Back

Tetrasodium EDTA disrupts Pseudomonas aeruginosa membrane integrity, shows suppressed resistance evolution and reduced cytotoxicity compared to meropenem

Orababa, O. Q.; Ayomikun, K.; Cornbill, C.; Uchechukwu, C. F.; Sharma, S.; Uzairue, L.; Reddy, N.; Gulati, R.; Oyedemi, B. M.; Harrison, F.

2026-08-11 microbiology
10.64898/2026.08.11.744140 bioRxiv
Show abstract

Pseudomonas aeruginosa remains one of the most important clinical pathogens for which new drugs are needed, due to its resistance machinery. Consequently, there is an increasing effort to develop new and effective treatments against this pathogen. We recently showed that tetrasodium ethylenediaminetetraacetic acid (tEDTA) exhibits promising antibacterial and antibiofilm activity against P. aeruginosa in advanced biofilm models. tEDTA is known to chelate divalent cations, with predicted effects on the outer membrane; however, a full understanding of how this kills P. aeruginosa is lacking. Also, it is currently not clear how slowly or rapidly P. aeruginosa will evolve resistance to this treatment. Using membrane disruption assays and RNA-seq, we showed that tEDTA disrupts bacterial membrane potential and permeabilises P. aeruginosa membranes. RNA-seq revealed the significant upregulation of genes involved in the transport of iron, phosphate, potassium, and magnesium ion. The arnABCD operon which is involved in lipid A biosynthesis was also upregulated. Using a 7-day evolutionary ramp approach, we showed that P. aeruginosa could not evolve resistance to tEDTA under strong selection. Lastly, we carried out a cytotoxicity assay with Human Epithelial type 2 (HEp-2) cells and showed that there was reduced cytotoxicity of tEDTA compared to meropenem. This study provides good insight into the mechanism of action of tEDTA and further evidence of its potential as an alternative to antibiotics for P. aeruginosa infections.

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.