Reveals of quercetin’s therapeutic effects on oral lichen planus based on network pharmacology approach and experimental validation

Zhao, Zichen; Ling-lin, Wang; Zhang, Mengna; Zhou, Chun‐Gao; Wang, Yanting; Ma, Jiang-Min; Yuan, Fan

doi:10.1038/s41598-022-04769-z

Cited by 16 publications

(13 citation statements)

References 49 publications

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“…Besides, anti-inflammatory and antioxidant aspects of QCT and its derivatives have been well recognized [ 18 ]. The recent network pharmacology study of QCT showed that the therapeutic effect on oral lichen planus was modulated by the inflammatory factor IL6 [ 19 ]. In terms of bone homeostasis, QCT could promote new local bone formations and could be applied to a bone-graft material [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Quercetin Anti-Osteoporosis Pharmacological Mechanisms with In Silico and In Vivo Models

Yuan

Cai

et al. 2022

Life

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Since osteoporosis critically influences the lives of patients with a high incidence, effective therapeutic treatments are important. Quercetin has been well recognized as a bone-sparing agent and thus the underlying mechanisms warrant further investigation. In the current study, the network pharmacology strategy and zebrafish model were utilized to explain the potential pharmacological effects of quercetin on osteoporosis. The potential targets and related signaling pathways were explored through overlapping target prediction, protein–protein interaction network construction, and functional enrichment analysis. Furthermore, we performed docking studies to verify the specific interactions between quercetin and crucial targets. Consequently, 55 targets were related to osteoporosis disease among the 159 targets of quercetin obtained by three database sources. Thirty hub targets were filtered through the cytoNCA plugin. Additionally, the Gene Ontology functions in the top 10 respective biological processes, molecular functions, and cell components as well as the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were depicted. The most significance difference in the KEGG pathways was the TNF signaling pathway, consisting of the Nuclear Factor Kappa B Subunit (NF-κB), Extracellular Regulated Protein Kinases (ERK) 1/2, Activator Protein 1 (AP-1), Interleukin 6 (IL6), Transcription factor AP-1 (Jun), and Phosphatidylinositol 3 Kinase (PI3K), which were probably involved in the pharmacological effects. Moreover, molecular docking studies revealed that the top three entries were Interleukin 1 Beta (IL1B), the Nuclear Factor NF-Kappa-B p65 Subunit (RelA), and the Nuclear Factor Kappa B Subunit 1 (NFKB1), respectively. Finally, these results were verified by alizarin red-stained mineralized bone in zebrafish and related qPCR experiments. The findings probably facilitate the mechanism elucidation related to quercetin anti-osteoporosis action.

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Section: Introductionmentioning

confidence: 99%

Exploring Quercetin Anti-Osteoporosis Pharmacological Mechanisms with In Silico and In Vivo Models

Yuan

Cai

et al. 2022

Life

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“…Recently, network science has found new applications in medical and drug discovery research [74]. Network pharmacology along with the system biology paradigm have both provided a breakthrough in drug discovery research by establishing the relationship between drug molecules and disease, and the potential therapeutic targets [75]. The development of multi-target therapeutics drug compounds is likely to be a promising paradigm for discovering drugs and offer a greater efficacy level with minimal or no adverse effects and toxicity compared to monotheraputic drug compounds [76].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Weighted Network Centrality Approach for Exploring Mechanisms of Action of the RP-RB Herbal Formula for Treating Rheumatoid Arthritis

