Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study

Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study View ORCID Profile Hoai-An Nguyen , View ORCID Profile Anton Y. Peleg , View ORCID Profile Jiangning Song , Jessica A. Wisniewski , Luke V. Blakeway , Gnei Z. Badoordeen , View ORCID Profile Ravali Theegala , Nhu Quynh Doan , Matthew H. Parker , View ORCID Profile David L. Dowe , View ORCID Profile Nenad Macesic doi: https://doi.org/10.1101/2025.05.13.25327501 Hoai-An Nguyen 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hoai-An Nguyen Anton Y. Peleg 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia 2 Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University , Melbourne, Australia 3 Centre to Impact AMR, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anton Y. Peleg Jiangning Song 3 Centre to Impact AMR, Monash University , Melbourne, Australia 4 Monash Biomedicine Discovery Institute, Department of Biochemistry & Molecular Biology, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jiangning Song Jessica A. Wisniewski 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luke V. Blakeway 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gnei Z. Badoordeen 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ravali Theegala 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ravali Theegala Nhu Quynh Doan 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew H. Parker 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site David L. Dowe 5 Department of Data Science & AI, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David L. Dowe Nenad Macesic 1 Department of Infectious Diseases, The Alfred Hospital and School of Translational Medicine, Monash University , Melbourne, Australia 3 Centre to Impact AMR, Monash University , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nenad Macesic For correspondence: nenad.macesic1{at}monash.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Objectives We aimed to conduct a comprehensive genomic analysis of ceftolozane/tazobactam (C/T) resistance mechanisms in Pseudomonas aeruginosa by combining novel institutional data with publicly available sequencing data. Methods We analysed 1,682 P. aeruginosa isolates, comprising 339 isolates from Alfred Hospital (Melbourne, Australia) and 1,343 isolates from six public datasets. All isolates underwent whole-genome sequencing and C/T broth microdilution (BMD) susceptibility testing. We assessed previously reported intrinsic and acquired resistance mechanisms. We then conducted a genome-wide association study (GWAS) and machine learning analysis to identify novel genes associated with resistance. We then evaluated the impact of mutations in these genes on MIC values and ceftolozane binding affinity. Results Among 1,682 P. aeruginosa isolates representing 527 distinct sequence types, 343/1,682 (20.4%) were C/T-resistant. Carbapenemase genes were detected in 206/1,682 (12.2%) isolates. Mutations in previously reported resistance-associated genes ( ftsI, mpl, ampD, ampC, ampR, oprD) were more frequent in resistant isolates but were also found in almost all susceptible isolates. Successive mutations conferred additive increases in MIC. Combined GWAS and machine learning analyses a priori identified five key genes significantly associated with resistance: ftsI , ampR , ampC , PA3329, and PA4311. Molecular docking simulation revealed that the R504C mutation in penicillin-binding protein 3 (PBP3), which is encoded by ftsI , reduced binding contacts and hydrogen bonds with ceftolozane, significantly decreasing binding affinity ( P =0.016). Conclusions Our analysis of 1,682 P. aeruginosa genomes demonstrated complex pathways to C/T resistance and showed that ftsI may play an underappreciated role. We discovered two previously unidentified genes associated with C/T resistance, whose function remains to be determined. Introduction Pseudomonas aeruginosa is a key Gram-negative pathogen, accounting for 9.4% of global antimicrobial resistance (AMR)-attributable deaths in 2021 1 . Multidrug-resistant (MDR) P. aeruginosa forms 15-30% of total P. aeruginosa infections 2 , severely limiting available treatment options. Consequently, ceftolozane-tazobactam (C/T) has emerged as a first-line treatment for MDR P. aeruginosa infections 3 . However, rates of C/T resistance in MDR P. aeruginosa can be as high as 18.4% 4 . This highlights the urgent need to understand C/T resistance mechanisms, in order to rapidly identify resistant isolates and better control their spread. There are multiple pathways contributing to C/T resistance 5 . Of these, structural alterations in AmpC β-lactamase represent most common mechanism 6 – 8 . AmpC hyperproduction, resulting from mutations in regulatory genes ( ampD , ampR , dacB, and mpl ), also confers resistance 6 , 7 . Acquired AMR genes such as carbapenemases or extended-spectrum β-lactamases