No abstract

No abstract

In Tibet, China, wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) has recently become one of the most destructive diseases on winter wheat. To identify races of the pathogen in Tibet, 261 isolates were obtained in 2010 and tested on seedlings of a standard set of 19 wheat indicator genotypes. Of the 261 isolates, 248 were identified as members of 19 known races (CYR17, CYR20, CYR21, CYR22, CYR23, CYR29, CYR31, CYR32, CYR33, Lov13-6, Su11-1, Su11-2, Su11-3, Su11-4, Su11-5, Su11-6, Su11-7, Su11-8, and Su11-13), and 13 identified as representatives of 4 new races. CYR32 and CYR33 were the most predominant. The number of races and their frequencies in Tibet were more similar to the fungal populations in Sichuan and Gansu provinces than to those in Yunnan, Qinghai, and Shaanxi provinces. The results suggest that Tibet is also a possible center of inoculum source and genetic variation for the stripe rust pathogen in addition to Sichuan and Gansu.

Two undescribed Tricholoma triterpenoids, namely tricholopardins C (1) and D (2), were isolated from the wild mushroom Tricholoma pardinum. Their structures with absolute configurations were elucidated by spectroscopic methods, as well as the single crystal X-ray diffraction. Compounds 1 and 2 were further obtained by chemical conversions from the known analogues. Compound 1 showed significant cytotoxicity to MCF-7 and Hela cell lines with IC50 values of 4.7 μM and 9.7 μM, respectively. Its mechanism of inducing MCF-7 cell apoptosis was studied briefly. Graphical Abstract

Background Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN. Methods Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated. Results A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine–cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb). Conclusion This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.