Science, Faculty of
DSpace
UBCV
Nguyen, Matthew Hoang Tu
Master of Science - MSc
University of British Columbia
Extended spectrum beta-lactamase (ESBL) producing Enterobacteriaceae such as E. coli and K. pneumoniae are found globally in hospital and community settings and represent a major healthcare challenge due to their limited effective treatment options. Several lineages are considered high-risk clones with an enhanced ability to spread and persist and increased pathogenicity and antimicrobial resistance (AMR). The prevalence of ESBL-producing Enterobacteriaceae in Qatar has steadily increased in the last two decades. Whole-genome sequencing can be used to characterize for the genomic characterization of these Qatari organisms; however, previous studies has focused only on core genes. Moreover, the genetic relationship of Qatari E. coli lineages to globally circulating high-risk clones has not been studied. This work presents an in-depth genomic characterization of ESBL-producing E. coli from a pediatric hospital in Qatar, with a focus on characterizing mobile genetic elements and plasmids. This work is also the first study to globally contextualize Qatari isolates against globally circulating lineages of ESBL-producing E. coli. This study reveals the presence of a putative emerging high-risk clone that may be endemic to Qatar, ST8881, that had not been previously studied. Through phylogenetic reconstruction from different genomic features, ST8881 is genetically distinguishable from other Qatari and global lineages by its mobile genetic element, virulence factor, and AMR marker profiles. This lineage is also characterized by factors affecting the stability of AMR genes: chromosomal integration of beta-lactamase genes, as well as the presence of a multiple-replicon plasmid encoding for five AMR genes. With Qatar being a major international travel hub, a high-risk clone could be easily exported; therefore, it is crucial to understand the genetic landscape of ESBL-producing E. coli in Qatar. The discovery and characterization of ST8881 provides a strong basis for future studies of AMR in Qatar.
GENOMIC CHARACTERIZATION AND GLOBAL CONTEXTUALIZATION OF ESBL-PRODUCING E. COLI FROM PEDIATRIC PATIENTS IN QATAR by Matthew Hoang Tu Nguyen B.Sc. (Hons), Simon Fraser University, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Matthew Hoang Tu Nguyen, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Genomic characterization and global contextualization of ESBL-producing E. coli from pediatric patients in Qatar submitted by Matthew Hoang Tu Nguyen in partial fulfilment of the requirements for the degree of Master of Science in Bioinformatics Examining Committee: Dr. William Hsiao, Associate Professor, Faculty of Heath Sciences, SFU Supervisor Dr. Inanc Birol, Professor, Department of Medical Genetics, UBC Supervisory Committee Member Dr. Robert Hancock, Professor, Department of Microbiology and Immunology, UBC Supervisory Committee Member Dr. Faraz Hach, Assistant Professor, Department of Urologic Sciences, UBC Additional Examiner iii Abstract Extended spectrum beta-lactamase (ESBL) producing Enterobacteriaceae such as E. coli and K. pneumoniae are found globally in hospital and community settings and represent a major healthcare challenge due to their limited effective treatment options. Several lineages are considered high-risk clones with an enhanced ability to spread and persist and increased pathogenicity and antimicrobial resistance (AMR). The prevalence of ESBL-producing Enterobacteriaceae in Qatar has steadily increased in the last two decades. Whole-genome sequencing can be used to characterize for the genomic characterization of these Qatari organisms; however, previous studies has focused only on core genes. Moreover, the genetic relationship of Qatari E. coli lineages to globally circulating high-risk clones has not been studied. This work presents an in-depth genomic characterization of ESBL-producing E. coli from a pediatric hospital in Qatar, with a focus on characterizing mobile genetic elements and plasmids. This work is also the first study to globally contextualize Qatari isolates against globally circulating lineages of ESBL-producing E. coli. This study reveals the presence of a putative emerging high-risk clone that may be endemic to Qatar, ST8881, that had not been previously studied. Through phylogenetic reconstruction from different genomic features, ST8881 is genetically distinguishable from other Qatari and global lineages by its mobile genetic element, virulence factor, and AMR marker profiles. This lineage is also characterized by factors affecting the stability of AMR genes: chromosomal integration of beta-lactamase genes, as well as the presence of a multiple-replicon plasmid encoding for five AMR genes. With Qatar being a major international travel hub, a high-risk clone could be easily exported; therefore, it is crucial to understand the genetic landscape of ESBL-producing E. coli in Qatar. The discovery and characterization of ST8881 provides a strong basis for future studies of AMR in Qatar. iv Lay Summary Qatar has seen an explosive increase in the prevalence of antimicrobial resistant E. coli. Because the country is a major travel hub connecting Eastern and Western countries and has a large expatriate population, understanding the types of strains present within the country is crucial to monitoring and tracking the transmission of antimicrobial resistant E. coli. The goal of this work was to characterize the different strains of E. coli found within a pediatric hospital in Qatar, and to compare them against the prevalent strains found around the world. The analysis showed the presence of a potentially emerging high-risk strain only seen in Qatar. This strain had not been studied before, and its uniqueness and prevalence in Qatar is crucial to the tracking of antimicrobial resistant E. coli. This high-risk strain could be easily exported out of Qatar; therefore, this work provides an important foundation for future studies of antimicrobial resistance. v Preface This work was completed under the supervision of Dr. William Hsiao, formally in the Department of Pathology and Laboratory Medicine, Faculty of Medicine, the University of British Columbia; and currently in the Faculty of Health Sciences and the Principal Investigator at the Center for Infectious Disease Genomics and One Health (CIDGOH) at Simon Fraser University. The project was a collaboration with Dr. Clement Tsui, Dr. Patrick Tang, and Dr. Andres Perez-Lopez from Sidra Medicine in Doha, Qatar. Dr. William Hsiao and Matthew Nguyen are jointly responsible for the conception and design of the study. The sequence dataset and antimicrobial phenotypic information was obtained by the collaborators at Sidra Medicine. All bioinformatics analyses were carried out by Matthew Nguyen, alongside the helpful guidance of Dr. William Hsiao, Dr. Zohaib Anwar, Jimmy Liu, David Tong, and Aishwarya Sridhar from CIDGOH, and Dr. Clement Tsui from Sidra Medicine. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................. xiv Acknowledgements ......................................................................................................................xv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 1.1 Extended spectrum β-lactamases producing Enterobacteriaceae .................................. 1 1.2 CTX-M β-lactamase variants .......................................................................................... 2 1.3 Global genetic landscape of ESBL-producing Escherichia coli ..................................... 3 1.4 ESBL-producing Escherichia coli in Qatar .................................................................... 5 1.5 Research objectives ......................................................................................................... 6 Chapter 2: Genomic characterization of extended-spectrum beta-lactamase producing E. coli from pediatric patients in Qatar ............................................................................................7 2.1 Introduction ..................................................................................................................... 7 2.2 Methods........................................................................................................................... 7 2.2.1 Bacterial isolates and associated metadata ................................................................. 7 2.2.2 Quality control and sequence processing .................................................................. 10 2.2.3 Genomic characterization pipeline ........................................................................... 10 vii 2.2.4 Identification of putative antimicrobial resistance mutations ................................... 12 2.3 Results ........................................................................................................................... 13 2.3.1 Quality control .......................................................................................................... 13 2.3.1.1 Sequencing quality ............................................................................................ 13 2.3.1.2 Contamination detection ................................................................................... 17 2.3.2 Characterization of STs by genomic features ........................................................... 19 2.3.2.1 Genomic distance as a baseline ......................................................................... 19 2.3.2.2 Multi-locus sequence typing ............................................................................. 22 2.3.2.3 Single nucleotide polymorphisms ..................................................................... 26 2.3.2.4 Mobile genetic elements ................................................................................... 28 2.3.2.4.1 Database search of mobile gene elements ................................................... 28 2.3.2.4.2 De novo prediction of mobile genetic elements .......................................... 32 2.3.2.5 Plasmids ............................................................................................................ 35 2.3.2.6 Virulence factors ............................................................................................... 37 2.3.2.7 Antibiotic resistance markers ............................................................................ 41 2.3.3 Clustering based on the core- and accessory-genomes ............................................. 43 2.3.4 Identification of putative antimicrobial resistance mutations ................................... 45 2.3.4.1 Predicting antimicrobial resistance using machine learning ............................. 45 2.3.4.2 Feature importance analysis .............................................................................. 