Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli

Abstract

There is urgent need to develop novel treatment strategies to reduce antimicrobial resistance. Collateral sensitivity (CS), where resistance to one antimicrobial increases susceptibility to other drugs, might enable selection against resistance during treatment. However, the success of this approach would depend on the conservation of CS networks across genetically diverse bacterial strains. Here, we examine CS conservation across diverse Escherichia coli strains isolated from urinary tract infections. We determine collateral susceptibilities of mutants resistant to relevant antimicrobials against 16 antibiotics. Multivariate statistical analyses show that resistance mechanisms, in particular efflux-related mutations, as well as the relative fitness of resistant strains, are principal contributors to collateral responses. Moreover, collateral responses shift the mutant selection window, suggesting that CS-informed therapies may affect evolutionary trajectories of antimicrobial resistance. Our data allow optimism for CS-informed therapy and further suggest that rapid detection of resistance mechanisms is important to accurately predict collateral responses.

Collateral sensitivity interactions between antibiotics depend on local abiotic conditions

“…Recent work revealed important genetic factors influencing the predictability of collateral sensitivity, but the importance of local abiotic conditions is still unclear. For example, high-throughput in vitro studies showed different replicate populations exposed to the same antibiotic sometimes acquire collateral sensitivity to another antibiotic, and sometimes do not 10,11 . This can be explained by different mutations, which vary in their phenotypic effects on resistance, spreading in different replicate populations 11,12,14,15 .…”

“…This raises the possibility that local environmental conditions could influence both the emergence of collateral sensitivity (by affecting which of the possible pathways to resistance are most strongly selected during antibiotic exposure) and its expression (by modifying the phenotypic effects of resistance alleles when bacteria are exposed to a second antibiotic). To date, research on collateral sensitivity interactions has focused on testing many combinations of antibiotics 5,6,9,14 , multiple strains 10 , or many replicate populations for individual antibiotic combinations 11 . Therefore, the role of local abiotic conditions in the emergence and expression of collateral sensitivity interactions remains unclear.…”

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

“…In recent years, significant efforts have been devoted to designing evolutionarily sound strategies that balance short-term drug efficacy with the long-term potential to develop resistance. These approaches describe a number of different factors that could modulate resistance evolution, including interactions between bacterial cells (3,4,5,6,7,8), synergy with the immune system (9), spatial heterogeneity (10,11,12,13,14,15), epistasis between resistance mutations (16,17), precise temporal scheduling (18,19,20,21), and statistical correlations between resistance profiles for different drugs (22,23,24,25,26,27,28,29,30,31).…”

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

“…These anti-resistance approaches exploit different features of the population dynamics, including competitive suppression between sensitive and resistance cells (14,15), synergy with the immune system (16), precise timing of growth dynamics or dosing (17,18), responses to subinhibitory drug doses (19), and band-pass response to periodic dosing (10). Resistance-stalling strategies may also exploit spatial heterogeneity (20,21,22,23,24,25), epistasis between resistance mutations (26,27), hospital-level dosing protocols (28,29), and regimens of multiple drugs applied in sequence (28,30,18,31,19,32) or combination (33,34,35,36,37,38,39,40), which may allow one to leverage statistical correlations between resistance profiles for different drugs (41,42,43,44,39,37,45,46,47,48). As a whole, these studies demonstrate the important role of community-level dynamics for understanding and predicting how bacteria respond and adapt to antibiotics.…”