Many approaches are followed to understand evolution of antibiotic resistance. The Adaptive Laboratory Evolution ( ALE) is used in most of the studies (Oz et al. 2014; Munck et al. 2014; Kim et al. 2018). In ALE, two approaches were followed, first, incremental increase in the antibiotic concentration and second method stepwise exposure to antibiotics (Jahn et al. 2017). In the current study, stepwise antibiotic exposure was used. The ciprofloxacin was the choice of antibiotic. One of the most effective antibiotics used to treat P. aeruginosa infections is the second-generation fluoroquinolone, ciprofloxacin; introduced in 1987, ciprofloxacin proved so effective at treating an infection that it rapidly joined the WHO list of medicines essential for basic healthcare (Wise et al. 1983). It has a broad spectrum of action with good tissue penetration, oral absorption and favourable pharmacokinetics, making it ideal for the treatment of a wide range of infections. Crucially, the presence of the cyclopropane moiety on the N atom of the heterocycle in ciprofloxacin increases its activity (compared with first generation fluoroquinolones such as norfloxacin) against P. aeruginosa by a factor of four (Jedrey et al. 2018). The second reason for the use of ciprofloxacin is its ability to induce more mutations by interfering with DNA replication and repair mechanism (Maxwell et al. 2015; Bush et al. 2020). Suzuki et al (2014), suggested that, the antibiotic resistance development in a gradual manner is unlikely. Evolution often happens in spurts (Neher 2013). In our experiments we observed that, the resistance increases by 25 and 50 folds after, 4 and 6 cycles respectively in clone A. After 10th cycle 100-fold increase in resistance was observed and this is the plateau of the resistance. P. aeruginosa when exposed to constant sub-inhibitory concentrations or increasing concentration of quaternary ammonium compounds showed a plateau in MIC after 10 cycles leading to ciprofloxacin resistance (Kim et al. 2018; Voumard et al. 2020). This is probably due to regrowth of bacteria tolerant to antibiotic after initial kill of 90–95% population as we have noted the MIC after the growth up to approximately 107cells. These tolerant bacteria remove ciprofloxacin by efflux, grows and also maintains the structure of outer membrane showing involvement of different mechanisms simultaneously i.e., initial stress response of the surviving population. The tolerant bacteria probably pump out ciprofloxacin by efflux and grow (Moen et al. 2012). This shows higher concentrations of antibiotics present in the broth (10X MIC) as in our case or in industrial sewage or clinical environment are more prone to antibiotic resistant bacteria than the sub-inhibitory or lower concentrations used in food processing, animal husbandry or veterinary conditions as pointed out by Gullberg et al (2011). Eagle (1948) noted that bacteria or fungi surviving above its bactericidal concentration had improved survival leading to antibiotic resistances. Survival of Staphylococci 1000-fold above its MIC in penicillin was also observed (Kirby 1945; Prasetyoputri et al. 2019). However, MIC does not give any phenotypic or genotypic knowledge of the bacterium therefore; we have characterized the mutants (A, B, C). The observations from this study and previous study clearly suggest the randomness of the event. The founder mutation will lead to multiple mutations. However, in the current study we have not sequenced the survivors of each cycle to screen for mutations. Our objective was to know what is the maximum resistance that could be achieved in ALE experiments.
The exposure of the wild type strain to ciprofloxacin 10 times its MIC showed lower growth rate as indicated with an increased lag phase (70 min) of the mutant strain (Fig. 1). This delay in growth probably is the strategy of bacteria to remain dormant, prevent itself from the harms of the antibiotic and prepare for the reproduction. The bacteria first become tolerant to in lag phase and this “tolerance by lag “leads to antibiotic resistance. (Fridman et al. 2014; Li et al. 2016). Fridman et al (2014) observed on exposing the Escherichia coli strain to ampicillin for 3, 5 and cycles increased the lag to 3.5, 6 and 10 hrs respectively; therefore, authors presume lag phase is optimized to tolerate antibiotic stress. S. Enteritidis mutants (SE-M1, SE-M2), which presented reduced susceptibility to ciprofloxacin, exhibited the same growth as the parental strain; however, the mutants that acquired resistance had longer lag phases than the parental strain and did not reach the same cell density in the stationary phase. On exposure to ciprofloxacin a longer lag phase of approximately 12 hrs was observed in S. Enteritidis and S. Typhimurium mutants (Zhang et al. 2017).
