Identification of Drug Candidates to Suppress Cigarette Smoke-induced Inflammation via Connectivity Map Analyses

Active Components of Wen Fei Fu Yang Qu Tan Fang and its Molecular Targets for Chronic Obstructive Pulmonary Disease Based on Network Pharmacology and Molecular Docking

To investigate the mechanism of Wen Fei Fu Yang Qu Tan Fang (WFFYQTF) in the treatment of chronic obstructive pulmonary disease (COPD) using network pharmacology and pharmacodynamics. The TCMSP database was utilized to identify the chemical components and molecular targets of WFFYQTF. Cytoscape software was employed to construct a "drug component-target" network. COPD risk genes and intersecting molecular targets of WFFYQTF were identified using GeneCards, OMIM, and DisGeNET databases. The STRING website was the place where protein-protein interaction (PPI) analysis was performed. Cytoscape topological analysis was applied for screening out key targets of WFFYQTF. GO and KEGG enrichment analyses were conducted using the DAVID database to elucidate the treatment targets of COPD with WFFYQTF. A total of 136 active components of WFFYQTF were identified, including key components such as quercetin, kaempferol, and luteolin, which were found to be particularly significant. Additionally, 412 drug targets and 7121 COPD risk genes were screened out, and 323 treatment targets of COPD with WFFYQTF were determined by Wayne analysis. Core targets identified via PPI analysis included SRC, STAT3, AKT1, HSP90AA1, and JUN. Pathways such as the hypoxia responce, inflammatory response, PI3K/AKT pathway, TH17 pathway and MAPK pathway were obtained with GO and KEGG enrichment analyses. Molecular docking results suggested that quercetin could be soundly bound to STAT3 and AKT1, and kaempferol to SRC. WFFYQTF can effectively impede COPD progression through the coordinated action of multiple components, targets, and pathways during treatment.

Mechanisms Underlying the Therapeutic Effects of JianPiYiFei II Granules in Treating COPD Based on GEO Datasets, Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations

BACKGROUND

JianPiYiFei (JPYF) II granules are a Chinese medicine for the treatment of chronic obstructive pulmonary disease (COPD). However, the main components and underlying mechanisms of JPYF II granules are not well understood. This study aimed to elucidate the potential mechanism of JPYF II granules in the treatment of COPD using network pharmacology, molecular docking, and molecular dynamics simulation techniques.

METHODS

The active compounds and corresponding protein targets of the JPYF II granules were found using the TCMSP, ETCM, and Uniport databases, and a compound-target network was constructed using Cytoscape3.9.1. The COPD targets were searched for in GEO datasets and the OMIM and GeneCards databases. The intersection between the effective compound-related targets and disease-related targets was obtained, PPI networks were constructed, and GO and KEGG enrichment analyses were performed. Then, molecular docking analysis verified the results obtained using network pharmacology. Finally, the protein-compound complexes obtained from the molecular docking analysis were simulated using molecular dynamics (MD) simulations.

RESULTS

The network pharmacological results showed that quercetin, kaempferol, and stigmasterol are the main active compounds in JPYF II granules, and AKT1, IL-6, and TNF are key target proteins. The PI3K/AKT signaling pathway is a potential pathway through which the JPYF II granules affect COPD. The results of the molecular docking analysis suggested that quercetin, kaempferol, and stigmasterol have a good binding affinity with AKT1, IL-6, and TNF. The MD simulation results showed that TNF has a good binding affinity with the compounds.

CONCLUSIONS

This study identified the effective compounds, targets, and related underlying molecular mechanisms of JPYF II granules in the treatment of COPD through network pharmacology, molecular docking, and MD simulation techniques, which provides a reference for subsequent research on the treatment of COPD.

