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The biodiversity and activity of microorganisms are crucial for litter decomposition, but how litter traits at different stages of decomposition drive changes in microbial communities has yet to be thoroughly explored. In the typical alpine hilly area of the Qinghai–Tibet Plateau, three types of litter at different decomposition stages were selected under a natural Picea crassifolia (Picea crassifolia Kom.) forest: undecomposed (A-1), partially decomposed (A-2), and fully decomposed (A-3). By measuring physicochemical indicators, microbial diversity, and the composition of the litter at different decomposition stages, this study investigates the community changes and responses of bacteria to litter characteristic changes at different decomposition levels. The results show that with the increase in decomposition level, bacterial diversity increases, community structure changes, and network complexity gradually increases, while the changes in fungal communities are insignificant. Structural equation modeling indicates that the first principal component (PC1) of litter properties is significantly negatively correlated with bacterial diversity and positively correlated with bacterial community composition. There is no significant correlation between fungal diversity and community composition, indicating a closer relationship between bacteria and litter characteristics than fungi. In summary, with an increase in litter decomposition level, the diversity and network complexity of bacterial and fungal communities will significantly increase, which is related to the changes in various litter characteristics. This study provides a scientific basis for the regulatory mechanism of litter decomposition and turnover in the alpine hilly area of the Qinghai–Tibet Plateau, specifically in Picea crassifolia forests. [ABSTRACT FROM AUTHOR]

Background: The causative relationship between chronic rhinosinusitis (CRS) and depression remains unclear. Herein we employed Mendelian randomization (MR) coupled with single-cell analysis to investigate the causality between CRS and depression., Methods: Data pertaining to CRS and depression were mined from the genome-wide association study database, and a single-cell dataset was sourced from the literature. To explore causality, we conducted bidirectional MR analysis using MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, with IVW representing the most important method. Further, sensitivity analysis was performed to evaluate the robustness of MR analysis results. Candidate genes were analyzed via single-cell combined MR analysis., Results: Forward MR analysis indicated depression as a risk factor for CRS when depression was the exposure factor and CRS was the outcome (OR = 1.425, P < 0.001). Reverse MR analysis revealed the same positive relationship between CRS and depression when CRS was the exposure factor and depression was the outcome (OR = 1.012, P = 0.038). Sensitivity analysis validated the robustness of bidirectional MR analysis results. Ten cell types (endothelial, ciliated, basal, myeloid, mast, apical, plasma, glandular, fibroblast, and T cells) were identified in the single-cell dataset. The network of receptor-ligand pairs showed that in normal samples, cell-cell interactions were present among various cell types, such as epithelial, mast, myeloid, and endothelial cells. In contrast, CRS samples featured only one specific receptor-ligand pair, confined to myeloid cells. TCF4 and MEF2C emerged as potentially crucial for CRS-associated depression development., Conclusions: Our findings suggest a bidirectional causal relationship between CRS and depression, offering a new perspective on the association between CRS and depression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhou, Yang, Li, Jin, Zhang and Yu.)