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Integrating Protein Interaction and Pathway Crosstalk Network Reveals a Promising Therapeutic Approach for Psoriasis Through Apoptosis Induction

Overview
Journal Sci Rep
Specialty Science
Date 2024 Sep 27
PMID 39333640
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Abstract

Psoriasis is a complex inflammatory skin disease manifested by altered proliferation and differentiation of keratinocytes with dysfunctional apoptosis. This study aimed to identify regulatory factors and comprehend the underlying mechanisms of inefficient apoptosis to open up promising therapeutic approaches. Incorporating human protein interactions, apoptosis proteins, and physical relationships of psoriasis-apoptosis proteins helped us to generate a psoriasis-apoptosis interaction (SAI) network. Subsequently, topological and functional analyses of the SAI network revealed effective proteins, functional modules, hub motifs, dysregulated pathways and transcriptional gene regulatory factors. Network pharmacology, molecular docking and molecular dynamics simulation methods identified the potential drug-target interactions. RELA, MAPK1, MAPK3, MMP9, IL1B, AKT1 and STAT1 were revealed as effective proteins. The MAPK1-MAPK3-RELA motif was identified as a hub regulator in the crosstalk between 41 pathways. Among all pathways, "lipid and atherosclerosis" was found to be the predominant pathway. Acetylcysteine, arsenic-trioxide, β-elemene, bortezomib and curcumin were identified as potential drugs to inhibit pathway crosstalk. Experimental verifications were performed using the literature search, GSE13355 and GSE14905 microarray datasets. Drug-protein-pathway interactions associated with apoptosis were deciphered. These findings highlight the role of hub motif-mediated pathway-pathway crosstalk associated with apoptosis in the complexity of psoriasis and suggest crosstalk inhibition as an effective therapeutic approach.

References
1.
Mach N, Plancade S, Pacholewska A, Lecardonnel J, Riviere J, Moroldo M . Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse. Sci Rep. 2016; 6:22932. PMC: 4785432. DOI: 10.1038/srep22932. View

2.
Mirzadeh A, Kobakhidze G, Vuillemot R, Jonic S, Rouiller I . In silico prediction, characterization, docking studies and molecular dynamics simulation of human p97 in complex with p37 cofactor. BMC Mol Cell Biol. 2022; 23(1):39. PMC: 9464413. DOI: 10.1186/s12860-022-00437-2. View

3.
Han H, Cho J, Lee S, Yun A, Kim H, Bae D . TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2017; 46(D1):D380-D386. PMC: 5753191. DOI: 10.1093/nar/gkx1013. View

4.
Bader G, Hogue C . An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003; 4:2. PMC: 149346. DOI: 10.1186/1471-2105-4-2. View

5.
Dutta K, Elmezayen A, Al-Obaidi A, Zhu W, Morozova O, Shityakov S . Seq12, Seq12m, and Seq13m, peptide analogues of the spike glycoprotein shows antiviral properties against SARS-CoV-2: An study through molecular docking, molecular dynamics simulation, and MM-PB/GBSA calculations. J Mol Struct. 2021; 1246:131113. PMC: 8283670. DOI: 10.1016/j.molstruc.2021.131113. View