An Integration of Network Pharmacology and Experimental Verification to Investigate the Mechanism of Guizhi to Treat Nephrotic Syndrome

Efficacy of Ganshuang granules on non-alcoholic fatty liver and underlying mechanism: a network pharmacology and experimental verification

OBJECTIVE

To investigate the potential pharmacological mechanisms of Ganshuang granules (, GSG) in treating non-alcoholic fatty liver (NAFLD).

METHODS

All the active components and targets of GSG were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Protein-Protein interaction network, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology function annotation of common targets were analyzed to predict the mechanisms of action of GSG in the treatment of NAFLD. Then, the mouse models of NAFLD were constructed in a diet-induced manner and treated with GSG. The levels of interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway-related proteins in the liver of mice in each group were measured by enzyme linked immunosorbent assay and Western blot, respectively.

RESULTS

Network pharmacology revealed a total of 159 potential targets of GSG for the treatment of NAFLD. Functional enrichment analysis indicated that the PI3K/AKT signaling pathway may be involved during GSG treatment of NAFLD. Further experiments showed that the significantly decreased alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total cholesterol, triglyceride and low-density lipoprotein cholesterol levels in NAFLD model mice serum after GSG treatment, as well as the expression levels of IL-6 and TNF-α in the liver. Furthermore, drug intervention increased the protein expression levels of phosphorylated-PI3K (P-PI3K) and P-AKT in the liver of the model group mice, and decreased the protein expression level of sterol regulatory element-binding protein 1.

CONCLUSION

We found that GSG is effective in treating NAFLD and the potential therapeutic targets may be involved in PI3K/AKT signaling pathway.

Qi-Po-Sheng-Mai granule ameliorates Ach-CaCl2 -induced atrial fibrillation by regulating calcium homeostasis in cardiomyocytes

BACKGROUND

Atrial fibrillation (AF) is one of the most common arrhythmias encountered in clinical settings. Currently, the pathophysiology of AF remains unclear, which severely limits the effectiveness and safety of medical therapies. The Chinese herbal formula Qi-Po-Sheng-Mai Granule (QPSM) has been widely used in China to treat AF. However, its pharmacological and molecular mechanisms remain unknown.

PURPOSE

The purpose of this study was to investigate the molecular mechanisms and potential targets of QPSM for AF.

STUDY DESIGN AND METHODS

The AF model was induced by Ach (66 μg/ml) and CaCl2 (10 mg/kg), and the dose of 0.1 ml/100 g was injected into the tail vein for 5 weeks. QPSM was administered daily at doses of 4.42 and 8.84 g/kg, and amiodarone (0.18 g/kg) was used as the positive control. The effect of QPSM on AF was assessed by electrocardiogram, echocardiography, and histopathological analysis. Then, we employed network pharmacology with single nucleus RNA sequencing (snRNA-Seq) to investigate the molecular mechanisms and potential targets of QPSM for AF. Furthermore, high performance liquid chromatography (HPLC) method was used for component analysis of QPSM, and molecular docking was used to verify the potential targets. Using the IonOptix single cell contraction and ion synchronization test equipment, single myocyte length and calcium ion variations were observed in real time. The expression levels of calcium Transporter-related proteins were detected by western blot and immunohistochemistry.

RESULTS

Based on an Ach-CaCl2-induced AF model, we found that QPSM treatment significantly reduced atrial electrical remodeling-related markers, such as AF inducibility and duration, and attenuated atrial dilation and fibrosis. Network pharmacology identified 52 active ingredients and 119 potential targets for QPSM in the treatment of AF, and 45 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched, among which calcium pathway had the greatest impact. Using single nucleus sequencing (snRNA-seq), we identified cardiomyocytes as the most differentially expressed in response to drug treatment, with nine differentially expressed genes enriched in calcium signaling pathways. High performance liquid chromatography and molecular docking confirmed that the core components of QPSM strongly bind to the key factors in the calcium signaling pathway. Additional experiments have shown that QPSM increases calcium transients (CaT) and contractility in the individual cardiomyocyte. This was accomplished by increasing the expression of CACNA1C and SERCA2a and decreasing the expression of CAMK2B and NCX1.

CONCLUSION

The present study has systematically elucidated the role of QPSM in maintaining calcium homeostasis in cardiomyocytes through the regulation of calcium transporters, which could lead to new drug development ideas for AF.