A synergistic mechanism of Liquiritin and Licochalcone B from Glycyrrhiza uralensis against COPD

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is a refractory respiratory disease mainly attributed to multiple pathological factors such as oxidative stress, infectious inflammation, and idiopathic fibrosis for decades. The medicinal plant Glycyrrhiza uralensis extract (ULE) was widely used to control respiratory diseases in China. However, the regulatory mechanism of scientific evidence to support the therapeutic benefits of ULE in the management of COPD is greatly limited.

PURPOSE

This study aims to discover the potential protection mechanism of ULE on COPD via a muti-targets strategy.

STUDY DESIGN AND METHODS

The present study set out to determine the potential protective effects of ULE on COPD through a multi-target strategy. In vivo and in vitro models of COPD were established using cigarette smoke and lipopolysaccharide to assess the protective effects of ULE. It was evaluated by measuring inflammatory cytokines and assessing pulmonary pathological changes. HPLC was used to verify the active compounds of the potential compounds that were collected and screened using HERB, works of literature, and ADME tools. The mechanisms of ULE in the treatment of COPD were explored using transcriptomics, connectivity-map, and network pharmacology approaches. The relevant targets were further investigated using RT-PCR, western blot, and immunohistochemistry. The HCK inhibitor (iHCK-37) was used to evaluate the potential mechanism of ULE's active compounds in the prevention of COPD.

RESULTS

ULE effectively protected the lungs of COPD mice from oxidative stress, inflammation, and fibrosis damage. After screening and verification using ADME properties and HPLC, 4 active compounds were identified in ULE: liquiritin (LQ), licochalcone B (LCB), licochalcone A (LCA), and echinatin (ET). Network pharmacology integrated with transcriptomics analysis showed that ULE mitigated oxidative stress, inflammation, and fibrosis in COPD by suppressing HCK. The combination of LCB and LQ was optimized for anti-inflammation, antioxidation, and anti-fibrosis activities. The iHCK-37 further validated the preventive treatment of LCB and LQ on COPD by inhibiting HCK to exert antioxidant, anti-inflammatory, and anti-fibrotic effects. The combination of LCB and LQ, in a 1:1 ratio, exerted synergistic antioxidative, anti-inflammatory, and anti-fibrotic effects in the treatment of COPD by downregulating HCK.

CONCLUSION

The combination of LCB and LQ performed a significant anti-COPD effect via downregulating HCK.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

Diterpenoid Vinigrol specifically activates ATF4/DDIT3-mediated PERK arm of unfolded protein response to drive non-apoptotic death of breast cancer cells

Vinigrol is a natural diterpenoid with unprecedented chemical structure, driving great efforts into its total synthesis in the past decades. Despite anti-hypertension and anti-clot ever reported, comprehensive investigations on bioactions and molecular mechanisms of Vinigrol are entirely missing. Here we firstly carried out a complete functional prediction of Vinigrol using a transcriptome-based strategy coupled with multiple bioinformatic analyses and identified "anti-cancer" as the most prominent biofunction ahead of anti-hypertension and anti-depression/psychosis. Broad cytotoxicity was subsequently confirmed on multiple cancer types. Further mechanistic investigation on several breast cancer cells revealed that its anti-cancer effect was mainly through activating PERK/eIF2α arm of unfolded protein response (UPR) and subsequent non-apoptotic cell death independent of caspase activities. The other two branches of UPR, IRE1α and ATF6, were functionally irrelevant to Vinigrol-induced cell death. Using CRISPR/Cas9-based gene activation, repression, and knockout systems, we identified the essential contribution of ATF4 and DDIT3, not ATF6, to the death process. This study unraveled a broad anti-cancer function of Vinigrol and its underlying targets and regulatory mechanisms. It paved the way for further inspection on the structure-efficacy relationship of the whole compound family, making them a novel cluster of PERK-specific stress activators for experimental and clinical uses.

Network pharmacology analysis uncovers the effect on apoptotic pathway by Bu-Fei formula for COPD treatment

ETHNOPHARMACOLOGICAL RELEVANCE

The Bu-Fei formula (BFF) has a positive effect on chronic obstructive pulmonary disease (COPD). However, its therapeutic mechanisms against COPD remain unknown.

