Fluoroquinolone-Specific Resistance Trajectories in E. coli and their Dependence on the SOS-Response

doi:10.21203/rs.3.rs-4939869/v1

Background

Fluoroquinolones are indispensable antibiotics used in treating bacterial infections in both human and veterinary medicine. However, resistance to these drugs presents a growing challenge. The SOS response, a DNA repair pathway activated by DNA damage, is known to influence resistance development, yet its role in fluoroquinolone resistance is not fully understood. This study aims to unfold the mechanisms of fluoroquinolone resistance by investigating the impact of the SOS response on bacterial adaptation.

Results

We exposed Escherichia coli to four fluoroquinolones - ciprofloxacin, enrofloxacin, levofloxacin, and moxifloxacin. Using a recA knockout mutant, deficient in the SOS response, as a control, we assessed how the presence or absence of this pathway affects resistance development. Our findings demonstrated that the rate of resistance evolution varied between the different fluoroquinolones. Ciprofloxacin and moxifloxacin exposures led to the most evident reliance on the SOS response for resistance, whereas enrofloxacin and levofloxacin exposed cultures showed less dependency. Whole genome analysis indicated distinct genetic changes associated with each fluoroquinolone, highlighting potential different pathways and mechanisms involved in resistance.

Conclusions

This study shows that the SOS response plays a crucial role in resistance development to certain fluoroquinolones, with varying dependencies per drug. The characteristic impact of fluoroquinolones on resistance mechanisms emphasizes the need to consider the unique properties of each antibiotic in resistance studies and treatment strategies. These findings are essential for improving antibiotic stewardship and developing more effective, tailored interventions to combat resistance.

Fluoroquinolones

Antibiotic resistance

de novo resistance

SOS response

Experimental evolution

Fluoroquinolones are broad-spectrum antibiotics commonly used to treat infections in both human and veterinary medicine (1, 2). Classified under the World Health Organization’s (WHO) AWaRe framework as high-priority critically important antimicrobials, these drugs are crucial due to their efficacy but pose a heightened risk for resistance development (3). Despite their importance and our broad knowledge of their mechanisms of action and corresponding bacterial resistance mechanisms, our understanding of how bacteria evolve to become resistant or adapt to fluoroquinolones remains limited.

The mode of action of fluoroquinolones revolves around targeting essential bacterial enzymes critical for DNA replication and repair, i.e. DNA gyrase (GyrA and GyrB) and DNA topoisomerase IV (ParC and ParE) (4). The bacterial resistance mechanisms include target site alterations, efflux pumps, and changes in membrane permeability (5). Since fluoroquinolones cause DNA damage and consequently have been shown to activate the bacterial SOS response, inhibiting the SOS response has been proposed as a potential approach to combat resistance evolution (6, 7).

The core of the SOS response is the activation of the RecA protein, induced by its interaction with single-stranded DNA (ssDNA) (8, 9). Once activated, RecA coordinates the self-cleavage of the SOS transcriptional repressor LexA (10), thereby initiating the transcriptional cascade of the SOS response. However, knocking out RecA does not entirely abolish antibiotic-induced mutations in Escherichia coli (E. coli) upon beta-lactam exposure, indicating the existence of a LexA/RecA-independent pathway capable of triggering the SOS response that has yet to be fully understood (11).

Utilizing an experimental evolution approach, we sought to study the dynamics governing the adaptation of E. coli to four fluoroquinolone antibiotics – ciprofloxacin, enrofloxacin, levofloxacin, and moxifloxacin – using a SOS-deficient mutant as biological control. The selection of these fluoroquinolones was based on their different chemical structures and widespread use in both veterinary and human healthcare settings, allowing for a broad exploration of the adaptive landscape. Yet, how and if they differ in inducing cellular changes that lead to de novo resistance is unknown. Through this study, we aimed to clarify the genetic and phenotypic changes occurring in both the wild type and SOS-knockout strain, thus highlighting the interplay between the induction of the SOS response and the evolution of resistance. Our research specifically focused on the role of the SOS response in adaptation to each fluoroquinolone and the distinct adaptation patterns associated with each antibiotic.

