Genomic insights into the fate of colistin resistance and Acinetobacter baumannii during patient treatment - Snitkin, Evan S.; Zelazny, Adrian M.; Gupta, Jyoti; NISC Comparative Sequencing Program; Palmore, Tara N.; Murray, Patrick R.; Segre, Julia A. (Genome Research, 2013)
Bacterial whole-genome sequencing (WGS) of human pathogens has provided unprecedented insights into the evolution of antibiotic resistance. Most studies have focused on identification of resistance mutations, leaving one to speculate on the fate of these mutants once the antibiotic selective pressure is removed. We performed WGS on longitudinal isolates of Acinetobacter baumannii from patients undergoing colistin treatment, and upon subsequent drug withdrawal. In each of the four patients, colistin resistance evolved via mutations at the pmr locus. Upon colistin withdrawal, an ancestral susceptible strain outcompeted resistant isolates in three of the four cases. In the final case, resistance was also lost, but by a compensatory inactivating mutation in the transcriptional regulator of the pmr locus. Notably, this inactivating mutation reduced the probability of reacquiring colistin resistance when subsequently challenged in vitro. On face value, these results supported an in vivo fitness cost preventing the evolution of stable colistin resistance. However, more careful analysis of WGS data identified genomic evidence for stable colistin resistance undetected by clinical microbiological assays. Transcriptional studies validated this genomic hypothesis, showing increased pmr expression of the initial isolate. Moreover, altering the environmental growth conditions of the clinical assay recapitulated the classification as colistin resistant. Additional targeted sequencing revealed that this isolate evolved undetected in a patient undergoing colistin treatment, and was then transmitted to other hospitalized patients, further demonstrating its stability in the absence of colistin. This study provides a unique window into mutational pathways taken in response to antibiotic pressure in vivo, and demonstrates the potential for genome sequence data to predict resistance phenotypes.
10.1101/gr.154328.112