RESUMEN
To investigate the mechanism of Wenshen Xuanbi Decoction (WSXB) in treating osteoarthritis (OA) via network pharmacology, bioinformatics analysis, and experimental verification. The active components and prediction targets of WSXB were obtained from the TCMSP database and Swiss Target Prediction website, respectively. OA-related genes were retrieved from GeneCards and OMIM databases. Protein-protein interaction and functional enrichment analyses were performed, resulting in the construction of the Herb-Component-Target network. In addition, differential genes of OA were obtained from the GEO database to verify the potential mechanism of WSXB in OA treatment. Subsequently, potential active components were subjected to molecular verification with the hub targets. Finally, we selected the most crucial hub targets and pathways for experimental verification in vitro. The active components in the study included quercetin, linolenic acid, methyl linoleate, isobergapten, and beta-sitosterol. AKT1, tumor necrosis factor (TNF), interleukin (IL)-6, GAPDH, and CTNNB1 were identified as the most crucial hub targets. Molecular docking revealed that the active components and hub targets exhibited strong binding energy. Experimental verification demonstrated that the mRNA and protein expression levels of IL-6, IL-17, and TNF in the WSXB group were lower than those in the KOA group (p < 0.05). WSXB exhibits a chondroprotective effect on OA and delays disease progression. The mechanism is potentially related to the suppression of IL-17 and TNF signaling pathways and the down-regulation of IL-6.
RESUMEN
BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. RESULTS: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. CONCLUSIONS: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO .