The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis
- aSchool of Biotechnology and Biomolecular Sciences,
- bSchool of Mathematics and Statistics, and
- dEvolution and Ecology Research Centre, University of New South Wales, Kensington, NSW 2052, Australia; and
- cSchool of Computing and Mathematics and Nanoscale Organisation and Dynamics Research Group, University of Western Sydney, Penrith South DC, NSW 1797, Australia
Abstract
The emergence of antibiotic resistance in Mycobacterium tuberculosis has raised the concern that pathogen strains that are virtually untreatable may become widespread. The acquisition of resistance to antibiotics results in a longer duration of infection in a host, but this resistance may come at a cost through a decreased transmission rate. This raises the question of whether the overall fitness of drug-resistant strains is higher than that of sensitive strains—essential information for predicting the spread of the disease. Here, we directly estimate the transmission cost of drug resistance, the rate at which resistance evolves, and the relative fitness of resistant strains. These estimates are made by using explicit models of the transmission and evolution of sensitive and resistant strains of M. tuberculosis, using approximate Bayesian computation, and molecular epidemiology data from Cuba, Estonia, and Venezuela. We find that the transmission cost of drug resistance relative to sensitivity can be as low as 10%, that resistance evolves at rates of ≈0.0025–0.02 per case per year, and that the overall fitness of resistant strains is comparable with that of sensitive strains. Furthermore, the contribution of transmission to the spread of drug resistance is very high compared with acquired resistance due to treatment failure (up to 99%). Estimating such parameters directly from in vivo data will be critical to understanding and responding to antibiotic resistance. For instance, projections using our estimates suggest that the prevalence of tuberculosis may decline with successful treatment, but the proportion of cases associated with resistance is likely to increase.
- antibiotic resistance
- approximate Bayesian computation
- bacterial evolution
- molecular epidemiology
- stochastic model
Footnotes
- 2To whom correspondence should be addressed. E-mail: m.tanaka{at}unsw.edu.au
-
Author contributions: S.A.S., A.R.F., and M.M.T. designed research; F.L. performed research; F.L., S.A.S., H.J., A.R.F., and M.M.T. contributed new reagents/analytic tools; F.L., S.A.S., A.R.F., and M.M.T. analyzed data; and F.L., S.A.S., A.R.F., and M.M.T. wrote the paper;.
-
Edited by Carl Bergstrom, University of Washington, Seattle, WA, and accepted by the Editorial Board June 22, 2009
-
The authors declare no conflict of interest.
-
This article is a PNAS Direct Submission. C.B. is a guest editor invited by the Editorial Board.