47 2.4 Discussion ..................................................................................................................... 47 Chapter 3: Global contextualization of extended-spectrum beta-lactamase producing E. coli from pediatric patients in Qatar ..........................................................................................54 3.1 Introduction ................................................................................................................... 54 viii 3.2 Methods......................................................................................................................... 54 3.2.1 Acquisition of global bacterial isolates ..................................................................... 54 3.2.2 Clustering of Qatari isolates alongside global isolates ............................................. 55 3.3 Results ........................................................................................................................... 56 3.3.1 Clustering alongside global isolates.......................................................................... 56 3.3.2 Clustering of Qatari sequence types of interest ........................................................ 59 3.3.2.1 Comparing Qatari CC4981 to global ST4981 and ST617 isolates ................... 59 3.3.2.2 Comparing Qatari ST8881 to global ST648 isolates ........................................ 61 3.3.3 Comparative genomics of ST8881 against globally circulating ST648 isolates ...... 63 3.3.3.1 Mobile genetic elements ................................................................................... 63 3.3.3.2 Plasmids ............................................................................................................ 65 3.3.3.3 Virulence factors ............................................................................................... 66 3.3.3.4 Antibiotic resistance markers ............................................................................ 67 3.4 Discussion ..................................................................................................................... 68 Chapter 4: Conclusion .................................................................................................................72 4.1 Analysis of the work within the context of current research ........................................ 72 4.2 Significance and contribution ....................................................................................... 73 4.3 Strengths and limitations............................................................................................... 74 4.4 Future work ................................................................................................................... 75 References .....................................................................................................................................76 Appendices ....................................................................................................................................83 Appendix A Isolate metadata and QC metrics .......................................................................... 83 Appendix B Isolate antimicrobial resistance phenotypes ....................................................... 112 ix Appendix C Building a sketch database for PopPUNK, clustering model selection, and hyperparameter optimization .................................................................................................. 130 C.1 Building a sketch database for PopPUNK .............................................................. 130 C.2 Clustering model selection and hyperparameter optimization................................ 133 Appendix D Predicting antimicrobial resistance mutations using machine learning ............. 134 D.1 Logistic regression model performance .................................................................. 134 D.2 Annotated feature importance results (random forests) .......................................... 135 D.3 Annotated feature importance results (logistic regression) .................................... 137 Appendix E SNP calling within reference genome plasmids ................................................. 139 E.1 SNP calling within the alignment to pEC958 ......................................................... 139 E.2 SNP calling within the alignment to pEC958B ...................................................... 140 Appendix F Clustering Qatari isolates alongside the pre-built E. coli PopPUNK database... 141 x List of Tables Table 2.1 All antibiotics tested on the Qatari isolates, along with the associated number of isolates for different MIC testing results. ....................................................................................... 9 Table 2.2 N50 statistic and assembled genome length of potential outliers. ................................ 17 Table 2.3 Counts of annotation type for SNPs in the Qatari genomes aligned to EC958. ........... 27 Table 4.1 Complete table of isolates along with their associated metadata and quality control metrics. ........................................................................................................................................ 111 Table 4.2 The antimicrobial resistance phenotypes (obtained through antimicrobial susceptibility testing) of each isolate. ............................................................................................................... 129 Table 4.3 Top five features obtained from the feature importance values of the trained random forests model for each drug......................................................................................................... 136 Table 4.4 Top five features obtained from the feature importance values of the trained logistic regression model for each drug. .................................................................................................. 138 xi List of Figures Figure 2.1 An example of a sequence that failed the per base sequence content module of FastQC. ......................................................................................................................................... 14 Figure 2.2 Plot of mean per-base sequence quality in the read. ................................................... 15 Figure 2.3 The number of contigs within each assembled genome, broken down by the length of contigs. .......................................................................................................................................... 16 Figure 2.4 Plot of length of assembled genomes against their associated N50 statistic. .............. 17 Figure 2.5 Proportion of top five organisms identified using Kraken2 in potential outliers. ....... 19 Figure 2.6 The neighbor-joining tree generated from the pairwise distance matrix produced by Mash. ............................................................................................................................................. 21 Figure 2.7 The core-genome MLST neighbor-joining tree shows that indeed there is a general separation of sequence types based on a set of 2513 loci. ............................................................ 23 Figure 2.8 The complete whole-genome MLST tree and the zoomed-in whole-genome MLST tree without the two outlier isolates. ............................................................................................. 25 Figure 2.9 The zoomed-in accessory-genome MLST tree without the two outlier isolates.. ....... 25 Figure 2.10 A heatmap of presence/absence of SNPs in each isolate predicted using Snippy..... 28 Figure 2.11 Heatmap of the number of each mobile genetic element per isolate predicted using MobileElementFind ...................................................................................................................... 30 Figure 2.12 A histogram showing the number of mobile genetic elements per isolate predicted using MobileElementFinder.......................................................................................................... 31 Figure 2.13 A histogram showing the number of mobile genetic elements per isolate predicted using MGEFinder.......................................................................................................................... 33 xii Figure 2.14 Heatmap of the number of each mobile genetic element per isolate predicted using MGEFinder. .................................................................................................................................. 34 Figure 2.15 A heatmap of the plasmids predicted within each isolate using PlasmidFinder. ...... 36 Figure 2.16 A heatmap of virulence factors predicted within each isolate using ABRicate with the VFDB virulence factor database. ............................................................................................ 38 Figure 2.17 A heatmap of virulence factors predicted within each isolate using ABRicate with the VFDB virulence factor database.. ........................................................................................... 40 Figure 2.18 A heatmap of AMR markers predicted within each isolate using AMRFinder. ....... 41 Figure 2.19 Phylogenetic tree generated from the core-genome distances of Qatari isolates. ..... 44 Figure 2.20 t-SNE of the accessory genome distances of Qatari isolates. .................................... 45 Figure 2.21 ROC AUC and F1 score of the RF model for each drug. .......................................... 46 Figure 3.1 Phylogenetic tree generated from the core-genome distances using PopPUNK. ........ 57 Figure 3.2 t-SNE of the accessory-genome distances. The node colors correspond to the cluster assigned by DBScan. .................................................................................................................... 58 Figure 3.3 Phylogenetic tree of Qatari ST4981 and ST10997 isolates alongside global ST4981 and ST617 isolates downloaded from the PATRIC database. ...................................................... 60 Figure 3.4 t-SNE generated from the accessory-genome distances obtained from PopPUNK of Qatari ST4981 and ST10997 alongside global ST4981 and ST617 isolates downloaded from the PATRIC database.......................................................................................................................... 61 Figure 3.5 Phylogenetic tree of Qatari ST8881 isolates alongside global ST648 and ST3177 isolates downloaded from the PATRIC database. ........................................................................ 62 xiii Figure 3.6 t-SNE generated from the accessory-genome distances obtained from PopPUNK of Qatari ST8881 alongside global ST648 and ST3177 isolates downloaded from the PATRIC database. ........................................................................................................................................ 