Back

Controlling Staphylococcus aureus by 2-phenol (2-MAMP) in a co-culture moderates the biofilm and virulence of Pseudomonas aeruginosa

Selvan, G. T.; Thirunavukkarasu, B.; Pravallika, N. N.; Vasudevan, S.; Palaniappan, B.; Solomon, A. P.

2021-07-04 microbiology
10.1101/2021.07.03.451022 bioRxiv
Show abstract

Staphylococcus aureus and Pseudomonas aeruginosa are the most encountered organisms in a polymicrobial chronic wound infection. Production of multiple virulence factors by this duo delays wound healing process. Notably, P. aeruginosa displays enhanced virulence in the presence of S. aureus by a peptidoglycan sensing mechanism. Thus, novel therapies are imperative to address polymicrobial infections effectively. Previously, it has been suggested that targeting S. aureus might be a possible approach to reduce the severity of P. aeruginosa in a polymicrobial infection. In this aspect, we have used 2-[(Methylamino)methyl]phenol (2-MAMP), our previously reported QS inhibitor to target S. aureus and phenotypically determine the virulence factors of P. aeruginosa under this condition. Analysis of major virulence factors of Pseudomonas viz. biofilm, pyocyanin and pyoveridine showed a significant reduction. The competitive index (CI) and relative increase ratio (RIR) were determined to understand the organisms interaction in co-culture. Results indicated competitiveness among the strains and on increasing ratios of S. aureus cells, co-existence was noticed. Further, as a sensible approach antibiotic - antivirulence drug combinations were tested on co-culture. Significant improvement in the growth inhibition was observed. Our preliminary results presented here would enable further research to address polymicrobial infection in a novel way.

Matching journals

The top 5 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.