Search Results (136)

Pinus massoniana Lamb. is an economically important conifer native to China. However, it is highly susceptible to the pine wood nematode (Bursaphelenchus xylophilus, PWN), the causal agent of pine wilt disease (PWD), resulting in substantial ecological and economic losses. To elucidate potential molecular defense mechanisms, 50 NAC (NAM, ATAF1/2, and CUC2) transcription factors (PmNACs) were identified in the P. massoniana genome. Phylogenetic analysis divided these PmNACs into seven subfamilies, and motif analysis identified ten conserved motifs associated with stress responses. Twenty-three genes were selected for expression analysis in various tissues and under exogenous salicylic acid (SA), methyl jasmonate (MeJA), and PWN infection. Six genes (PmNAC1, PmNAC8, PmNAC9, PmNAC17, PmNAC18, and PmNAC20) were significantly up-regulated by both hormonal treatment and PWN infection, implying their involvement in JA/SA-mediated immune pathways. Functional characterization showed PmNAC8 is a nuclear-localized transcription factor with autoactivation activity. Furthermore, transient overexpression of PmNAC8 in Nicotiana benthamiana induced reactive oxygen species (ROS) accumulation and necrotic lesions. Collectively, these results elucidate NAC-mediated defense responses to PWN infection in P. massoniana and identify candidate genes for developing PWD-resistant pine varieties. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus Steiner & Buhrer (pine wood nematodes, PWN), impacts forest carbon sequestration and climate change. However, satellite-based PWD monitoring is challenging due to the limited spatial resolution of Sentinel’s MSI sensor, which reduces its sensitivity to subtle biochemical alterations in foliage. We have, therefore, developed a slope product index (SPI) for effective detection of PWD using single-date satellite imagery based on spectral gradients in the visible and near-infrared (VNIR) range. The SPI was compared against 15 widely used vegetation indices and demonstrated superior robustness across diverse test sites. Results show that the SPI is more sensitive to changes in chlorophyll content in the PWD detection, even under potentially confounding conditions such as drought. When integrated into Random Forest (RF) and Back-Propagation Neural Network (BPNN) models, SPI significantly improved classification accuracy, with the multivariate RF model achieving the highest performance and univariate with SPI in BPNN. The generalizability of SPI was validated across test sites in distinct climate zones, including Zhejiang (accuracyZ_Mean = 88.14%) and Shandong (accuracyS_Mean = 78.45%) provinces in China, as well as Portugal. Notably, SPI derived from Sentinel-2 imagery in October enables more accurate and timely PWD detection while reducing field investigation complexity and cost. Full article

Pine wilt disease (PWD), a destructive pine forest disease caused by pine wood nematode (PWN), Bursaphelenchus xylophilus, has led to huge economic losses and ecological environment damage. Thaumatin-like proteins (TLPs) are the products of a complex gene family involved in host defense and a wide range of developmental processes in fungi, plants, and animals. In this study, a tlp gene of B. xylophilus (Bxtlp) (GenBank: OQ863020.1) was amplified via PCR and cloned into the expression vector pET-15b to construct the recombinant vector PET-15b-Bxtlp, which was then transformed into Escherichia coli BL-21(DE3). The recombinant protein was successfully purified using Ni-NTA affinity chromatography. The effect of the Bxtlp gene on the vitality and pathogenicity of PWNs was elucidated through RNA interference (RNAi) and overexpression. Bxtlp dsRNA significantly reduced the feeding, motility, spawning, and reproduction abilities of PWN; shortened its lifespan; and increased the female–male ratio. In contrast, the recombinant BxTLP markedly enhanced the reproductive ability of PWN. In addition, Bxtlp dsRNA increased reactive oxygen species (ROS) content in nematodes, while the recombinant BxTLP was confirmed to have antioxidant capacity in vitro. Furthermore, the bioassays on Pinus thunbergii saplings demonstrated that Bxtlp could significantly influence PWN pathogenicity. Overall, we speculate that Bxtlp affects the pathogenicity of PWNs mainly via regulating ROS levels, the motility, and hatching of PWN. Full article

Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission ability, it has caused significant damage to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine learning/deep learning algorithms with Sentinel-1/2 images in the Google Earth Engine cloud platform to implement province-wide PWD monitoring in Anhui Province, China. The study also analyzed the spatial distribution of PWD in Anhui Province from two perspectives—spatiotemporal patterns and influencing factors—aiming to investigate the spatiotemporal evolution patterns and the impact of influencing factors on the occurrence of PWD. The results show that (1) the random forest model exhibited the strongest performance, followed by the CNN model, while the DNN model performed the worst. Using the RF model to monitor PWD and calculate the affected area in Anhui Province from 2019 to 2024 yielded errors within 30% compared to official statistics. (2) PWD in Anhui Province showed a clear clustering trend, with global Moran’s indices all exceeding 0.79 from 2019 to 2024. The LISA map revealed a spread pattern from south to north and from west to east. (3) Topographic and temperature factors had the greatest influence on PWD distribution. SHAP analysis indicated that topographic and climatic factors were the primary drivers of PWD-affected areas, with slope and temperature being the two most significant contributing factors. This study helps to rapidly and accurately identify outbreak areas during epidemics and enables precise quarantine measures and targeted control efforts. Full article

