Pine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entropy (MaxEnt) model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China. Further, the potential distribution was modeled using the current (1970–2000) and the projected (2050 and 2070) climate data based on two representative concentration pathways (RCP 2.6 and RCP 8.5), and fairly robust prediction results were obtained. Our model identified that the area south of the Yangtze River in China was the most severely affected place by pine wilt disease, and the eastern foothills of the Tibetan Plateau acted as a geographical barrier to pest distribution. Bioclimatic variables related to temperature influenced pine trees’ distribution, while those related to precipitation affected B. xylophilus’s distribution. In the future, the suitable area of B. xylophilus will continue to increase; the shifts in the center of gravity of the suitable habitats of the three pine species and B. xylophilus will be different under climate change. The area ideal for pine trees will migrate slightly northward under RCP 8.5. The pine species will continue to face B. xylophilus threat in 2050 and 2070 under the two distinct climate change scenarios. Therefore, we should plan appropriate measures to prevent its expansion. Predicting the distribution of pine species and the impact of climate change on forest diseases is critical for controlling the pests according to local conditions. Thus, the MaxEnt model proposed in this study can be potentially used to forecast the species distribution and disease risks and provide guidance for the timely prevention and management of B. xylophilus.
There is a bidirectional relationship between inflammatory bowel disease (IBD) and depression/anxiety. Emerging evidences indicate that the liver may be involved in microbiota-gut-brain axis. This experiment focused on the role of melatonin in regulating the gut microbiota and explores its mechanism on dextran sulphate sodium- (DSS-) induced neuroinflammation and liver injury. Long-term DSS-treatment increased lipopolysaccharide (LPS), proinflammation cytokines IL-1β and TNF-α, and gut leak in rats, breaking blood-brain barrier and overactivated astrocytes and microglia. Ultimately, the rats showed depression-like behavior, including reduction of sucrose preference and central time in open field test and elevation of immobility time in a forced swimming test. Oral administration with melatonin alleviated neuroinflammation and depression-like behaviors. However, melatonin supplementation did not decrease the level of LPS but increase short-chain fatty acid (SCFA) production to protect DSS-induced neuroinflammation. Additionally, western blotting analysis suggested that signaling pathways farnesoid X receptor-fibroblast growth factor 15 (FXR-FGF 15) in gut and apoptosis signal-regulating kinase 1 (ASK1) in the liver overactivated in DSS-treated rats, indicating liver metabolic disorder. Supplementation with melatonin markedly inhibited the activation of these two signaling pathways and its downstream p38. As for the gut microbiota, we found that immune response- and SCFA production-related microbiota, like Lactobacillus and Clostridium significantly increased, while bile salt hydrolase activity-related microbiota, like Streptococcus and Enterococcus, significantly decreased after melatonin supplementation. These altered microbiota were consistent with the alleviation of neuroinflammation and metabolic disorder. Taken together, our findings suggest melatonin contributes to reshape gut microbiota and improves inflammatory processes in the hippocampus (HPC) and metabolic disorders in the liver of DSS rats.
Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.
Dendrobium officinale is an important traditional Chinese medicinal plant and crop, which contains many kinds of medicinal components. The quality of medicinal plants is closely related to the ecological factors in a growing environment. The main components of D. officinale determined in this study were polysaccharides, total alkaloids and total flavonoids. In addition, this study dealt with the correlation of these components to 16 ecological factors under three different cultivation modes (Greenhouse, Bionic, Wild; Lu’an, Anhui Province, China). The relationship between ecological factors and quality factors was analyzed step by step using correlation analysis, principal component analysis and stepwise multiple linear regression. Eight ecological factors: maximum relative humidity, minimum relative humidity, maximum temperature, sunshine duration, soil pH, soil total nitrogen, soil total phosphorus and soil available phosphorus were considered as key factors that influenced the main medicinal qualities of cultivated D. officinale. This study provides an insight for exploring the complex relationship between ecological factors and D. officinale medicinal value in artificial cultivation.
1. Hyphantria cunea is a problematic invasive species that has caused considerable damage to agricultural and forestry ecosystems in China.2. We compared 19 bioclimate variables between its native country (the United States of America) and China to elucidate the environmental factors associated with its presence. Using present climate data and projected future climate data (2050 and 2070) based on two Representative Concentration Pathways (RCPs 2.6 and 8.5), we used MaxEnt to predict the suitable habitat areas for H. cunea in the USA and China. Using these analyses, we predicted the niche change of H. cunea in China using the 'ecospat' package in R.3. Our model predicts that climate change will generally increase the extent of suitable habitat in both China and the USA, although low latitude areas will be limited by future climate change. We also report low niche overlap between the USA and Chinese populations of H. cuena and significant differences in bioclimate variables associate with its presence in each country. Therefore, our results suggest H. cunea has adapted to several climatic conditions in China. This indicates the niche of H. cunea may shift and adapt to novel environmental conditions over the course of its spread.4. Overall, the analysis of the environmental characteristics, niche changes, and suitable areas of H. cunea provides a basis for pest control and management strategies aimed at preventing further spread. Our methods may also be used to study other similar invasive species.
Spartina alterniflora is a perennial herb native to the American Atlantic coast and is the dominant plant in coastal intertidal wetlands. Since its introduction to China in 1979, it has quickly spread along the coast and has caused various hazards. To control the further spread of S. alterniflora in China, we first reconstructed the history of the spread of S. alterniflora in its invasion and origin countries. We found that S. alterniflora spreads from the central coast to both sides of the coast in China, while it spreads from the west coast to the east coast in America. Furthermore, by comparing 19 environmental variables of S. alterniflora in its invasion and origin countries, it was found that S. alterniflora is more and more adaptable to the high temperature and dry environment in the invasion country. Finally, we predicted the suitable areas for this species in China and America using the maximum entropy (MaxEnt) model and ArcGIS. Overall, through analysis on the dynamic and trend of environmental characteristics during the invasion of S. alterniflora and predicting its suitable area in the invasion area, it guides preventing its reintroduction and preventing its further spread of the species has been found. It has reference significance for studying other similar alien plants and essential enlightening relevance to its invasion and spread in similar areas.
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