Network pharmacology and molecular docking-based analysis of protective mechanism of MLIF in ischemic stroke

Mengting Lv; Qiuzhen Zhu; Xinyu Li; Shanshan Deng; Yuchen Guo; Junqing Mao; Yuefan Zhang

doi:10.3389/fcvm.2022.1071533

Network pharmacology and molecular docking-based analysis of protective mechanism of MLIF in ischemic stroke

Front Cardiovasc Med. 2022 Nov 18:9:1071533. doi: 10.3389/fcvm.2022.1071533. eCollection 2022.

Authors

Mengting Lv¹, Qiuzhen Zhu², Xinyu Li¹, Shanshan Deng¹, Yuchen Guo³, Junqing Mao⁴, Yuefan Zhang¹

Affiliations

¹ School of Medicine, Shanghai University, Shanghai, China.
² The Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ College of Pharmacology, Anhui University of Chinese Medicine, Hefei, China.
⁴ Department of Clinical Pharmacy, Jiading Branch of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Objective: This study aimed to evaluate the potential mechanism by which Monocyte locomotion inhibitory factor (MLIF) improves the outcome of ischemic stroke (IS) inflammatory injury.

Methods: Potential MLIF-related targets were predicted using Swiss TargetPrediction and PharmMapper, while IS-related targets were found from GeneCards, PharmGKB, and Therapeutic Target Database (TTD). After obtaining the intersection from these two datasets, the Search Tool for Retrieval of Interacting Genes/Protein (STRING11.0) database was used to analyze the protein-protein interaction (PPI) network of the intersection and candidate genes for MLIF treatment of IS. The candidate genes were imported into the Metascape database for Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The top 20 core genes and the "MLIF-target-pathway" network were mapped using the Cytoscape3.9.1. Using AutoDock Vina1.1.2, the molecular docking validation of the hub targets and MLIF was carried out. In the experimental part, transient middle cerebral artery occlusion (tMCAO) and oxygen and glucose deprivation (OGD) models were used to evaluate the protective efficacy of MLIF and the expression of inflammatory cytokines and the putative targets.

Results: MLIF was expected to have an effect on 370 targets. When these targets were intersected with 1,289 targets for ischemic stroke, 119 candidate therapeutic targets were found. The key enriched pathways were PI3K-Akt signaling pathway and MAPK signaling pathway, etc. The GO analysis yielded 1,677 GO entries (P < 0.01), such as hormone stimulation, inflammatory response, etc. The top 20 core genes included AKT1, EGFR, IGF1, MAPK1, MAPK10, MAPK14, etc. The result of molecular docking demonstrated that MLIF had the strong binding capability to JNK (MAPK10). The in vitro and in vivo studies also confirmed that MLIF protected against IS by lowering JNK (MAPK10) and AP-1 levels and decreasing pro-inflammatory cytokines (IL-1, IL-6).

Conclusion: MLIF may exert a cerebral protective effect by inhibiting the inflammatory response through suppressing the JNK/AP-1 signaling pathway.

Keywords: JNK; ischemic stroke (IS); molecular docking; monocyte locomotion inhibitory factor (MLIF); network pharmacology.