Dependency on recA for resistance evolution differs between fluoroquinolones.

We could observe a clear pattern in both strains by comparing the onset of clinically relevant resistance between the tested antibiotics. Both MG1655 and ΔrecA acquired resistance the fastest when exposed to moxifloxacin. The slowest resistance development was observed during ciprofloxacin exposure. An overview of the number of transfers it took to reach the EUCAST breakpoints can be found in Table 1.

Table 1

Comparison average transfers until clinical resistance
Fluoroquinolone	MG1655	ΔrecA
Ciprofloxacin	7.7±0.6	17±5.3
Enrofloxacin	5.3±0.6; 9*±1	14.3±2.5; 26^()±2.8
Levofloxacin	6.7±1.2	11.3±3.2
Moxifloxacin	3.3±0.6	7.7±2.5

The table lists the average transfers ± standard deviation until the strains reached the resistance breakpoint according to the EUCAST/CLSI guidelines (see Table S3 in the supplementary data).

*Horses. **Poultry. (*) One replicate could not reach the resistance breakpoint until the experiment ended.

Relative to the wild-type strain, the knockout mutant (ΔrecA) displayed a higher inconsistency in adaptation rates to the tested fluoroquinolones between the replicates. In general, the ΔrecA mutant showed an unpredictable response to antibiotic exposure, often leading to the loss of cultures after incubation in antibiotic concentrations they already adapted to before. The starting concentrations for the evolution experiment with the knockout mutant were lower, reflective of the decreased minimum inhibitory concentration (MIC) of the naive mutant observed during initial MIC testing. The starting concentrations, which equalled ¼ of the starting MICs can be found in Table S4.

Knocking out recA led to a distinct impairment of adaptation to all tested fluoroquinolones (Fig. 1). However, there were notable differences between technical replicates, and there also seemed to be a distinction between the different fluoroquinolones. The strongest dissimilarity between ΔrecA and the wild-type could be observed upon moxifloxacin exposure. While the wild-type could adapt to moxifloxacin concentrations of 32, 512, and 1024 ug/mL, ΔrecA could only be exposed to a maximum moxifloxacin concentration of 1, 2, or 4 ug/mL, respectively. Similar patterns were found in the ciprofloxacin cultures. Two of the three ΔrecA replicates could not adapt to ciprofloxacin concentrations above 1 ug/mL during the tested time frame. This pattern supports the hypothesis that targeting the SOS response in bacteria could be a valuable approach to prevent the development of antimicrobial resistance to some antibiotics. However, one of the ciprofloxacin-exposed ΔrecA replicates reached the EUCAST threshold of 0.5 ug/mL after 11 transfers and could survive a final concentration of 4 ug/mL. Yet, this final concentration was considerably lower than the final concentrations of the wild-type replicates, i.e. 128 ug/mL and 256 ug/mL of ciprofloxacin. A detailed account of the concentrations at each transfer cycle is available in the supplementary data (Table S5 and Table S6).
Different fluoroquinolones result in distinct mutational patterns.

To study the influence of the four tested fluoroquinolones on the mutational trajectories of the wild-type and the mutant, the genome of every culture was sequenced before the evolution experiment as well as after 14 and 30 transfer cycles. MG1655 and ΔrecA both acquired mutations that have commonly been described in fluoroquinolone-resistant strains, i.e. in the genes gyrA, gyrB, parC and parE (5, 16, 17), with the highest frequency of mutations in gyrA. Both strains also developed mutations that have been described to accelerate antibiotic resistance (acrR, and marR) (5, 17, 18). Mutations in these genes were the most prevalent and abundant overall, along with mutations in soxR and dinG. In addition, both strains gained mutations in genes specific for either the wild-type or the knockout mutant. Genes that were only mutated in one replicate were ignored in this analysis. After this filtering, the specific mutations for the strains occurred in rpoC, mprA, eptB, and rrlF for MG1655, and dinG and rbsR for ΔrecA, respectively. Mutations in acrR, gyrA, gyrB, and marR occurred in both strains before transfer 14. Mutations in parC and parE were acquired by single replicates before transfer 14 but were more abundant at the end of the experiment (Fig. 2). This early appearance of gyrA mutations and the later onset of parC mutations is coherent with earlier observations (19, 20). Single nucleotide polymorphisms (SNPs) were the most common type of mutations observed.

