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Rates and Mechanisms of Bacterial Mutagenesis from Maximum-Depth Sequencing

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ABSTRACT

In 1943, Luria and Delbrück used a phage resistance assay to establish spontaneous mutation as a driving force of microbial diversity1. Mutation rates are still studied using such assays, but these can only examine the small minority of mutations conferring survival in a particular condition. Newer approaches, such as long-term evolution followed by whole-genome sequencing 2, 3, may be skewed by mutational “hot” or “cold” spots 3, 4. Both approaches are affected by numerous caveats 5, 6, 7 (see Supplemental Information). We devise a method, Maximum-Depth Sequencing (MDS), to detect extremely rare variants in a population of cells through error-corrected, high-throughput sequencing. We directly measure locus-specific mutation rates in E. coli and show that they vary across the genome by at least an order of magnitude. Our data suggest that certain types of nucleotide misincorporation occur 104-fold more frequently than the basal rate of mutations, but are repaired in vivo. Our data also suggest specific mechanisms of antibiotic-induced mutagenesis, including downregulation of mismatch repair via oxidative stress; transcription-replication conflicts; and in the case of fluoroquinolones, direct damage to DNA.

No MeSH data available.


Schematic depicting the mathematical derivation of the false positive rate of MDS due to polymerase error. (a) The origin of various terms used in equations 2-7. (b) Illustration of an example calculation of false positive rate given more “intuitive” values of N, R, and p. The false positive rate is calculated in a way that accounts for the possibility that an error in one or more “linear” cycles propagates to a whole family of reads. The number of reads with an error (k) is Poisson distributed according to equation 2. The probability of a false positive is the sum of the probabilities that all R reads come from one of k families, for all possible k, according to equation 3. Note that in practice, p<10−6, and in our study N=12, R>2, making the false positive rate much lower (see Fig. 1).
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Figure 14: Schematic depicting the mathematical derivation of the false positive rate of MDS due to polymerase error. (a) The origin of various terms used in equations 2-7. (b) Illustration of an example calculation of false positive rate given more “intuitive” values of N, R, and p. The false positive rate is calculated in a way that accounts for the possibility that an error in one or more “linear” cycles propagates to a whole family of reads. The number of reads with an error (k) is Poisson distributed according to equation 2. The probability of a false positive is the sum of the probabilities that all R reads come from one of k families, for all possible k, according to equation 3. Note that in practice, p<10−6, and in our study N=12, R>2, making the false positive rate much lower (see Fig. 1).

Mentions: As discussed briefly in the main text, in our assay, the total polymerase error rate Epol,X→Y can be derived as follows (for visual aid, see Extended Data Fig. 10). After exponential PCR, there are N pools of reads, each derived from one of the original linear amplification steps. The probability of having k pools derive from an original polymerase error is binomially distributed. Furthermore, because pN<<1, the distribution is Poisson. (2)(Nk)pk(1−p)N−k≈(Np)kk!e−Np


Rates and Mechanisms of Bacterial Mutagenesis from Maximum-Depth Sequencing
Schematic depicting the mathematical derivation of the false positive rate of MDS due to polymerase error. (a) The origin of various terms used in equations 2-7. (b) Illustration of an example calculation of false positive rate given more “intuitive” values of N, R, and p. The false positive rate is calculated in a way that accounts for the possibility that an error in one or more “linear” cycles propagates to a whole family of reads. The number of reads with an error (k) is Poisson distributed according to equation 2. The probability of a false positive is the sum of the probabilities that all R reads come from one of k families, for all possible k, according to equation 3. Note that in practice, p<10−6, and in our study N=12, R>2, making the false positive rate much lower (see Fig. 1).
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Related In: Results  -  Collection

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Figure 14: Schematic depicting the mathematical derivation of the false positive rate of MDS due to polymerase error. (a) The origin of various terms used in equations 2-7. (b) Illustration of an example calculation of false positive rate given more “intuitive” values of N, R, and p. The false positive rate is calculated in a way that accounts for the possibility that an error in one or more “linear” cycles propagates to a whole family of reads. The number of reads with an error (k) is Poisson distributed according to equation 2. The probability of a false positive is the sum of the probabilities that all R reads come from one of k families, for all possible k, according to equation 3. Note that in practice, p<10−6, and in our study N=12, R>2, making the false positive rate much lower (see Fig. 1).
Mentions: As discussed briefly in the main text, in our assay, the total polymerase error rate Epol,X→Y can be derived as follows (for visual aid, see Extended Data Fig. 10). After exponential PCR, there are N pools of reads, each derived from one of the original linear amplification steps. The probability of having k pools derive from an original polymerase error is binomially distributed. Furthermore, because pN<<1, the distribution is Poisson. (2)(Nk)pk(1−p)N−k≈(Np)kk!e−Np

View Article: PubMed Central - PubMed

ABSTRACT

In 1943, Luria and Delbr&uuml;ck used a phage resistance assay to establish spontaneous mutation as a driving force of microbial diversity1. Mutation rates are still studied using such assays, but these can only examine the small minority of mutations conferring survival in a particular condition. Newer approaches, such as long-term evolution followed by whole-genome sequencing 2, 3, may be skewed by mutational &ldquo;hot&rdquo; or &ldquo;cold&rdquo; spots 3, 4. Both approaches are affected by numerous caveats 5, 6, 7 (see Supplemental Information). We devise a method, Maximum-Depth Sequencing (MDS), to detect extremely rare variants in a population of cells through error-corrected, high-throughput sequencing. We directly measure locus-specific mutation rates in E. coli and show that they vary across the genome by at least an order of magnitude. Our data suggest that certain types of nucleotide misincorporation occur 104-fold more frequently than the basal rate of mutations, but are repaired in vivo. Our data also suggest specific mechanisms of antibiotic-induced mutagenesis, including downregulation of mismatch repair via oxidative stress; transcription-replication conflicts; and in the case of fluoroquinolones, direct damage to DNA.

No MeSH data available.