Effect of Icariin on Peripheral Blood Dendritic Cells and Th17/Treg Balance in Myocardial Remodeling Model of Dahl Salt-sensitive Rats / 中国实验方剂学杂志

ObjectiveTo investigate the effect of icariin （ICA）-mediated vitamin D system on peripheral blood dendritic cells （DCs） and helper T cells 17 （Th17）/regulatory T cells （Treg） balance in myocardial remodeling model of Dahl salt-sensitive rats. MethodFifty SPF Dahl salt-sensitive rats were divided into model group， vitamin D group （3×10-5 mg·kg-1·d-1）， and high-， medium-， and low-dose ICA groups （120， 60， 30 mg·kg-1·d-1）， and 10 Dahl salt-resistant rats were used as normal group. The myocardial remodeling model was established by feeding rats with a high-salt diet containing 8% NaCl. After six weeks of modeling， the normal group and the model group were given an equal volume of ultrapure water by gavage， and other groups were continuously administrated for six weeks. Cardiac echocardiography， hematoxylin-eosin （HE） staining， and Masson staining were used to observe the pathological changes in cardiac structure and fibrosis. The levels of serum 25（OH）D3， B-type N-terminal pro-brain natriuretic peptide （NT-ProBNP）， interleukin （IL）-17， transforming growth factor （TGF）-β1， IL-12， and IL-10 were detected by enzyme-linked immunosorbent assay （ELISA）. The phenotype of peripheral blood DCs and the ratio of Th17/Treg cells of rats were detected by flow cytometry. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to detect the mRNA and protein expressions of vitamin D receptor （VDR），1α-hydroxylase （CYP27B1）， and 24-hydroxylase （CYP24A1） in peripheral blood DCs of rats. ResultCompared with the control group， the rats in the model group had pathological changes such as disordered arrangement of myocardial cells and cytoplasmic hypertrophy and swelling. Myocardial collagen fibers proliferated significantly， and the arrangement of myocardial fibers was disordered. The levels of serum 25（OH）D3 and IL-10 were significantly decreased， and the levels of serum IL-17， TGF-β1， IL-6， IL-12， and NT-ProBNP were significantly increased （P<0.05）. The costimulatory molecules CD40， CD80， CD86， and MHC-Ⅱ were highly expressed in the peripheral blood DCs， and the expression of CD11 and CD11b was lower （P<0.05）. The proportion of Th17 cells in the peripheral blood was significantly increased， and the proportion of Treg cells was decreased. The ratio of Th17/Treg was increased （P<0.05）. The mRNA and protein expressions of CYP24A1 in peripheral blood DCs increased， and the mRNA and protein expressions of CYP27B1 and VDR decreased （P<0.05）. Compared with the model group， the arrangement of myocardial fibers in each drug administration group was relatively regular， and the swelling of myocardial cells was significantly reduced. The pathological morphology of myocardial tissue was improved to varying degrees. The pathological changes in myocardial tissue were improved and alleviated to varying degrees. The drug could reduce the serum levels of NT-ProBNP， IL-17， TGF-β1， IL-6， and IL-12 and increase the level of serum 25（OH）D3 and IL-10 （P<0.05）. The expression of costimulatory molecules CD40， CD80， CD86， and MHC-Ⅱ in the peripheral blood DCs of rats was decreased， and the expression of CD11 and CD11b molecules was increased （P<0.05）. The drug could reduce the proportion of Th17 cells in peripheral blood and the ratio of Th17/Treg cells and increase the proportion of Treg cells （P<0.05）. It could decrease the mRNA and protein expressions of CYP24A1 in peripheral blood DCs of rats and elevate the mRNA and protein expression of VDR and CYP27B1 （P<0.05）. ConclusionICA can regulate the phenotype of peripheral blood DCs and the ratio of Th17/Treg cells by regulating the vitamin D system and play a role in improving myocardial remodeling from the perspective of immune balance.

Mechanism of Epimedium intervention in heart failure based on network pharmacology and molecular docking technology.

To analyze the pharmacological mechanism of Epimedium in regulating heart failure (HF) based on the network pharmacology method, and to provide a reference for the clinical application of Epimedium in treating HF. Obtaining the main active ingredients and their targets of Epimedium through TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) database. Access to major HF targets through Genecards, OMIM, PharmGKB, Therapeutic Target Database, Drug Bank database. Protein interaction analysis using String platform and construction of PPI network. Subsequently, Cytoscape software was used to construct the "Epimedium active ingredient-heart failure target" network. Finally, the molecular docking is verified through the Systems Dock Web Site. The core active ingredients of Epimedium to regulate HF are quercetin, luteolin, kaempferol, etc. The core targets are JUN, MYC, TP53, HIF1A, ESR1, RELA, MAPK1, etc. Molecular docking validation showed better binding activity of the major targets of HF to the core components of Epimedium. The biological pathways that Epimedium regulates HF mainly act on lipid and atherosclerotic pathways, PI3K-Akt signaling pathway, and chemoattractant-receptor activation. And its molecular functions are mainly DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and neurotransmitter receptor activity. This study reveals the multi-component, multi-target and multi-pathway mechanism of action of Epimedium in regulating mental failure, and provides a basis for the clinical development and utilization of Epimedium to intervene in HF.

Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network.

Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an artificial neural network and validated its diagnostic efficacy in a public dataset. We calculated heart failure-related differentially expressed genes and obtained 24 candidate genes by random forest classification, and selected the top 6 genes as important genes for subsequent analysis. The prediction weights of the genes of interest were determined by the neural network model and the model scores were evaluated in 2 independent sample datasets (GSE16499 and GSE57338 datasets). Since the weights of RNA-seq predictions for constructing neural network models were theoretically more suitable for disease classification of RNA-seq data, the GSE57338 dataset had the best performance in the validation results. The diagnostic model derived from our study can be of clinical value in determining the likelihood of HF occurring through cardiac biopsy. In the meantime, we need to further investigate the accuracy of the diagnostic model based on the results of our study.