suggesting a potential expansion trend that requires further validation. In contrast, the Jiulongpo population showed neutrality values near zero and a non-unimodal mismatch distribution, suggesting that this population has remained relatively stable without experiencing recent expansion. AMOVA results revealed that 86.71% of the total genetic variation occurred within populations, whereas 13.29% was attributable to differences among populations (p < 0.01, Table S3), indicating that the majority of genetic diversity in B. xylophilus is derived from intra-population variation.

2.3. Distribution of Pinewood Nematode in M. alternatus Across Five Districts of Chongqing

The distribution of B. xylophilus in M. alternatus was assessed by quantifying the number of nematodes carried per 20 mg of thorax and abdomen tissue in five districts (Yongchuan, Beibei, Tongnan, Rongchang, and Jiangbei) of Chongqing (Figure S3). The results revealed significant differences in nematode loads across these regions (p < 0.0001). In Yongchuan, the average nematode count per 20 mg of thorax and abdomen tissue was 23.28 and 55.08, respectively. In Beibei, the corresponding values were 21.52 and 61.80, while in Tongnan, they were 22.28 and 64.30. For Rongchang, the averages were 23.08 and 63.10, and for Jiangbei, the averages were 27.94 and 65.74 (Figure 5). The statistical analysis showed highly significant differences in nematode counts per 20 mg of thorax and abdomen tissue across the five districts, highlighting regional variability in nematode load within M. alternatus.

2.4. Diversity and Differential Analysis of Endophytic Bacterial Communities in Pine Wood

Samples from diseased and healthy pine wood were collected from Gele Mountain in Shapingba District, Chongqing, and subjected to morphological and molecular identification. Diseased samples were designated as H1, H2, and H3, while healthy samples were designated as N1, N2, and N3 in triplicate. The V3–V4 region of the bacterial 16S rRNA gene was sequenced using the Illumina HiSeq platform (Illumina, San Diego, CA, USA), and the raw sequence data of bacterial communities from both healthy and diseased samples were obtained (Table S4). The analysis identified 197 OTUs in the H group and 192 OTUs in the N group, with 13 OTUs unique to the H group, 8 OTUs unique to the N group, and 184 shared OTUs. Taxonomic classification revealed a total of 6 phyla, 9 classes, 32 orders, 46 families, 72 genera, and 42 species. Within the H group, 170 shared OTUs were identified across the three replicates, while H1, H2, and H3 contained 7, 20, and 13 unique OTUs, respectively. Similarly, the N group contained 166 shared OTUs, with N1, N2, and N3 containing 5, 20, and 19 unique OTUs, respectively. These results suggest an increase in bacterial community richness in diseased pine samples compared to healthy ones (Figure 6a). Alpha diversity indices, including the Shannon, Simpson, and Chao1 indices, were higher in the H group than in the N group, indicating greater species diversity and richness in the diseased samples (Table S5). Non-metric multidimensional scaling (NMDS) analysis revealed significant differences in microbial community structures between the H and N groups (Stress = 0.0000), which were further supported by PERMANOVA analysis (R2 = 0.996, p < 0.05; Figure 6b,c). Statistical analysis of the Alpha diversity indices confirmed significant differences in the Shannon and Simpson indices between diseased and healthy samples, with microbial diversity being significantly higher in the H group (Figure 6d,e).
At the phylum level, Proteobacteria dominated both groups, accounting for 77% in the H group and 71% in the N group. Actinobacteria showed a threefold difference in relative abundance between the two groups, while Bacteroidetes accounted for 14% in the H group and only 2% in the N group, representing an eightfold difference. Significant variations were observed in bacterial phyla between the diseased and healthy samples (Figure 6f). At the genus level, Burkholderia-Caballeronia-Paraburkholderia was the dominant bacterial genus in the H group, whereas Luteibacter dominated the N group. Sphingomonas was present in both groups, while Mycobacterium showed marked differences, accounting for 3% in the H group and 19% in the N group. ANOVA analysis indicated significant differences in genus-level composition between the two groups (Figure 6g). The most notable differences were observed in Luteibacter, Mycobacterium, and Burkholderia-Caballeronia-Paraburkholderia (p < 0.01), aligning with the taxonomic distribution results. Functional annotation using KEGG and COG databases revealed that genes associated with metabolism were the most abundant, followed by genetic information processing and environmental information processing. Functional pathway analysis identified 25 metabolic pathways, with amino acid transport and metabolism, as well as translation, ribosomal structure, and biogenesis, showing significant differences between the H and N groups (Figure 6i and Table S6).