represent another major mechanism due to their ability to hydrolyse ceftolozane 8 , 9 . Previous studies on C/T resistance have largely focused on case series and well-described mechanisms 7 , 8 , 10 . However, the complex interplay between different resistance mechanisms remains poorly understood, and the identification of novel resistance determinants has been limited. To address these gaps, we aimed to comprehensively determine C/T resistance mechanisms using novel data from our institution combined with all publicly-available P. aeruginosa genomes with matched C/T susceptibility phenotypes. Specifically, we aimed to assess prevalence of known resistance mechanisms, then identify novel mechanisms through a genome-wide association study (GWAS) and machine learning (ML) approaches ( Figure 1 ). Download figure Open in new tab Figure 1. Overall workflow of the study. This figure was created with BioRender.com. Materials and methods Bacterial isolate collection The study was reviewed and approved by the Alfred Hospital Ethics Committee (Project No. 185/21). We analysed 339 P. aeruginosa isolates collected between 2004-2021 at the Alfred Hospital (Melbourne, Australia), a 638-bed quaternary hospital with a cystic fibrosis and lung transplant state referral service. Consecutive P. aeruginosa isolates were recovered from clinical specimens. The isolates consisted of both matt and mucoid morphotypes and were stored in glycerol broth at –80°C. Repeat isolates from the same patient were included if they had a different morphotype and/or susceptibility profile (as determined by Vitek 2; BioMérieux, France) and were collected more than a week apart. All isolates underwent short-read sequencing (Illumina) (see Supplementary Methods ). We identified six public datasets containing both whole-genome sequencing (WGS) data and antimicrobial susceptibility testing (AST) results 5 , 11 – 15 . To ensure standardised resistance determination, we included only datasets where minimum inhibitory concentrations (MIC) were determined using broth microdilution (BMD), the reference method. The in-house sequence files have been deposited in NCBI project ID PRJNA1220180. Antimicrobial susceptibility testing For our in-house isolates, we conducted BMD using the ThermoFisher Sensititre plate (code: DKMGN) to determine C/T MIC values. We used the European Committee on Antimicrobial Susceptibility Testing (EUCAST) v15.0 breakpoints (MIC ≤ 4mg/L) to classify isolates as ‘susceptible’ or ‘resistant’ 16 . WGS data processing De novo short-read assembly was generated using Unicycler v0.5.0 17 . Species confirmation and multi-locus sequence typing (MLST) were conducted via Pathogenwatch ( https://pathogen.watch ). Acquired AMR genes were detected using AMRFinderPlus v3.11.18 18 . For chromosomal mutations analysis, we assessed mutations in ten genes previously linked to C/T resistance, as described by Cortes-Lara et al. 5 . Additionally, we included oprD due to its involvement in resistance following C/T exposure (see Supplementary Methods) 7 . Identifying variants associated with C/T resistance We conducted GWAS on susceptible/resistant phenotypes using a mixed-effects model implemented in pyseer v1.3.10 19 . Unitigs were used as testing variants, with a phylogeny-based pairwise distance matrix incorporated to control for population structure (see Supplementary Methods). Association mapping was performed using the annotated PAO1 reference genome (Genbank: GCA_000006765.1). To reduce false positives and computational load, we applied a two-step filtering strategy: first identifying GWAS-significant variants, then retaining only those that also contributed to ML-based C/T resistance prediction (see Supplementary Methods). Binding affinity analysis We assessed the binding affinity between ceftolozane and proteins encoded by GWAS-significant genes, comparing both wild-type and mutant variants. 3D structures of proteins were predicted using ColabFold, an AlphaFold-based tool 20 . Molecular docking simulations were performed using AutoDock Vina v1.2.5 21 , and results were visualised using PyMOL 22 . Results Data characteristics We analysed 1,682 P. aeruginosa genomes from four continents (Asia, Europe, North America, Oceania) ( Supplementary File 1 ), including Alfred Hospital isolates (339/1,682, 20.2%) and six public datasets (1,343/1,682, 79.8%) 5 , 11 – 15 . C/T resistance was noted in 343/1,682 (20.4%) isolates. We detected 527 sequence types (STs) ( Supplementary Figure 1 ). P. aeruginosa ST235, a well-recognised global MDR lineage was most frequent (163/1,682, 9.7%) and accounted for 78/343 (22.7%) of C/T resistant isolates. Resistance rates in common ST groups can be found in Supplementary Table 1 . Prevalence of acquired AMR genes We first analysed acquired C/T resistance determinants. Carbapenemase genes were noted in 206/1,682 (12.2%) isolates ( Supplementary Table 