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Network Pharmacology-Based Mechanistic Investigation of Jinshui Huanxian Formula Acting on Idiopathic Pulmonary Fibrosis.

ABSTRACT: Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease with high incidence, morbidity, and mortality rates. Jinshui Huanxian formula (JHF) is an empirical formula that targets the pathogenesis of lung-kidney qi deficiency and phlegm-blood stasis in pulmonary fibrosis (PF). The purpose of this study was to explore JHF's potential pharmacological mechanisms in IPF therapy using network intersection analysis. JHF's primary active components and corresponding target genes were predicted using various databases. Two sets of IPF disease genes were obtained from the DisGeNET and GEO databases and two sets of IPF drug targets were collected. The disease and drug target genes were analyzed. The JHF target genes that intersected with IPF's differentially expressed genes were identified to predict JHF's targets of action in IPF. The functions and pathways of predicted targets acting on IPF were analyzed using the DAVID and KEGG pathway databases. Finally, the resulting drug target mechanisms were validated in a rat model of PF. The initial analyses identified 494 active compounds and 1,304 corresponding targets for JHF. The intersection analysis revealed four common genes for the JHF targets, IPF disease, and anti-IPF drugs in the KEGG database. Furthermore, these genes were targeted by several JHF compounds. Seventy-two JHF targets were closely related to IPF, which suggests that they are therapeutically relevant. Target screening revealed that they regulate IPF through 18 pathways. The targets' molecular functions included regulation of oxidoreductase activity, kinase regulator activity, phosphotransferase activity, and transmembrane receptor protein kinase activity. In vivo experiments showed that JHF alleviated the degree of PF, including decreases in collagen deposition and epithelial-mesenchymal transition. This study systematically explored JHF's mechanisms to identify the specific target pathways involved in IPF. The generated pharmacological network, paired with in vivo validation, elucidates the potential roles and mechanisms of JHF in IPF therapy.

SUBMITTER: Liu T

PROVIDER: S-EPMC8279870 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients' condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this study, a gas chromatography-mass spectrometry (GC-MS) metabonomics strategy was established to obtain metabolite information. A random forest algorithm was used to search for potential biomarkers of PQ poisoning, and data mining and network pharmacological analysis were used to evaluate the active components, drug-disease targets, and key pathways of Xuebijing (XBJ) injection in the treatment of PQ-induced pulmonary fibrosis. Targets from the network pharmacology analysis and metabolites from plasma metabolomics were jointly analyzed to select crucial metabolic pathways. Finally, molecular docking technology and in vitro experiments were used to verify the pathway targets to further reveal the potential mechanisms underlying the antipulmonary fibrosis effect of XBJ. Metabonomics studies showed that l-valine, glycine, citric acid, d-mannose, d-galactose, maltose, l-tryptophan, and arachidonic acid contributed more to the differentiation of different groups than other metabolites. Compared with the control group, the PQ poisoning group had higher levels of l-valine, glycine, citric acid, l-tryptophan, and arachidonic acid, and lower levels of d-mannose, d-galactose, and maltose. After treatment with XBJ injection, the relative levels of these metabolites were reversed. The network pharmacological analysis screened a total of 180 targets, mainly involving multiple signaling pathways and metabolic pathways, which jointly played an antipulmonary fibrosis effect. Based on the combined analysis of 180 targets and 8 different metabolites, arachidonic acid metabolism was selected as the key metabolic pathway. Molecular docking analysis showed that the XBJ compound had strong binding activity with the target protein. Western blot results showed that XBJ injection could reduce the inflammatory response by downregulating the expressions of p-p65, p-IKBα, and p-IKKβ, thus inhibiting the development of PQ-induced pulmonary fibrosis. In summary, the combined results from metabolomics and network pharmacology studies showed that Xuebijing has the characteristics of multitarget, multichannel, and multicomponent action in the treatment of pulmonary fibrosis caused by PQ.

Project description:ObjectiveTo elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.MethodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.ResultsThe ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.ConclusionIn this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a "multicompound, multitarget, and multipathway" manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