63 Figure 3.7 Heatmap for the MGE profile of ST8881 and ST648. ................................................ 64 Figure 3.8 Heatmap for the plasmid profile of ST8881 and ST648. ............................................ 65 Figure 3.9 Heatmap for the virulence factor profile of ST8881 and ST648. ................................ 66 Figure 3.10 Heatmap for the AMR profile of ST8881 and ST648. .............................................. 67 Figure 4.1 The example fit between two Qatari isolates to determine an optimal k-mer range. 131 Figure 4.2 The example fit between two global isolates to determine an optimal k-mer range. 132 Figure 4.3 ROC AUC and F1 score of the LR model for each drug. ......................................... 139 Figure 4.5 Heatmap produced by the presence/absence matrix of SNPs within the pEC958B plasmid. ....................................................................................................................................... 140 Figure 4.6 Phylogenetic tree of the Qatari isolates alongside the PopPUNK included E. coli database. ...................................................................................................................................... 141 xiv List of Abbreviations AG Accessory-genome AMR Antimicrobial resistance BGMM Bayesian Gaussian mixture model CG Core-genome DBSCAN Density-based spatial clustering of applications with noise ESBL Extended spectrum beta-lactamases HGT Horizontal gene transfer IS Insertion sequence LR Logistic regression MGE Mobile genetic element MLST Multi-locus sequence typing RF Random forests ROC AUC Area under the receiver operating characteristic curve SNP Single nucleotide polymorphism ST Sequence type WG Whole-genome WGS Whole-genome sequencing xv Acknowledgements I would like to thank my supervisor, Dr. William Hsiao, for his guidance and support during this project. I am grateful to have been given the opportunity to benefit from his knowledge and expertise, which has allowed me to grow as a researcher. I would like to thank my committee members, Dr. Inanc Birol and Dr. Hancock, for their constructive feedback and support throughout my degree. Their suggestions have been instrumental during the course of this project. I would also like to thank the collaborators at Sidra Medicine, especially Clement Tsui, for their help and feedback throughout the project. I would like to thank all the members of the Centre for Infectious Disease Genomics and One Health (CIDGOH), especially those in the Genomics team, for the helpful discussions and feedback during group meetings. I would like to thank all my close friends who have supported me during my degree, through the ups and downs. Finally, I want to give the biggest thanks to my parents, for their love and support not just during this degree, but also throughout my life. None of this would have been possible without them. I would also like to acknowledge that this work was supported by the NSERC-CREATE and NSERC CGS-M awards. xvi Dedication I dedicate this thesis to my parents for their endless support through my degree and my life.1 Chapter 1: Introduction 1.1 Extended spectrum β-lactamases producing Enterobacteriaceae Enterobacteriaceae is a family of Gram-negative, facultative anaerobic, non-sporing bacilli [1]. This family of bacteria is a frequent cause of urinary tract, bloodstream, and respiratory infections, especially in hospital and healthcare settings [2]. Two common Enterobacteriaceae are Escherichia coli and Klebsiella pneumoniae, the former of which is a frequent cause of urinary tract infections, while the latter is implicated in pneumoniae but can also cause infections in the urinary tract [2, 3]. These two species are of special concern because they have a high global rate of antimicrobial resistance (AMR) [4–6]. E. coli, followed by K. pneumoniae, was responsible for the most deaths attributed to AMR in 2019 [6]. Within the pediatric population, urinary tract infections are the most common bacterial infections; in particular, these infections are the most common cause of febrile illness in children [7]. β-lactam drugs are some of the most prescribed antibiotics for treating Enterobacteriaceae-caused infections [8]. Examples of such antibiotics include cephalosporins, monobactams, and carbapenems. The mechanism behind β-lactam antibiotics is focused on inhibiting the synthesis of peptidoglycan, a vital component of bacterial cell walls [9]. Specifically, β-lactams target penicillin-binding proteins, which then interrupts the transpeptidation process, leading to lysis [9]. However, such widespread use of β-lactam antibiotics has caused the spread of antibiotic resistant Enterobacteriaceae. The most important mechanism for β-lactam resistance involves the production of β-lactamases, enzymes produced by bacteria that hydrolyze the β-lactam amide [10] and thus inactivate the antibiotic. One group of β-lactamases are of special concern: extended spectrum β-lactamases (ESBLs) cause AMR by hydrolyzing a wider range of newer β-lactam antibiotics, including third-generation 2 cephalosporins and monobactams. However, ESBLs do not confer resistance to cephamycins and carbapenems [11]. ESBLs are inhibited by β-lactamase inhibitors such as clavulanic acid and tazobactam (Peirano and Pitout, 2019). Although ESBL-producing Enterobacteriaceae were originally susceptible to carbapenems, the restricted options for treating infections caused by such bacteria have led to the increased usage of carbapenems, which has led to the emergence and global spread of carbapenemase-producing bacteria [12]. There are three main types of ESBLs: TEM, SHV and CTX-M types [13]. E. coli and K. pneumoniae are the major ESBL-producing Enterobacteriaceae worldwide, and have been declared by the Infectious Diseases Society of America as two of the six drug-resistant microbes that must be urgently targeted by new therapies [14]. K. pneumoniae with TEM and SHV ESBL types were the primary cause of global hospital-related outbreaks in the 1990s, however, since the 2000s, CTX-Ms have become the most common global ESBL among Enterobacteriaceae, especially in E. coli (Peirano and Pitout, 2019). 1.2 CTX-M β-lactamase variants There are currently over 220 CTX-M β-lactamases enzymes which are divided into five subfamilies (CTX-M-1, CTX-M-2, CTX-M-8, CTX-M-9, and CTX-M-25) based on their amino acid identities [12]. blaCTX-M-14 (part of the CTX-M-9 subfamily) and blaCTX-M-15 (part of the CTX-M-1 subfamily) are the predominant genotypes globally [15], including in the Middle East. The only exceptions are China, South-East Asia, South Korea, Japan and Spain where CTX-M-9 is dominant, and South America where CTX-M-2 is the major CTX-M variant [12, 15]. The global dissemination of CTX-M genes can be attributed to horizontal gene transfer (HGT) on 3 conjugative plasmids and mobile genetic elements (MGEs) such as insertion sequences (ISs), transposons and integrons [16, 17]. Plasmids of the IncF family have been shown to predominantly carry blaCTX-M-15 [18]. blaCTX-M-14 is carried on multiple plasmid types, such as IncF in the East Asia [19] and IncK in Western Europe [20]. There are also a number of identical plasmids found across genetically diverse strains; these are called “epidemic plasmids” [21]. Some examples include pHK01 which carries blaCTX-M-14 and pEK516 which encodes blaCTX-M-15 [22, 23]. A worrying aspect of blaCTX-M containing plasmids is their ability to be co-resistant to other antibiotic classes, by capturing additional resistance determinant genes (e.g. carbapenemase genes) [15]. Therefore, the presence and spread of epidemic plasmids that may be vectors for multiple classes of antibiotic drugs is of great concern. MGEs have also played a critical role in the dissemination of blaCTX-M genes. The most commonly associated IS with blaCTX-M genes is ISEcp1 which encodes a transposase and acts as a strong promoter for blaCTX-M expression [24]. IS26 is another IS that has been shown to be important in the dissemination of CTX-M-1 and CTX-M-9 variants. Again, the major concern about MGEs that carry blaCTX-M genes is the presence of co-resistance-mediating genes of other antibiotic classes, such as fluoroquinolones, aminoglycosides and trimethoprim [25]. 1.3 Global genetic landscape of ESBL-producing Escherichia coli The prevalence of ESBL-producing E. coli has become a serious problem worldwide. They are found globally in both hospital and community settings [11]. They have also been found in the environment, previously being isolated in different water sources [26]. There have 4 also been cases of asymptomatic carriers of ESBL-producing E. coli [27], meaning that the environmental spread of these bacteria occur through both human and animal populations. Bacterial clones are isolates that have a close common phylogenetic ancestor, with high-risk clones those having an enhanced ability to spread and persist, with increased pathogenicity or AMR [28]. These high-risk clones are found globally and have been the biggest driver of AMR spread around the world. Examples of globally circulating high-risk E. coli clones are: sequence type (ST) 131 (ST131), ST155, ST393, ST69, ST405, ST38, ST73, and ST648 [29–31]. ST131, in particular, is the most studied high-risk clone because of its global prevalence alongside its association with blaCTX-M-15 and multidrug resistance. ST131 also carries more virulence factors than other AMR E. coli [32]. A pan-genome refers to the shared genes within all strains of bacteria, and comprises of core genes which are present in all genomes and accessory genes which contains genes that may are not present in all genomes [33]. There have been previous studies comparing the pan-genome of commensal and pathogenic E. coli; however, no associations between the pan-genome and AMR were established [34]. Most studies that aim to characterize E. coli strains focus on the core-genome or specific sets of accessory genes (mainly AMR markers and virulence factors). There are few studies that perform pan-genome analyses for ESBL-producing E. coli, but they prove useful such as in a recent study which revealed a tailocin specific for ST131 [35]. However, E. coli is incredibly diverse; the number of core genes shared among all strains is in the thousands whereas there are tens of thousands of accessory genes [36]. Therefore, the pan-genome can be used to find relationships between E. coli lineages as well as for the molecular characterization of E. coli subtypes. 