2 ), including serine carbapenemase ( bla GES , bla KPC ) and metallo-β-lactamase genes ( bla IMP , bla VIM , bla NDM ). Carbapenemase acquisition strongly predicted C/T resistance, with 202/206 (98.1%) carbapenemase-positive isolates being C/T resistant. Only bla GES-5 was found in C/T-susceptible isolates (4/206, 1.9%). The most common carbapenemase was bla NDM-1 (37/206, 18.0%), followed by bla IMP-4 (31/206, 15.0%), bla IMP-1 (29/206, 14.1%), bla VIM-2 (25/206, 12.1%) and bla GES-5 (22/206, 10.7%). Dual carbapenemases were identified in six isolates. The bla OXA-50 family was excluded from analysis as these enzymes are intrinsic to P. aeruginosa 23 . Among acquired bla OXA genes, bla OXA-10 , bla OXA-14 , bla OXA-141 , bla OXA-210 , and bla OXA-796 were significantly more prevalent in C/T-resistant isolates ( Supplementary Figure 2 ). VEB-type β-lactamases were detected only in C/T-resistant isolates (8/343, 2.0%), with 4/8 co-harboring additional carbapenemases: two with bla KPC-2 , one with bla VIM-5 , and one with bla NDM-1 . Analyses of known chromosomal C/T resistance mechanisms We then assessed prevalence of mutations in a curated list of eleven intrinsic genes associated with C/T resistance ( ampC and its regulators, mexAB-oprM overexpression pathway, ftsI, galU and oprD , Figure 2A-C , Supplementary Table 3 ) 5 . We specifically excluded isolates with acquired resistance determinants that likely conferred C/T resistance alone (carbapenemase genes, VEB β-lactamases, acquired OXA β-lactamases with significantly higher prevalence in resistant isolates), as the impact of additional mutations would be unclear. This resulted in a subset of 1,447 isolates (including 115 resistant isolates), which we will refer to as the ‘intrinsic resistance’ dataset. Mutations in genes associated with resistance were noted in all resistant isolates but also in 1,323/1,332 (99.3%) susceptible isolates, however the majority of these have previously been described as natural polymorphisms ( Supplementary Table 3 ) 5 . Download figure Open in new tab Figure 2. (A) Impact of mutations in chromosomal genes known to affect C/T susceptibility. P-values were calculated using the Chi-square test. (B) Pathways to C/T resistance at gene level. Each unique combination of mutations in genes associated with C/T resistance is shown as a unique pathway. (C) Distinct pathways to C/T resistance, collapsed to resistance mechanisms. Here, the genes associated with C/T resistance are collapsed into mechanistic groupings. Each unique combination of mutations in mechanistic groupings is shown as a unique pathway. (D, E) MIC values with respect to the cumulation of mutations (all non-synonymous mutations [D] vs high-impact mutations only [E]) in chromosomal genes known for C/T resistance. Dots and vertical bars indicate mean and standard deviation (SD) of MIC values, respectively. We therefore focused on mutations with a previously reported high impact on C/T susceptibility. These were significantly more frequent in resistant than susceptible isolates (in order of significance): ftsI (24/115 resistant vs. 22/1,332 susceptible isolates, P =4.11×10 −28 ), ampC (19/115 vs 55/1,332, P =2.58×10 −8 ), mpl (12/115 vs. 44/1,332, P =3.82×10 −4 ), ampD (9/115 vs 37/1,332, P =0.007), and ampR (13/115 vs 73/1,332, P =0.02). In terms of specific mutations, for ftsI the R504C mutation was most prevalent (21/115 [18.3%] resistant vs 17/1332 [1.3%] susceptible isolates). Notably, it was distributed across 18 unique STs in the resistant group. The previously reported F533L and E466K mutations were not found in C/T-resistant isolates 24 , 25 . For known ampC mutations 26 , E247G was detected in one resistant and one susceptible isolate. Deletions in the G229-E247 region (△G at L237, △P241-A246, and △R238-L244) were found in four resistant isolates and no susceptible isolates. Inactivated oprD was also more frequent in resistant isolates (77/115 [66.9%] vs 626/1,332 [47,0%], P =6.03X 10 −5 ). Frameshift due to indels was the most common mutation in both resistant (26/115, 22.6%) and susceptible (249/1,332, 18.7%) isolates. For other canonical C/T resistance genes, mutations in the mexAB-oprM efflux pump regulators ( nalC , nalD , and mexR ) were very frequent in the resistant group, occurring in 111/115 (96.5%). Mutations in galU and dacB were noted in 3/115 (2.6%) and 39/115 (33.9%) resistant isolates, respectively. Analysis of combinations of mutations in C/T resistance-associated genes revealed 67 distinct mutational pathways in resistant isolates ( Figure 2B ). Multiple mutations were common, with only 1/67 (1.5%) pathways involving a single mutation. Notably, all resistant isolates harboured at least one mutation in either ampC or its overexpression regulators ( ampR , ampD , mpl, dacB ). At the AMR mechanism level, collapsing ampC -related genes and mexAB-oprM -related genes into