Distinct patterns could be found at the end of the experiment when examining the individual antibiotic-associated mutations (see network in Fig. 2, and Fig. 3). Whereas no acrR mutations could be observed in any of the ciprofloxacin-exposed strains, it was the only fluoroquinolone that could be associated with rpoD mutations. Next to moxifloxacin, it was also the only fluoroquinolone linked to soxR mutations. Overall, soxR mutations were more abundant in the knockout strains than in the wild-type. Mutations in gyrB were the least common in enrofloxacin-exposed strains, with no occurrence in any of the knockout replicates. In contrast, all levofloxacin-exposed ΔrecA replicates had at least a single mutation in gyrB. Exposure to levofloxacin led to mutations in marR in all cultures, whereas exposure to moxifloxacin resulted in a mutation in this gene for only one of the wild-type replicates. Adaptation to moxifloxacin was possible for all ΔrecA replicates without mutations in parC. Noteworthily, one of the moxifloxacin-exposed wild-type replicates could adapt to a concentration of 512 ug/mL without any gyrA, parC, or parE mutation.

Our evolved strains showed a higher prevalence of D87 than S83 mutations in the DNA gyrase subunit A (see Table 2). 79% of the strains had a D87 mutation while only 29% of the evolved strains had a mutation in S83. This pattern was pronounced in the knockout mutant – while 75% of the evolved strains carried a D87 mutation, only a single strain (8%) carried an S83 mutation at the end of the evolution experiment. In addition, 75% of the knockout strains had only a single mutation in gyrA, whereas 83% of the wild-type had two mutations in the DNA gyrase.

Table 2

Overview *gyrAB* and *parCE* Mutations
Strain	Replicate	Concentration	FQ	gyrA		gyrB		parC		parE
Strain	Replicate	Concentration	FQ	Type	Effect	Type	Effect	Type	Effect	Type	Effect
MG1655	1	128	CIP	Complex	DS82GD	SNP	Q465P			SNP	Q459P
	2	128		SNP, SNP	D87G, S83L	SNP	Q465P	SNP	E84G
	3	256		SNP, SNP	D87G, G81C	SNP, SNP	E466D, I471T	SNP	S80R	SNP	E460K, T447A
	1	32	ENR	SNP, SNP	D87G, S83L	DEL, SNP	D399_L403del, S464Y	INS, SNP	D519dup, S80R	SNP	S425P
	2	512		SNP	D87Y	SNP	E446D	SNP	S80R
	3	256		SNP, SNP	D87G, G81C			SNP	G78C
	1	128	LEV	SNP, SNP	D87G, S83A	SNP	S464A	SNP	A30V
	2	64		SNP, SNP	D87Y, S83L					INS	V461_H462insL
	3	256		SNP, SNP	A119E, D87N	SNP	S464F	SNP	S80I	SNP	I437F
	1	512	MOX			SNP	V453F
	2	32		SNP, SNP, MNP	A614V, D87N, S83V	SNP	E466A	SNP	S80I	SNP	E460V
	3	1024		SNP, Complex	D87G, SA83LP			SNP	S80R	SNP	V461G
ΔrecA	1	1	CIP	SNP	D87G	SNP	S464F	SNP	S80R	INS	H51dup
	2	4		SNP	D87Y	DEL	R457_K460del			SNP	K390T
	3	1		SNP	A119E			SNP	R614G
	1	32	ENR		A119E
	2	8		SNP, DEL	D87G, P618del					SNP	K610T
	3	1		SNP, SNP	D82N, D87G				G78D
	1	64	LEV	SNP	D87Y	SNP, SNP	D105E, D426E			SNP	I437L
	2	2		SNP	D87G	SNP, SNP	D426E, F458L	SNP	R96C
	3	2		SNP, SNP	D87G, S83L	SNP	E446D	SNP	S80R	SNP	V461G
	1	4	MOX	SNP	D87Y	SNP	V453L			SNP	R481C
	2	2		SNP	D82G					SNP	L502F
	3	1		SNP	D87G	SNP	T469P			SNP	E449V