2.5. Diversity and Species Distribution of Endophytic Fungal Communities in Pine Wood

The ITS1 region of fungal DNA from healthy and diseased samples was sequenced using the Illumina HiSeq platform to obtain raw data (Table S7). The fungal communities in the H and N groups yielded 212 and 206 OTUs, respectively. Taxonomic classification identified 4 phyla, 15 classes, 32 orders, 47 families, 65 genera, and 57 species. At 97% similarity, clustering resulted in 223 OTUs in the H group and 222 OTUs in the N group, including 9 unique OTUs in the H group, 8 unique OTUs in the N group, and 214 shared OTUs. Within the H group, 118 OTUs were shared across replicates, with H1, H2, and H3 containing 87, 11, and 13 unique OTUs, respectively. In the N group, 104 OTUs were shared, while N1, N2, and N3 contained 102, 20, and 22 unique OTUs, respectively. These findings indicate an increased fungal community richness in diseased samples compared to healthy ones (Figure 7a). NMDS and PERMANOVA analyses showed no significant differences in fungal community structure between the H and N groups (Stress = 0.0000) (Figure 7b,c). Alpha diversity indices, including the Shannon and Simpson indices, were higher in the H group, suggesting greater fungal diversity and abundance in diseased samples. While differences in the Shannon index were not statistically significant (p > 0.05), the Simpson index showed significant differences (p < 0.01), indicating overall higher fungal diversity in the H group (Figure 7d,e, Table S8).
At the phylum level, Ascomycota was the dominant fungal phylum in the H group, accounting for 92% of the community, compared to 52% in the N group. In contrast, Basidiomycota was more abundant in the N group (44%) than in the H group (5%), demonstrating significant differences in fungal phylum composition between diseased and healthy samples (Figure 7f). At the genus level, Ceratobasidium was the dominant genus in healthy samples, representing 43.5% of the fungal community, while Leptographium was the dominant genus in diseased samples, accounting for 34.6% (Figure 7g). ANOVA analysis confirmed significant differences in fungal genera between the two groups (p < 0.01). Ceratobasidium was significantly more abundant in the N group, whereas Leptographium (e.g., blue-stain fungus of Pinus armandii) was significantly enriched in the H group (Figure 7h).

3. Discussion

Pine wilt disease, caused by Bursaphelenchus xylophilus, continues to pose a serious threat to forest ecosystems in southwestern China [30]. Our regional investigation confirms that the disease is widely established across Chongqing, with higher detection frequencies in urbanized and historically affected districts. This spatial pattern likely reflects the combined effects of human-mediated dispersal, timber transportation, and vector activity, emphasizing the importance of sustained regional surveillance and integrated management [31]. The strong association between blue-stain symptoms, insect infestation, and molecular confirmation indicates that these morphological characteristics remain useful field indicators; however, the greater sensitivity of molecular diagnostics highlights the necessity of combining morphological and molecular approaches to avoid underestimation of infection rates.
From a population genetic perspective, the dominance of a single widespread haplotype across all sampled regions suggests either a common invasion origin or the selective advantage of a particular lineage. Such a pattern is often linked to founder effects followed by rapid expansion. Meanwhile, the occurrence of region-specific haplotypes indicates ongoing local differentiation, implying that environmental pressures may be shaping adaptive divergence despite an overall shared genetic background. The predominance of intra-population genetic variation suggests that most diversity is maintained within local populations rather than structured among geographic regions, possibly reflecting frequent local reproduction combined with limited natural long-distance dispersal. Signals of recent demographic expansion further indicate that the population may not yet have reached equilibrium and retains considerable invasive potential [32]. Together, these findings suggest strong adaptive capacity, which may complicate long-term containment and management strategies.
In addition to genetic patterns, substantial restructuring of microbial communities was observed following nematode infection [33]. The increased microbial diversity in diseased trees suggests that pine wilt disease involves broader ecological disruption rather than a simple single-organism infection. The enrichment of Burkholderia-related taxa in diseased samples supports a cooperative pathogenic framework in which bacteria may enhance tissue degradation and facilitate nematode proliferation [34]. Conversely, the reduction in potentially protective taxa, such as Luteibacter, may weaken host defense mechanisms, contributing to disease progression [35,36]. Fungal community shifts further reinforce this synergistic model, as the enrichment of blue-stain fungi likely accelerates vascular dysfunction and resin depletion, thereby promoting host mortality [37]. Collectively, these patterns support a multi-organism interaction model in which nematodes, bacteria, and fungi act synergistically during pathogenesis, with microbial metabolic intensification potentially accelerating host decline.
Several limitations should be acknowledged. The genetic analysis was based on a single mitochondrial marker, and incorporation of nuclear markers or whole-genome approaches would provide a more comprehensive understanding of population structure. Moreover, microbiome analyses were correlation-based and cannot establish direct causality between specific taxa and pathogenicity, necessitating further experimental validation. Finally, temporal dynamics were not assessed, and seasonal variation may influence both genetic and microbial patterns. Despite these limitations, this study provides an integrated ecological and evolutionary perspective on pine wilt disease in Chongqing and offers a foundation for future research on pathogen adaptation and disease management.

