Silversol ® (a Colloidal Nanosilver Formulation) Inhibits Growth of Antibiotic-Resistant Staphylococcus aureus by Disrupting Its Physiology in Multiple Ways.
Nidhi ThakkarGemini GajeraDilip MehtaVijay KothariPublished in: Pharmaceutics (2024)
Antibiotic-resistant strains of Staphylococcus aureus are being viewed as a serious threat by various public health agencies. Identifying novel targets in this important pathogen is crucial to the development of new effective antibacterial formulations. We investigated the antibacterial effect of a colloidal nanosilver formulation, Silversol ® , against an antibiotic-resistant strain of S. aureus using appropriate in vitro assays. Moreover, we deciphered the molecular mechanisms underlying this formulation's anti- S. aureus activity using whole transcriptome analysis. Lower concentrations of the test formulation exerted a bacteriostatic effect against this pathogen, and higher concentrations exerted a bactericidal effect. Silversol ® at sub-lethal concentration was found to disturb multiple physiological traits of S. aureus such as growth, antibiotic susceptibility, membrane permeability, efflux, protein synthesis and export, biofilm and exopolysaccharide production, etc. Transcriptome data revealed that the genes coding for transcriptional regulators, efflux machinery, transferases, β-lactam resistance, oxidoreductases, metal homeostasis, virulence factors, and arginine biosynthesis are expressed differently under the influence of the test formulation. Genes ( argG and argH ) involved in arginine biosynthesis emerged among the major targets of Silversol ®' s antibacterial activity against S. aureus .
Keyphrases
- staphylococcus aureus
- drug delivery
- public health
- genome wide
- biofilm formation
- pseudomonas aeruginosa
- escherichia coli
- candida albicans
- nitric oxide
- gene expression
- single cell
- transcription factor
- methicillin resistant staphylococcus aureus
- electronic health record
- endothelial cells
- dna methylation
- machine learning
- genome wide identification
- cystic fibrosis
- antimicrobial resistance
- oxidative stress
- single molecule
- deep learning
- cell wall
- atomic force microscopy
- data analysis