functional groups revealed seven distinct resistance pathways ( Figure 2C ). Among these, only modifications in AmpC and its regulatory genes emerged as a standalone pathway capable of conferring C/T resistance. While AmpC overexpression represents the main mechanism of resistance to C/T, additional mechanisms are often required to reach high-level resistance, highlighting the complexity of the mutational resistome in P. aeruginosa . We next investigated the impact of multiple mutations on MIC, firstly by including both low- and high-impact mutations ( Figure 2D , Supplementary Figure 3A ), then high-impact mutations only ( Figure 2E , Supplementary Figure 3B ). In both analyses, we observed an increasing trend in MIC with a higher number of mutations. High-impact mutations were associated with higher MIC increases, with a single mutation able to achieve resistant MICs (mean: 4.73 mg/L, SD: 11.64, Figure 2E ) per EUCAST breakpoints (4 mg/L). When considering both low- and high-impact mutations, six or more mutations were required to achieve the same effect ( Figure 2D ) . Identifying novel C/T resistance mechanisms through GWAS and ML analysis Using the ‘intrinsic resistance’ dataset (n=1,447), we conducted GWAS and identified 311 significant variants out of 2,698,635 tested unitigs ( Supplementary Figure 4 ). Mapping to the PAO1 reference genome annotated 280 unitigs corresponding to 73 genes ( Supplementary Table 4 ). Top associated genes included ftsI ( P =5.05×10 −23 ), hsbR ( P =5.85×10 −20 ), ampR ( P =3.52×10 −18 ), ampC ( P =6.50×10 −15 ), and several hypothetical proteins, including PA3329 ( P =1.47×10 −15 ), PA4311 ( P =6.86×10 −15 ), and PA2418 ( P =1.58×10 −13 ) ( Figure 3A ). Download figure Open in new tab Figure 3. (A) Genes selected by GWAS study, with the maximum P-value (y-axis) and the average minimum allele frequency (MAF) (x-axis). (B) Genes (y-axis) selected by both GWAS and ML approach, and their occurrence in ML models (x-axis). Only genes with MAF > 0.1 are shown. We then developed ML models to predict binary C/T resistance using the same dataset, achieving an average area under receiver operator curve (AUROC) of 0.65, and log-loss of 0.26 ( Supplementary Figure 5 ). Across all model iterations, 386 unitigs were selected. To refine the list of candidates, we intersected GWAS-significant unitigs with those selected by the models, applying thresholds of a minimum allele frequency above 0.1 and presence in more than 5 model iterations ( Figure 3B , Supplementary Table 4 ). This filtering yielded five key genes: ftsI and ampR were selected in all models, while PA3329, PA4311, and ampC appeared in 9/10, 7/10, and 6/10 models, respectively. Binding affinity assessment for significant variants ftsI emerged as a key determinant of C/T resistance in analysis of known resistance mechanisms, as well as GWAS/ML analysis. These findings and the fact that PBP3 is a target of ceftolozane prompted investigation of mutation impacts on in silico binding affinity 27 . We identified four non-synonymous mutations in GWAS-significant unitigs mapped to ftsI : R504C (n=38), F507I (n=1), T514A (n=1), and R504H (n=1). Among these, only R504C was significantly associated with resistant isolates (21/115 [18.3%], P =2.30×10 −26 ) while the others were exclusively found in susceptible isolates. Molecular docking analysis revealed significantly higher binding affinity between ceftolozane and wild type PBP3 compared to mutant PBP3 (R504C) (-3.520 vs -2.814 kcal/mol, P =0.016) ( Supplementary Figure 6 ), with loss of contact at A244 and single hydrogen bonds at Q265 and T267 (cf. double bonds in wild type PBP3) ( Figure 4 ). These changes potentially explain reduced drug binding and consequent resistance. Download figure Open in new tab Figure 4. 3D structure visualization of the interaction of ceftolozane (pink) to R504C mutant PBP3 (Panel A) and wild type PBP3 (Panel B). Dashed yellow lines indicate hydrogen bonds. The R504C mutant ( Panel A ) shows loss of contact at A244 and single hydrogen bonds at positions Q265 and T267 (cf. double bonds in wild type PBP3 – Panel B ). We extended our docking analysis to the novel proteins encoded by PA3329 and PA4311. The PA3329-encoded protein showed no meaningful binding affinity to ceftolozane (mean: 924.393 kcal/mol, SD: 288.148). The PA4311-encoded protein demonstrated binding affinity (-1.493 kcal/mol, 1.459) with potential binding sites at D116, Q271, R292 and G294 ( Supplementary Figure 7 ). Putative functions of novel C/T resistance determinants To understand putative functions of PA3329 and P4311, we examined their genomic neighbourhood, predicted Gene Ontology functions 28 and conducted a database and literature search (see Supplementary Methods and Supplementary Table 5) . PA3329 is located in an eight-gene operon (PA3327-PA3334) that includes fabH2 (beta-keto-acyl-ACP synthase) and acp3 (acyl