Multi-omics informed mathematical model for meropenem and tobramycin against hypermutable Pseudomonas aeruginosa.

Abstract

Hypermutable P. aeruginosa isolates frequently display resistance emergence during treatment. Mechanisms of such resistance emergence have not been explored using dynamic hollow-fiber studies and multi-omics-informed mathematical modeling. Two hypermutable and heteroresistant P. aeruginosa isolates, CW8 (MIC_meropenem=8mg/L, MIC_tobramycin=8mg/L) and CW44 (MIC_meropenem=4mg/L, MIC_tobramycin=2mg/L), were studied. Both isolates had genotypes resembling those of carbapenem- and aminoglycoside-resistant strains. Achievable lung fluid concentration-time profiles following meropenem at 1 or 2g every 8h (3-h infusion) and tobramycin at 5 or 10mg/kg body weight every 24h (0.5-h infusion), in monotherapy and combinations, were simulated over 8 days. Total and resistant bacterial counts were determined. Resistant colonies and whole population samples at 191h were whole-genome sequenced, and population transcriptomics performed at 1 and 191h. The multi-omics analyses informed mechanism-based modeling of total and resistant populations. While both isolates eventually displayed resistance emergence against all regimens, the high-dose combination synergistically suppressed resistant regrowth of only CW8 up to ∼96h. Mutations that emerged during treatment were in pmrB, ampR, and multiple efflux pump regulators for CW8, and in pmrB and PBP2 for CW44. At 1h, mexB, oprM and ftsZ were differentially downregulated in CW8 by the combination. These transcriptomics results informed inclusion of mechanistic synergy in the mechanism-based model for only CW8. At 191h, norspermidine genes were upregulated (without a pmrB mutation) in CW8 by the combination, and informed the adaptive loss of synergy in the model. Multi-omics information enabled mechanism-based modeling to describe the bacterial response of both isolates simultaneously. IMPORTANCE: Pseudomonas aeruginosa causes serious bacterial infections in people with cystic fibrosis (pwCF), and has numerous resistance mechanisms. Current empirical approaches to informing antibiotic regimen selection have important limitations. This study exposed two P. aeruginosa clinical isolates to concentration-time profiles of meropenem and tobramycin as would be observed in lung fluid of pwCF. The combination elicited different bacterial count profiles between the isolates, despite similar bacterial baseline characteristics. We found differences between the isolates in the expression of a key resistance mechanism against meropenem at 1h, and expression that implied a loss of cell membrane permeability for tobramycin without the expected DNA mutation. This information enabled mathematical modeling to accurately describe all bacterial profiles over time. For the first time, this multi-omics informed modeling approach using DNA and RNA data was applied to a hollow-fiber infection study. Using bacterial molecular insights with mechanism-based mathematical modeling has high potential for ultimately informing personalized antibiotic therapy.