Identification of gene modules and hub genes associated with Colletotrichum siamense infection in mango using weighted gene co-expression network analysis.

Colletotrichum siamense is a hemibiotrophic ascomycetous fungus responsible for mango anthracnose. The key genes involved in C. siamense infection remained largely unknown. In this study, we conducted weighted gene co-expression network analysis (WGCNA) of RNA-seq data to mine key genes involved in Colletotrichum siamense-mango interactions. Gene modules of Turquoise and Salmon, containing 1039 and 139 respectively, were associated with C. siamense infection, which were conducted for further analysis. GO enrichment analysis revealed that protein synthesis, organonitrogen compound biosynthetic and metabolic process, and endoplasmic reticulum-related genes were associated with C. siamense infection. A total of 568 proteins had homologs in the PHI database, 370 of which were related to virulence. The hub genes in each module were identified, which were annotated as O-methyltransferase (Salmon) and Clock-controlled protein 6 (Turquoise). A total of 24 proteins exhibited characteristics of SCRPs. By using transient expression in Nicotiana benthamiana, the SCRPs of XM_036637681.1 could inhibit programmed cell death (PCD) that induced by BAX (BCL-2-associated X protein), suggesting that it may play important roles in C. siamense infection. A mango-C. siamense co-expression network was constructed, and the mango gene of XM_044632979.1 (auxin-induced protein 15A-like) was positively associated with 5 SCRPs. These findings help to deepen the current understanding of necrotrophic stage in C. siamense infection.

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.

BACKGROUND: Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance. METHODS: We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China's most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment. RESULTS: Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy. CONCLUSIONS: We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.

Evaluating physiological changes of grass and semishrub species with seasonality for understanding the process of shrub encroachment in semiarid grasslands.

Shrub encroachment occurs worldwide, especially in arid and semiarid grasslands. Changes in soil water in different layers affect the process of shrub encroachment. Understanding the biological and physiological responses of plant species to shrub encroachment is essential for explaining shrub encroachment. The dominant species in six typical plant communities changed from Stipa bungeana Trin. to Artemisia ordosica Krasch., representing different shrub-encroached grasslands. The gravimetric soil water content (SWC) and enzyme and osmotic adjustment compounds of the dominant species across shrub encroachment stages and growing seasons were measured to explain the shrub encroachment. Results showed that SWC decreased and then increased during the growing seasons. With the process of shrub encroachment, SWC first increased, then decreased. With increasing soil depth, SWC increased or decreased. Across seasons with decreasing SWC, enzyme activity decreased and then increased, and malondialdehyde content and osmotic adjustment compounds increased. With the process of shrub encroachment, enzyme activity, malondialdehyde content and osmotic adjustment compounds increased or decreased. The two dominant species (S. bungeana and A. ordosica) enhanced their drought resistance abilities by regulating their antioxidant systems and osmotic adjustment compounds when soil water in a specific layer was not over the threshold. We recommend increasing the clay content to increase the water holding capacity in the surface soil layer to restore the zonal vegetation of S. bungeana.

Changes in levels of enzymes and osmotic adjustment compounds in key species and their relevance to vegetation succession in abandoned croplands of a semiarid sandy region.

Reclamation of cropland from grassland is regarded as a main reason for grassland degradation; understanding succession from abandoned cropland to grassland is thus crucial for vegetation restoration in arid and semiarid areas. Soil becomes dry when cropland is reverted to grassland, and enzyme and osmotic adjustment compounds may help plants to adapt to a drying environment. Croplands that were abandoned in various years on the Ordos Plateau in China, were selected for the analysis of the dynamics of enzymes and osmotic adjustment compounds in plant species during vegetation succession. With increasing number of years since abandonment, levels of superoxide dismutase increased in Stipa bungeana, first decreased and then increased in Lespedeza davurica and Artemisia frigida, and fluctuated in Heteropappus altaicus. Levels of peroxidase and catalase in the four species fluctuated; levels of proline, soluble sugar, and soluble protein either decreased or first increased and then generally decreased. According to a drought resistance index, the drought resistance of the four species was ranked in descending order as follows: S. bungeana > A. frigida > H. altaicus > L. davurica. The drought resistance ability of the different species was closely linked with vegetation succession from communities dominated by annual and biennial species (with main accompanying species of L. davurica and H. altaicus) to communities dominated by perennial species (S. bungeana and A. frigida) when soil became dry owing to increasing evapotranspiration after cropland abandonment. The restoration of S. bungeana steppe after cropland abandonment on the Ordos Plateau is recommended both as high-quality forage and for environmental sustainability.