All three mutants in the current study were sensitive to heat and osmotic stress (Table 1). Cellular responses, antimicrobial exposure, and other growth-compromising stresses, have all been linked to the development of antimicrobial resistance in Gram-negative bacteria resulting from the stimulation of protective changes to cell physiology, activation of resistance mechanisms, and induction of resistance mutations (Poole 2012). However, these mutants were resistant to different antibiotics like ampicillin, kanamycin, tetracycline and chloramphenicol which belong to different classes of antibiotics viz: beta-lactam, aminoglycoside, tetracyclines and chloramphenicol along with fluoroquinolones having different modes of action. The resistance to different generations of ciprofloxacin was also observed (Table 2). Thus, ciprofloxacin induced antibiotic resistance cause multidrug resistance. The antibiotics probably have common targets or common mechanism to develop resistance. Jahn et al (2017) have shown that E. coli can adapt to resistant mutation in different antibiotics like amikacin, piperacillin and tetracycline irrespective of method of selection or adaptation. Recently E. coli and S. Typhimurium mutants resistant to tetracycline (tigecycline), beta-lactam (mecillinam) and antimicrobial peptide (protamine) showed increased sensitivity to antibiotic nitrofurantoin under laboratory condition (Roemhild et al. 2020).
Colony size of bacteria is phenotype variation to study genetic diversity and intermediate exposure to antibiotics may cause small size colonies (Lee et al. 2018). Small colony size and long lag phase has been associated with exposure to aminoglycoside stresses in P. aeruginosa (Wei et al. 2011). In clinical isolates of S. aureus small colony variants have been linked to antibiotic resistant infections (Cao et al. 2017). The bacteria may show phenotypic switching of colony size shape or cell morphology to adapt to a hostile environment and this needs to be studied further. In this study small colony size has probably helped in ciprofloxacin resistance adaptation.
After 20 cycles of exposure to ciprofloxacin, 3 clones were selected for WGS. The objective was to understand, what mutations led to resistance? Second objective was to find out whether all the clones have originated from the same parental strain or each evolved independently? There could be multiple evolutionary pathways which lead to each clone. However, sequencing each clone and its ancestor was beyond the scope of this work. The whole genome comparison of three clones suggests that, they are derived from a single parental strain. This strain may have a founder mutation, probably in gyrase or in DNA repair pathway. In an earlier study on evolution of resistance in S. aureus after 22 days of antibiotic exposure (trimethoprim, ciprofloxacin and neomycin) WGS was carried out. They sequenced 120 clones and found that treatments with alternating antibiotics changed the spectrum of resistance mutations. These genetic constraints affected the rate of evolution of mutations associated with the cross resistance amongst the drugs (Kim et al. 2014). E. coli on exposure to amikacin, tetracycline and piperacillin at the end of the study 96 clones were sequenced and found that cross resistance is similar in the two approaches used in the study leading to similar phenotypic and genotypic changes (Jahn et al. 2017).
We report for the first-time approximately 40,000 SNPs in a single isolate on cyclic exposure to ciprofloxacin. No such report on single isolate is observed in literature. Ten clinical Pseudomonas aeruginosa mutants resistant to amikacin showed 18,876 nSNPs while, 81 nSNPs were identified in pipercellin resistant isolates (Ramanathan et al. 2017). Similarly, in ten clinical isolates of Acinetobacter baumanii 11,387 SNPs in the coding region, 42 INDELS and 33 antibiotic related genes were observed. Total 74,713 SNPs have been reported in 60 isolates of MDR E. coli recently from India (Pu et al. 2019; Ragupathi et al. 2020). The clone A showed 113 INDELS. Out of these 10 (9 insertions and 1 deletion) were in the coding region while others in the non-coding regions. These were present in genes not essential for antibiotic resistance. In Mycobacterium tuberculosis short indels are shown to cause antibiotic resistance upon disruption which are not only dispensable and important in highly resistant outbreak but also important in evolution (Godfroid et al. 2020). WGS of 25 clinical isolates of pneumococcal strains from children showed different drug resistant profiles ranging from 131–171 indels and SNPs in the range 16,103–28,128 with different drug resistant profiles (Pan et al. 2018) however, the contribution of indels in antibiotic evolution is poorly understood.
All clones in the present study showed 80% transitions and 20% transversions. In earlier studies also, similar ratio of 1:4 ratio of transversion: transition was observed in multidrug resistant E. coli, Mycobacterium tuberculosis and human genome (Guo et al. 2017; Payne et al. 2019; Ragupathi et al. 2020). In a previous study, it has been shown that, fluoroquinolone (norfloxacin) antibiotics disrupt DNA repair pathways. This impaired mismatch repair pathway may also contribute to skewed ratios of transition to transversion. (Jørgensen et al. 2013). The widespread biasness observed in transition: transversion ratios are unknown. Two main hypotheses to explain this phenomenon are; first the mutational hypothesis which shows there is transitional biasness and higher transitional rate in coding and noncoding regions therefore transition mutational rates of polymerases are higher than transversion rates. [49,50]. The second selective hypothesis states that nonsynonymous transitions conserve important biochemical properties of original amino acids therefore natural selection does not favour transversions (Vogel and Kopun 1977; Miyata et al. 1979; Zhang 2000; Lyons and Lauring 2017). The synonymous mutations do not affect the encoded amino acid and therefore, have no role in adaptation or fitness of a bacterial cell. However, laboratory evolved populations of P. fluorescence and S. Typhimurium have shown increased fitness due to synonymous mutation (Lind et al. 2010; Bailey et al. 2014). This shows our knowledge about gene function and their role in bacterial genome evolution is still scarce as pointed out by Bryant et al (2012).
