SRA

The natural mutation rate is a key metric for population genetic tools. At present, the mutation rate for E. coli is assumed to be the same as M. tuberculosis when these calculations are performed. This assumption is unlikely to be valid since a preponderance of evidence demonstrates that genome structure, generation time, population size, environmental lifestyle, and mutational mechanisms differ between M. tuberculosis and E. coli. All of these factors are known to influence natural mutation rates, and their estimates. The aim of this proposal is to sequence a unique set of 12 well characterized clinical strains of M. tuberculosis. Sequence analysis of these strains will allow direct calculation of a natural mutation rate for this organism. The strains were identified during a study of M. tuberculosis micro-evolution in high incidence, isolated Aboriginal Canadian communities. The unique features of these communities have allowed us to identify several clones of M. tuberculosis that have circulated continuously over an 18 year period (1986-2004). We propose sequencing of 3 separate clones isolated 8 different times throughout the study period (i.e. a total of 8 strains). In addition, we have identified 4 closely related organisms associated with reactivation disease acquired between 1904-1910, 1919-1925, 1947-1964 and 1966-1972. We propose whole genome sequencing of these 4 strains in order to extend the sampling window to 100 years (1904-2004). The situation is analogous to repeated passaging of laboratory strains in vitro, except that we are sampling M. tuberculosis strains from their natural environment, with all the complexities of transmission between human hosts and within host immune-pathogen dynamics. This dataset of 12 genome sequences is ideally suited to Bayesian coalescent inference of the mutation rate. We believe whole genome sequencing of these strains will allow the precise calculation of a mutation rate for this organism in its natural environment. Having an accurate measurement of the natural mutation rate will allow much more powerful analyses of emerging genomic data, and will aid the translation of this data into concrete achievements in tuberculosis control.