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

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

Phospholipids play an essential role as a barrier between cell content and the extracellular environment and regulate various cell signaling processes. Phosphatidylcholine (PtdCho) is one of the most abundant phospholipids in plant, animal, and some prokaryote cell membranes. In plants and some parasites, the biosynthesis of PtdCho begins with the amino acid serine, followed mainly through a phosphoethanolamine N-methyltransferase (PMT)-mediated biosynthetic pathway to phosphocholine (pCho). Because the PMT-mediated pathway, referred to as the phosphobase methylation pathway, produces a series of important primary and specialized metabolites for plant development and stress response, understanding the PMT enzyme is a key aspect of engineering plants with improved stress tolerance and fortified nutrients. Importantly, given the very limited phylogenetic distribution of PMTs, functional analysis and the identification of inhibitors targeting PMTs have potential and positive impacts in humans and in veterinary and agricultural fields. Here, we describe detailed basic knowledge and practical research methods to enable the systematic study of the biochemical and biophysical functions of PMT. The research methods described in this chapter are also applicable to the studies of other ubiquitous S-adenosyl-l-methionine (SAM)-dependent methyltransferases in all kingdoms.

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.