Exploring the extent of uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli

Background Antimicrobial resistance (AMR) in E. coli is a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to a variants in a curated reference database with an implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study we evaluated the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility-phenotype prediction in E. coli . Methods We assembled a global dataset of 9001 E. coli sequences of which 8586 had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes extracted using the NCBI AMRFinder tool. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (70% match) and alleles (ARGs, 100% match). Results The performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet FDA thresholds for any of the eight antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, a small number of genes explained most resistance although a proportion could not be explained by known ARGs; this ranged from 75.1% for co-amoxiclav to 3.4% for ciprofloxacin. Only 17,177/36,637 (47%) of ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1292 unique ARGs of which 158 (12%) were present ≥10 times, 374 (29%) were present 2-9 times and 760 (59%) only once. Simulated accumulation curves revealed that discovery of new (100%-match) ARGs present more than once in the dataset plateaued relatively quickly whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0.76 (95% CI 0.72-0.79, p<0.001)) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed 7 times in Oxfordshire always being found elsewhere. Finally, using the example of bla TEM-1 , we demonstrated that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences (e.g. two common, uncatalogued bla TEM-1 alleles with only synonymous mutations compared to the known reference were associated with reduced resistance to co-amoxiclav [aOR 0.57, 95%CI 0.34-0.93, p=0.03] and piperacillin-tazobactam [aOR 0.54, 95%CI 0.32-0.87, p=0.01]). Conclusions Overall we highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We would like to thank the authors of the datasets used in this study for making their data freely available for public use. The computational aspects of this research were funded from the NIHR Oxford BRC with additional support from the Wellcome Trust Core Award Grant Number 203141/Z/16/Z. SL was funded by an MRC Clinical Research Training Fellowship MR/T001151/1. ASW and TEAP are also supported by the NIHR Oxford Biomedical Research Centre. ASW is an NIHR Senior Investigator. NS is an NIHR Oxford BRC Senior Fellow. This research is supported by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. This research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All assemblies are available at 10.6084/m9.figshare.22220212 and associated metadata can be found in supplementary dataset 1. All code used for the analysis can be found at https://github.com/samlipworth/resistome_variation where there is also a binder environment in which the key aspects of the analysis can be replicated.