4. Materials and Methods

4.1. Collection of Dead Pine Trees Suspected of Being Infected with Pine Wilt Disease

Sampling of dead pine trees suspected of being affected by pine wilt disease was collected from forest stands in parts of the trunk were selected, and an electric saw and an axe were used to cut them into small wooden blocks, each approximately 5 cm in length and 1 to 2 cm in diameter. In late August 2019, suspected pinewood nematode-infected dead pine trees were collected from six districts (counties) in Chongqing: 17 from Nan’an, 20 from Shapingba, 108 from Jiangjin, 18 from Xiushan, 20 from Kaizhou, and 12 from Tongnan, totaling 192 samples. The samples from each region were randomly divided into three groups for morphological identification of the pinewood nematode. In late August 2020, suspected pinewood nematode-infected dead pine trees were collected from six districts (counties) in Chongqing: 392 from Beibei, 49 from Shapingba, 93 from Jiulongpo, 20 from Dianjiang, 36 from Jiangjin, and 28 from Banan, totaling 618 samples. The samples from each region were randomly divided into three groups for molecular identification of the pinewood nematode (Figure 1).

4.2. Collection of M. alternatus

M. alternatus was collected using traps, placed in forest clearings in the forest farm, 1.5 to 2.0 m above the ground, with a density of 7 traps/km2. Three sampling points (a total of 3 km2) were established per region, deploying 21 traps total. Traps were brought back to the laboratory, and the samples were stored in anhydrous ethanol.

4.3. Morphological Identification of B. xylophilus

Nematodes were extracted from pine wood samples using the Baermann funnel method [5]. After allowing the setup to stand for 24 h, the water stopper at the bottom of the funnel was opened, and 5 to 10 mL of the suspension was collected using a small beaker. A temporary glass slide was prepared, and the sample was examined under a light microscope to determine the presence of pinewood nematodes. Morphological identification of B. xylophilus was based on the following diagnostic characteristics: males possess copulatory spicules with a distinct distal bulge; females exhibit a prominent vulval flap; the tail is relatively short and blunt, typically lacking a clearly defined terminus. When present, the mucron at the tail tip is generally less than 24 µm in length.

4.4. Molecular Identification of B. xylophilus

DNA was randomly extracted from samples collected from three different regions. Nematode suspensions were obtained using the Baermann funnel method (as described in Section 4.3). Two milliliters of suspension from each group were centrifuged, and DNA was extracted using the kit’s lysis buffer. DNA of the nematodes was extracted using the TaKaRa MiniBEST Universal DNA Extraction Kit(Code No. 9765, TaKaRa Bio Inc., Kusatsu, Shiga, Japan; Ver. 5.0), following the manufacturer’s instructions. PCR was performed in a 25 µL reaction: 2 µL of DNA template, 12.5 µL of Mix (containing dNTPs, 10× PCR buffer with Mg2+, and Taq DNA polymerase), 9.5 µL ultrapure water, and 0.5 µL of each primer (10 µM). The primers recommended by the national standard GB/T 23476-2009 Pine Wood Nematode Disease Quarantine Technical Regulations were used [38]: forward primer 5′-CGGGTCATGGCTGGAGGTATCGT-3′, reverse primer 5′-TGGCTCAATGGCAAATCCTTCGTA-3′, the amplified region length is 724 bp. The primers recommended by the industry standard LY/T 2350-2014 Pine Wood Nematode Disease Quarantine Technical Regulations were used: forward primer 5′-CTACGTGCTGTTGTTGAGTTGGC-3′, reverse primer 5′-TGGTGCCTAACATTGCGCGA-3′, the amplified region length is 403 bp. PCR products were verified using 1% agarose gel electrophoresis. LY/T 2350-2014 was used for molecular identification due to its higher sensitivity, capable of detecting single nematodes in samples where GB/T 23476-2009 may fail (Figure S2), ensuring maximum detection accuracy in field-collected samples.