5 1.4 ESBL-producing Escherichia coli in Qatar The Arabian Peninsula include Bahrain, Iraq, Jordan, Kuwait, Oman, Qatar, the Kingdom of Saudi Arabia, the United Arab Emirates, and Yemen. The region has one of the highest prevalence rates of ESBL-producing Enterobacteriaceae, mainly due to the overuse and misuse of antibiotics in animals and humans, the lack of infection control measures, and the continuous mobilization of populations [37]. There has also been a rise in carbapenem-resistant phenotypes in the Arabian Peninsula, sometimes in conjunction with an ESBL-producing phenotype. The majority of studies focused on characterizing AMR Enterobacteriaceae has been focused on Saudi Arabia; studies based in Qatar are uncommon but does not correlate with AMR incidence within the country. Between 2010 and 2018, the percentage of ESBL-producing E. coli isolated from pediatric patients increased from 18% to 31.7% [38]. The study also found that resistance was primarily caused by CTX-M-1 variants [38]. A second study by [39] showed that 39.5% of E. coli isolated from pediatric cases of acute gastroenteritis were multi-drug resistant, with 22.4% being ESBL-producers. However, these studies relied on polymerase chain reaction to identify AMR genes. Higher-resolution genomics methods, such as whole-genome sequencing (WGS), are able to detect AMR genes and mutations with higher sensitivity and accuracy [40]. This is incredibly useful for the surveillance of AMR. One recent study by [41], the first of which to use a WGS approach to characterize ESBL-producing E. coli from pediatric patients in Qatar, found CTX-M enzymes to be the most prevalent ESBLs, with a predominance of blaCTX-M-15. They also mention the frequent co-resistance to antibiotic classes other than β-lactams. The genomic sequences from this study is currently the only publicly available WGS Qatari E. coli isolates available on NCBI. However, the study focused mainly on the core genome, with genetic relatedness only inferred based on core genome single nucleotide polymorphism (SNPs). 6 1.5 Research objectives Although there has been previous work to characterize ESBL-producing E. coli in Qatar using WGS, none have leveraged the accessory genome alongside the core genome in order to more thoroughly and robustly characterize the circulating strains within the Qatari pediatric community. In a collaboration with the authors of [41], this work offers a more in-depth characterization of Qatari ESBL-producing E. coli in pediatric patients, with a particular emphasis on the accessory genome and MGEs. There also has not been any previous work that leveraged the large amounts of WGS sequences available from around the world in order to compare the Qatari ESBL-producing E. coli against global isolates. The use of comparative genomics would allow Qatari isolates to be compared against the global E. coli genetic landscape. This work will also aim to understand the Qatari E. coli genetic landscape within the global context. This will determine whether all identified Qatari isolates are known circulating strains, or whether there are any putative E. coli strains that may only be circulating within Qatar and the Middle East. I hypothesize that there will be two groups of lineages present in Qatar: one group containing well-known globally circulating high-risk clones, and one group containing lesser-known lineages, descendants of high-risk clones, that may have arisen within the country. A deep understanding of the ESBL-producing E. coli strains within Qatar is important for the surveillance of AMR, particularly because a large proportion of the Qatari population consists of expatriates, and Qatar remains a major international travel hub that connects Europe to East Asia. 7 Chapter 2: Genomic characterization of extended-spectrum beta-lactamase producing E. coli from pediatric patients in Qatar 2.1 Introduction Overall, there has been a shortage of studies characterizing ESBL-producing E. coli in Qatar. Even greater is the lack of studies using WGS for molecular characterization. This chapter will expand on the previous study from [41] which characterized ESBL-producing E. coli from pediatric patients in Qatar, but focused mainly on the core genome. This work incorporates the characterization of both the core and accessory genome in order to gain further insights. It is especially crucial to characterize the MGEs within the Qatari isolates, as they are important mechanisms for the spread of AMR and virulence factors. 2.2 Methods 2.2.1 Bacterial isolates and associated metadata The raw WGS reads of 256 isolated E. coli isolates were obtained directly from the authors of [41]. All isolates were recovered from screening and clinical specimens collected between January and December of 2018, in the general pediatrics, neonatal ICU and pediatric ICU units at Sidra Medicine in Doha, Qatar. The WGS reads are available as paired-end fastq.gz format on the Sequence Read Archive (BioProject: PRJNA599369). Associated AMR phenotypic data (contained within a metadata file) was obtained from the Supplementary Materials (https://www.frontiersin.org/articles/10.3389/fmicb.2020.581711/full#supplementary-material) of the same study. The metadata file contains the date of sampling, location of sampling 8 (specifically the pediatric unit), and specimen type of each isolate. Each isolate also contains an ST inferred from PubMLST, a multi-locus sequence typing (MLST) panel of seven core genes. ST inference was obtained using mlst, a tool by [42]. The AMR profile for each isolate consists of three phenotypes for various antibiotics. The phenotypes are susceptible, resistant, or intermediate. 14 antibiotics were assessed using antimicrobial susceptibility testing (Table 2.1). 9 Table 2.1 All antibiotics tested on the Qatari isolates, along with the associated number of isolates for different MIC testing results. Not all antibiotics were tested for all isolates. The full antimicrobial resistance phenotypes for all isolates is in Appendix B .Antibiotic Drug class Drug subclass # resistant # susceptible # intermediate Ceftriaxone β-lactam Cephalosporin 251 2 1 Ceftazidime β-lactam Cephalosporin 125 102 27 Cefepime β-lactam Cephalosporin 147 80 27 Aztreonam β-lactam Monobactam 125 27 19 Amoxicillin-clavulanate β-lactam Penam 71 137 46 Piperacillin-tazobactam β-lactam Penam 20 222 12 Ertapenem β-lactam Carbapenem 12 241 1 Meropenem β-lactam Carbapenem 8 246 0 Gentamicin Aminoglycoside N/A 45 208 1 Amikacin Aminoglycoside N/A 1 252 1 Ciprofloxacin Fluoroquinolone N/A 100 149 5 Levofloxacin Fluoroquinolone N/A 95 136 5 Trimethoprim-sulfamethoxazole Diaminopyrimidine/sulfonamide N/A 167 87 0 Nitrofurantoin Nitrofuran N/A 5 234 15 10 2.2.2 Quality control and sequence processing Initial sequencing quality control of the raw WGS reads was performed using FastQC (v0.11.8) [43]. Preliminary contamination detection was done using Kraken 2 (v2.1.1) (Wood et al., 2019) with the standard database (1/27/2021 collection). Kraken 2 performs taxonomic classification of each read in a sequence using exact k-mer matches to a database of archaea, viral, bacterial and human reference sequences. Supporting contamination detection was performed through quality control checks of the assembled reads, performed using QUAST (v5.0.2) [45]. FastQC, Kraken 2 and QUAST output files were compiled, analyzed and visualized using MultiQC (v1.11) [46]. Adaptors and low-quality reads were removed from the raw WGS sequences using Trim Galore! (v0.6.3) [47]. SPAdes (v3.13.0) [48] was used to perform de novo assembly on the trimmed reads. 2.2.3 Genomic characterization pipeline Multiple genomic features were identified from the Qatari isolates using various tools. As a baseline, the genomic distances between isolates were calculated using Mash (v2.2) [49], which performs k-mer based distance estimation using MinHash. SNPs were called on the raw reads using Snippy (v4.4.5) [50] with the EC958 reference (RefSeq assembly accession: GCF_000285655.3). This reference core-genome genome represents a globally disseminated multidrug resistant strain of ST131 E. coli [51] and was also used in the study by [41]. The core SNPs from Snippy were used in analysis. Core-genome (cgMLST) and whole-genome (wgMLST) multi-locus sequence typing was performed using MentaLiST (v0.1.9) [52]. cgMLST calling used the 2513 loci cgMLST scheme (downloaded in September 2020) from 11 cgMLST.org Nomenclature Server [53]. The wgMLST scheme comprises of 25002 loci obtained from Enterobase (downloaded in September 2020) [53] through PubMLST. An accessory-genome analysis (agMLST) was also performed, by removing all core genes from the wgMLST results and only analyzing the accessory genome. MGEs were identified using two methods: a database search based on sequence similarity using MobileElementFinder (v1.0.3) [54] and a de novo prediction from the raw reads and assembled genomes using MGEFinder (v1.0.6) [55]. Candidate mobile elements were then annotated for AMR genes, transposases and prophage elements using ResFinder (v4.0) [56], ISEScan (v1.7.2) [57], and PHASTER [58] respectively. Plasmid replicons were predicted using PlasmidFinder [59], then plasmids and IS were reconstructed from the assembled contigs using MOB-suite [60]. AMR markers were identified using AMRFinderPlus (v3.10.20) [61], and virulence factors were identified using ABRicate (v1.0.0) [62] with the included E. coli VF database, which contains E. coli-specific virulence factors from VFDB [63]. For these genomic features, heatmaps were produced for visualization purposes. Hierarchical clustering was performed and compared to ST metadata in order to determine whether the STs of interest can be characterized by a given genomic feature. Moreover, in order to investigate these genomic features in further detail, they were annotated using Bakta (v1.5.1) [64]. Phylogenetic reconstruction was done on all called genomic features. Most features had to be transformed into a pairwise distance matrix calculated using the hamming distance. Mash, however, outputs a pairwise distance matrix directly. Phylogenetic trees were built from distance matrices using QuickTree (v2.5) [65] and visualized using Microreact [66]. PopPUNK (v2.4.0) [67] was used to cluster the Qatari isolates according to their accessory and core genomes. A database was built from the 251 uncontaminated assembled 12 genomes using pp-sketchlib (v1.7.0) [68] (details in 1.1.1.1.1Appendix A ). A Bayesian Gaussian mixture model (BGMM) was fitted to the database using PopPUNK (details in 1.1.1.1.1Appendix A ). The model was then optimized using PopPUNK’s refinement method, which tries to improve the model by optimizing the network score. This refinement method was run three separate times on the model: once refining only the core genome, once refining only the accessory genome, and once refining both together at once. As a final output, PopPUNK outputs a list of cluster assignments for each isolate, as well as a t-SNE plot of the accessory genome distances, and a neighbor-joining tree from the core genome distances. 