Targeting Iron - Respiratory Reciprocity Promotes Bacterial Death

Discovering new bacterial signaling pathways offers unique antibiotic strategies. Here, through an unbiased resistance screen of 3,884 gene knockout strains, we uncovered a previously unknown non-lytic bactericidal mechanism that sequentially couples three transporters and downstream transcription to lethally suppress respiration of the highly virulent P. aeruginosa strain PA14 - one of three species on the WHO's 'Priority 1: Critical' list. By targeting outer membrane YaiW, cationic lacritin peptide 'N-104' translocates into the periplasm where it ligates outer loops 4 and 2 respectively of the inner membrane transporters FeoB and PotH to respectively suppress both ferrous iron and polyamine uptake. This broadly shuts down transcription of many biofilm-associated genes, including ferrous iron-dependent TauD and ExB1. The mechanism is innate to the surface of the eye enhanced by synergistic coupling with thrombin peptide GKY20. This is the first example of an inhibitor of multiple bacterial transporters. Mid-log P. aeruginosa PA14 cells were incubated without treatment or with 5 or 20 uM lacritin N-104 (or 20 uM N-80/C-25 as negative control peptide) for 45 min at 35oC. RNA isolation from the cells was carried out using the RNeasy mini kit (74104, Qiagen). The quality of the RNA samples was examined prior to sequencing by using gel electrophoresis, which revealed two sharp peaks with very low background. An RNA Integrity Number equivalent (RINe) of 8.4-9.2 and a concentration of approximately 40-60 ng/ul were obtained. Three independent RNA samples were sequenced for each experimental condition. Library construction and sequencing were executed at the Genome Analysis and Technology Core of the University of Virginia. Specifically, mRNA enrichment was conducted using the NEBNext® rRNA Depletion Kit (Bacteria) (E7850L, New England BioLabs). Transcriptome sequencing was then performed on a Next Seq 500 Mid Output platform, with 150-bp pair-end reads produced. The range of raw reads for each sample varied from 8-10 million, thus ensuring an optimal depth of sequencing. The analysis of RNA-seq data was conducted at the Bioinformatics Core facility of the University of Virginia.