Much of the worldwide dissemination of antibiotic resistance has been driven by resistance gene associations with mobile genetic elements (MGEs), such as plasmids and transposons. Although increasing, our understanding of resistance spread remains relatively limited, as methods for tracking mobile resistance genes through multiple species, strains and plasmids are lacking. We have developed a bioinformatic pipeline for tracking variation within, and mobility of, specific transposable elements (TEs), such as transposons carrying antibiotic-resistance genes. TETyper takes short-read whole-genome sequencing data as input and identifies single-nucleotide mutations and deletions within the TE of interest, to enable tracking of specific sequence variants, as well as the surrounding genetic context(s), to enable identification of transposition events. A major advantage of TETyper over previous methods is that it does not require a genome reference. To investigate global dissemination of Klebsiella pneumoniae carbapenemase (KPC) and its associated transposon Tn4401, we applied TETyper to a collection of over 3000 publicly available Illumina datasets containing blaKPC. This revealed surprising diversity, with over 200 distinct flanking genetic contexts for Tn4401, indicating high levels of transposition. Integration of sample metadata revealed insights into associations between geographic locations, host species, Tn4401 sequence variants and flanking genetic contexts. To demonstrate the ability of TETyper to cope with high-copy-number TEs and to track specific short-term evolutionary changes, we also applied it to the insertion sequence IS26 within a defined K. pneumoniae outbreak. TETyper is implemented in python and is freely available at https://github.com/aesheppard/TETyper.

Introduction: Risk factors for carbapenemase-producing Enterobacterales (CPE) acquisition/ infection and associated clinical outcomes have been evaluated in the context of clonal, species-specific outbreaks. Equivalent analyses for complex, multi-species outbreaks, which are increasingly common, are lacking. Methods: Between December 2010 and January 2017, a caseecontrol study of Klebsiella pneumoniae carbapenemase (KPC)-producing organism (KPCO) acquisition was undertaken using electronic health records from inpatients in a US academic medical centre and longterm acute care hospital (LTACH) with ongoing multi-species KPCO transmission despite a robust CPE screening programme. Cases had a first KPCO-positive culture >48 h after admission, and included colonizations and infections (defined by clinical records). Controls had at least two negative perirectal screens and no positive cultures. Risk factors for KPCO acquisition, first infection following acquisition, and 14-day mortality following each episode of infection were identified using multi-variable logistic regression. Results: In 303 cases (89 with at least one infection) and 5929 controls, risk factors for KPCO acquisition included: longer inpatient stay, transfusion, complex thoracic pathology, mechanical ventilation, dialysis, and exposure to carbapenems and b-lactam/b-lactamase

22 23 Much of the worldwide dissemination of antibiotic resistance has been driven by resistance gene 24 associations with mobile genetic elements (MGEs), such as plasmids and transposons. Although 25 increasing, our understanding of resistance spread remains relatively limited, as methods for 26 tracking mobile resistance genes through multiple species, strains and plasmids are lacking. We have 27 developed a bioinformatic pipeline for tracking variation within, and mobility of, specific 28 transposable elements (TEs), such as transposons carrying antibiotic resistance genes. TETyper takes 29 short-read whole-genome sequencing data as input and identifies single-nucleotide mutations and 30 deletions within the TE of interest, to enable tracking of specific sequence variants, as well as the 31 surrounding genetic context(s), to enable identification of transposition events. To investigate global 32 dissemination of Klebsiella pneumoniae carbapenemase (KPC) and its associated transposon Tn4401, 33 we applied TETyper to a collection of >3000 publicly available Illumina datasets containing blaKPC. 34 This revealed surprising diversity, with >200 distinct flanking genetic contexts for Tn4401, indicating 35 high levels of transposition. Integration of sample metadata revealed insights into associations 36 between geographic locations, host species, Tn4401 sequence variants and flanking genetic 37 contexts. To demonstrate the ability of TETyper to cope with high copy number TEs and to track 38 specific short-term evolutionary changes, we also applied it to the insertion sequence IS26 within a 39 defined K. pneumoniae outbreak. TETyper is implemented in python and is freely available at 40 https://github.com/aesheppard/TETyper. 41 42 43 44 IMPACT STATEMENT 45 46Whole-genome sequencing (WGS) of bacterial pathogens has revolutionised the analysis of global 47 and within-outbreak transmission pathways. However, the study of antibiotic resistance 48 dissemination is more challenging, as resistance genes are often associated with mobile genetic 49 elements (MGEs) that enable gene exchange between different host bacteria. Therefore, standard 50 WGS approaches that focus on host strain relationships may not be informative for understanding 51 resistance gene dissemination. We have developed a bioinformatic tool for analysing WGS data from 52 the perspective of a specific MGE-resistance gene association. The outputs produced identify 53 variation within the MGE, as well as signatures of MGE mobility. This information can then be used 54to track the movement of the resistance gene, thus overcoming previous limitations by defining 55 relationships from a resistance gene perspective, rather than a host-strain perspective. In an 56 epidemiological context, this can provide insight into specific transmission pathways, thus informing 57 infection control within outbreak scenarios, as well as increasing our understanding of global 58 pathways of resistance dissemination. 59 60 61 62