Anti- Vibrio parahaemolyticus compounds from Streptomyces parvus based on Pan-genome and subtractive proteomics

Front Microbiol. 2023 Jul 6:14:1218176. doi: 10.3389/fmicb.2023.1218176. eCollection 2023.

Abstract

Introduction: Vibrio parahaemolyticus is a foodborne pathogen commonly found in seafood, and drug resistance poses significant challenges to its control. This study aimed to identify novel drug targets for antibacterial drug discovery.

Methods: To identify drug targets, we performed a pan-genome analysis on 58 strains of V. parahaemolyticus genomes to obtain core genes. Subsequently, subtractive proteomics and physiochemical checks were conducted on the core proteins to identify potential therapeutic targets. Molecular docking was then employed to screen for anti-V. parahaemolyticus compounds using a in-house compound library of Streptomyces parvus, chosen based on binding energy. The anti-V. parahaemolyticus efficacy of the identified compounds was further validated through a series of experimental tests.

Results and discussion: Pangenome analysis of 58 V. parahaemolyticus genomes revealed that there were 1,392 core genes. After Subtractive proteomics and physiochemical checks, Flagellar motor switch protein FliN was selected as a therapeutic target against V. parahaemolyticus. FliN was modeled and docked with Streptomyces parvus source compounds, and Actinomycin D was identified as a potential anti-V. parahaemolyticus agent with a strong binding energy. Experimental verification confirmed its effectiveness in killing V. parahaemolyticus and significantly inhibiting biofilm formation and motility. This study is the first to use pan-genome and subtractive proteomics to identify new antimicrobial targets for V. parahaemolyticus and to identify the anti-V. parahaemolyticus effect of Actinomycin D. These findings suggest potential avenues for the development of new antibacterial drugs to control V. parahaemolyticus infections.

Keywords: Streptomyces parvus; Vibrio parahaemolyticus; bioflim; pan-genome; subtractive proteomics.