Exploration of the possible mechanisms of Ling Gui Zhu Gan decoction in nephrotic syndrome based on network pharmacology, molecular docking and molecular dynamics simulation

This study aimed to explore the possible mechanisms of Ling Gui Zhu Gan decoction (LGZGD) in the treatment of nephrotic syndrome (NS) using network pharmacology combined with molecular docking and molecular dynamics simulation. The active ingredients of LGZGD and their targets were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Swiss Target Prediction database. The NS targets were retrieved from Genecards, OMIM and Drugbank databases. Next, the intersecting targets of drug and disease were imported into the String database for protein-protein interaction network analysis, and the core targets were identified through topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed in the Metascape platform. Finally, molecular docking and molecular dynamics simulation were performed for further validation. The network analysis showed that 109 active ingredients of LGZGD were associated with 105 targets in NS. The key active ingredients (quercetin, kaempferol, naringenin, licochalcone A, formononetin, beta-sitosterol) and the core targets (IL6, AKT1, TNF, VEGFA, TP53, JUN, IL1B, CASP3, EGFR, and STAT3) were further identified. Enrichment analysis indicated that multiple biological processes and pathways, including AGE-RAGE, PI3K-Akt, JAK-STAT, and HIF-1 signaling pathways, might be regulated by LGZGD in the treatment of NS. Molecular docking and molecular dynamics simulation results further indicated that the key active ingredients of LGZGD could stably bind to the core targets through hydrogen bonding and hydrophobic interaction. This study demonstrates that the active ingredients of LGZGD may regulate multiple targets, biological processes and signaling pathways in NS. Our findings may provide a theoretical basis for further studies on LGZGD in the treatment of NS.

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Objectives

Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.

Methods

Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.

Results

Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.

Conclusion

A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Integrating Network Pharmacology and Transcriptomic Strategies to Explore the Pharmacological Mechanism of Hydroxysafflor Yellow A in Delaying Liver Aging

Aging affects the structure and function of the liver. Hydroxysafflor yellow A (HSYA) effectively improves liver aging (LA) in mice, but the potential mechanisms require further exploration. In this study, an integrated approach combining network pharmacology and transcriptomics was used to elucidate the potential mechanisms of HSYA delay of LA. The targets of HSYA were predicted using the PharmMapper, SwissTargetPrediction, and CTD databases, and the targets of LA were collected from the GeneCards database. An ontology (GO) analysis and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation of genes related to HSYA delay of LA were performed using the DAVID database, and Cytoscape software was used to construct an HSYA target pathway network. The BMKCloud platform was used to sequence mRNA from mouse liver tissue, screen differentially expressed genes (DEGs) that were altered by HSYA, and enrich their biological functions and signaling pathways through the OmicShare database. The results of the network pharmacology and transcriptomic analyses were combined. Then, quantitative real-time PCR (qRT-PCR) and Western blot experiments were used to further verify the prediction results. Finally, the interactions between HSYA and key targets were assessed by molecular docking. The results showed that 199 potentially targeted genes according to network pharmacology and 480 DEGs according to transcriptomics were involved in the effects of HSYA against LA. An integrated analysis revealed that four key targets, including HSP90AA1, ATP2A1, NOS1 and CRAT, as well as their three related pathways (the calcium signaling pathway, estrogen signaling pathway and cGMP-PKG signaling pathway), were closely related to the therapeutic effects of HSYA. A gene and protein expression analysis revealed that HSYA significantly inhibited the expressions of HSP90AA1, ATP2A1 and NOS1 in the liver tissue of aging mice. The molecular docking results showed that HSYA had high affinities with the HSP90AA1, ATP2A1 and NOS1 targets. Our data demonstrate that HSYA may delay LA in mice by inhibiting the expressions of HSP90AA1, ATP2A1 and NOS1 and regulating the calcium signaling pathway, the estrogen signaling pathway, and the cGMP-PKG signaling pathway.

Experimental Study on the Mechanism of Cinnamaldehyde Ameliorate Proteinuria Induced by Adriamycin

Objective. Cinnamaldehyde (CA) is the main active component of Guizhi (Cinnamomi ramulus) to ameliorate adriamycin- (ADR-) induced proteinuria in rats. However, the underlying mechanism of CA against proteinuria remains unclear. The aim of this study was to investigate the action mechanisms of CA to treat proteinuria. Methods. 13 rats were randomly selected from 78 SD rats as control group, and the other rats were injected with ADR (3 mg/kg/time) twice through tail vein on day 1 and day 8 for modeling. After modeling, the rats were randomly divided into 5 groups as follows: ADR group, ADR+CA low-dose group, ADR+CA middle-dose group, ADR+CA high-dose group and Benazepril group with 13 rats in each group. The urine of SD rats was collected for 24 h, urine protein, creatinine and urea nitrogen were detected, renal index was calculated, and HE staining and western blot were performed. Results. The 24 h urine volume and urine protein, renal function, and renal histopathology got worse significantly in the ADR group. To western blot, CA downregulated the protein expression of ACE and Ang-2 and upregulated the protein expression of ACE2 in RAS signaling pathway. Conclusion. The underlying action mechanism of CA to treat NS might mainly be achieved by regulating RAS signaling pathway.