The observed patterns of mutations associated with the development of resistance varied strongly between the four fluoroquinolones used in this study. Examining the adaptation to fluoroquinolone exposure and its dependency on the SOS response, revealed intriguing dynamics. Notably, both the ΔrecA knockout and wild-type strains developed clinical levels of resistance most rapidly following moxifloxacin exposure, whereas the slowest adaptation occurred with ciprofloxacin exposure. Furthermore, our analysis revealed that strains exposed to moxifloxacin and ciprofloxacin exhibited the strongest reliance on RecA for their adaptation, in stark contrast to those exposed to enrofloxacin and levofloxacin. These findings suggest that the effectiveness of impairing the SOS response as a strategy to inhibit resistance evolution may depend not only on biological variation but also on the specific fluoroquinolone used. Interestingly, the difference in SOS response induction between different fluoroquinolones is not commonly recognized. Instead, it is often assumed that the cellular response to different antibiotics within the same class is interchangeable (21–27).

Although the differences in the chemical properties of each fluoroquinolone are evident, their impact on cellular uptake and target binding remains unclear. A computational study on quinolone-gyrase binding revealed varying affinities of moxifloxacin, ciprofloxacin, and levofloxacin for gyrase (28). While the commonly observed S83L mutation in gyrA is known to confer high-level resistance to ciprofloxacin and levofloxacin, its impact on moxifloxacin resistance appears to be less significant, likely due to moxifloxacin's improved binding to the GyrA protein (28). Additionally, the study highlighted that mutations at D87 and R121 are more critical binding sites for ciprofloxacin, levofloxacin, and moxifloxacin than the S83 site (28). Consistent with these findings, our evolved strains showed a higher prevalence of D87 mutations over S83 mutations in the DNA gyrase (see Table 2), suggesting that the differing binding affinities of these fluoroquinolones lead to distinct impacts on resistance evolution.

The genomic analysis of the resistant cultures revealed distinct mutation profiles when comparing the effects of different fluoroquinolones. We observed fewer mutations in marR in moxifloxacin-exposed strains compared to those exposed to other antibiotics, particularly levofloxacin, which induced marR mutations in all cultures. The mar regulon has been proposed not only to play a role in reduced drug uptake and the regulation of the AcrAB efflux pump but also in the repair of quinolone-induced DNA damage (29–31). However, it is important to note that changes in SoxS expression, partially regulated by soxR, have been suggested as an alternative to mar overexpression in the development of resistance (31). Our analysis indicates that moxifloxacin exposure results in a higher prevalence of acrR and soxR mutations compared to the other fluoroquinolones, suggesting a potential shift in AcrAB efflux pump expression in the moxifloxacin-resistant strains via these regulons (32). Nevertheless, despite this, efflux might not be the primary resistance mechanism in moxifloxacin-resistant cultures, likely due to the chemical complexity and hydrophobic properties of the antibiotic (33).