Project description:Overview: Idiopathic pulmonary fibrosis (IPF) is a disease caused by many factors, eventually resulting in lung function failure. Jinbei oral liquid (JBOL) is a traditional Chinese clinical medicine used to treat pulmonary diseases. However, the pharmacological effects and mechanism of the action of JBOL on IPF remain unclear. This study investigated the protective effects and mechanism of the action of JBOL on IPF using network pharmacology analysis, followed by in vivo and in vitro experimental validation. Methods: The components of JBOL and their targets were screened using the TCMSP database. IPF-associated genes were obtained using DisGeNET and Drugbank. The common targets of JBOL and IPF were identified with the STRING database, and a protein-protein interaction (PPI) network was constructed. GO and KEGG analyses were performed. Sprague-Dawley rats were injected with bleomycin (BLM) to establish an IPF model and treated orally with JBOL at doses of 5.4, 10.8, and 21.6 ml/kg. A dose of 54 mg/kg of pirfenidone was used as a control. All rats were treated for 28 successive days. Dynamic pulmonary compliance (Cdyn), minute ventilation volume (MVV), vital capacity (VC), and lung resistance (LR) were used to evaluate the efficacy of JBOL. TGF-β-treated A549 cells were exposed to JBOL, and epithelial-to-mesenchymal transition (EMT) changes were assessed. Western blots were performed. Results: Two hundred seventy-eight compounds and 374 targets were screened, and 103 targets related to IPF were identified. Core targets, including MAPK1 (ERK2), MAPK14 (p38), JUN, IL-6, AKT, and others, were identified by constructing a PPI network. Several pathways were involved, including the MAPK pathway. Experimentally, JBOL increased the levels of the pulmonary function indices (Cdyn, MVV, and VC) in a dose-dependent manner and reduced the RL level in the BLM-treated rats. JBOL increased the epithelial marker E-cadherin and suppressed the mesenchymal marker vimentin expression in the TGF-β-treated A549 cells. The suppression of ERK1/2, JNK, and p38 phosphorylation by JBOL was validated. Conclusion: JBOL had therapeutic effects against IPF by regulating pulmonary function and EMT through a systemic network mechanism, thus supporting the need for future clinical trials of JBOL.

Project description:The incidence of pulmonary fibrosis (PF), a progressively fatal disease, has increased in recent years. However, there are no effective medicines available. Previous results have shown that sinensetin probably has some curative effects on PF. Therefore, this paper aims to predict the targets of sinensetin using a network pharmacology method and to confirm its effects and functional targets in PF using a mouse PF model. First, network pharmacology analysis showed that sinensetin has 105 functional targets, and 1,698 gene targets closely relate to PF. The intersection of the functional targets and gene targets produced 52 targets for the treatment of PF with sinensetin. The PPIs (protein-protein interactions) led to several potential key target genes, including MAPK1, EGFR, SRC, and PTGS2. The results of GO and KEGG analyses suggested the crucial function of apoptosis in PF and its involvement in the PI3K signaling pathway. Subsequently, we tested the molecular docking of sinensetin with the PI3K protein using the AutoDock4 software. The results showed that sinensetin could fit well into several binding sites of the PI3K protein. Furthermore, we constructed a PF mouse model through one-off intratracheal instillation of bleomycin and then intragastrically administered different concentrations of sinensetin to the model mice. Twenty-eight days later, the mice were sacrificed, and the lung tissues, serum, and bronchoalveolar lavage fluid (BALF) were collected. The in vivo tests showed that the body weight of model mice increased slightly compared with that of PF mice after intragastric sinensetin. HE and Masson staining suggested a certain extent of reduction in the pathology of lung tissues. The expression of collagens I and III, as well as hydroxyproline in the lung tissues, was reduced to a certain extent. IL-6 levels in the serum and BALF decreased markedly. The expression of vimentin and α-SMA in pulmonary tissues decreased. Cell apoptosis, as well as P-PI3K and P-AKT levels, in lung tissues also reduced. In summary, network pharmacology and in vivo test results suggest sinensetin causes an effective delay in the progression of pulmonary fibrosis, and the functional mechanism is likely related to PI3K-AKT signaling.

Project description:BackgroundNo single pulmonary function test captures the functional effect of emphysema in idiopathic pulmonary fibrosis (IPF). Without experienced radiologists, other methods are needed to determine emphysema extent. Here, we report the development and validation of a formula to predict emphysema extent in patients with IPF and emphysema.MethodsThe development cohort included 76 patients with combined IPF and emphysema at the Royal Brompton Hospital, London, United Kingdom. The formula was derived using stepwise regression to generate the weighted combination of pulmonary function data that fitted best with emphysema extent on high-resolution computed tomography. Test cohorts included patients from two clinical trials (n = 455 [n = 174 with emphysema]; NCT00047645, NCT00075998) and a real-world cohort from the Royal Brompton Hospital (n = 191 [n = 110 with emphysema]). The formula is only applicable for patients with IPF and concomitant emphysema and accordingly was not used to detect the presence or absence of emphysema.ResultsThe formula was: predicted emphysema extent = 12.67 + (0.92 x percent predicted forced vital capacity) - (0.65 x percent predicted forced expiratory volume in 1 second) - (0.52 x percent predicted carbon monoxide diffusing capacity). A significant relationship between the formula and observed emphysema extent was found in both cohorts (R2 = 0.25, P < 0.0001; R2 = 0.47, P < 0.0001, respectively). In both, the formula better predicted observed emphysema extent versus individual pulmonary function tests. A 15% emphysema extent threshold, calculated using the formula, identified a significant difference in absolute changes from baseline in forced vital capacity at Week 48 in patients with baseline-predicted emphysema extent < 15% versus ≥ 15% (P = 0.0105).ConclusionThe formula, designed for use in patients with IPF and emphysema, demonstrated enhanced ability to predict emphysema extent versus individual pulmonary function tests.Trial registrationNCT00047645; NCT00075998.

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Network Pharmacology-Based Mechanistic Investigation of Jinshui Huanxian Formula Acting on Idiopathic Pulmonary Fibrosis.

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