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Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection.

Ford CB, Lin PL, Chase MR, Shah RR, Iartchouk O, Galagan J, Mohaideen N, Ioerger TR, Sacchettini JC, Lipsitch M, Flynn JL, Fortune SM - Nat. Genet. (2011)

Bottom Line: Based on the distribution of SNPs observed, we calculated the mutation rates for these disease states.The pattern of polymorphisms suggests that the mutational burden in vivo is because of oxidative DNA damage.We show that Mtb continues to acquire mutations during disease latency, which may explain why isoniazid monotherapy for latent tuberculosis is a risk factor for the emergence of isoniazid resistance.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA.

ABSTRACT
Tuberculosis poses a global health emergency, which has been compounded by the emergence of drug-resistant Mycobacterium tuberculosis (Mtb) strains. We used whole-genome sequencing to compare the accumulation of mutations in Mtb isolated from cynomolgus macaques with active, latent or reactivated disease. We sequenced 33 Mtb isolates from nine macaques with an average genome coverage of 93% and an average read depth of 117×. Based on the distribution of SNPs observed, we calculated the mutation rates for these disease states. We found a similar mutation rate during latency as during active disease or in a logarithmically growing culture over the same period of time. The pattern of polymorphisms suggests that the mutational burden in vivo is because of oxidative DNA damage. We show that Mtb continues to acquire mutations during disease latency, which may explain why isoniazid monotherapy for latent tuberculosis is a risk factor for the emergence of isoniazid resistance.

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The mutational capacity of strains from latency and reactivated disease is similar to that of strains from active disease or in vitro growth(a–c) Mutation rate (μ was estimated based on the number of unique SNPs (m) observed in each condition (4 active, 3 latent, 7 reactivated). This calculation was performed over a range of generation times (g, 18–240 hours per generation) to allow for the uncertainty in growth rate in vivo. The probability of observing μ when g is fixed at any given time was determined to build the probability distribution function around each estimate and to define the 95% confidence intervals. The single base mutation rate of the bacterium during in vitro growth (μin vitro) was determined by fluctuation analysis (Supplementary Figs. 1a–c) and is indicated by an arrow. In each clinical condition, μ20 (the predicted mutation rate if the generation time in vivo were as rapid as the generation time in vitro) is similar to μin vitro. Generation time in vivo is predicted to be substantially slower than in vitro, and thus the mutation rate must be proportionally higher to produce the observed number of SNPs. (d) Given the uncertainty in generation time, a mutation rate per day can be calculated to determine the rate at which mutations occur regardless of generation time. Mutations occur at a similar rate per day regardless of the disease status of the host. Error bars represent 95% confidence intervals.
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Figure 3: The mutational capacity of strains from latency and reactivated disease is similar to that of strains from active disease or in vitro growth(a–c) Mutation rate (μ was estimated based on the number of unique SNPs (m) observed in each condition (4 active, 3 latent, 7 reactivated). This calculation was performed over a range of generation times (g, 18–240 hours per generation) to allow for the uncertainty in growth rate in vivo. The probability of observing μ when g is fixed at any given time was determined to build the probability distribution function around each estimate and to define the 95% confidence intervals. The single base mutation rate of the bacterium during in vitro growth (μin vitro) was determined by fluctuation analysis (Supplementary Figs. 1a–c) and is indicated by an arrow. In each clinical condition, μ20 (the predicted mutation rate if the generation time in vivo were as rapid as the generation time in vitro) is similar to μin vitro. Generation time in vivo is predicted to be substantially slower than in vitro, and thus the mutation rate must be proportionally higher to produce the observed number of SNPs. (d) Given the uncertainty in generation time, a mutation rate per day can be calculated to determine the rate at which mutations occur regardless of generation time. Mutations occur at a similar rate per day regardless of the disease status of the host. Error bars represent 95% confidence intervals.

Mentions: Because of the inherent uncertainty in the generation time of Mtb in vivo, we estimated the mutation rate across a broad range of generation times (18–240 hours), calculating the rate that would be required to generate the number of polymorphisms identified by WGS (Fig. 3a–c). In order to compare the mutation rate of bacteria from each clinical condition, we derived a lower limit for the bacterial mutation rate in vivo, which we define as the predicted mutation rate per generation if the in vivo generation time were equivalent to the in vitro generation time of 20 hours, μ(20hr). While Mtb is likely to have a much longer generation time in vivo, especially during prolonged latent infection, we use μ(20hr) as a highly conservative boundary estimate of the in vivo mutation rate that allows us to directly compare the mutational capacity of the bacterium in different in vivo conditions. Strikingly, we found that the bacterial population's capacity for mutation, μ(20hr), during latency (2.71×10−10) and reactivated disease (3.03×10−10) is equivalent to that of Mtb from animals with active disease (2.01 ×10−10) (Table 1). Mutation rate can also be calculated as the number of mutations that occur per day of infection rather than per generation. We therefore calculated the mutation rate per day required for the bacterial populations in each disease state to acquire the number of polymorphisms that we identified by WGS (Fig. 3d). Our data indicate that in macaques with active, latent and reactivated disease, the bacterial populations acquire mutations at the same rate over time, regardless of the number of bacterial replications that have occurred.


Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection.