Ochieng

Dombi

Hussain

2022

Preprint

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Aim : This study presents an efficient weighted network centrality measureapproach and its application in network pharmacology for exploring mechanismsof action of the Ruellia prostrata (RP) and Ruellia bignoniiflora (RB) herbalformula for treating rheumatoid arthritis. Method: A tripartite network of RP-RB compositive compound-knowntherapeutic targets-putative target genes was first built. The interconnectivityscore was used to prioritizes network candidate network components based ontheir overall connectivity with shared neighborhood network components. A newinterconnectivity based weighted centrality score calculated from traditionalnetwork centrality measure was used identify and explore relationships of themajor network components. The functionality of our proposed method wasdemonstrated by an application in network pharmacology. Result : Network pharmacology results revealed that out of 196 compounds 22were actively interacting with known rheumatoid arthritis therapeutic targets andputative target genes. A multi-level network simulation identifies 33 majorcandidate targets including 8 compositive compounds, 10 therapeutic targets and15 putative target genes. The gene ontology and KEGG pathway enrichmenttarget validation demonstrated that candidate putative target genes were frequently involved in TNF, CCR5, IL-17 and G-protein coupled receptorssignaling pathways which are critical in the progression of rheumatoid arthritis.The molecular docking simulation and molecular descriptor analysis indicatedthat Glyceryl diacetate-2-Oleate, Hexadecanoic acid, 2,3-bis(acetyloxy)propylester, Glycidyl oleate, and Oleoyl chloride had a high binding affinity on theirmajor targets with their molecular descriptor properties indicating their potentialdrug likeliness for the treatment of rheumatoid arthritis. Conclusion : The findings of this study provides promising results that couldlead to design and discovery alternative drug compounds for management andtreatment of rheumatoid arthritis. Although our approach is purely based on insilico method, clinical experiments are still needed to test and validate thehypotheses of our computational methods.

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“…In Figure 9, four transcription factors were reported (i.e., RELA, STAT3, TP53, STAT1). TP53 has been reported to be overexpressed in OLP [16,62] and related to malignant transformation in OLP [63].…”

Section: Methods Rank Term Overlap P-valuementioning

confidence: 99%

GENEvaRX: A Novel AI-Driven Method and Web Tool Can Identify Critical Genes and Effective Drugs for Lichen Planus

Turki

Taguchi

2023

Preprint

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Lichen planus (LP) is an autoimmune disorder diagnosed based on physical symptoms and lab tests. Examples of symptoms include flat bumps, and itchy and purplish skin, while lab tests include a shave biopsy of the lesion. When the pathology report shows consistency with LP and is negative for potential triggers for an allergy test and hepatitis C, a dermatologist typically prescribes corticosteroid in the form of pills or injection into the lesion to treat the symptoms. To understand the molecular mechanism of the disease and thereby overcome issues associated with disease treatment, there is a need to identify potential effective drugs, drug targets, and therapeutic targets associated the LP. Hence, we propose a novel computational framework based on new constrained optimization to support vector machines coupled with enrichment analysis. First, we downloaded three gene expression datasets (GSE63741, GSE193351, GSE52130) pertaining to healthy and LP patients from the gene expression omnibus (GEO) database. We then processed each dataset and entered it into our computational framework to select important genes. Finally, we performed enrichment analysis of selected genes, reporting the following results. Our methods outperformed baseline methods in terms of identifying disease and skin tissue. Moreover, we report 5 drugs (including, dexamethasone, retinoic acid, and quercetin), 45 unique genes (including PSMB8, KRT31, KRT16, KRT19, KRT17, COL3A1, LCE2D, LCE2A), and 23 unique TFs (including NFKB1, STAT1, STAT3) reportedly related to LP pathogenesis, treatments, and therapeutic targets. Our methods are publicly available in the GENEvaRX web server at https://aibio.shinyapps.io/GENEvaRX/.

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Reveals of quercetin’s therapeutic effects on oral lichen planus based on network pharmacology approach and experimental validation

Cited by 16 publications

References 49 publications

Exploring Quercetin Anti-Osteoporosis Pharmacological Mechanisms with In Silico and In Vivo Models

Exploring Quercetin Anti-Osteoporosis Pharmacological Mechanisms with In Silico and In Vivo Models

An Efficient Weighted Network Centrality Approach for Exploring Mechanisms of Action of the RP-RB Herbal Formula for Treating Rheumatoid Arthritis

GENEvaRX: A Novel AI-Driven Method and Web Tool Can Identify Critical Genes and Effective Drugs for Lichen Planus

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