Phenotype-First Diagnostic Framework for Tracking Fluoroquinolone Resistance in Escherichia coli

Background: Fluoroquinolone (FQ) resistance in Escherichia coli (E. coli) undermines empiric therapy and often coincides with multidrug resistance (MDR). Because sequencing is not routinely available in many laboratories, we evaluated a phenotype-first, sequencing-independent diagnostic framework deployable on standard platforms. Methods: We profiled 45 archived E. coli isolates for susceptibility (Clinical and Laboratory Standards Institute [CLSI]-guided), extended-spectrum β-lactamase (ESBL) and AmpC β-lactamase (AmpC) phenotypes, MDR, and multiple-antibiotic resistance (MAR) indices. Ten founders (five FQ-susceptible [FQ-S], five low-level resistant [LLR]) seeded 20 parallel lineages exposed to stepwise ciprofloxacin. We tracked minimum inhibitory concentrations (MICs), collateral resistance, growth kinetics, and biofilm biomass using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for identification, automated and reference antimicrobial susceptibility testing (AST), growth-curve analysis, and crystal violet microtiter assays. The intended use is a sequencing-independent workflow for routine laboratories-especially where whole-genome sequencing is not readily available-working with archived or prospective clinical E. coli. This workflow is best applied when local FQ nonsusceptibility threatens empiric reliability; inputs include standard ID/AST with simple growth and biofilm assays. Primary outputs include: (i) MIC trajectories with time to high-level resistance (HLR), (ii) ΔMAR-summarized collateral resistance with class-level susceptible-to-resistant conversions, and (iii) concise fitness/biofilm summaries to guide empiric-policy refresh and early de-escalation. Results: At baseline, ciprofloxacin nonsusceptibility was 40.0%; ESBL and AmpC phenotypes were confirmed in 28.9% and 15.6%, respectively; 46.7% met the MDR definition; and the median MAR index was 0.29. During evolution, 70% of lineages reached HLR (MIC ≥ 4 μg/mL), with earlier conversion from LLR versus FQ-S founders (median 7 vs. 11 passages). Collateral resistance emerged most often to third-generation cephalosporins (3GCs), trimethoprim-sulfamethoxazole, and tetracyclines, while carbapenem activity was preserved. MAR increased in parallel with rising MICs. Resistance acquisition imposed modest fitness costs (slightly reduced growth rates and longer lag phases) that were partly offset under subinhibitory ciprofloxacin, whereas biofilm biomass changed little. Conclusions: this phenotype-first, routine-laboratory workflow rapidly maps FQ resistance and clinically relevant co-selection in E. coli. In high-resistance settings, empiric FQ use is difficult to justify, and MAR trends provide practical co-selection signals for stewardship. This reproducible framework complements genomic surveillance and is directly applicable where sequencing is unavailable.