4.5. Correlation Analysis Between Pine Wilt Disease, Blue Stain, and Insect Infestation

The calculation formulas are as follows:
Pine wilt disease detection rate (%) = number of samples detected with pine wilt disease/total number of samples in each district (county) × 100;
Blue stain rate (%) = number of blue stain samples of dead wood with pine wilt disease/number of samples detected with pine wilt disease × 100;
Insect infestation rate (%) = number of insect infestation samples of dead wood with pine wilt disease/number of samples detected with pine wilt disease × 100.
All data were first organized and summarized in Microsoft Excel. Normality and homogeneity of variance were assessed using GraphPad 10 software. After meeting these assumptions, two-way ANOVA was performed to evaluate differences among groups, and Pearson correlation coefficients were calculated to analyze the relationships between pine wilt disease detection rate, blue stain rate, and insect infestation rate [39].

4.6. Distribution of B. xylophilus in M. alternatus

In June and July 2020, M. alternatus specimens were randomly collected from traps across five districts (counties) in western Chongqing: Tongnan, Yongchuan, Beibei, Rongchang, and Jiangbei forest farms. From each district, 50 beetles were randomly selected, preserved in anhydrous ethanol, and subsequently processed. Each specimen was surface-dried with sterile gauze, after which the thoracic and abdominal regions were dissected using sterilized needles and scalpels. Approximately 20 mg of tissue from each region was weighed on an electronic balance, homogenized in 1 mL of sterile water, and centrifuged at 7000 rpm for 3 min. The supernatant was discarded, retaining the pellet containing pinewood nematodes. Temporary slides were prepared from the pellet and examined under an optical microscope at 100× magnification (10× ocular, 10× objective).

4.7. Sequencing and Microbial Diversity Analysis of Pine Wood Samples

Healthy and diseased pine wood samples were collected from the main stems of Pinus trees in Gele Mountain, Shapingba District, Chongqing. Samples were transported to the laboratory on the same day, cut into wood chips, and stored at −20 °C until further processing. After morphological and molecular identification (see Section 4.3 and Section 4.4), the diseased and healthy samples were repeated three times, with the diseased samples being H1, H2, and H3, and the healthy samples being N1, N2, and N3. The total DNA of the pinewood samples was extracted, and the primers used for the bacterial 16SV3+V4 region were 338F: 5′-ACTCCTACGGGAGGCAGCA-3′, 806R: 5′-GGACTACHVGGGTWTCTAAT-3′ [39]; the primers used for the fungal ITS1 region were ITS1F: 5′-CTTGGTCATTTAGAGGAAGTAA-3′, ITS1R: 5′-GCTGCGTTCTTCATCGATGC-3′ [40].
High-throughput paired-end sequencing was performed on the Illumina HiSeq platform (Illumina, San Diego, CA, USA). Sequencing reads were quality-filtered, chimeras removed, and clustered into OTUs at 97% similarity, followed by taxonomic annotation against the Silva (bacteria) and UNITE (fungi) databases [41,42,43,44]. Microbial community composition and diversity at multiple taxonomic levels (phylum to species) were analyzed and visualized using standard bioinformatics pipelines [45,46]. Alpha and beta diversity indices were calculated, and statistical differences among groups were assessed using ANOVA. Functional prediction of bacterial communities was conducted based on 16S rRNA gene sequences [47].