carrier protein), suggestive of lipid/lipopolysaccharide synthesis 29 , 30 ( Supplementary Figure 8) . The predicted Gene Ontology terms for PA3329 indicate a role in cellular anatomical structure (Supplementary Table 5) . PA3329 has also been implicated in the CreBC two-component system which is involved in β-lactam stress response 31 . PA4311 is annotated as a glycosyltransferase 32 ( https://www.pseudomonas.com/feature/show?id=111460 ). The predicted Gene Ontology terms also indicate glycosyltransferase activity and a role in cellular anatomical structure ( Supplementary Table 5 ). In P. aeruginosa , glycosyltransferase reactions are essential for lipopolysaccharide synthesis, which is located in the outer leaflet of the outer membrane 33 . Based on this evidence, we hypothesise that both PA3329 and PA4311 are involved in outer membrane remodelling that affects ceftolozane influx. Discussion This comprehensive genomic analysis of 1,682 P. aeruginosa isolates has revealed multiple mechanisms contributing to C/T resistance, including previously known resistance determinants ( ftsI , ampC , and ampR ) and novel candidates (PA3329 and PA4311). ftsI emerged as a key contributor to C/T resistance across multiple analyses. These findings were underscored by several methodological strengths. Our study leveraged a large, geographically diverse dataset incorporating both novel and public genome sequences to provide robust statistical power and broad representativeness. Additionally, our multi-modal approach, which combines GWAS, ML, and molecular docking, enabled cross-validation of findings through complementary methodologies. Ceftolozane is a potent inhibitor of PBP3 encoded by ftsI 27 . Our study noted that mutations in ftsI were highly associated with C/T resistance, being present in 24/115 resistant vs 22/1,332 susceptible isolates ( P =4.11×10 −28 ), specifically the R504C mutation. This was then further confirmed in our GWAS and ML analyses, which both identified ftsI as the top-ranking contributor to C/T resistance. Our docking analysis then demonstrated that mutant ftsI (R504C) significantly reduced ceftolozane binding affinity, providing a mechanistic explanation for resistance. The role of PBP3 in β-lactam resistance has been increasingly recognised, not only in P. aeruginosa 7 , but also across various Enterobacterales 34 , 35 . Although the R504C mutation was previously documented to emerge following C/T exposure 10 , its importance in C/T resistance has potentially been underappreciated, as research has primarily focused on β-lactamases 8 , 36 . Furthermore, its co-occurrence with other mutations may have obscured its individual contribution to resistance 7 , 8 , 10 , highlighting the value of GWAS approach in identifying specific resistance determinants. Our analysis of genes previously associated with C/T resistance 5 showed that they likely have differing contributions when studied in a diverse international dataset. We applied a multi-modal approach to assess the contribution of resistance genes by applying both a traditional genomic analysis looking at genes associated with C/T resistance in the literature, and GWAS and ML approaches to allow us to identify key resistance genes a priori. Mutations in ampC and its regulator genes were found in all resistant isolates. Amongst these some were confirmed to have strong contributory effects (e.g. ampC and ampR ) but others contributed less (e.g. GWAS suggested limited contributions from ampD ). In some C/T resistance genes we detected very few mutations (e.g., galU in 2.6% of resistant isolates). These findings highlight the complex nature of C/T resistance mechanisms in P. aeruginosa . To try to account for some of these interactions, we assessed the impact of multiple mutations and noted stepwise increases in MIC with the number of mutations, both when considering all non-synonymous mutations and when considering high-impact mutations alone. The contribution of horizontally acquired β-lactamase genes to C/T resistance has been well described 8 , 9 , specifically key carbapenemases including bla VIM , bla IMP , bla NDM , and bla KPC . While we noted these carbapenemases in 12.2% isolates in our study, we also showed that specific bla OXA and bla VEB genes may play an important role. Four bla OXA genes ( bla OXA-14 , bla OXA-141 , bla OXA-210 , and bla OXA-796 ) and all bla VEB genes were found exclusively in resistant isolates. These findings confirm prior studies that identified bla OXA gene acquisition as a common cause of C/T resistance (particularly bla OXA-14 ) 8 , as well as noting an association between bla VEB gene acquisition and C/T resistance 37 . Our study had several limitations. Firstly, the retrospective nature of the analysis and potential sampling bias in the dataset may affect generalisability. Secondly, while docking analysis suggests reduced ceftolozane binding to