Adaptation to enrofloxacin was associated with a lower frequency of gyrB and parE mutations compared to the other fluoroquinolones tested. This observation is particularly interesting given the chemical similarity between enrofloxacin and ciprofloxacin. Despite this, enrofloxacin appears to have a distinct binding affinity and resistance profile compared to ciprofloxacin. The two fluoroquinolones might interact differently with their target enzymes, but further investigation into their unique mechanisms is required. Furthermore, two special cases were observed: one wild-type strain exposed to moxifloxacin and one knockout strain exposed to enrofloxacin, both of which achieved adaptation to high antibiotic concentrations without acquiring a gyrA mutation. These findings suggest that high-level fluoroquinolone resistance can develop without the typically observed gyrA mutations, indicating alternative mechanisms or pathways activated that need to be further studied.

The biological variability observed among our replicates could partially be attributed to specific single nucleotide polymorphisms (SNPs). For instance, one of the three moxifloxacin-exposed wild-type replicates acquired an S80I mutation in parC. This replicate was only able to reach a concentration of 32 µg/mL, compared to the corresponding replicates, which reached 1024 and 512 µg/mL. This amino acid substitution increases mutation rates in E. coli under moxifloxacin exposure by 1300-fold (34), potentially leading to a reduction in population fitness due to the accumulation of deleterious mutations. Such lower fitness can be further diminished by passage through strong bottlenecks (Muller’s ratchet) (35–37) or by driving out the most adapted genotype as the population becomes overwhelmed with the mutational backflow (38). While a higher mutation rate can be beneficial for acquiring resistance through multiple mutations, as is necessary for high-level fluoroquinolone resistance (39, 40), it remains unclear at what point this benefit becomes a disadvantage. Indeed, in natural E. coli isolates, weak mutators have been found to have the highest levels of antibiotic resistance (41).

It is noteworthy that Schedletzky and co-workers reported different mutation rates associated with the S80I mutation in parC, depending on the antibiotic used (34). Ciprofloxacin exposure resulted in a lower mutation rate increase (40-fold compared to the 1300-fold increase observed during moxifloxacin exposure), which may have had a less detrimental effect on the population’s fitness. This finding aligns well with our observed phenotypes. For instance, the wild-type strain that acquired this mutation could only reach a concentration of 32 µg/mL under moxifloxacin exposure, while another wild-type replicate with the same mutation adapted to a levofloxacin concentration of 256 µg/mL, the highest observed among the replicates. Moreover, moxifloxacin has a greater impact on the growth rate at sub-MIC levels than ciprofloxacin (42), yet it induces the SOS response to a lesser extent (43). This observation can be explained by the increased SOS response at higher metabolic rates, driven by the elevated ROS levels in faster-metabolizing cells (44).

The mutations identified in this study play vital roles in the development of fluoroquinolone resistance, yet it is important to acknowledge that these mutations may not act independently. The interaction between mutations, known as epistasis, can significantly shape the resistance phenotype (45). The presence of one mutation may influence the effect of another, leading to non-linear evolutionary trajectories that affect the adaptability and fitness of bacterial populations. Furthermore, the resistance phenotypes observed in this study are likely influenced not only by the specific mutations but also by the broader genetic background of the bacterial strains and the conditions under which the experiments were performed. The impact of a particular mutation can vary significantly depending on the surrounding genomic context, potentially leading to different resistance levels in different strains or under varying environmental stresses.

In addition to directly contributing to resistance, some of the mutations identified in this study may play compensatory roles by modifying the fitness costs associated with fluoroquinolone resistance. Mutations in regulatory genes, for instance, can exert broad effects on gene expression and cellular pathways, potentially rebalancing cellular metabolism that might otherwise be impaired by resistance mutations (46). These compensatory mutations are crucial for maintaining bacterial fitness in the presence of antibiotics, allowing the organism to sustain growth and survival under selective pressure. Furthermore, they play a crucial role in the emergence of resistance. Without these compensatory changes, resistant cultures would be outcompeted by more fit, susceptible populations in antibiotic-free environments (47).

