Quantifying DNA Breaks in Fluoroquinolone- Treated Bacterial Populations

Abstract

Bacterial persistence is a tolerance mechanism that has been linked to chronic and recurrent infections. Persisters are phenotypic variants that can survive high antibiotic concentrations that kill non-persister cells within a bacterial population, and surviving persisters give rise to a new population with similar sensitivity to the antibiotic. Evidence suggests that persister and non-persister cells incur DNA damage, so DNA repair mechanisms are important for persister formation. The variation in DNA damage quantities incurred by populations with different survival fractions has not yet been defined, thus this thesis sought to investigate the relationship between observed damage, damage repair, and persistence. Quantification of DNA breaks observed in Escherichia coli (E. coli) populations treated with levofloxacin (LEVO) was attempted by employing two breaks-labeling techniques proposed in literature: DSB-Seq and qDSB-Seq. Both DSB-Seq and qDSB-Seq were designed for use with mammalian and yeast cells only, so E. coli-specific parameters were determined. Analysis of the SOS response to DNA damage during recovery from antibiotic treatment revealed that cells should recover in LB medium for 45 min prior to breaks labeling to ensure that labeled breaks correspond to permanent damage induced by LEVO. Conduction of DSB-Seq with selected E. coli parameters demonstrated that more than 80% of the starting DNA is typically lost during the labeling and capture steps prior to sequencing. Given the low yields, DNA concentrations requirements for sequencing were frequently not met after purification of labeled breaks. Analysis of preliminary sequencing data testing the protocol was limited by a low percentage of sequencing reads that mapped to the reference E. coli genome. Nevertheless, this thesis demonstrates that applications of DSB-Seq and qDSB-Seq can be extended to E. coli to detect LEVO-induced DNA damage. Additional optimization of parameters for this methodology would allow for comparison across populations with different survival to LEVO treatments, thus highlighting important differences contributing to persistence.