Exploring the bioactive compounds of Feiduqing formula for the prevention and management of COVID-19 through network pharmacology and molecular docking

Background:To explore the effective chemical constituents of Feiduqing formula for prevention and treatment of coronavirus disease 2019(COVID-19).Methods:The compounds and action targets of twelve herbal medicines in Feiduqing formula were collected via Traditional Chinese Medicine Systems Pharmacology Database and Analytic Platform.The genes corresponding to the targets were queried through the UniProt database.The“herbal medicine-ingredient-target”network was established by Cytoscape software.The Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed by Database for Annotation,Visualization and Integrated Discovery.Molecular docking was used to analyze the binding force of core active compounds of Feiduqing formula with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and angiotensin converting enzyme II(ACE2).Results:The“herbal medicine-ingredient-target”network included 434 nodes and 1948 edges,including 222 components such as quercetin,kaempferol,luteolin,etc.The key targets are PTGS2,HSP90AA1,PTGS1,ESR1,AR,NOS2,etc.Gene Ontology function enrichment analysis revealed 2530 items,including RNA polymerase II-specific,response to oxidative stress,transcription factor activity,etc.Kyoto Encyclopedia of Genes and Genomes pathway enrichment screened 169 signal pathways,including Human cytomegalovirus infection,Kaposi sarcoma-associated herpesvirus infection,Hepatitis B,Hepatitis C,IL-17,TNF,etc.The results of molecular docking showed that quercetin,luteolin,β-sitosterol,stigmasterol and other core active compounds have a certain degree of affinity with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2.Conclusion:The active compounds of Feiduqing formula may have a therapeutic effect on COVID-19 pneumonia through the action on PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2,and regulating many signaling pathways.

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Premenstrual dysphoric disorder(PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations(CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types,respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.