mutant PBP3, in vitro studies are required to confirm these findings. Lastly, we hypothesised that PA3329 and PA4311 are involved in outer membrane remodelling however future functional validation is crucial to testing this hypothesis and determining their role in resistance. However, the emergence of mutations and interactions between multiple genes complicates the identification of key resistance drivers. In conclusion, our study rigorously determined the genetic underpinnings of C/T resistance and revealed complex pathways leading to C/T resistance. ampC and its regulators were confirmed to play an important role but we demonstrated that ftsI may be a more significant contributor than previously recognised. We also identified two novel determinants whose functions remain to be determined. The hypotheses generated by our study present an opportunity for validation through both computational and experimental approaches to understand the functional mechanisms of these newly identified determinants. Data Availability The in-house sequence files have been deposited in NCBI project ID PRJNA1220180 Author contributions HA.N, N.M. and A.Y.P. conceived the study. J.A.W. designed and supervised sampling and collection of bacterial isolates. J.A.W., L.V.B., G.Z.B, R.T., N.Q.D., and M.H.P. collected the bacterial isolates, performed bacterial characterization, conducted genome sequencing and broth microdilution. HA.N. preprocessed WGS data. HA.N., J.S., D.L.D., and N.M. conceptualised machine learning analyses. HA.N. conducted GWAS, developed machine learning models. HA.N. and N.M. analysed all results. HA.N. and N.M. wrote the manuscript with comments and feedback of all of the co-authors. All authors read and approved the manuscript. Funding This work was supported by the National Health and Medical Research Council of Australia (Emerging Leader 1 Fellowship APP1176324 to N.M. and Practitioner Fellowship APP1117940 to A.Y.P) and the Australian Medical Research Future Fund (FSPGN000048). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Competing interests N.M. has received research support from GlaxoSmithKline, unrelated to the current study. A.Y.P. has received research funding from MSD through an investigator-initiated research project. All other authors declare no conflict of interest. Acknowledgement This work was supported by Monash eResearch Centre, including the M3 service. This work was also supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the NCRIS-funded Australian Research Data Commons (ARDC). Footnotes ↵ † No longer at the institution where the work was performed. In this revised manuscript, we did not include blaOXA-50 family genes, as these are intrinsic to P. aeruginosa isolates. We also excluded natural polymorphisms in chromosomal genes that are known to be associated with ceftolozane/tazobactam resistance. In the previous version, mutations in dacB were not reported due to missing annotations in the reference genome; these mutations are now included. References 1. ↵ Naghavi M , Vollset SE , Ikuta KS , et al. Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050 . The Lancet . 2024 ; 404 ( 10459 ): 1199 – 1226 . doi: 10.1016/S0140-6736(24)01867-1 OpenUrl CrossRef PubMed 2. ↵ Horcajada JP , Montero M , Oliver A , et al. Epidemiology and Treatment of Multidrug-Resistant and Extensively Drug-Resistant Pseudomonas aeruginosa Infections . Clinical Microbiology Reviews . 2019 ; 32 ( 4 ):10.1128/cmr.00031-19. doi : doi: 10.1128/cmr.00031-19 OpenUrl CrossRef 3. ↵ Macesic N , Uhlemann A-C , Peleg AY . Multidrug-resistant Gram-negative bacterial infections . The Lancet . 2025 ; 405 ( 10474 ): 257 – 272 . doi: 10.1016/s0140-6736(24)02081-6 OpenUrl CrossRef PubMed 4. ↵ Kostoulias X , Chang CC , Wisniewski J , et al. Antimicrobial susceptibility of ceftolozane-tazobactam against multidrug-resistant Pseudomonas aeruginosa isolates from Melbourne, Australia . Pathology . 2023 ; 55 ( 5 ): 663 – 668 . doi: 10.1016/j.pathol.2023.03.009 OpenUrl CrossRef PubMed 5. ↵ Cortes-Lara S , Barrio-Tofiño Ed , López-Causapé C , et al. Predicting Pseudomonas aeruginosa susceptibility phenotypes from whole genome sequence resistome analysis . Clinical Microbiology and Infection . 2021 ; 27 ( 11 ): 1631 – 1637 . doi: 10.1016/j.cmi.2021.05.011 OpenUrl CrossRef PubMed 6. ↵ Cabot G , Bruchmann S , Mulet X , et al. Pseudomonas aeruginosa Ceftolozane-Tazobactam Resistance Development Requires Multiple Mutations Leading to Overexpression and Structural Modification of AmpC . Antimicrobial Agents and Chemotherapy . 2014 ; 58 ( 6 ): 3091 – 3099 . doi : doi: 10.1128/aac.02462-13 OpenUrl Abstract / FREE Full Text 7. ↵ Tamma PD , Beisken S , Bergman Y , et al. Modifiable Risk Factors for the Emergence of Ceftolozane-tazobactam Resistance . Clinical Infectious Diseases . 