In all three clones the ciprofloxacin induced mutation frequency across the genome was uniform except the plasmid (psLT) (Fig. 2A) There are many genes in the current study had large number of SNPs. However, the number of mutations per 1000 bp was uniform across the genome. Hence, bigger genes like STM_4261 (siiE) have more mutations compared smaller genes like marR which have fewer mutations. However, there are many reports of biased mutation frequency concentrated in certain region of genome. In a recent study Foster et al (2013), reported that mutations are not randomly distributed along the chromosome rather, mutations fall in a wave-like pattern that is repeated in an almost exact mirror image in the two separately replicated halves (replicores) of the E. coli chromosome (Martincorena et al. 2012; Foster et al. 2013). Mutation is difficult to study because it is a highly noisy process and because it affects variation in a manner that is highly entangled with the effects of natural selection. To characterize the effects of mutation, we need to acknowledge these complications and find creative ways to address them. Future studies will undoubtedly take advantage of the increasing ability to examine variation at the whole-genome level to reveal much more about mutation and how it acts as an engine of evolution in bacteria (Hershberg 2015).
Mutations were uniformly distributed across the genome. Therefore, multiple metabolic pathways are affected. Since, selection was based on the survival, all the three clones were able to grow on rich medium albeit slow. Most of the metabolic pathways are functioning or alternative pathways are functioning to keep the metabolism going. However, compared to wild type the mutants grew slow and formed small colony. Survival and growth in high antibiotic concentration are the result of contribution of multiple adjustments in many metabolic pathways. The mutant should have corrupted target or targets that are not affected by antibiotic. All three mutants had mutations in DNA replication and repair pathways. The increase in MIC of ciprofloxacin is known to be by alterations in primary and secondary drug targets gyrA, gyrB the DNA gyrases or parC, parE the topoisomerases and regulator genes of efflux pumps like (marR, acrR and soxR); recently RNA polymerases rpoB, rpoC are also shown to improve import of ciprofloxacin in E. coli (Ricci et al. 2006; Pietsch et al. 2017). In previous studies it has been shown that, a single mutation in gyrA at S83 orA87 was observed in fluoroquinolone resistant Salmonella from human and animal origin. Similarly, resistance linking gyrB mutation at (S464T) and mutations at S80 or G78 of parC has been reported in ciprofloxacin resistant Salmonella (Chen et al. 2007). The second possibility is pumping out the antibiotic or restriction of antibiotic entering into the cell. Active efflux and decrease in drug permeability also contribute to resistance to many antibiotics including fluoroquinolones in clinical isolates of Salmonella, E. coli, Pseudomonas etc; (Toprak et al. 2011; Redgrave et al. 2014) acrAB-tolC efflux pump is the prominent one in S. Typhimurium. This pump consists of periplasmic accessory protein AcrA, transporter inner membrane protein AcrB and outer membrane channel protein (TolC). The mutants had multiple mutations in transporter proteins. It has been shown that ciprofloxacin affects cell viability by causing oxidative stress. We observed at least 45 genes involved in oxidative stress were mutated. However, none of these mutations were biologically tested for their contribution for the antibiotic resistance. The antibiotic resistance observed in the current study may be cumulative effect of mutations in many pathways or mutation in few genes. This needs further validation by detailed physiological studies. (Toprak et al. 2011; Redgrave et al. 2014).
There are few observations which we were unable to explain. The pSLT plasmid is a 90 kb native plasmid of S. Typhimurium. The entire plasmid had only two mutations. Since plasmid replication and maintenance are also carried out by genes on genome and expected similar mutation rate as on the main genome. The transformation experiments in E. coli have suggested that probably the mechanism of replication and maintenance is different in chromosome and plasmid. The other reason could be our dose of ciprofloxacin used in this study was high and cost of maintaining resistant plasmid will be ameliorated in evolution experiments as observed by Svara and Rankin, (2011).
The important shortcoming of the current study was, we were unable to track the mutations from the first cycle to 20th cycle of antibiotic exposure. This would have given an insight into possible origin of hypermutation phenotype. However, clones A, B and C all share more than 98 % of the mutations; suggest that all have originated from one hyper mutant.