AIM OF THE STUDY

To explore BFF's therapeutic effect on COPD and pharmacological mechanisms.

MATERIALS AND METHODS

First, the effect of BFF on rats with COPD was studied. Rats were randomly assigned to the blank, COPD, BFF treatment, and aminophylline (APL) treatment groups. From weeks 1-8, the COPD model was established by Klebsiella pneumoniae (KP) and cigarette smoke. Then, rats were given corresponding treatment for 8 weeks. The lung function of the rats was analyzed by whole-body plethysmography and pulmonary function testing, lung histopathology by electron microscopy and hematoxylin and eosin staining, and protein levels by immunohistochemistry. Next, the key components and targets of BFF in COPD were screened by network pharmacology analysis. Finally, the possible mechanism was verified through molecular docking and in vivo experiments.

RESULTS

BFF significantly improved lung function and lung histopathology in COPD rats and inhibit inflammation and collagen deposition in lung tissues. Also, 46 bioactive compounds and 136 BFF targets related to COPD were identified; among them, 3 compounds (quercetin, luteolin, and nobiletin) and 6 core targets (Akt1, BCL2, NF-κB p65, VEGFA, MMP9, and Caspase 8) were the key molecules associated with the mechanisms of BFF. The target enrichment analysis suggested that BFF's mechanisms might involve the apoptosis-related pathway; this possibility was supported by the molecular docking data. Lastly, BFF was indicated to increase the expression of core target genes and the production of apoptosis-related proteins.

CONCLUSIONS

BFF affects COPD by regulating the apoptosis-related pathways and targets.

LINCS Dataset-Based Repositioning of Dutasteride as an Anti-Neuroinflammation Agent

Neuroinflammation is often accompanied by central nervous system (CNS) injury seen in various CNS diseases, with no specific treatment. Drug repurposing is a strategy of finding new uses for approved or investigational drugs, and can be enabled by the Library of Integrated Network-based Cellular Signatures (LINCS), a large drug perturbation database. In this study, the signatures of Lipopolysaccharide (LPS) were compared with the signatures of compounds contained in the LINCS dataset. To the top 100 compounds obtained, the Quantitative Structure-Activity Relationship (QSAR)-based tool admetSAR was used to identify the top 10 candidate compounds with relatively high blood-brain barrier (BBB) penetration. Furthermore, the seventh-ranked compound, dutasteride, a 5-α-reductase inhibitor, was selected for in vitro and in vivo validation of its anti-neuroinflammation activity. The results showed that dutasteride significantly reduced the levels of IL-6 and TNF-α in the supernatants of LPS-stimulated BV2 cells, and decreased the levels of IL-6 in the hippocampus and plasma, and the number of activated microglia in the brain of LPS administration mice. Furthermore, dutasteride also attenuated the cognitive impairment caused by LPS stimulation in mice. Taken together, this study demonstrates that the LINCS dataset-based drug repurposing strategy is an effective approach, and the predicted candidate, dutasteride, has the potential to ameliorate LPS-induced neuroinflammation and cognitive impairment.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples

OBJECTIVE

To develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.

METHODS

The 236 molecular profiles from 800 de-identified human serum samples from the Department of Defense Serum Repository were classified as smokers or non-smokers based on direct measurement of serum cotinine levels. A machine-learning pipeline was used to classify smokers and non-smokers from their molecular profiles.

RESULTS

The refined supervised support vector machines with recursive feature elimination predicted smokers and non-smokers with 78% accuracy on the independent held-out set. Several of the identified classifiers of smoking status have previously been reported and four additional miRNAs were validated with experimental tobacco smoke exposure in mice, supporting the computational approach.

CONCLUSIONS

We developed and validated a pipeline that shows retrospective analysis of post-exposure serum samples can identify environmental exposures.

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.