Network Pharmacology-Based Strategy for Exploring the Pharmacological Mechanism of Honeysuckle (Lonicer japonica Thunb.) against Newcastle Disease.

Abstract

Objective:

Newcastle disease causes huge economic losses in the global poultry industry. An efficient treatment is needed to deal with the variable immunogenicity of the Newcastle disease virus (NDV). This study utilized network pharmacology to study the potential therapeutic targets of Honeysuckle (Lonicer japonica Thunb.) against Newcastle disease.

Methods:

Venny online analysis was used to analyze the potential overlapping targets of Honeysuckle and Newcastle disease. Hub genes were obtained using the STRING database and Cytoscape 3.8.2 software. Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway enrichment analysis using the DAVID online tool were performed on these targets.

Results:

Twenty-five overlapping targets were identified. The PPI network construction results included 23 nodes of 25 genes and 95 edges. It was found that the IL-6 node had the largest degree. STAT1 and IRF1, CASP9, and CASP3 had the same as well as strongest interaction strengths. GO functions, such as "cytokine activity," had a regulatory effect on NDV. The "Toll-like receptor signaling Pathway" "Nod-like receptor signaling pathway," "RIG-I-like receptor signaling pathway," and "Apoptosis," which were obtained using KEGG analysis, also indicated that these pathways can act on NDV to enhance immune function.

Conclusions:

In this study, the potential targets and mechanisms of action of Honeysuckle against Newcastle disease were explored through network pharmacology, which provided a theoretical basis for the treatment of Newcastle disease and provided new ideas for the development of traditional Chinese medicine for the poultry industry.

• Authors: Yi Lu, Wen A Huang, Zhi B He, Shan Li, Jun Liu
• Journal: Evidence-based complementary and alternative medicine : eCAM (Evid Based Complement Alternat Med) Vol. 2022 Pg. 9265094 ( 2022) ISSN: 1741-427X [Print] United States
• PMID: 35422871 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Yi Lu et al.