4.8. Genetic Diversity of B. xylophilus Carried by M. alternatus in Chongqing

Between July and August 2020, M. alternatus samples were collected from 11 districts in Chongqing, including Beibei (31), Jiulongpo (30), Shapingba (34), Jiangbei (6), Yubei (5), Rongchang (7), Tongliang (10), Dadukou (9), Banan (14), Gaoxin (12), and Hechuan (4). A total of 31 sub-sites were established, yielding 162 trap-based beetle samples. Adult M. alternatus individuals collected from the traps were preserved in absolute ethanol. Total DNA was extracted from the mid-posterior thorax and abdominal tissues of M. alternatus. PCR amplification of the mitochondrial COI gene was performed using primers specific to B. xylophilus (LepF: 5′-ATTCAACCAATCATAAAGATATTGG-3′; LepR: 5′-TAAACTTCTGGATGTCCAAAAAATCA-3′). The PCR conditions were as follows: initial denaturation at 94 °C for 30 s; 35 cycles of 98 °C for 10 s, 52 °C for 30 s, and 72 °C for 40 s; followed by a final extension at 72 °C for 2 min. PCR products were detected by agarose gel electrophoresis and visualized using a gel imaging system. Target bands of the expected size were excised and sequenced.
The 11 districts were treated as 11 geographic populations for genetic diversity analysis of B. xylophilus mtCOI sequences. Population genetic parameters, including the number of haplotypes, haplotype diversity (Hd), and nucleotide diversity (π), were calculated using DnaSP v6. Neutrality tests (Tajima’s D and Fu and Li’s D) were conducted in DnaSP. A haplotype network was constructed using PopART. Genetic distances were calculated using the Kimura 2-parameter (K2P) model in MEGA 7.0, and a UPGMA phylogenetic tree was constructed based on mtCOI sequences of the 11 geographic populations. Genetic differentiation (Fst) and analysis of molecular variance (AMOVA) were performed using Arlequin v3.5 with 1000 permutations. Historical population dynamics were analyzed in DnaSP v6, including mismatch distribution analysis.

5. Conclusions

Pine wilt disease in Chongqing shows uneven distribution, with blue-stain symptoms and insect infestation strongly correlating with nematode presence, highlighting their value as diagnostic indicators. Population genetic analysis of B. xylophilus revealed a dominant haplotype, high intra-population diversity, and signs of recent expansion, reflecting its adaptive potential and rapid dispersal. Endophytic microbial communities differed markedly between healthy and diseased trees, with specific bacterial and fungal taxa potentially enhancing nematode pathogenicity or supporting host health. These findings provide actionable insights for forest management and suggest that future studies should explore the causal relationships between microbial communities and nematode virulence to improve control strategies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants15050775/s1: Figure S1: Disease symptoms of pine wilt disease. Figure S2: National standards and industry standards molecular results of B. xylophilus. Figure S3: Haplotype network of B. xylophilus geographical populations based on the mtCOI gene sequence. Figure S4: Geographical distribution map of a sample of Pinnacle aspen in Chongqing. Table S1: Correlation coefficients of morphological and molecular identification with blue stain and insect infestation. Table S2: Sample Collection Information. Table S3: AMOVA analysis based on mtCOI gene sequence. Table S4: Microbial sequencing data of pine wood with B. xylophilus disease and healthy pine wood. Table S5: Alpha Diversity Index (endophytic bacteria). Table S6: KEGG pathways in two pine wood samples. Table S7: Microbial sequencing data of pine wood with Bursaphelenchus xylophilus disease and healthy pine wood (endophytic fungi). Table S8: Alpha Diversity Index (endophytic fungi).

