Structural and biochemical analysis of phosphoethanolamine methyltransferase from the pine wilt nematode Bursaphelenchus xylophilus

Lee, Soon Goo; Chung, Michelle; DeMarsilis, Antea J.; Holland, Cynthia K.; Jaswaney, Rohit V.; Jiang, Cherry; Kroboth, Jakob H.P.; Kulshrestha, Kevin; Marcelo, Raymundo; Meyyappa, Vidhya M.; Nelson, Grant B.; Patel, Janki; Petronio, Alex J.; Powers, Samantha K.; Qin, Peter R.; Ramachandran, Mythili; Rayapati, Divya; Rincon, John A.; Rocha, Andréia Maria Camargos; Ferreira, Joan Gabriel Rodinho Nunes; Steinbrecher, Micah K.; Yao, Kaisen; Zhang, Eric; Zou, Angela J.; Gang, Margery; Sparks, Melanie; Cascella, Barrie; Cruz, Wilhelm S.; Jez, Joseph M.

doi:10.1016/j.molbiopara.2020.111291

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…While most research was focusing on understanding PWN outbreaks from physiological, molecular, or genetic studies [8][9][10], recent evidence indicates that those spatial distributions of PWN are also important in mitigating and controlling PWM outbreak. However, obtaining such information in a timely and accurate manner remains an open challenge [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Evaluation of the novel endophytic fungus Chaetomium ascotrichoides 1‐24‐2 from Pinus massoniana as a biocontrol agent against pine wilt disease caused by Bursaphelenchus xylophilus

Kamaruzzaman,

Zheng,

Zhou

et al. 2024

Pest Management Science

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BACKGROUNDBursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD), is an ever‐increasing threat to Pinus forests worldwide. This study aimed to develop biological control of PWD by the application of endophytic fungi isolated from healthy pine trees.RESULTSWe successfully isolated a novel endophytic fungal strain 1‐24‐2 from branches of healthy Pinus massoniana. The culture filtrates (CFs) of strain 1‐24‐2 exhibited strong nematicidal activity against Bursaphelenchus xylophilus, with a corrected mortality rate of 99.00%. Based on the morphological and molecular characteristics, the isolated strain 1‐24‐2 was identified as Chaetomium ascotrichoides. In the in‐planta assay, pine seedlings (2‐years‐old) treated with 1‐24‐2 CFs + pine wood nematode (T2) showed a significant control effect of 80%. A total of 24 toxic compounds were first identified from 1‐24‐2 CFs through gas chromatography–mass spectrometry (GC–MS) analysis, from which O‐methylisourea, 2‐chlorobenzothiazole, and 4,5,6‐trihydroxy‐7‐methylphthalide showed robust binding sites at Tyr119 against phosphoethanolamine methyltransferase (PMT) protein of Bursaphelenchus xylophilus by molecular docking approach and could be used as potential compounds for developing effective nematicides. Interestingly, strain 1‐24‐2 produces toxic volatile organic compounds (VOCs), which disturb the natural development process of B. xylophilus, whose total number decreased by up to 83.32% in the treatment group as compared to control and also reduced Botrytis cinerea growth by up to 71.01%.CONCLUSIONOur results highlight the potential of C. ascotrichoides 1‐24‐2 as a promising biocontrol agent with solid nematicidal activity against B. xylophilus. This is the first report of C. ascotrichoides isolated from P. massoniana exhibiting strong biocontrol potential against B. xylophilus in the world. © 2024 Society of Chemical Industry.

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Functional analysis of phosphoethanolamine N-methyltransferase in plants and parasites: Essential S-adenosylmethionine-dependent methyltransferase in choline and phospholipid metabolism

Bare¹,

Thomas²,

Etoroma³

et al. 2023

Biochemical Pathways and Environmental Responses in Plants: Part B

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Structural and biochemical analysis of phosphoethanolamine methyltransferase from the pine wilt nematode Bursaphelenchus xylophilus

Cited by 4 publications

References 39 publications

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

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

Evaluation of the novel endophytic fungus Chaetomium ascotrichoides 1‐24‐2 from Pinus massoniana as a biocontrol agent against pine wilt disease caused by Bursaphelenchus xylophilus

Functional analysis of phosphoethanolamine N-methyltransferase in plants and parasites: Essential S-adenosylmethionine-dependent methyltransferase in choline and phospholipid metabolism

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