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Aim To apply network pharmacology, molecular docking, and in vitro experimental techniques to predict as well as verify the antidepressant pharmacological mechanisms of Mengyao Anshen Buxin Liuwei pills. Methods TCMSP database and literature mining were used to obtain the active ingredients of Anshen Buxin six flavor pills , Swiss Target Prediction was applied to predict the ingredient related target information, and Cytoscape was employed to construct a medicinal herb ingredient target network. Depression targets were retrieved through GeneCards , Drugbank , OMIM and other databases. STRING database was used to obtain protein interaction relationship network information. DAVID database was used for GO biological process enrichment analysis and KEGG signaling pathway enrichment analysis. Autodock vina software was applied for molecular docking validation. In vitro injury model was established in BV2 microglial cells, cell viability was assessed by CCK-8 assay, and the mRNA expression of relevant core targets was assessed by qPCR. Results A total of 34 active components of Anshen Buxin Liuwei pills were screened, involving 140 potential targets and 59 core targets, involving 99 signaling pathways. Molecular docking results showed that betulinic acid, stigmasterol p-stiosterol 10 active components such as sitosterol and quercetin had good binding ability with AKT1, APP, ALB, MAPK3, VE GFA and MAPK 1 targets. The re suits in vitro showed that the activity of BV2 cells increased significantly compared with the model group. Anshen Buxin Liuwei pills could regulate the mRNA expression of each core target. Conclusion Anshen Buxin Liuwei pills may play an antidepressant role mainly through serotonin synaptic and other signaling pathways and related core targets.
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Objective: To analyze family-based haplotype frequencies of HLA-A, -B, -C, -DRB1 and -DQB1 genes and their clinical significance. Methods: The data of HLA genotyping in 3568 families undergoing related haploidentical transplantation between 2012 and 2017 at the First Affiliated Hospital of Soochow University were retrospectively evaluated. The HLA genotyping was performed by PCR amplification with sequence-based typing (PCR-SBT) and sequence-specific oligonucleotide probe (PCR-SSOP) methods. The family genetic analysis and haplotype frequencies were also investigated. Results: All the families were divided into 3 groups, including group1 of 1 422 entire families; group2 of 1 310 patients and either of their parents or one of their children; group3 of 836 patients and their HLA≥5/10 matched sibling donors. In the haplotypes with frequencies greater than 0.1% in group1+ group2, the frequency of A*11∶01-B*40∶01-C*03∶04-DRB1*11∶01-DQB1*03∶01, A*02∶07-B*51∶01-C*14∶02-DRB1*09:01-DQB1*03∶03 were significantly different between group1 and group2 (P=0.029, 0.033) . The frequency of A*11∶01-B*46∶01-C*01∶02∶01G-DRB1*09∶01-DQB1*03∶03 was significantly different between group1 and group3 (P=0.035) . The frequency of A*02∶01-B*40∶01-C*07∶02-DRB1*09∶01-DQB1*03∶03 was significantly different between group1 and group2 (P=0.034) , or group1 and group3 (P=0.034) . The frequency of A*24∶02-B*13∶01-C*03∶04-DRB1*12∶02-DQB1*03:01 was significantly different between group2 and group3 (P=0.046) . Conclusion: In this study, we summarize the prevalence of haplotype frequencies in terms of HLA-A, -B, -C, -DRB1 and-DQB1. Based on the database of family haplotype analysis, patients and donor candidates are sorted with matched HLA genotype while unmatched HLA haplotype. Even in patients without entire family information, HLA haplotype analysis assists in choosing the optimal related or unrelated donors.