Biology subjects

Mohammed Ismail, W.

Publications and source records attributed to Mohammed Ismail, W..

2 recordsLinked to original sources

bioRxiv2020

New complexities of SOS-induced untargeted mutagenesis in Escherichia coli as revealed by mutation accumulation and whole-genome sequencing

When its DNA is damaged, Escherichia coli induces the SOS response, which consists of about 40 genes that encode activities to repair or tolerate the damage. Certain alleles of the major SOS-control genes, recA and lexA, cause constitutive expression of the response, resulting in an increase in spontaneous mutations. These mutations, historically called "untargeted", have been the subject of many previous studies. Here we re-examine SOS-induced mutagenesis using mutation accumulation followed by whole-genome sequencing (MA/WGS), which allows a detailed picture of the types of mutations induced as well as their sequence-specificity. Our results confirm previous findings that SOS expression specifically induces transversion base-pair substitutions, with rates averaging about 60-fold above wild-type levels. Surprisingly, the rates of G:C to C:G transversions, normally an extremely rare mutation, were induced an average of 160-fold above wild-type levels. The SOS-induced transversion showed strong sequence specificity, the most extreme of which was the G:C to C:G transversions, 60% of which occurred at the middle base of 5'GGC3'+5'GCC3' sites although these sites represent only 8% of the G:C base pairs in the genome. SOS-induced transversions were also DNA strand biased, occurring, on average, 2- to 4- times more often when the purine was on the leading strand template and the pyrimidine on the lagging strand template than in the opposite orientation. However, the strand bias was also sequence specific, and even of reverse orientation at some sites. By eliminating constraints on the mutations that can be recovered, the MA/WGS protocol revealed new complexities to the nature of SOS "untargeted" mutations. HighlightsO_LIThe SOS response to DNA damage induces "untargeted" mutations C_LIO_LISOS-mutations are revealed by mutation accumulation and whole genome sequencing C_LIO_LISOS-mutations are both sequence and DNA-strand biased C_LIO_LIG:C to C:G transversions are particularly highly induced by SOS C_LIO_LIG:C to C:G transversions are extremely sequence and DNA-strand biased C_LI

genetics↗

bioRxiv2019

Clonal reconstruction from time course genomic sequencing data

BackgroundBacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations.\n\nResultsIn this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli.\n\nConclusionWe developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments.\n\nAvailabilityThe program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE

genomics↗