Surfactin effectively inhibits Staphylococcus aureus adhesion and biofilm formation on surfaces.
Jin LiuWei LiXiaoyu ZhuHaizhen ZhaoYingjian LuChong ZhangZhaoxin LuPublished in: Applied microbiology and biotechnology (2019)
Biosurfactants are amphiphilic compounds that composed of hydrophilic and hydrophobic moieties, which possess the ability of self-organizing between phases, reducing the interfacial tension, and forming aggregates such as micelles. This spontaneous process results in significant changes in surface properties that directly influence the adherence of microorganisms. In this study, the ability of surfactin, a biosurfactant produced by Bacillus subtilis in reducing adhesion and disrupting the presence of biofilm of Staphylococcus aureus (S. aureus) on several surfaces, was investigated. Significant biofilm removal was observed on glass, polystyrene, and stainless steel surfaces. Furthermore, we explored the probable mechanism about how surfactin affected S. aureus biofilm formation. Based on our findings, surfactin had a significant effect on the polysaccharides production and especially decreased the percentage of alkali-soluble polysaccharide in biofilms. It also down-regulated the expression of icaA and icaD significantly, which are necessary for the important constituents to take shape of staphylococcal biofilm. In addition, it was found that the lipopeptide affected the quorum sensing (QS) system in S. aureus through regulating the auto inducer 2 (AI-2) activity, which has been reported to be negative for biofilm formation in S. aureus. These above properties could be applied in developing surfactin as a potential pre-coating agent on material surfaces to prevent S. aureus biofilm formation.
Keyphrases
- biofilm formation
- bacillus subtilis
- staphylococcus aureus
- candida albicans
- pseudomonas aeruginosa
- methicillin resistant staphylococcus aureus
- escherichia coli
- poor prognosis
- drug delivery
- ionic liquid
- transcription factor
- artificial intelligence
- molecular dynamics simulations
- machine learning
- deep learning
- skeletal muscle
- human health
- atomic force microscopy