Ford CB, Lin PL, Chase MR, Shah RR, Iartchouk O, Galagan J, Mohaideen N, Ioerger TR, Sacchettini JC, Lipsitch M, Flynn JL, Fortune SM - Nat. Genet. (2011)

The mutational capacity of strains from latency and reactivated disease is similar to that of strains from active disease or in vitro growth(a–c) Mutation rate (μ was estimated based on the number of unique SNPs (m) observed in each condition (4 active, 3 latent, 7 reactivated). This calculation was performed over a range of generation times (g, 18–240 hours per generation) to allow for the uncertainty in growth rate in vivo. The probability of observing μ when g is fixed at any given time was determined to build the probability distribution function around each estimate and to define the 95% confidence intervals. The single base mutation rate of the bacterium during in vitro growth (μin vitro) was determined by fluctuation analysis (Supplementary Figs. 1a–c) and is indicated by an arrow. In each clinical condition, μ20 (the predicted mutation rate if the generation time in vivo were as rapid as the generation time in vitro) is similar to μin vitro. Generation time in vivo is predicted to be substantially slower than in vitro, and thus the mutation rate must be proportionally higher to produce the observed number of SNPs. (d) Given the uncertainty in generation time, a mutation rate per day can be calculated to determine the rate at which mutations occur regardless of generation time. Mutations occur at a similar rate per day regardless of the disease status of the host. Error bars represent 95% confidence intervals.
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Related In: Results  -  Collection

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Figure 3: The mutational capacity of strains from latency and reactivated disease is similar to that of strains from active disease or in vitro growth(a–c) Mutation rate (μ was estimated based on the number of unique SNPs (m) observed in each condition (4 active, 3 latent, 7 reactivated). This calculation was performed over a range of generation times (g, 18–240 hours per generation) to allow for the uncertainty in growth rate in vivo. The probability of observing μ when g is fixed at any given time was determined to build the probability distribution function around each estimate and to define the 95% confidence intervals. The single base mutation rate of the bacterium during in vitro growth (μin vitro) was determined by fluctuation analysis (Supplementary Figs. 1a–c) and is indicated by an arrow. In each clinical condition, μ20 (the predicted mutation rate if the generation time in vivo were as rapid as the generation time in vitro) is similar to μin vitro. Generation time in vivo is predicted to be substantially slower than in vitro, and thus the mutation rate must be proportionally higher to produce the observed number of SNPs. (d) Given the uncertainty in generation time, a mutation rate per day can be calculated to determine the rate at which mutations occur regardless of generation time. Mutations occur at a similar rate per day regardless of the disease status of the host. Error bars represent 95% confidence intervals.
Mentions: Because of the inherent uncertainty in the generation time of Mtb in vivo, we estimated the mutation rate across a broad range of generation times (18–240 hours), calculating the rate that would be required to generate the number of polymorphisms identified by WGS (Fig. 3a–c). In order to compare the mutation rate of bacteria from each clinical condition, we derived a lower limit for the bacterial mutation rate in vivo, which we define as the predicted mutation rate per generation if the in vivo generation time were equivalent to the in vitro generation time of 20 hours, μ(20hr). While Mtb is likely to have a much longer generation time in vivo, especially during prolonged latent infection, we use μ(20hr) as a highly conservative boundary estimate of the in vivo mutation rate that allows us to directly compare the mutational capacity of the bacterium in different in vivo conditions. Strikingly, we found that the bacterial population's capacity for mutation, μ(20hr), during latency (2.71×10−10) and reactivated disease (3.03×10−10) is equivalent to that of Mtb from animals with active disease (2.01 ×10−10) (Table 1). Mutation rate can also be calculated as the number of mutations that occur per day of infection rather than per generation. We therefore calculated the mutation rate per day required for the bacterial populations in each disease state to acquire the number of polymorphisms that we identified by WGS (Fig. 3d). Our data indicate that in macaques with active, latent and reactivated disease, the bacterial populations acquire mutations at the same rate over time, regardless of the number of bacterial replications that have occurred.

Bottom Line: Based on the distribution of SNPs observed, we calculated the mutation rates for these disease states.The pattern of polymorphisms suggests that the mutational burden in vivo is because of oxidative DNA damage.We show that Mtb continues to acquire mutations during disease latency, which may explain why isoniazid monotherapy for latent tuberculosis is a risk factor for the emergence of isoniazid resistance.

View Article: PubMed Central - PubMed

Affiliation: Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA.

ABSTRACT
Tuberculosis poses a global health emergency, which has been compounded by the emergence of drug-resistant Mycobacterium tuberculosis (Mtb) strains. We used whole-genome sequencing to compare the accumulation of mutations in Mtb isolated from cynomolgus macaques with active, latent or reactivated disease. We sequenced 33 Mtb isolates from nine macaques with an average genome coverage of 93% and an average read depth of 117×. Based on the distribution of SNPs observed, we calculated the mutation rates for these disease states. We found a similar mutation rate during latency as during active disease or in a logarithmically growing culture over the same period of time. The pattern of polymorphisms suggests that the mutational burden in vivo is because of oxidative DNA damage. We show that Mtb continues to acquire mutations during disease latency, which may explain why isoniazid monotherapy for latent tuberculosis is a risk factor for the emergence of isoniazid resistance.

Show MeSH
Related in: MedlinePlus