2.2.4 Identification of putative antimicrobial resistance mutations Identification of putative AMR genes was also performed using machine learning. By using feature importance analysis, specific SNPs and genes can be correlated with different AMR phenotypes. The phenotypes were provided from the original study [41]. All intermediate phenotypes were removed. Logistic regression (LR) and random forest (RF) models were trained on the SNPs called by Snippy. As each SNP is a feature, the feature matrix comprises of 500,313 features and 251 samples (or examples). All SNPs were annotated by Bakta. Model performance was evaluated using 5-fold cross-validation. The scoring metrics used for evaluating performance are the area under the receiver operating characteristic curve (ROC AUC) and the F1 score of the resistant class. Feature importance was determined using the LR coefficient values and the RF feature importance values, which are based on the Gini index for decision trees. 13 2.3 Results 2.3.1 Quality control 2.3.1.1 Sequencing quality Initial sequencing quality control of the raw WGS reads revealed some minor issues concerning sequencing quality. FastQC indicates that 149 sequences have failed in terms of per-base sequence content, with the rest of the isolates containing warnings with regards to this statistic. This is a measure of the proportion of each of the called normal DNA bases for each base position within a sequence. Therefore, a failure is indicated when the difference between A and T, or G and C is greater than 20% in any position [43] (example seen in Figure 2.1). The sequences that failed this module generally have a large difference between pairs of bases within the first 20 bases and last 10 bases of a read. This is common in libraries which were fragmented with transposases [43]. Therefore, the failure of the Qatari sequences for this quality measure is expected, as the paired-end libraries were constructed using Nextera XT [41]. The Nextera XT (Illumina, San Diego, CA) uses a transposome to fragment and tag DNA with adapter sequences. Trim Galore! was used to remove these low-quality regions of reads, and remove adapter sequences. 14 Figure 2.1 An example of a sequence that failed the per base sequence content module of FastQC (E15). FastQC also shows 57 sequences that failed in terms of sequence quality (Figure 2.2), which measures the mean quality value across each base position in a read. Although there is a general degradation of quality over the read length, the failed sequences have a larger drop than those that passed, especially at the end of a read where the Phred score can be as low as seven. Since the issue appears systematic, it appears this may be a sequencing issue. Many sequences that failed this module also contained a higher number of N nucleotides at the end of the reads. Although low quality portions are trimmed out of the beginning and end of a read, the lower quality of these sequences cannot be circumvented with Trim Galore, especially if the drop if a read drops in quality is not only at the beginning or end of the read. Therefore, it is important to keep in mind the sequence quality when doing downstream sequencing processing and analysis. 15 Figure 2.2 Plot of mean per-base sequence quality (measured using the Phred score) in the read. Red lines indicate sequences that have failed this FastQC module (greater than 1% error rate), yellow lines indicate sequences that contain warnings (between 0.2% and 1% error rate), and green lines indicate sequences that passed quality control (less than 0.2% error rate). The quality of the assembled contigs were also assessed using QUAST, revealing that many of the genomes are of lower assembly quality. Most genomes contain a large number contigs, with many in the thousands of contigs. There are several isolates that are outliers, with over 5000 contigs. Moreover, the genomes have many contigs of small lengths, with the majority of contigs being between 0-1000 bp (Figure 2.3). There are also multiple genomes that have unusually large assembled genome lengths along with low N50 statistic (Table 2.2, Figure 2.4). The N50 statistic is the contig length such that longer or equal-length contigs produce 50% of the bases of the assembly, therefore a low N50 score indicates a poor assembly of low quality 16 containing a large number of fragmented contigs. Since E. coli has a genome size between 4.5 to 5.5 Mb [69], the combination of these two statistics may indicate these genomes may be contaminated and could be considered outliers. Figure 2.3 The number of contigs within each assembled genome, broken down by the length of contigs. Overall there are many small contigs in each genome. There are also a number of standout genomes with abnormally large number of contigs. Isolate ID N50 (Kbp) Length (Mbp) E10 3.1 6.5 E103 92.0 6.0 E273 2.7 8.3 E288 21.8 6.1 17 E324 11.4 8.7 E424 22.3 6.0 E53 1.6 8.9 Table 2.2 N50 statistic and assembled genome length of potential outliers. The full metadata and quality control metrics for all isolates is in Appendix A . Figure 2.4 Plot of length of assembled genomes against their associated N50 statistic. There are four genomes with abnormally large genome sizes, but also extremely low N50 statistic (red box). 2.3.1.2 Contamination detection Preliminary contamination detection using Kraken2 revealed that although over 85% of all reads in the isolates are classified as Enterobacteriaceae, the majority of reads for many isolates are classified as a genus other than Escherichia. In Figure 2.5, the Kraken2 output of the 18 seven potential outliers from Table 2.2 are shown. Even though the majority of the reads are classified as “Other” when looking at the species level, these reads are mostly classified as Enterobacteriaceae when looking at the family level. Two of the isolates mentioned in Table 2.2 showed high levels of contamination, determined by a large number of reads classified by Kraken2 as a genus other than Escherichia. 19.3% and 5.9% of reads, from E103 and E324 respectively, were classified as Klebsiella. The other potential outliers did not have considerable contamination from any one genus or family. Another indication of contamination is by looking at the genome assembly. If the assembled genome length is larger than expected, it is likely that the genome is contaminated by another organism. Therefore, since E53 and E273 have an abnormally large genome size, so they will also be considered outliers. E103, E324, E53, E273 and E10 are therefore considered outliers due to contamination and are thus removed from downstream analysis. 19 Figure 2.5 Proportion of top five organisms identified using Kraken2 in potential outliers from Table 2.2. 2.3.2 Characterization of STs by genomic features 2.3.2.1 Genomic distance as a baseline As a baseline, a phylogenetic tree was produced from the pairwise distance matrix obtained from Mash [49]. Each sequence is sketched to produce a k-mer representation. The genomic distance is then estimated by the MinHash algorithm, which estimates the Jaccard index between two sketches. This method is based on nucleotide content and is therefore agnostic to specific variant types. For this baseline, the outliers were not discarded in order to observe their distance to the rest of the isolates. 20 As expected, a general separation (or clustering) by STs is observed (Figure 2.6). ST131 is grouped together in its own clade. Two separate subclades of ST131 are observed. Moreover, there is a single ST131 isolate that is clearly distant from all others; this is E324, one of the outliers outlined previously. ST38 is also generally well separated but contains a few other isolates of different STs within its clade. ST10, however, is divided into two separate clades, with many different isolates intermingled within the clades. The other major globally circulating STs, ST73 and ST1193 form their own monophyletic group and are therefore completely separated from other STs. The five outliers are also highlighted in Figure 2.7. E324 is clearly present on the left, being much more distantly related from the rest of the ST131 isolates. E103 (blue box) also has a larger branch length compared to other closely related isolates. However, E10 is nestled in a cluster of many different STs, with no discernable significant difference in branch lengths, meaning that its nucleotide identity does not clearly differentiate it as an outlier. There are also two isolates (red square) that are separated from all other isolates: these are E53 and E273. 21 Figure 2.6 The neighbor-joining tree generated from the pairwise distance matrix of Qatari isolates, produced by Mash. The node colors and outside band correspond to STs referenced in the legend. Outliers mentioned in Section 2.3.1.2 are the nodes encircled in blue. Only the isolates from STs of interest (globally circulating lineages and putative Qatari lineages of interest) have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs (in most cases they are singletons of an ST). The STs can be distinguished based on their pairwise Mash distances, which provides a genetically context-independent analysis. E324 ST4981 & ST10997 22 2.3.2.2 Multi-locus sequence typing The neighbor-joining tree of the cgMLST (Figure 2.7) shows an expected general separation of STs based on the 2513 loci in the cgMLST scheme. The major globally prevalent STs (ST10, ST38, ST73, ST131, and ST1193) group together in their respective clade. However, there are instances of isolates from other STs mixed within the clades of globally prevalent STs. ST10 has different STs within its clade: ST32, ST34, ST167, ST229, ST752, and ST1695. Moreover, ST38 has E131 of ST315 and E26 of ST3268 within its clade. There are also two clades of potential interest, containing STs that have previously not been extensively characterized. One such clade consists of three ST4981 and two ST10097 isolates. These STs are not consistently separated through their core genome. Moreover, this clade is particularly distant from all other STs, only being very distantly related to ST10. The other potential clade of interest consists of six ST8881 isolates, and neighbors a clade of four ST648 isolates. Since ST648 is an emerging virulent strain [70], ST8881 may be of particular interest due to it not having been previously studied and its relatedness to ST648. 