2020 ; 73 ( 11 ): e4599 – e4606 . doi: 10.1093/cid/ciaa1306 OpenUrl CrossRef 8. ↵ Fraile-Ribot PA , Cabot G , Mulet X , et al. Mechanisms leading to in vivo ceftolozane/tazobactam resistance development during the treatment of infections caused by MDR Pseudomonas aeruginosa . Journal of Antimicrobial Chemotherapy . 2017 ; 73 ( 3 ): 658 – 663 . doi: 10.1093/jac/dkx424 OpenUrl CrossRef 9. ↵ Karlowsky JA , Lob SH , Estabrook MA , et al. Susceptibility profile and β-lactamase content of global Pseudomonas aeruginosa isolates resistant to ceftolozane/tazobactam and/or imipenem/relebactam—SMART 2016–21 . JAC-Antimicrobial Resistance . 2023 ; 5 (3) doi: 10.1093/jacamr/dlad080 OpenUrl CrossRef 10. ↵ Rubio AM , Kline EG , Jones CE , et al. In Vitro Susceptibility of Multidrug-Resistant Pseudomonas aeruginosa following Treatment-Emergent Resistance to Ceftolozane-Tazobactam . Antimicrobial Agents and Chemotherapy . 2021 ; 65 ( 6 ): 10 . 1128 /aac.00084-21. doi : doi: 10.1128/aac.00084-21 OpenUrl CrossRef 11. ↵ Barrio-Tofiño Ed , López-Causapé C , Cabot G , et al. Genomics and Susceptibility Profiles of Extensively Drug-Resistant Pseudomonas aeruginosa Isolates from Spain . Antimicrobial Agents and Chemotherapy . 2017 ; 61 ( 11 ): 10 . 1128 /aac.01589-17. doi : doi: 10.1128/aac.01589-17 OpenUrl CrossRef 12. del Barrio-Tofiño E , Zamorano L , Cortes-Lara S , et al. Spanish nationwide survey on Pseudomonas aeruginosa antimicrobial resistance mechanisms and epidemiology . Journal of Antimicrobial Chemotherapy . 2019 ; 74 ( 7 ): 1825 – 1835 . doi: 10.1093/jac/dkz147 OpenUrl CrossRef PubMed 13. Teo Jocelyn Q-M , Lim Jie C , Tang Cheng Y , et al. Ceftolozane/Tazobactam Resistance and Mechanisms in Carbapenem-Nonsusceptible Pseudomonas aeruginosa . mSphere . 2021 ; 6 ( 1 ): 10 . 1128 /msphere.01026-20. doi: 10.1128/msphere.01026-20 OpenUrl CrossRef 14. Stanton RA , Campbell D , McAllister GA , et al. Whole-Genome Sequencing Reveals Diversity of Carbapenem-Resistant Pseudomonas aeruginosa Collected through CDC’s Emerging Infections Program, United States, 2016-2018 . Antimicrobial Agents and Chemotherapy . 2022 ; 66 ( 9 ):e00496-22. doi : doi: 10.1128/aac.00496-22 OpenUrl CrossRef PubMed 15. ↵ Sastre-Femenia MÀ , Fernández-Muñoz A , Gomis-Font MA , et al. Pseudomonas aeruginosa antibiotic susceptibility profiles, genomic epidemiology and resistance mechanisms: a nation-wide five-year time lapse analysis . The Lancet Regional Health – Europe . 2023 ; 34 doi: 10.1016/j.lanepe.2023.100736 OpenUrl CrossRef 16. ↵ European Committee on Antimicrobial Susceptibility Testing (EUCAST) . Breakpoint tables for interpretation of MICs and zone diameters. Version 15.0, valid from 2025-01-01 . 2023 . 17. ↵ Wick RR , Judd LM , Gorrie CL , Holt KE . Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads . PLOS Computational Biology . 2017 ; 13 ( 6 ): e1005595 . doi: 10.1371/journal.pcbi.1005595 OpenUrl CrossRef PubMed 18. ↵ Feldgarden M , Brover V , Gonzalez-Escalona N , et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence . Scientific Reports . 2021/06/16 2021 ; 11 ( 1 ): 12728 . doi: 10.1038/s41598-021-91456-0 OpenUrl CrossRef PubMed 19. ↵ Lees JA , Galardini M , Bentley SD , Weiser JN , Corander J. pyseer: a comprehensive tool for microbial pangenome-wide association studies . Bioinformatics . 2018 ; 34 ( 24 ): 4310 – 4312 . doi: 10.1093/bioinformatics/bty539 OpenUrl CrossRef PubMed 20. ↵ Mirdita M , Schütze K , Moriwaki Y , Heo L , Ovchinnikov S , Steinegger M . ColabFold: making protein folding accessible to all . Nature Methods . 2022/06/01 2022 ; 19 ( 6 ): 679 – 682 . doi: 10.1038/s41592-022-01488-1 OpenUrl CrossRef PubMed 21. ↵ Eberhardt J , Santos-Martins D , Tillack AF , Forli S . AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings . Journal of Chemical Information and Modeling . 2021/08/23 2021 ; 61 ( 8 ): 3891 – 3898 . doi: 10.1021/acs.jcim.1c00203 OpenUrl CrossRef PubMed 22. ↵ Schrödinger LLC . The PyMOL Molecular Graphics System, Version 1.8 . 2015 . 23. ↵ Mack Andrew R , Hujer Andrea M , Mojica Maria F , et al. β-Lactamase diversity in Pseudomonas aeruginosa . Antimicrobial Agents and Chemotherapy . 2025 ; 69 ( 3 ): e00785 – 24 . doi: 10.1128/aac.00785-24 OpenUrl CrossRef PubMed 24. ↵ Hernández-García M , García-Castillo M , García-Fernández S , et al. Distinct epidemiology and resistance mechanisms affecting ceftolozane/tazobactam in Pseudomonas aeruginosa isolates recovered from ICU patients in Spain and Portugal depicted by WGS . Journal of Antimicrobial Chemotherapy . 2020 ; 76 ( 2 ): 370 – 379 . doi: 10.1093/jac/dkaa430 OpenUrl CrossRef 25. ↵ Simner PJ , Beisken S , Bergman Y , Posch AE , Cosgrove SE , Tamma PD . Cefiderocol Activity Against Clinical Pseudomonas aeruginosa Isolates Exhibiting Ceftolozane-Tazobactam Resistance . Open Forum Infectious Diseases . 2021 ; 8 ( 7 ): ofab311 . doi: 10.1093/ofid/ofab311 OpenUrl CrossRef PubMed 26. ↵ Díaz-Cañestro M , Periañez L , Mulet X , et al. Ceftolozane/tazobactam for the treatment of multidrug resistant Pseudomonas aeruginosa: experience from the Balearic Islands . European Journal of Clinical Microbiology & Infectious Diseases . 