SynopsisIntroductionCiprofloxacin resistance in Escherichia coli is widespread and adds to the burden of E. coli infections. Reviews assessing the genetic basis of ciprofloxacin resistance have mostly been qualitative. However, to allow for the prediction of a resistance phenotype of clinical relevance based on genotypic characteristics, it is essential to quantify the contribution of prevalent genotypic determinants to resistance. We carried out a systematic review to assess the relative contribution of currently known genomic resistance determinants to the minimum inhibitory concentration (MIC) of ciprofloxacin in E. coli.MethodsPubMed and Web of Science were searched for English language studies that assessed both ciprofloxacin MIC and the presence or introduction of genetic determinants of ciprofloxacin resistance in E. coli. We included experimental and observational studies without time restrictions. Medians and ranges of MIC fold changes were calculated for each resistance determinant and for combinations of determinants.ResultsWe included 66 studies, describing 604 E. coli isolates that carried at least one genetic resistance determinant. Genes coding for targets of ciprofloxacin (gyrA and parC) are strongest contributors to ciprofloxacin resistance, with median MIC fold increases ranging from 24 (range 4-133) for single Ser83Leu (gyrA) mutants to 1533 (range 256-8533) for triple Ser83Leu, Asp87Asn/Gly (gyrA) and Ser80Ile/Arg (parC) mutants. Other resistance mechanisms, including efflux, physical blocking or enzymatic modification, conferred smaller increases in ciprofloxacin MIC (median MIC fold increases typically around 15, range 1-125). However, the (combined) presence of these other resistance mechanisms further increases resistance with median MIC fold increases of up to 4000, and even in the absence of gyrA and parC mutations up to 250.ConclusionThis report provides a comprehensive and quantitative overview of the contribution of different genomic determinants to ciprofloxacin resistance in E. coli. Additionally, the data demonstrate the complexity of resistance phenotype prediction from genomic data and could serve as a reference point for studies aiming to address ciprofloxacin resistance prediction using genomics, in E. coli.