23 Figure 2.7 The core-genome MLST neighbor-joining tree shows that indeed there is a general separation of sequence types based on a set of 2513 loci. The node colors and outside band correspond to STs referenced in the legend. Only the isolates from STs of interest have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs. As expected, STs are very well distinguished by their core genomes. ST4981 and ST10997 are observed on a monophyletic clade. Similarly, ST8881 is also observed on a monophyletic clade neighboring ST648. The neighbor-joining tree of the wgMLST also shows a general separation between STs (Figure 2.8), however the tree has much longer branch lengths, attributable to the increase of variation in the accessory genome. Once again, we see that for ST10 and ST38, the isolates are 24 not fully separated. The wgMLST clade of ST38 contains the same STs as the ones in the cgMLST (ST38, ST315, and ST3268), however, it is interesting to note that ST10 also contains one of the potential clades of interest aforementioned: the clade with ST10997 and ST4981. Moreover, in this case, ST10997 and ST4981 are completely separated, each in their own subclade. Another important observation from the wgMLST neighbor-joining tree is the presence of two potential outliers (red box). There are two isolates, E167 of ST10993 and E406 of ST131 that are together on a clade but have extremely large branch lengths to the rest of the genomes within the tree. Finally, the other clade of interest, consisting of ST8881, still neighbors a clade of ST648. ST10 ST38 ST8881 25 Figure 2.8 The complete whole-genome MLST tree (on the left) and the zoomed-in whole-genome MLST tree (on the right) without the two outlier isolates. The node colors and outside band correspond to STs referenced in the legend. Only the isolates from STs of interest have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs. On the left, two outliers are observed (red box). However, overall, the STs are also distinguishable by their whole-genome MLST profile. ST8881 as well as ST4981 and ST10997 remain in their respective monophyletic clades. However, ST10 and ST38 are not cleanly separated compared to the other STs. The accessory-genome MLST neighbor-joining tree (Figure 2.9) is very similar to the wgMLST tree, indicating that the trends seen in the wgMLST are not simply dominated by the variation seen within the cgMLST. The exact same trends and observations observed in the wgMLST tree are found within the agMLST tree. Figure 2.9 The zoomed-in accessory-genome MLST tree without the two outlier isolates. The node colors and outside band correspond to STs referenced in the legend. Only the isolates from STs of interest have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs. The tree is very similar to that of the whole-genome MLST, with the same trends and observations observed, indicating that STs can indeed be distinguished based on their accessory genome. 26 2.3.2.3 Single nucleotide polymorphisms Within the 251 Qatari isolates, there are a total of 500,313 SNP positions, 120,144 of which are core SNPs. Since the reference genome that was used for SNP calling was the EC958 genome, there are three sequences within the reference: one whole-genome sequence, and two plasmid sequences (pEC958 and pEC958B). One plasmid is much smaller than the other, with the large one having a length of 135,602 bp and the smaller one being only 4080 bp long. Within the genome, there are 490,455 SNPs (120,144 core SNPs), within the large plasmid there are 9413 SNPs (no core SNPs), and within the small plasmid there are 445 SNPs (no core SNPs). The EC958 is an E. coli ST131 reference genome, therefore the Qatari ST131 isolates have much fewer called variants than other STs (Figure 2.10). Overall, the STs are separated well by their SNP profile. However, there are two groups of ST131 isolates: one that is much more closely related to the EC958 reference genome, and one that contains several additional sets of closely-situated SNPs. Otherwise, we see clear clusters for the other STs, and observe a mostly uniform SNP distribution within an ST. One exception is ST10, which appears to have three subclusters that are closely related. The SNP profile for regions aligned to the two plasmids of the reference genome do not cleanly separate the STs. Shown in Appendix E , the heatmaps and dendrogram obtained from hierarchical clustering do not cluster the STs together. For the larger plasmid, pEC958, only ST1193 is clustered together, while the other STs do not follow any pattern. There are also a number of isolates that do not contain any SNPs within the plasmid. This is observed more frequently in the second smaller plasmid, pEC958B. Most isolates do not contain any SNPs within this plasmid. 27 After annotating the SNPs against the reference genome’s GenBank annotations, the observed distribution is presented in Table 2.3. A majority of the SNPs are within coding sequences, with the majority of the rest in intergenic regions. There are a small number of those in RNA regions. Coding sequence Intergenic region rRNA tRNA ncRNA Number of variants 447 009 53 133 66 39 16 Table 2.3 Counts of annotation type for SNPs in the Qatari genomes aligned to EC958. 28 Figure 2.10 A heatmap of presence/absence of SNPs in each isolate predicted using Snippy. The dendrogram on the left was produced through hierarchical clustering of the SNP calls. The colored bars indicate the respective ST from the list of globally prevalent ST alongside STs from clades of interest. ST131 is separated into two clades, while ST10 is separated into three groups. ST8881 is in a monophyletic clade neighboring ST648. ST4981 and ST10997 are together on a monophyletic clade, but do not separate cleanly within themselves. 2.3.2.4 Mobile genetic elements 2.3.2.4.1 Database search of mobile gene elements Using MobileElementFinder, there were a total of 170 different MGEs predicted within the 251 isolates (Figure 2.11). Generally, the STs are clustered together from their MGE profile. 29 The number of MGEs per isolate ranges from 9 to 34 (Figure 2.12). ST10 isolates, however, appear to not consistently cluster together. It is also interesting to note that ST4981 and ST10997 are mostly clustered together, between isolates of ST10. However, there is a single isolate of ST4981 (E197) that is separate from this cluster. All isolates in ST4981 and ST10997 have a set of six MGEs that are consistently predicted within their genomes: ISEc1, ISEc26, ISEch5, ISKpn8, MITEEc1, MITEYpe1. 30 Figure 2.11 Heatmap of the number of each mobile genetic element per isolate predicted using MobileElementFinder. The dendrogram on the left was produced through hierarchical clustering of the MGE profile. The colored bars indicate the respective ST from the list of globally prevalent ST alongside STs from clades of interest. Only a subset of MGEs is labelled. The major STs are well distinguished by their MGE profile. The exception is ST10, which is very diverse and does not cluster together. ST8881 is contained within its own monophyletic clade. ST4981 and ST10997 cluster together, but do not cleanly separate from each other. 31 Figure 2.12 A histogram showing the number of mobile genetic elements per isolate predicted using MobileElementFinder. ST8881 is also observed to cluster together alongside ST648, although there is a single isolate of ST131 nested within this cluster (E141). The ST8881 isolates can be divided into two groups: those with exactly 15 MGEs, and those with less than 15 MGEs. E200, E336 and E444 have the exact same MGE profile, consisting of 13 different MGEs, with two copies of ISEc38 and two copies of MITEEc1. The other group of ST8881 isolates does not have a consistent MGE profile. There is a set of seven MGEs that are consistently predicted within all ST8881 isolates: IS609, IS6100, ISEc30, ISEc40, ISEc9, ISEch14, MITEEc1. However, compared to all other Qatari isolates, there is no MGE that is unique to ST8881. 32 2.3.2.4.2 De novo prediction of mobile genetic elements MGEFinder has four methods of predicting MGEs from an initial sequence alignment to the reference genome. These methods are based on the identification of candidate termini pairs (terminal ends of an IS element). This is done by searching for clipped-end sites which are then used to build a consensus sequence of the terminal end. These high-quality terminal ends are then paired up with oppositely oriented termini to identify the candidate insertion [55]. The first method is by aligning candidate termini pairs to the reference genome to infer the full sequence. The second is to align the candidate termini pairs to an assembly. For this method, there are three quality levels for the inferred sequence. The highest quality is an inferred sequence with full context, meaning the inferred sequence has 25 base pairs of context on both sides. This is followed by an inferred sequence with half context, meaning one side of the MGE is at the end of the contig. Finally, the lowest quality is an inferred sequence without context. Because of the low quality of assembled genomes, only inferred sequences with full context will be analyzed. The third method of predicting MGEs is by attempting to overlap the termini. The final method is to align the termini of candidate pairs to query sequences within a database. When running the entire MGEFinder workflow, this database is dynamically-constructed by candidate IS elements from the previous steps that have passed a series of quality control metrics. The results obtained from MGEFinder, which predicts MGEs de novo, were somewhat similar to those obtained via MobileElementFinder. However, MGEFinder consistently predicts more MGEs per genome than MobileElementFinder (Figure 2.13). This is due to an increase in sensitivity with MGEFinder, especially with regards to repetitive sequences. However, this may also be due to false positives due to the low coverage of the sequence, resulting in increased soft-clipping. The heatmap of the MGE calls alongside the dendrogram produced through 33 hierarchical clustering (Figure 2.14) shows that STs are generally clustered together. ST8881 and ST648 are neighboring clusters, and ST4981 and ST10997 are clustered together. However, it is interesting to note that there are two separate clusters of ST131. Similarly to the MobileElementFinder results, there are multiple clusters of ST10 in the heatmap, suggesting it is highly heterogenous in its MGE profile. Figure 2.13 A histogram showing the number of mobile genetic elements per isolate predicted using MGEFinder. The number of predicted MGEs is consistently higher per isolate for MGEFinder than MobileElementFinder, however, a similar bimodal distribution is observed. 34 Figure 2.14 Heatmap of the number of each mobile genetic element per isolate predicted using MGEFinder. The dendrogram on the left was produced through hierarchical clustering of the MGE profile. The colored bars indicate the respective ST from the list of globally prevalent ST alongside STs from clades of interest. Only a subset of MGEs is labelled. In this case, the STs continue to be mainly distinguishable. However, ST131 is now split into two clusters. ST10 is split into three clusters. ST8881, however, consists of its own monophyletic clade. ST4981 and ST10997 is also on its own monophyletic clade, and the two STs are distinguishable from each other. Overall, there are some interesting observations about the MGEs predicted by MGEFinder compared to MobileElementFinder. The MITEEc1 and MITEYpe1 predicted in nearly all isolates using MobileElementFinder were not predicted in any isolates by MGEFinder. 