All strains adapted to some extent to the fluoroquinolones, regardless of whether they possessed a functional recA gene. However, it remains unclear whether this adaptation occurred due to RecA-independent activation of the SOS response or through the activation of other pathways. Although ciprofloxacin exposure appears to downregulate the mismatch repair system (48), inactivation of this system alone is not sufficient to acquire ciprofloxacin-resistance-inducing mutations if the SOS response is impaired (49). It has been proposed that parts of the SOS response can be activated independently of the LexA/RecA-regulon upon beta-lactam exposure, leading to resistance-inducing mutations through the upregulation of dinB, an error-prone DNA polymerase that is part of the SOS response (50). However, our observations indicate that a dinB knockout mutant can adapt to amoxicillin and enrofloxacin at nearly the same rate as a wild-type E. coli (Teichmann et al., in preparation), suggesting that this DNA polymerase is not essential for adaptation. Considering this, alternative mechanisms, such as adaptive amplification and amplification-mutagenesis, have also been proposed (51).

This study sheds light on the complex dynamics of bacterial adaptation to fluoroquinolones, highlighting the critical role of the SOS response and the unique mutational trajectories linked with different antibiotics within this class. Our findings demonstrate that the pathways leading to resistance are not uniform across all fluoroquinolones; instead, they vary depending on the specific drug. The differential reliance on the recA gene and the distinct mutation profiles observed across fluoroquinolone-exposed cultures emphasizes the necessity of a tailored approach when studying antibiotic resistance mechanisms. This suggests that studies relying predominantly on ciprofloxacin, for example, may not fully capture the broader range of responses provoked by the entire class of fluoroquinolones.

These insights have profound implications for clinical practice and antibiotic stewardship. The variability in the resistance evolution pathways suggests that strategies designed to curb the development of resistance must be tailored to the specific fluoroquinolone in use. Moreover, recognizing distinct mutational profiles associated with different fluoroquinolones can inform more effective screening and monitoring of resistant isolates, potentially leading to improved therapeutic outcomes.

It is important to acknowledge that resistance mechanisms can vary substantially across different bacterial species. The genetic and physiological differences between species may influence how they acquire and express resistance. For example, variations in cell wall structure, efflux pump systems, and DNA repair pathways could result in different evolutionary responses to fluoroquinolones. Thus, while our results provide valuable insights into resistance mechanisms in E. coli, further research is necessary to evaluate whether similar patterns of mutation and adaptation occur in other clinically relevant bacteria.

In summary, our research highlights the importance of considering the unique properties of each fluoroquinolone when designing studies and implementing clinical practices. To develop more targeted and effective strategies for combating antibiotic resistance these differences should be explored, particularly in the context of SOS response-independent pathways.

Strains, growth conditions, and antimicrobial agents

E. coli strain MG1655 was used as the wild-type strain. The single gene knockout mutant JW2669 (ΔrecA636::kan) was chosen from the KEIO collection and ordered from Horizon Discovery Ltd (12). The knockout mutant contained a resistance cassette with flanking FLP recognition elements which was removed using the pCP20 method before starting the experiments (13). Liquid or solid lysogeny broth (LB) (10 g/L NaCl) was used to culture the bacteria. Both strains were grown with a starting OD₆₀₀ of 0.1 at 37°C and 200 rpm overnight. For weekend incubations the starting OD₆₀₀ was reduced to 0.01 and the incubation temperature set to 30°C. Fluoroquinolone stocks were purchased from Sigma Aldrich and stored after preparation (10 mM) and filter sterilization either at 4°C for up to one week or at -20°C for a maximum of a month.

Minimum inhibitory concentrations (MIC)

MICs were measured twice a week in duplicate for each strain by the broth microdilution method described in (14). In brief, each strain was inoculated into the wells with a starting OD₆₀₀ of 0.05. Readings were performed in a microtiter plate reader every 10 min with 5 min shaking intervals. The MIC was set to the minimal concentration of the antibiotic which inhibited bacterial growth to an OD < 0.2 after 24 h incubation.

