ABSTRACT
As a consequence of the tight linkages between plants, soil, and microorganisms, we hypothesized the variations in plant species would change soil and microbial stoichiometry. Here, we examined the plant leaf carbon (C):nitrogen (N):phosphorus (P) ratios of nine species coming from three plant functional groups (PFGs) in the riparian zones of Hulunbuir steppe during near-peak biomass. The soil C:N:P, microbial biomass carbon (MBC):microbial biomass nitrogen (MBN), and extracellular enzyme's C:N:P were also assessed using the soils from each species. We found that plant tissue, soil nutrient, microbial, and enzyme activity stoichiometry significantly differed among different PFGs. Plant leaf and soil nutrient ratios tended to be similar (p > 0.05) between different species within the same PFGs. The variations in leaf C:N:P significantly correlated with the changes in soil C:N:P and MBC:MBN ratios. The homeostatic coefficients (H) < 1 suggested the relationships between plants and their resources C:N:P ratios might be non-homeostatic in the examined riparian zone. By assessing plant tissue and its soil nutrient stoichiometry, this study provided a perspective to understand the linkages of plant community, soil nutrient, and microbial characteristics.
ABSTRACT
BACKGROUND: Patients with major depressive disorder (MDD) have an increased risk of breast cancer (BC), implying that these two diseases share similar pathological mechanisms. This study aimed to identify the key pathogenic genes that lead to the occurrence of both triple-negative breast cancer (TNBC) and MDD. METHODS: Public datasets GSE65194 and GSE98793 were analyzed to identify differentially expressed genes (DEGs) shared by both datasets. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape to identify key PPI genes using cytoHubba. Hub DEGs were obtained from the intersection of hub genes from a PPI network with genes in the disease associated modules of the Weighed Gene Co-expression Network Analysis (WGCNA). Independent datasets (TCGA and GSE76826) and RT-qPCR validated hub gene expression. RESULTS: A total of 113 overlapping DEGs were identified between TNBC and MDD. The PPI network was constructed, and 35 hub DEGs were identified. Through WGCNA, the blue, brown, and turquoise modules were recognized as highly correlated with TNBC, while the brown, turquoise, and yellow modules were similarly correlated with MDD. Notably, G3BP1, MAF, NCEH1, and TMEM45A emerged as hub DEGs as they appeared both in modules and PPI hub DEGs. Within the GSE65194 and GSE98793 datasets, G3BP1 and MAF exhibited a significant downregulation in TNBC and MDD groups compared to the control, whereas NCEH1 and TMEM45A demonstrated a significant upregulation. These findings were further substantiated by TCGA and GSE76826, as well as through RT-qPCR validation. CONCLUSIONS: This study identified G3BP1, MAF, NCEH1 and TMEM45A as key pathological genes in both TNBC and MDD.