The pine wood nematode, a microscopic worm-like organism, is the primary cause of Pine Wilt Disease (PWD), a serious threat to pine forests, as infected trees can die within a few months. In this study, we aim to predict the occurrence of PWD by leveraging geographical and meteorological features, with a particular focus on incorporating interpretability through explainable AI (XAI). Unlike conventional models that utilize features from a single point of location, our approach considers surrounding environmental factors (patches) and employs a channel grouping mechanism to aggregate features effectively, enhancing prediction accuracy. Experimental results demonstrate that the proposed model based on convolutional neural network (CNN) outperforms traditional point-wise models, achieving a 9.7% higher F1-score. Experimental results show that data augmentation further improved performance, while interpretability analysis identified precipitation and temperature-related features as the most significant contributors. The developed CNN model provides a robust and interpretable framework, offering valuable insights into the spatial and environmental dynamics of PWD occurrence. Full article

The pinewood nematode (PWN), Bursaphelenchus xylophilus, is a pathogenic organism that causes pine wilt disease (PWD). To date, several molecular diagnostic methods have been developed; however, rapid, convenient, and inexpensive field diagnostic tools for detecting PWN are still limited. In this study, an enzyme-mediated duplex exponential amplification (EmDEA) method for detecting PWN from extracted nematodes or pinewood sawdust was developed and tested. This method comprised an EmDEA molecular test kit, which consisted of freeze-dried enzyme pellets that can be stored at room temperature (approximately 20–25 °C) for one year, a dry block heater, and a portable isothermal fluorescence amplification instrument. The whole procedure was completed within 30 min. The EmDEA assay could detect a single PWN at all life stages from a mixture of other nematode species or from pinewood sawdust. The detection limit was 10 copies (plasmid weight 32.66 ag) or 1/500 of that of a single adult PWN per reaction. Therefore, the EmDEA assay has potential applications in PWN detection in the field, as well as quarantine inspection in international trade. Moreover, modification of primers and probes will allow the rapid identification of other nematode species. Full article

Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a significant threat to global pine forests and calls for the development of innovative management strategies. Microbial control emerges as an effective, cost-efficient, and environmentally sustainable approach to eliminate the damage from PWD. This review consolidates molecular mechanisms in the microbiological control of PWD, which focus on three core strategies: microbial control activity against PWN, biological control of vector insects, and the enhancement of host tree resistance to nematode infections. The review thoroughly evaluates integrated control strategies in which microbial control is used in traditional management practices. Recent studies have pinpointed promising microbial agents for PWN control, such as nematophagous microorganisms, nematicidal metabolites, parasitic fungi that target vector insects, and microbes that boost plant resistance. In particular, the control potential of volatile organic compounds (VOCs) produced by microorganisms against PWN and the enhancement of pine resistance to PWN by microorganisms were emphasized. Moreover, we assessed the challenges and opportunities associated with the field application of microbiological control agents. We emphasized the feasibility of multi-strategy microbial integrated control, which provides a framework for future studies on microbial-based PWD control strategies. Full article

Pine Wilt Disease (PWD) is a highly infectious and lethal disease that severely threatens global pine forest ecosystems and forestry economies. Early and accurate detection of infected trees is crucial to prevent large-scale outbreaks and support timely forest management. However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. To address these challenges, we propose an improved object detection model, YOLOv8-MFD, designed for accurate and efficient detection of PWD-infected trees from UAV imagery. The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. Experimental results on a UAV-based forest dataset demonstrate that YOLOv8-MFD achieves a precision of 92.5%, a recall of 84.7%, an F1-score of 88.4%, and a mAP@0.5 of 88.2%. Compared to baseline models such as YOLOv8 and YOLOv10, our method achieves higher accuracy while maintaining acceptable computational cost (11.8 GFLOPs) and a compact model size (10.2 MB). Its inference speed is moderate and still suitable for real-time deployment. Overall, the proposed method offers a reliable solution for early-stage PWD monitoring across large forested areas, enabling more timely disease intervention and resource protection. Furthermore, its generalizable architecture holds promise for broader applications in forest health monitoring and agricultural disease detection. Full article