Author Contributions

Conceptualization, H.Y., Y.Z. and G.C.; Methodology, L.J.; Software, H.Y.; Validation, H.Y., L.J. and X.H.; Investigation, S.C. and G.M.; Resources, Z.B.; Data curation, H.Y.; Writing—original draft, H.Y.; Writing—review & editing, F.J.; Visualization, L.J.; Supervision, G.C.; Project administration, Z.B.; Funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partly supported by the Natural Science Foundation of China (32472083), the Central Guidance Fund for Local Science and Technology Development (2023ZYDF087) and the chief scientist innovation project of State Tobacco Monopoly Administration/China National Tobacco Corporation (702023CK0870).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical distribution of dead pine samples suspected of being infected with pine wilt disease in Chongqing. The blue font represents the western region, the red represents the central urban area, the orange represents the southwestern region, the yellow represents the central region, the purple represents the northeastern region, the green represents the southeastern region.
Figure 1. Geographical distribution of dead pine samples suspected of being infected with pine wilt disease in Chongqing. The blue font represents the western region, the red represents the central urban area, the orange represents the southwestern region, the yellow represents the central region, the purple represents the northeastern region, the green represents the southeastern region.
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Figure 2. The occurrence of pine wilt disease and its correlation analysis. (a) Microscopic identification of dead pine trees suspected to be infected with pine wilt disease, along with observations of blue stain and infestation rate in Chongqing. (b) Molecular detection of dead pine trees suspected to be infected with pine wilt disease, as well as blue stain and infestation rate in Chongqing. Each treatment included three biological replicates. Data were analyzed using one-way ANOVA followed by Tukey’s HSD test for multiple comparisons. Groups labeled with different letters are significantly different (p < 0.05). (c,d) Correlation analysis among morphological detection, molecular detection, blue stain, and infestation levels in pinewood. |r| = 0.8–1.0 indicates a very strong correlation; |r| = 0.6–0.8, strong; |r| = 0.4–0.6, moderate; |r| = 0.2–0.4, weak; |r| = 0.0–0.2, very weak.
Figure 2. The occurrence of pine wilt disease and its correlation analysis. (a) Microscopic identification of dead pine trees suspected to be infected with pine wilt disease, along with observations of blue stain and infestation rate in Chongqing. (b) Molecular detection of dead pine trees suspected to be infected with pine wilt disease, as well as blue stain and infestation rate in Chongqing. Each treatment included three biological replicates. Data were analyzed using one-way ANOVA followed by Tukey’s HSD test for multiple comparisons. Groups labeled with different letters are significantly different (p < 0.05). (c,d) Correlation analysis among morphological detection, molecular detection, blue stain, and infestation levels in pinewood. |r| = 0.8–1.0 indicates a very strong correlation; |r| = 0.6–0.8, strong; |r| = 0.4–0.6, moderate; |r| = 0.2–0.4, weak; |r| = 0.0–0.2, very weak.
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Figure 3. Phylogenetic relationships and genetic distances among 11 B. xylophilus populations in Chongqing. UPGMA analysis based on mtCOI sequences clustered eight populations into one branch, while Beibei, Shapingba, and Rongchang formed distinct clades. Genetic distances ranged from 0.00051 to 0.00600, with the greatest distance between Beibei and Shapingba. Note: BB: Beibei; TL: Tongliang; YB: Yubei; JLP: Jiunongpo; JB: Jiangbei; HC: Hechuan; GX: Gaoxin; DDK: Dadukou; BN: Banan; RC: Rongchang; SPB: Shapingba.
Figure 3. Phylogenetic relationships and genetic distances among 11 B. xylophilus populations in Chongqing. UPGMA analysis based on mtCOI sequences clustered eight populations into one branch, while Beibei, Shapingba, and Rongchang formed distinct clades. Genetic distances ranged from 0.00051 to 0.00600, with the greatest distance between Beibei and Shapingba. Note: BB: Beibei; TL: Tongliang; YB: Yubei; JLP: Jiunongpo; JB: Jiangbei; HC: Hechuan; GX: Gaoxin; DDK: Dadukou; BN: Banan; RC: Rongchang; SPB: Shapingba.
