Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Abstract

Increasing levels of antibiotic resistance in many bacterial pathogen populations is a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria is exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Avian-specificSalmonella entericaSerovar Gallinarum transition to endemicity is accompanied by localized resistome and mobilome interaction

“…Humanity appeared powerless against epidemics until the advent of antimicrobials, which are still the primary agents used to combat S. Gallinarum infection (24,25). As a manifestation of localization-related forces, the emergence of AMR allows pathogens to acquire additional fitness benefits (26).…”

Explaining the stable coexistence of drug-resistant and -susceptible pathogens: the Resistance Acquisition Purifying Selection model

“…While we cannot currently know if this number is correct, it is encouraging to see that the number is at least of an order of magnitude that seems plausible. It would be of interest to see whether phylogenetic approaches can be used to determine if resistant strains indeed have a lower R0 compared to non-resistant strains [33,34].…”

A phylogenetic approach to studying the roles of within-host evolution and between-host transmission of resistance for clinicalEscherichia coliinfections

“…PipTaz is the most extreme case, as there are 53 origins of resistance in our dataset, and 51 of these origins only affect a single sample (a cluster size of 1), whereas only two origins affect two samples (only 4% of clusters are larger than size 1). This suggests that strains resistant to PipTaz are unlikely to be transmitted to other patients, possibly because of fitness costs of the genes or mutations that cause resistance (Pennings, Ogbunugafor, and Hershberg 2020; Melnyk, Wong, and Kassen 2015; Andersson and Hughes 2011; Helekal et al 2022). On the other extreme, Ciprofloxacin resistance is often found in sizable clusters.…”