Pine wilt disease (PWD) is characterized by rapid transmission, high mortality rates, and difficulty in control, resulting in severe and destructive impacts on both the ecological environment and socioeconomic development in China. Due to the lack of significant symptoms in infected trees during the early stages of the disease, improving the accuracy of early detection has become a major challenge in PWD monitoring. In recent years, the rapid advancement of UAV-based hyperspectral remote sensing technology has provided a promising approach for the early detection of PWD. In this study, we selected classic canopy pigment and moisture content indices to construct a set of recognition indicators. The optimal canopy pigment index (CI) and canopy moisture content index (WASCOSBNDI) were then chosen through significance testing and derivative analysis. Based on the asynchronous variations in canopy moisture and pigment content during the development of PWD, the CI, WASCOSBNDI, and CI-WASCOSBNDI models were developed using a multi-threshold segmentation method to identify trees at different stages of infection. The results demonstrate that the CI-WASCOSBNDI model achieved the highest accuracy in detecting infection stages, with an overall classification accuracy of 92.78%. In comparison, the CI and WASCOSBNDI models achieved classification accuracies of 81.34% and 89.84%, respectively. For the early stage infected trees, which are the primary focus of this study, the CI-WASCOSBNDI model exhibited the best performance with an accuracy rate exceeding 70%, significantly outperforming the other models. Furthermore, the timing of infection in early stage trees significantly influenced the model’s detection accuracy, with trees closer to the disease outbreak period being more easily identified. These findings provide a reference for the accurate early monitoring of PWD using UAV hyperspectral imagery. Full article

Pinus massoniana Lamb. (masson pine) is a critical species for afforestation in southern China but faces severe threats from pine wilt disease (PWD) caused by Bursaphelenchus xylophilus. To accelerate disease-resistant breeding, this study investigated the effects of cryopreservation on the embryonic capacity of the embryogenic callus as well as the effects of abscisic acid (ABA), polyethylene glycol 8000 (PEG 8000) and phytagel concentration on the somatic embryo’s maturation and germination. Furthermore, the impact of transplanting substrates on the survival and growth of regenerated plantlets were evaluated. The results showed that cryopreservation at −196 °C effectively maintained the embryogenic potential of the callus, with post-thaw tissues exhibiting superior somatic embryo maturation capacity compared to the long-term subcultured callus (38.4 vs. 13.2 embryos/mL). Key maturation parameters were systematically optimized: ABA concentration at 6 mg/L in the suspension culture maximized embryo yield of 24.1 somatic embryos/mL, while PEG 8000 at 130 g/L in solid medium achieved peak embryo production of 38.4 somatic embryos/mL, and the maximum of 26.6 somatic embryos/mL when the concentration of phytagel was 3.5 g/L. The highest germination rate of 29.8% was observed with 130 g/L PEG in the maturation medium. The highest survival rate (56.5%) and maximum plant height (22.3 cm) after 12 months of transplantation were achieved in substrates consisting of soil and vermiculite, which outperformed those containing varying proportions of mushroom residue. This study establishes a scalable protocol for the mass propagation of PWD-resistant P. massoniana, integrating cryopreservation and maturation media optimization, which offers dual benefits for disease-resistant breeding and sustainable germplasm conservation. Full article

Pine wilt disease (PWD) poses a significant threat to pine forest health, making sanitation thinning of infected trees and cultivation of disease-resistant pine stands crucial measures for forest ecosystem restoration. To date, limited studies have systematically investigated how post-sanitation planting of pine-wilt-disease-resistant Pinus species affects soil microbiome, especially regarding bacterial and fungal diversity characteristics, functional succession patterns, and community assembly processes. In this study, we performed a comparative analysis of soil microbial community characteristics and biochemical properties between experimental plots subjected to sanitation thinning and those replanted with disease-resistant pine species. The results indicated that compared to the sanitation-thinned experimental plot, the disease-resistant experimental plots (Pinus taeda experimental plot and Pinus thunbergii experimental plot) exhibited significantly higher activities of β-glucosidase (S-β-GC), N-acetyl-β-D-glucosidase (S-NAG), and soil arylsulfatase (S-ASF). Compared with the sanitation logging stands, our analysis revealed that the Pinus taeda experimental plot and Pinus thunbergii experimental plot exhibited significantly higher fungal community evenness (OTUs), greater species abundance (OTUs), and more unique fungal taxa. Furthermore, the edaphic properties—specifically soil moisture content (SMC), pH levels, and total potassium (TK)—significantly influenced the structures of soil bacterial and fungal communities. Compared to the sanitation-thinned experimental plot, wood saprotrophic fungi and ectomycorrhizal fungi exhibited increased abundance in both the P. taeda experimental plot and Pinus thunbergii experimental plot. Furthermore, the null models indicated that both the P. taeda experimental plot and P. thunbergii experimental plot enhanced the undominated processes of bacteria and fungi. In summary, our data elucidate the differences in bacterial and fungal responses between pine forests undergoing thinning due to infected trees and those cultivated for disease resistance. This deepens our understanding of microbial functions and community assembly processes within these ecosystems. Full article

Bursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD), poses a severe global threat to coniferous forests. Esteya vermicola, an endoparasitic nematophagous fungus, exhibits promising biocontrol potential against this pinewood nematode. The vesicular transport system, evolutionarily conserved in eukaryotes, is essential for fungal pathogenicity. Based on our genome sequence of E. vermicola CBS115803, we identified EvSec22, a gene encoding a SNARE protein implicated in vesicular transport process. This study investigates the role of EvSec22 in E. vermicola during nematode infection, utilizing our optimized gene knockout methodology. Infection assays revealed that EvSec22 deletion significantly impaired the pathogenicity of E. vermicola against B. xylophilus. Phenotypic analyses revealed that the ΔEvSec22 mutant exhibited suppressed hyphal growth, reduced conidiation, and abnormal septal spacing. Furthermore, the mutant showed significantly diminished tolerance to osmotic stress (sorbitol) and oxidative stress (hydrogen peroxide). Overall, the EvSec22 gene is associated with the virulence of E. vermicola CBS115803 against B. xylophilus, and its deletion also affects the normal growth of E. vermicola and its tolerance to abiotic stress. This study providing new insights into SNARE protein functions in fungal biocontrol agents. Full article

To examine the role of asymptomatic infected trees in the spread of pine wilt disease (PWD), we established two study sites in a coastal black pine forest in 2020: one in a heavily damaged site and the other in a slightly damaged site. Half of the trees in each site were treated with a nematicide injection to suppress nematode activity. Tree health, assessed by resin exudation and external symptoms, was monitored for four years. In the slightly damaged site, asymptomatic infected trees emerged within 20 m of infected trees, and even with nematicide treatment, trees within 2 m of infected trees became asymptomatic infected. However, nematicide treatment allowed temporarily asymptomatic infected trees to survive or recover. These findings suggest that combining nematicide injection with the felling of neighboring infected trees can effectively suppress PWD. Full article

Plants, as producers in ecosystems, are an integral part of biodiversity in terms of their species diversity. Plant diversity not only enhances the quality of ecosystem services, but also provides habitat for a wide range of plants and animals. The invasion of pine wilt disease (PWD) has posed a significant threat to plant diversity in China, but it is not clear whether this threat would be significantly different in natural and planted forests. In this study, we collected a long time series of refined forest subcompartment data on PWD occurrence and plant diversity sample survey data to analyze the loss and recovery time of plant diversity in China caused by PWD invasion, especially the degree of impact on plant diversity in natural and planted forests. The results showed that after PWD invasion, the plant diversity levels of China’s national, natural, and planted forests reached a minimum in the third year of invasion, with a loss of 9.1%, 6.46%, and 9.82%, respectively, relative to the pre-invasion levels. Starting from the third year of invasion, the plant diversity levels of the three recovered gradually at different rates, among which there was a significant difference in the speed of recovery between natural forests and planted forests, which took two and three years to recover to the original level of plant diversity, respectively. This study revealed the differences in the response of plant diversity to PWD invasion between natural and planted forests and provided a theoretical basis for local governments and managers in preventing and controlling PWD and protecting plant diversity. Full article

Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), is one of the most serious threats to pine forests worldwide. The fungus Esteya vermicola, with its lunate conidia capable of parasitizing the PWN, has shown promise as an efficient biological control agent against PWD. Solid-state fermentation (SSF) is preferred for large-scale applications in the field, as it facilitates microbial agent transport and ensures a long shelf life. However, research on enhancing the yield of lunate conidia from E. vermicola through SSF is limited. In this study, we initially achieved a yield of 3.04 × 10⁸ conidia/g using a basic SSF medium composed of wheat bran, corn flour, and soybean flour. To improve this yield, we employed an orthogonal experimental design (OED) to identify the optimal medium composition, which required a wheat bran-to-corn flour-to soybean flour ratio of 7:2:1 (w/w/w), a substrate-to-water ratio of 1:0.7 (w/v), and the addition of 1.33% (w/w) glucose, 1.33% (w/w) yeast extract fermentation, and 1.33% (w/w) MgSO₄. Using the response surface methodology (RSM), we calculated the optimal fermentation conditions, which were 24.9 °C, 78.0% relative humidity (RH), an inoculation volume of 16.3% (v/w), and a fermentation time of 7.1 days. Under these conditions, the yield of lunate conidia reached a maximum of 16.58 × 10⁸ conidia/g, a 4.45-fold increase after optimization. This study improved the yield of E. vermicola lunate conidia and provides insights for developing biopesticides based on this strain. Full article