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Figure 4. Mismatch distribution analysis of B. xylophilus populations. (a) overall (11 geographic populations combined), (b) Beibei, (c) Jiangbei, (d) Rongchang, and (e) Jiulongpo. Unimodal distributions and significant negative neutrality test values in the overall and Beibei populations indicate recent expansion. Jiangbei and Rongchang populations show unimodal distributions with non-significant negative neutrality tests, suggesting possible expansion. Jiulongpo population exhibits a multimodal distribution and positive neutrality test values, indicating demographic stability.
Figure 4. Mismatch distribution analysis of B. xylophilus populations. (a) overall (11 geographic populations combined), (b) Beibei, (c) Jiangbei, (d) Rongchang, and (e) Jiulongpo. Unimodal distributions and significant negative neutrality test values in the overall and Beibei populations indicate recent expansion. Jiangbei and Rongchang populations show unimodal distributions with non-significant negative neutrality tests, suggesting possible expansion. Jiulongpo population exhibits a multimodal distribution and positive neutrality test values, indicating demographic stability.
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Figure 5. Analysis of distribution difference in B. xylophilus in the body of the pine brown longicorn (n = 50). This value is the mean of 20 mg of aspergillus tissue carrying nematodes. The statistical analyses were performed using two-way ANOVA (*** p < 0.001).
Figure 5. Analysis of distribution difference in B. xylophilus in the body of the pine brown longicorn (n = 50). This value is the mean of 20 mg of aspergillus tissue carrying nematodes. The statistical analyses were performed using two-way ANOVA (*** p < 0.001).
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Figure 6. Pinewood nematode alters bacterial community structure in pine trees: Diseased pines show higher bacterial diversity and increased community abundance compared to healthy pines. (a) OTU Venn,184 shared OTUs, 13 unique OTUs in diseased (H) group, 8 unique OTUs in healthy (N) group; (b,c) Disease pine wood vs. healthy pine wood NMDS, PERMANOVA diagram; (d,e) Diseased pine (H) and healthy pine (N) Shannon, Simpson index. (f) Microbial relative abundance map of two samples at the phylum level; (g) Species analysis of genus-level differences in microbial communities of two pine wood samples; (h) Microbial relative abundance map of two samples at the genus level; (i) The COG pathway in two pine wood samples. The statistical analyses were performed using Student’s t-test (** 0.001 < p < 0.01).
Figure 6. Pinewood nematode alters bacterial community structure in pine trees: Diseased pines show higher bacterial diversity and increased community abundance compared to healthy pines. (a) OTU Venn,184 shared OTUs, 13 unique OTUs in diseased (H) group, 8 unique OTUs in healthy (N) group; (b,c) Disease pine wood vs. healthy pine wood NMDS, PERMANOVA diagram; (d,e) Diseased pine (H) and healthy pine (N) Shannon, Simpson index. (f) Microbial relative abundance map of two samples at the phylum level; (g) Species analysis of genus-level differences in microbial communities of two pine wood samples; (h) Microbial relative abundance map of two samples at the genus level; (i) The COG pathway in two pine wood samples. The statistical analyses were performed using Student’s t-test (** 0.001 < p < 0.01).
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Figure 7. Endophytic fungal communities in healthy and diseased pine wood. (a) Venn diagram showing unique and shared OTUs between groups. Diseased samples (H) exhibited higher fungal richness (212 OTUs) compared to healthy samples (N, 206 OTUs), with 214 shared OTUs; (b,c) NMDS ordination (stress = 0.000) and PERMANOVA results demonstrating no significant structural differences (p > 0.05) in fungal communities between health states; (d,e) Alpha diversity indices revealing significantly higher fungal diversity in diseased samples (Simpson index, p < 0.01), though Shannon index differences were not significant (p > 0.05); (f) Phylum-level community composition showing relative abundance shifts between groups; (g) Microbial relative abundance map of two samples at the genus level; (h) Species analysis of genus-level differences in microbial communities of two pine wood samples. The statistical analyses were performed using Student’s t-test (** 0.001 < p < 0.01).
