Could Pine Wood Nematode (Bursaphelenchus xylophilus) Cause Pine Wilt Disease or Even Establish inside Healthy Trees in Finland Now—Or Ever?

Pine wilt disease (PWD) caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus) can, in suitable conditions, lead to mass mortality of susceptible trees. In the European Union, PWN is a quarantine pest. To support PWN risk management in Finland, we assessed the suitability of the Finnish present and future climate for both PWD and PWN establishment inside susceptible healthy trees. The former was done using the mean summer temperature concept and the latter by relating annual growing degree days to the likelihoods of PWN extinction and establishment inside healthy trees. The likelihoods were derived from the previously published modelling of PWN population dynamics for 139 locations in Germany. Both assessments were conducted using 10 × 10 km resolution climate data from 2000–2019 and Finland-specific climate change projections for 2030–2080. The results indicate that the present Finnish climate is too cool for both PWD and PWN establishment inside healthy trees. Furthermore, even global warming does not appear to turn the Finnish climate suitable for PWD or PWN establishment inside healthy trees by 2080, except under the worst-case representative concentration pathway scenario (RCP8.5). Consequently, giving top priority to PWN when allocating resources for biosecurity activities in Finland might deserve reconsideration.

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.

Transcriptomic and Coexpression Network Analyses Revealed Pine Chalcone Synthase Genes Associated with Pine Wood Nematode Infection

Pine wood nematode (PWN) causes serious diseases in conifers, especially pine species. To investigate the transcriptomic profiles of genes involved in pine-PWN interactions, two different pine species, namely, Pinus thunbergii and P. massoniana, were selected for this study. Weighted gene coexpression network analysis (WGCNA) was used to determine the relationship between changes in gene expression and the PWN population after PWN infection. PWN infection negatively affects the expression of most genes in pine trees, including plant defense-related genes such as genes related to plant hormone signal transduction, plant-pathogen interactions, and the MAPK signaling pathway in plants. However, the expression of chalcone synthase genes and their related genes were proportional to the changes in nematode populations, and chalcone synthase genes were dominant within the coexpression module enriched by genes highly correlated with the nematode population. Many genes that were closely related to chalcone synthase genes in the module were related to flavonoid biosynthesis, flavone and flavonol biosynthesis, and phenylpropanoid biosynthesis. Pine trees could actively adjust their defense strategies in response to changes in the number of invasive PWNs, but the sustained expression of chalcone synthase genes should play an important role in the inhibition of PWN infection.

Nematicidal activities of aureothin and alloaureothin from endophytic bacteria, Streptomyces sp. AE170020, against the pine wood nematode, Bursaphelenchus xylophilus

Endophytic bacteria, a rich source of bioactive secondary metabolites, are ideal candidates for environmentally benign agents. In this study, an endophytic strain, Streptomyces sp. AE170020, was isolated and selected for the purification of nematicidal substances based on its high nematicidal activity. Two highly active components, aureothin and alloaureothin, were identified, and their chemical structures were determined using spectroscopic analysis. Both compounds suppressed the growth, reproduction, and behavior of Bursaphelenchus xylophilus. In in vivo experiments, the extracts of strain Streptomyces sp. AE170020 effectively suppressed the development of pine wilt disease in four-year-old plants of Pinus densiflora. The potency of secondary metabolites isolated from endophytic strains suggests applications in controlling Bursaphelenchus xylophilus and opens an avenue for further research on exploring bioactive substances against the pine wood nematode.

Trehalose in pine wood nematode participates in DJ3 formation and confers resistance to low-temperature stress

Background Recently, pine wood nematode (PWN, Bursaphelenchus xylophilus) has been found in the extreme cold area of northeast China. The third-stage dispersal juvenile (DJ3) of PWN, which is a long-lived stress-resistant stage, plays an important role in the process of PWN spreading to low-temperature areas, as this stage can survive under unfavorable conditions. Results Weighted correlation network analysis (WGCNA) was used to analyze the expression patterns of 15,889 genes included in 21 RNA-Seq results of PWN at DJ3 and the other 6 different stages, and a total of 12 coexpression modules were obtained. Among them, the magenta module has the highest correlation with DJ3, which included a total of 652 genes. KEGG enrichment analysis showed that most of the genes in the magenta module were involved in metabolic processes, which were related to autophagy and longevity regulation. These pathways included starch and sucrose metabolism, which contains trehalose metabolism. To explore the function of trehalose in DJ3 formation and survival under − 20 °C, a trehalose-6-phosphate synthase encoding gene (Bx-tps), a trehalose-6-phosphate phosphatase encoding gene (Bx-tpp) and 7 trehalase encoding genes (Bx-tres) were identified and investigated. The expression of these 9 genes was related to the formation of DJ3. A treatment under − 20 °C induced the accumulation of trehalose. The survival rate of DJ3 at -20 °C reduced after silencing of any of these trehalose metabolism genes. Further analysis suggested that two trehalose synthesis genes were highly correlated with DJ3 and might be involved in autophagy by regulating with energy conversion related genes. Conclusions The above results indicated that trehalose metabolism promotes DJ3 formation and helps DJ3 survive at -20 °C. Although trehalose accumulation is favorable for DJ3 to cope with low-temperature stress, multiple trehalose metabolism genes need to work together. There may be a multi-path regulated physiological process involving trehalose synthesis genes under low-temperature stress resistance. This physiological process may regulate the formation and maintenance of DJ3 through autophagy and energy conversion.