2018/11/01 2018 ; 37 ( 11 ): 2191 – 2200 . doi: 10.1007/s10096-018-3361-0 OpenUrl CrossRef PubMed 27. ↵ Moyá B , Zamorano L , Juan C , Ge Y , Oliver A . Affinity of the New Cephalosporin CXA-101 to Penicillin-Binding Proteins of Pseudomonas aeruginosa . Antimicrobial Agents and Chemotherapy . 2010 ; 54 ( 9 ): 3933 – 3937 . doi : doi: 10.1128/aac.00296-10 OpenUrl Abstract / FREE Full Text 28. ↵ Kulmanov M , Guzmán-Vega FJ , Duek Roggli P , Lane L , Arold ST , Hoehndorf R . Protein function prediction as approximate semantic entailment . Nature Machine Intelligence . 2024/02/01 2024 ; 6 ( 2 ): 220 – 228 . doi: 10.1038/s42256-024-00795-w OpenUrl CrossRef 29. ↵ Therisod H , Weissborn AC , Kennedy EP . An essential function for acyl carrier protein in the biosynthesis of membrane-derived oligosaccharides of Escherichia coli . Proceedings of the National Academy of Sciences . 1986/10/01 1986 ; 83 ( 19 ): 7236 – 7240 . doi: 10.1073/pnas.83.19.7236 OpenUrl Abstract / FREE Full Text 30. ↵ Chen W , Wang B , Gruber JD , Zhang Y-M , Davies C . Acyl Carrier Protein 3 Is Involved in Oxidative Stress Response in Pseudomonas aeruginosa. Original Research . Frontiers in Microbiology . 2018 - September-20 2018;Volume 9 - 2018 doi: 10.3389/fmicb.2018.02244 OpenUrl CrossRef PubMed 31. ↵ Zamorano L , Moyà B , Juan C , Mulet X , Blázquez J , Oliver A . The Pseudomonas aeruginosa CreBC Two-Component System Plays a Major Role in the Response to β-Lactams, Fitness, Biofilm Growth, and Global Regulation . Antimicrobial Agents and Chemotherapy . 2014 ; 58 ( 9 ): 5084 – 5095 . doi: 10.1128/aac.02556-14 OpenUrl Abstract / FREE Full Text 32. ↵ Winsor GL , Griffiths EJ , Lo R , Dhillon BK , Shay JA , Brinkman Fiona SL . Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database . Nucleic Acids Research . 2016 ; 44 ( D1 ): D646 – D653 . doi: 10.1093/nar/gkv1227 OpenUrl CrossRef PubMed 33. ↵ Maldonado RF , Sá-Correia I , Valvano MA . Lipopolysaccharide modification in Gram-negative bacteria during chronic infection . FEMS Microbiology Reviews . 2016 ; 40 ( 4 ): 480 – 493 . doi: 10.1093/femsre/fuw007 OpenUrl CrossRef PubMed 34. ↵ Le Terrier C , Nordmann P , Buchs C , Poirel L . Effect of modification of penicillin-binding protein 3 on susceptibility to ceftazidime-avibactam, imipenem-relebactam, meropenem-vaborbactam, aztreonam-avibactam, cefepime-taniborbactam, and cefiderocol of Escherichia coli strains producing broad-spectrum β-lactamases . Antimicrobial Agents and Chemotherapy . 2024 ; 68 ( 4 ): e01548 – 23 . doi: 10.1128/aac.01548-23 OpenUrl CrossRef PubMed 35. ↵ Mushtaq S , Vickers A , Woodford N , Livermore DM . Activity of aztreonam/avibactam and ceftazidime/avibactam against Enterobacterales with carbapenemase-independent carbapenem resistance . International Journal of Antimicrobial Agents . 2024/03/01/ 2024 ; 63 ( 3 ): 107081 . doi: 10.1016/j.ijantimicag.2023.107081 OpenUrl CrossRef PubMed 36. ↵ Gomis-Font MA , Cabot G , López-Argüello S , et al. Comparative analysis of in vitro dynamics and mechanisms of ceftolozane/tazobactam and imipenem/relebactam resistance development in Pseudomonas aeruginosa XDR high-risk clones . Journal of Antimicrobial Chemotherapy . 2022 ; 77 ( 4 ): 957 – 968 . doi: 10.1093/jac/dkab496 OpenUrl CrossRef PubMed 37. ↵ Livermore DM , Mushtaq S , Ge Y , Warner M . Activity of cephalosporin CXA-101 (FR264205) against Pseudomonas aeruginosa and Burkholderia cepacia group strains and isolates . International Journal of Antimicrobial Agents . 2009 /11/01/ 2009;34(5):402-406. doi: 10.1016/j.ijantimicag.2009.03.021 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted August 25, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study Hoai-An Nguyen , Anton Y. Peleg , Jiangning Song , Jessica A. Wisniewski , Luke V. Blakeway , Gnei Z. Badoordeen , Ravali Theegala , Nhu Quynh Doan , Matthew H. Parker , David L. Dowe , Nenad Macesic medRxiv 2025.05.13.25327501; doi: https://doi.org/10.1101/2025.05.13.25327501 Share This Article: Copy Citation Tools Complex pathways to ceftolozane-tazobactam resistance in clinical Pseudomonas aeruginosa isolates: a genomic epidemiology study Hoai-An Nguyen , Anton Y. Peleg , Jiangning Song , Jessica A. Wisniewski , Luke V. Blakeway , Gnei Z. Badoordeen , Ravali Theegala , Nhu Quynh Doan , Matthew H. Parker , David L. Dowe , Nenad Macesic medRxiv 2025.05.13.25327501; doi: https://doi.org/10.1101/2025.05.13.25327501 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15231) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3338) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9239) Radiology and Imaging (2203) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0202a0529cdaa64',t:'MTc3OTgzMzk5NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();