Figure 7. Endophytic fungal communities in healthy and diseased pine wood. (a) Venn diagram showing unique and shared OTUs between groups. Diseased samples (H) exhibited higher fungal richness (212 OTUs) compared to healthy samples (N, 206 OTUs), with 214 shared OTUs; (b,c) NMDS ordination (stress = 0.000) and PERMANOVA results demonstrating no significant structural differences (p > 0.05) in fungal communities between health states; (d,e) Alpha diversity indices revealing significantly higher fungal diversity in diseased samples (Simpson index, p < 0.01), though Shannon index differences were not significant (p > 0.05); (f) Phylum-level community composition showing relative abundance shifts between groups; (g) Microbial relative abundance map of two samples at the genus level; (h) Species analysis of genus-level differences in microbial communities of two pine wood samples. The statistical analyses were performed using Student’s t-test (** 0.001 < p < 0.01).
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Table 1. Genetic diversity of neutrality test of B. xylophilus among 11 geographical populations based on the mtCOI gene sequence.
Table 1. Genetic diversity of neutrality test of B. xylophilus among 11 geographical populations based on the mtCOI gene sequence.
PopulationSample NumberHaplotypeNumber of HaplotypesHaplotype Diversity, Hd ± SDNucleotide Diversity, Pi ± SDFu Ang Li’s DTajima’s D
BB31Hap1 (29) Hap3 (1) Hap4 (1)30.12700 ± 0.080000.01485 ± 0.01353−5.49276 **−2.81586 ***
JLP30Hap1 (30)10.000000.000000.000000.00000
JB6Hap1 (6)10.60000 ± 0.215000.00231 ± 0.00103−1.26013−1.23311
YB5Hap1 (5)10.000000.000000.000000.00000
RC7Hap1 (6) Hap5 (1)20.28600 ± 0.196000.00137 ± 0.00094−1.29591−1.23716
TL10Hap1 (10)10.000000.000000.000000.00000
DDK9Hap1 (9)10.000000.000000.000000.00000
BN14HapI (14)10.000000.000000.000000.00000
GX12HapI (12)10.000000.000000.000000.00000
HC4Hap1 (4)10.000000.000000.000000.00000
SPB34Hap1 (26) Hap2 (8)20.37100 ± 0.079000.00097 ± 0.000200.580400.77304
Total1625-0.12900 ± 0.035000.00215 ± 0.00175−10.31880 **−2.78962 ***
Note: Hap1–Hap5 indicate haplotypes; SD = standard deviation. Numbers in parentheses represent the count of each haplotype in each geographic population. Neutrality tests: ** p < 0.01; *** p < 0.001.
Table 2. Estimates of genetic distance (bottom left) and genetic differentiation index (top right) among 11 geographical populations of B. xylophilus.
Table 2. Estimates of genetic distance (bottom left) and genetic differentiation index (top right) among 11 geographical populations of B. xylophilus.
PopulationBBBNDDKGXHCJBJLPRCSPBTLYB
BB-−0.02957−0.05402−0.03708−0.08772−0.08772−0.001080.110320.18028 **−0.0473−0.10846
BN0.00527-0.000000.000000.000000.000000.000000.106380.136240.000000.00000
DDK0.005270.00000-0.000000.000000.000000.000000.038170.103350.000000.00000
GX0.005270.000000.00000-0.000000.000000.000000.081970.124900.000000.00000
HC0.005270.000000.000000.00000-0.000000.00000−0.098040.022660.000000.00000
JB0.005270.000000.000000.000000.00000-0.00000−0.024390.068820.000000.00000
JLP0.005270.000000.000000.000000.000000.00000-0.247310.19947 ** 0.000000.00000
RC0.005790.000510.000510.000510.000510.000510.00051-0.111670.05405−0.05528
SPB0.006000.000720.000720.000720.000720.000720.000720.00124-0.111380.05008
TL0.005270.000000.000000.000000.000000.000000.000000.000510.00072-0.00000
YB0.005270.000000.000000.000000.000000.000000.000000.000510.000720.00000-
Note: BB: Beibei; TL: Tongliang; YB: Yubei; JLP: Jiunongpo; JB: Jiangbei; HC: Hechuan; GX: Gaoxin; DDK: Dadukou; BN: Banan; RC: Rongchang; SPB: Shapingba,   ** p < 0.01 in genetic differentiation index.
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Yang, H.; Jiang, L.; Hu, X.; Chen, S.; Jia, F.; Ma, G.; Huang, K.; Bai, Z.; Zheng, Y.; Chen, G. Investigation of Pine Wilt Disease in Chongqing: From Field Occurrence and Genetic Diversity to Endophytic Microbial Composition and Functional Analysis. Plants 2026, 15, 775. https://doi.org/10.3390/plants15050775

AMA Style

Yang H, Jiang L, Hu X, Chen S, Jia F, Ma G, Huang K, Bai Z, Zheng Y, Chen G. Investigation of Pine Wilt Disease in Chongqing: From Field Occurrence and Genetic Diversity to Endophytic Microbial Composition and Functional Analysis. Plants. 2026; 15(5):775. https://doi.org/10.3390/plants15050775

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Yang, Haorong, Lan Jiang, Xu Hu, Shan Chen, Fan Jia, Guanhua Ma, Kuo Huang, Ziqin Bai, Yang Zheng, and Guokang Chen. 2026. "Investigation of Pine Wilt Disease in Chongqing: From Field Occurrence and Genetic Diversity to Endophytic Microbial Composition and Functional Analysis" Plants 15, no. 5: 775. https://doi.org/10.3390/plants15050775

APA Style

Yang, H., Jiang, L., Hu, X., Chen, S., Jia, F., Ma, G., Huang, K., Bai, Z., Zheng, Y., & Chen, G. (2026). Investigation of Pine Wilt Disease in Chongqing: From Field Occurrence and Genetic Diversity to Endophytic Microbial Composition and Functional Analysis. Plants, 15(5), 775. https://doi.org/10.3390/plants15050775

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