35 It is important to note that these miniature inverted-repeat transposable elements are very short, only being around 123 bp in length. This is because MGEFinder was developed to genotype large insertions, rather than small ones. For the case of MGEFinder, ST8881 is still grouped together based on their MGE profile but is not divided into two groups. The isolates range from 42 to 46 MGEs per genome. ST8881 has a set of 34 MGEs that is consistently predicted by MGEFinder, with two MGE having multiple copies within all isolates. The first of these MGEs (c31813) is predicted in 174 different isolates, the most out of any other MGE. Upon doing a BLAST query on ISFinder of this 768 bp long representative sequence, the top results all match the IS1 family of transposases in E. coli. Furthermore, the length of c31813 is also the same as the ISKpn14 IS within the IS1 family. Interestingly, however, this IS was only predicted in 67 isolates using MobileElementFinder. The second MGE that is found multiple times within ST8881 isolates is c9039 and is predicted in 112 different isolates. This MGE is 1329 bp long and a BLAST search on ISFinder matches IS2 from the IS3 family of insertion sequences. Interestingly, this MGE is not predicted in any isolate using MobileElementFinder, which may reveal the benefit of de novo MGE prediction. ST10997 and ST4981 are grouped together based on their MGE profile and have between 60 and 64 MGEs per genome. These two STs have a set of 46 MGEs that is consistently predicted, with the same two MGEs as ST8881 present with multiple copies: c31813 and c9039. 2.3.2.5 Plasmids Hierarchical clustering of the plasmid replicon profile from PlasmidFinder did not produce any concordant clusters with regards to STs (Figure 2.15). There were seven isolates with no predicted plasmid replicons, all from different STs. Overall, 52 different replicons were 36 predicted among all isolates. The most commonly found plasmid replicon was IncFIB(AP001918), predicted in 188 (74.3%) isolates. The number of copies of any single replicon found within a single isolate ranges from one to four. Figure 2.15 A heatmap of the plasmid replicons predicted within each isolate using PlasmidFinder. The dendrogram on the left was produced through hierarchical clustering of the plasmid replicon profile. The colored bars indicate the respective ST from the list of globally prevalent STs alongside STs from clades of interest. Only a subset of plasmids is labelled. No clear separation of STs is observed based on their plasmid replicon profile. 37 The ST8881 isolates are not fully clustered together according to their plasmid profile. E155 and E248 are separated from the rest of the ST8881 which are clustered together. There were four different plasmid replicons found among the ST8881 isolates, with two replicons found in all isolates: IncFIB(AP001918) and IncFII. IncFIA was also identified in all but one of the ST8881 isolates (E248). The last replicon was Col(pHAD28), only predicted within a single ST8881 isolate (E318). There were no replicons that were only found in ST8881 isolates. The ST4981 and ST10997 isolates are not clustered together. The only replicons found in all ST4981 and ST10997 isolates are IncFIB(AP001918) and IncFIB(K). 2.3.2.6 Virulence factors A total of 745 different virulence factors were predicted within the Qatari isolates. These include genes that encode for hypothetical proteins that were predicted; removing these genes yields 603 virulence factors. Hierarchical clustering shows many of the major STs show a clear clustering (Figure 2.16). All ST131, for example, are clustered together. Similarly, ST1193 and ST73 are in their own clusters as well. ST4981 and ST10997 are clustered together, while ST8881 clusters alongside ST648 and is closely related to some ST38 isolates. 38 Figure 2.16 A heatmap of virulence factors predicted within each isolate using ABRicate with the VFDB virulence factor database. This does not include genes that encode for hypothetical proteins. The dendrogram on the left was produced through hierarchical clustering of the virulence factor profile. The colored bars indicate the respective ST from the list of globally prevalent STs alongside STs from clades of interest. Only a subset or virulence factors is labelled. All ST131 isolates cluster together, but ST10 is diverse and does not cleanly cluster together. ST8881 is a monophyletic clade neighboring ST648. ST4981 and ST10997 isolates cluster together, but are not separable from each other. ST8881 isolates form their own cluster alongside a cluster of ST648 isolates. The number of virulence factors within ST8881 isolates ranges from 176 to 190. There is a set of 155 virulence factors that is found within all ST8881 isolates. 39 Interestingly, when performing hierarchical clustering only on the 183 genes that encode hypothetical proteins, the STs still show a clear clustering (Figure 2.17). Moreover, ST38 clusters better using only the genes encoding hypothetical proteins; instead of 2 separate clusters, there is now only a single cluster. It is also interesting to note that there are large groups of genes that can be used to separate the STs. For example, for ST131, we see a large cluster of genes that is absent in other STs. 40 Figure 2.17 A heatmap of virulence factors predicted within each isolate using ABRicate with the VFDB virulence factor database. This only includes genes that encode for hypothetical proteins. The dendrogram on the left was produced through hierarchical clustering of the gene profile. The colored bars indicate the respective ST from the list of globally prevalent STs alongside STs from clades of interest. Only a subset or virulence factors is labelled. The same trends are observed as in Figure 2.16. The STs are distinguishable based on both known virulence factors and putative hypothetical protein virulence factors. 41 2.3.2.7 Antibiotic resistance markers There was a total of 124 different AMR markers identified within the Qatari genomes. Hierarchical clustering of the antibiotic resistance marker profile shows a general clustering of STs (Figure 2.18). Interestingly, we can see that ST131 is divided into three separate clusters. Figure 2.18 A heatmap of AMR markers predicted within each isolate using AMRFinder. The dendrogram on the left was produced through hierarchical clustering of the AMR profile. The colored bars indicate the respective ST from the list of globally prevalent STs alongside STs from clades of interest. Only a subset of AMR markers is labelled. Some STs are more cleanly distinguished than others based on their AMR marker profile. ST10 does not cluster, and ST131 is separated into three clusters. ST8881 however, is a monophyletic clade with a single unique AMR marker profile. ST4981 and ST10997 are also on a monophyletic clade, and their profiles are distinguishable. 42 All ST8881 isolates are clustered together according to their AMR marker profile. A unique set of 18 markers are present in all isolates. This also corresponds with ST8881’s unique AMR phenotype, derived from antimicrobial susceptibility testing. However, this is only assuming that all ST8881 isolates have the same resistance phenotype, since half of these isolates have “intermediate” resistance to Amoxicillin-clavulanate and Nitrofurantoin. Moreover, half of the isolates have not been tested for Aztreonam resistance. Therefore, the conclusion that all ST8881 isolates have the same phenotype relies on the assumption that all isolates are resistant to Amoxicillin-clavulanate and Aztreonam, and susceptible to Nitrofurantoin. However, with the same AMR marker profile, this is a strong assumption. With the above assumption, ST8881 isolates are resistant to ceftriaxone, ceftazidime, cefepime, amoxicillin-clavulanate, aztreonam, gentamicin, ciprofloxacin, levofloxacin, and trimethoprim-sulfamethoxazole. In broad terms, this means they are resistant to all cephalosporins, monobactams, fluoroquinolones, diaminopyrimidines, and sulfonamides tested. AMRFinder identified AMR markers for these antibiotics except for aztreonam. Cephalosporin resistance is encoded by blaCTX-M-15, fluoroquinolone resistance is encoded by point mutations in gyrA, parC and parE, diaminopyrimidines (trimethoprim) resistance is encoded by dfrA12, and sulfonamide resistance is encoded by sul1. Moreover, gentamicin resistance is encoded by aac(3)-IIe and aac(6’)-Ib-cr5. Other β-lactam resistance markers include a partial hit to blaOXA (which could encode for amoxicillin resistance) and a complete hit of blaTEM-1. However, there are no identified markers for aztreonam. Aztreonam is usually encoded by a point mutation within ftsI which is present within the AMRFinder database. There are two point mutations in ftsI observed in other Qatari isolates (ftsI_I336IKYRI and ftsI_N337NYRIN observed in two and six isolates respectively), but none of them are observed within any ST8881 isolates. There are 43 also several AMR markers that confer resistance to antibiotics that were not assessed by antimicrobial susceptibility testing: mph(A) conferring resistance to macrolides, catB3 conferring resistance to chloramphenicol, a point mutation in cyaA conferring resistance to fosmidomycin, a point mutation in glpT conferring resistance to Fosfomycin, and the presence of qacEdelta1 which confers resistance to antiseptics. Although ST10997 and ST4981 generally cluster together, there is a single isolate of ST10997 (E249) that is distant from the rest of the cluster. Moreover, the AMR phenotype profile of ST4981 differs from ST10997. There is also much discrepancy between the two ST10997 isolates, with different phenotypes for five different antibiotics. 2.3.3 Clustering based on the core- and accessory-genomes The cluster assignments by BGMM tend to correlate very well with the STs. All ST73 and ST1193 isolates are assigned their own respective unique clusters (Figure 2.19). ST10997 and ST4981 are also in their own cluster, as well as ST8881 and ST648. However, ST10 and ST38 are much more diverse, having isolates that are assigned a range of different clusters. ST10, in particular, is comprised of 15 different clusters. ST131 is also comprised of two separate clusters. Each cluster forms its own monophyletic group within a greater clade that contains all ST131 isolates. The t-SNE of the accessory-genome distances (Figure 2.20) show that there are up to 7 distinct separable groups of isolates. However, within each group, it is hard to separate out the STs based on only their accessory genome. Only a few STs (or groups of) are clearly separable: ST10997 and ST4981, ST8881 and ST648, and ST131. ST131, specifically, is interesting as the two aforementioned clusters are separable based on their accessory genome. 