Abstract
Background
Pseudomonas aeruginosa is a persistent and difficult-to-treat pathogen in many patients, especially those with cystic fibrosis (CF). Herein, we describe our experience managing a young woman suffering from CF with XDR P. aeruginosa who underwent lung transplantation. We highlight the contemporary difficulties reconciling the clinical, microbiological, and genetic information.
Methods
Mechanism-based-susceptibility disk diffusion synergy testing with double and triple antibiotic combinations aided in choosing tailored antimicrobial combinations to control the infection in the pre-transplant period, create an effective perioperative prophylaxis regimen, and manage recurrent infections in the post-transplant period. Thirty-six sequential XDR and PDR P. aeruginosa isolates obtained from the patient within a 17-month period, before and after a double-lung transplant were analyzed by whole genome sequencing (WGS) and RNAseq in order to understand the genetic basis of the observed resistance phenotypes, establish the genomic population diversity, and define the nature of sequence changes over time
Results
Our phylogenetic reconstruction demonstrates that these isolates represent a genotypically and phenotypically heterogeneous population. The pattern of mutation accumulation and variation of gene expression suggests that a group of closely related strains was present in the patient prior to transplantation and continued to evolve throughout the course of treatment regardless of antibiotic usage.Our findings challenge antimicrobial stewardship programs that assist with the selection and duration of antibiotic regimens in critically ill and immunocompromised patients based on single-isolate laboratory-derived resistant profiles. We propose that an approach sampling the population of pathogens present in a clinical sample instead of single colonies be applied instead when dealing with XDR P. aeruginosa, especially in patients with CF.
Conclusion
In complex cases such as this, real-time combination testing and genomic/transcriptomic data could lead to the application of true “precision medicine” by helping clinicians choose the combination antimicrobial therapy most likely to be successful against a population of MDR pathogens present.
Disclosures
Federico Perez, MD, MS, Accelerate (Research Grant or Support)Merck (Research Grant or Support)Pfizer (Research Grant or Support) Robert A. Bonomo, MD, Entasis, Merck, Venatorx (Research Grant or Support)
Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies
