Back

Unveiling the Anti-Adhesive Potential of Tetraspanin CD9 Peptides against Pseudomonas aeruginosa in Human Keratinocytes

Murad, K.; Ab-Rahim, S.; Al-Talib, H.

2023-10-23 microbiology
10.1101/2023.10.23.563549 bioRxiv
Show abstract

Multidrug-resistant P. aeruginosa strains are becoming a public health problem worldwide, causing numerous nosocomial infections. Adhesion of bacteria to host cells is a crucial step in infection, hence interruption of this stage can reduce bacterial infection. Tetraspanin CD9 was chosen for this study as it has been implicated in the pathogenesis of bacterial infections in a previous study. The aim of this study is to investigate the adhesion inhibition of tetraspanin CD9 peptides against P. aeruginosa in human keratinocytes. HaCaT cells were infected with P. aeruginosa, prior to treatment with CD9 peptides. The CD9 peptides cytotoxicity testing was determined by MTT assay. Bacterial adhesion was also determined quantitatively by counting viable bacterial cells and qualitatively by Giemsa staining and transmission electron microscope. Inflammatory markers (IL-8 and IL-6) expression was measured by Elisa assay. CD9 peptides did not affect HaCaT cell viability and inflammatory markers release. This study successfully demonstrated that CD9 peptides reduced P. aeruginosa adherence. Colonies produced by P. aeruginosa isolates treated with CD9 peptides were significantly reduced. Giemsa staining and TEM showed that treated samples had lower bacterial density and were located farther from the cells. These data suggest that tetraspanin CD9 peptides as the potential therapeutic approach against P. aeruginosa due to its property that inhibits bacterial adhesion without killing the bacteria, whereby at the same time does not adversely affect the nature of host cells.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.