Evolution experiment

Evolution experiments were performed as described previously (15). At the beginning of the experiment, the MICs of both strains were determined. The starting concentration for each combination of strain and antibiotic equalled ¼ of the MIC for the specific strain and can be found in the supplementary data (Table S4). After overnight incubation, the OD₆₀₀ was measured. If the value was above 65% of the OD₆₀₀ of the previous culture, the culture was considered to have adapted to the antibiotic, and the antimicrobial concentration was increased 2-fold in fresh medium for the next incubation. If the yield was below this threshold, the culture was transferred to a fresh medium with the same antimicrobial concentration as used the previous day. In parallel, each bacterial strain was cultured without antibiotic exposure serving as biological controls. Three biological replicates were performed for each condition. The experiments were stopped after 30 transfers.

DNA isolation and whole genome sequencing

The DNA of each culture was isolated at the start, after 14 transfers, and at the end of the evolution experiment (30 transfers). After pelleting, phenol-chloroform-isoamyl alcohol (PCI) (Carl Roth) isolation was used to extract the DNA from the whole population. Genomic DNA libraries were created according to the manufacturer’s protocol using NEBNext Ultra II FS DNA Library Prep kit for Illumina (New England BioLabs) in combination with NEBNext multiplex oligos for Illumina (96 Unique Dual Index Primer Pairs; New England BioLabs). The samples were sequenced using a NextSeq 500/550 Mid Output V2.5 kit (Illumina).

Bioinformatic analysis

The bioinformatics analysis pipeline involved several steps to assess the quality of the sequencing data, trim low-quality reads, align the reads to the reference genome, and perform variant calling. First, the quality of the raw reads was evaluated using FastQC v0.11.9 and MultiQC v1.11. Next, Trim Galore was used to remove low-quality bases and adapter sequences from the reads (https://github.com/FelixKrueger/TrimGalore). The Trim Galore analysis was performed using v0.6.7 and cutadapt v1.18 with paired-end trimming. The adapter sequence 5' – AGATCGGAAGAGC − 3' corresponding to Illumina TruSeq libraries was auto-detected. The maximum allowed error rate was set to the default value of 0.1 during adapter trimming. Additionally, a minimum required adapter overlap of 1 base pair and a minimum sequence length of 20 base pairs for both reads were enforced to ensure stringent trimming criteria. The trimmed reads were then aligned to the reference genome using Snippy v4.6.0, a tool specifically designed for bacterial variant calling from short-read sequencing data (https://github.com/tseemann/snippy). As reference genomes, U00096 Escherichia coli K-12 sub-strain MG1655, and NZ_CP009273 Escherichia coli BW25113, also derived from K-12, were used. The sequences were obtained from the NCBI database. Finally, variant calling was performed using Snippy to identify single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels) in comparison to the reference genome. The final data analysis and visualization were performed in Microsoft Excel, R, and Cytoscape. Upon comparison with the reference genomes U00096 and NZ_CP009273, we found that both our susceptible E. coli strains exhibited notable genetic variations that were considered for further downstream analysis.

Acknowledgements
We thank Kaisa Thorell for her help with Snippy during the genomic analyses. We also appreciate the contributions of Pau Jorba Adolff and Chiara Varriale, who assisted with the evolution experiments.

Funding declaration
This study was financed by the Netherlands Food and Consumer Product Safety Authority.

Clinical trial number
not applicable

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No competing interests reported.

Supplementarydata.docx

Fluoroquinolone-Specific Resistance Trajectories in E. coli and their Dependence on the SOS-Response

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Abstract

Background

Results

Conclusions

Figures

Background

Results

Discussion

Conclusion

Materials and Methods

Strains, growth conditions, and antimicrobial agents

Minimum inhibitory concentrations (MIC)

Evolution experiment

DNA isolation and whole genome sequencing

Bioinformatic analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1