44 Figure 2.19 Phylogenetic tree generated from the core-genome distances of Qatari isolates. The node colors correspond to assigned BGMM clusters, while the outside band corresponds to STs shown in the legend. Only the isolates from STs of interest have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs. Overall, BGMM cluster assignments correlate well with STs. However, ST131 is separate into two clusters, and ST10 comprises of multiple different assigned clusters showing its diversity. ST8881 is clustered alongside ST648. However, ST4981 and ST10997 are assigned their own unique cluster and are clearly a monophyletic clade. 45 Figure 2.20 t-SNE of the accessory genome distances of Qatari isolates. Dot colors correspond to the assigned cluster by BGMM. Based on their accessory genome, ST131 is clearly split into two, with corresponding cluster assignments by BGMM. ST8881 is not clearly separable from ST648 whilst ST4981 and ST10997 are clearly separable from the rest of the isolates. 2.3.4 Identification of putative antimicrobial resistance mutations 2.3.4.1 Predicting antimicrobial resistance using machine learning The distribution of phenotypes for the E. coli isolates is very unbalanced. Because of the sampling strategy, for most antimicrobials, there is a larger proportion of resistant isolates. From a preliminary analysis, only the following drugs could be used for training a model: ceftazidime, ST131 ST8881 ST4981 & ST10997 46 cefepime, amoxicillin-clavulanate, gentamicin, ciprofloxacin, levofloxacin, and trimethoprim-sulfamethoxazole. For all other drugs, the proportion of the minority class is less than 10% of the sample size. Out of these drugs, there is only one example where the resistant class is the minority class: gentamicin where the resistant class comprises of 19% of the isolates. Overall, the exact same trend is observed for the prediction models on various drugs (Figure 2.21 andFigure 4.3). Amoxicillin and gentamicin have worse F1 scores than ROC AUC because they are the only drugs that have the resistant class as the minority class. Between the models, RF either has comparable performance to LR, or significantly better in the case of gentamicin. However, for the other drugs, the F1 score indicates that the model is indeed learning from the large SNP feature space and may be informative in determining putative AMR-associated mutations. Figure 2.21 ROC AUC and F1 score of the RF model for each drug. The drugs are ceftazidime (CAZ), cefepime (FEP), amoxicillin-clavulanate (AMC), gentamicin (GEN), ciprofloxacin (CIP), levofloxacin (LVX), and trimethoprim-sulfamethoxazole (SXT). Prediction performance is not ideal, with some drugs having F1 scores below 0.5 (e.g. AMC and GEN). 47 2.3.4.2 Feature importance analysis There is no consensus in the top ten features for the two models when comparing the LR coefficients and the RF feature importance values. For both models, the feature importance values and coefficients are very small (Table 4.3 and Table 4.4). The majority of top mutations are not annotated as being within AMR-associated genes. The only example for the RF model is sugE for trimethoprim. Over expression of sugE has been linked to increased resistance to quaternary ammonium compounds, or biocides [71]. There are also two other genes that are found to be the top genes in the RF model. gsiB is classified as a highly important gene associated with amoxicillin and ceftazidime. rplD encodes for a 50S ribosomal protein and is a highly important gene associated with levofloxacin and trimethoprim. The top mutations identified by the LR model are much less diverse. The hyb family of genes is associated with ceftazidime, ciprofloxacin, cefepime, and levofloxacin resistance. The lptG gene is associated with levofloxacin and ciprofloxacin resistance; moreover, it was also found to be important by the RF model for levofloxacin resistance as well. The gss gene was identified as important for ceftazidime and cefepime resistance, but also was identified by the RF model for ciprofloxacin resistance. Two mutations of mdtC have been identified as important for ciprofloxacin and levofloxacin resistance. Finally, three mutations of mgtA and two mutations of yjjX were identified as important for trimethoprim resistance. 2.4 Discussion Most genomic features that were used to characterize the Qatari isolates can be used to distinguish between STs, specifically the predominant STs. Since Mash is a k-mer based method to determine genomic distances between isolates, it serves as a good baseline to build a 48 phylogenetic tree based on the pairwise distances of isolates. In contrast to building phylogenetic trees using genomic features such as SNPs or MGEs, Mash k-mers are not context aware, meaning they rely only on nucleotide identity to determine distances, and have no knowledge of the underlying genetics of each k-mer. In addition to being a baseline, the phylogenetic tree can be used to assess outliers, isolates that have an unusually large distances to all other isolates. In this instance, two of the outliers are clearly presented by Mash (E324 and E103), while the third outlier is not distantly related to other isolates and remains within a clade that contains a variety of STs. Although E273 and E53 are not as clear as outliers, they are also on independent branches of the phylogenetic tree. This demonstrates Mash as a rapid method of building a phylogenetic tree, which can then be used for further phylogenetic analysis or as a secondary process in identifying probable outliers. All the genomic features apart from plasmids were able to be used to separate out most of the prevalent STs. The most prevalent ST is ST131 (16.8%), followed by ST10 and ST38 (both 8.2%). Many of the other globally circulating high-risk clones [29–31] were also present within the samples: ST155 (2.0%), ST69 (2.0%), ST405 (2.0%), ST73 (3.1%), and ST648 (1.6%). Other frequently found STs are ST1193 (3.9%), ST48 (2.3%) and ST8881 (2.4%). ST1193 is documented as an emerging high-risk clone [72] which is becoming as prevalent as ST131 in many areas of the world. ST48 is a single locus variant of ST10, and is part of the same clonal complex [73]. ST8881 however, has been documented, with only one prior work having identified it in one E. coli isolate among a sample in Bangladesh [74]. Being one of the most prevalent STs, ST8881 is of particular interest because of its novelty. ST4981 and ST10997 are other STs of interest, because based on the genomic features, the two STs are genetically similar while being genetically distinct from all other isolates by being in their own monophyletic group. 49 Clonal complex can be defined as STs that differ from each other by no more than two of the seven cgMLST loci [75], ST4981 and ST10997 can be considered clonal complexes because they differ by only the purA loci according the pubMLST scheme. Moreover, both of these STs can be considered a clonal complex with ST617, as they differ to ST617 by the fumC and purA (only in the case of ST10997) loci. Therefore, ST8881 along with the clonal complex consisting of ST10997 and ST4981 will be of particular interest. This clonal complex will be called CC4981 in this work. The unique nature of ST8881 and CC4981 is also supported by the cluster assignments of BGMM. In the core-genome phylogenetic tree, CC4981 is in a monophyletic group that is relatively distant from other Qatari isolates, while also being assigned its own unique cluster. ST8881, however, is not assigned a unique cluster, but rather is clustered together with ST648. This can be explained by the limited number of ST648 isolates alongside the genetic closeness of ST648 and ST8881 isolates. The accessory-genome t-SNE also shows that these aforementioned clusters are somewhat separable from the rest of the isolates, further supporting their importance and uniqueness in the Qatari E. coli genetic landscape. ST8881 is phenotypically characterized by its unique AMR phenotype profile. Although similar to ST648’s AMR phenotype, all ST8881 are susceptible to piperacillin-tazobactam, whereas some ST648 isolates are resistant to that antibiotic. Moreover, all ST8881 isolates were resistant to gentamicin, whereas one ST648 isolate was susceptible. Using MOB-suite, a subset of the AMR markers in ST8881 can be linked to plasmids and MGEs. Fosfomycin resistance, fosmidomycin resistance, cephalosporin resistance (via CTX-M-15), blaTEM-1 β-lactam resistance, all encoded by genes as well as all quinolone resistant point mutations, are within the chromosome. Gentamicin resistance is encoded by a gene within the ISPam4 repetitive element 50 on the chromosome. Macrolide-resistance encoding via mph(A), sulfonamide resistance, biocide resistance, streptomycin resistance, and trimethoprim resistance is encoded by genes found on a plasmid with three replicons: IncFIA, IncFIB, and IncFIC. erm(B) macrolide resistance is encoded by a sequence on the same plasmid. Finally, blaOXA, a β-lactam resistance gene, along with aac(6')-Ib-cr5, a fluoroquinolone- and aminoglycoside-resistance associated gene, and catB3, a chloramphenicol-resistance associated gene, are found on an insertion element part of the IS6 family. While PlasmidFinder was not able to identify these plasmids, MOB-suite was able to reconstruct them from the assembled genomes, increasing sensitivity. The IS6 insertion element is of particular interest, because although this IS is present on Qatari ST648 isolates as well, ST8881 isolates have an incomplete gene for blaOXA, whereas ST648 isolates have the full blaOXA-1 gene. This incomplete blaOXA gene in ST8881 may explain its susceptibility to piperacillin-tazobactam. Another significant difference between ST8881 and ST648 is the presence of aac(3)-IIe, a gene encoding for gentamicin. All ST8881 isolates have this gene, but it is not uniformly present within the ST648 isolates. ST8881 isolates are also consistently different from ST648 by one virulence factor: a bifunctional enterobactin receptor/adhesin protein that is present in all ST8881 isolates but absent from all ST648 isolates. This virulence factor was on the chromosome, and not on extrachromosomal elements. The chromosomal integration of β-lactamase genes (CTX and TEM genes) is of particular interest because they are traditionally disseminated through plasmids. However, there has been a recent increase in reports of chromosomal integration of blaCTX-M genes [76]. No insertions sequences, however, were found to flank the chromosomal β-lactamase genes in ST8881 isolates. Therefore, it is unclear whether this is truly a case of chromosomal integration, or a case of failed plasmid reconstruction due to low quality reads and assembled contigs. 51 The presence of multiple-replicon plasmids is also of particular concern. Plasmids usually contain a single replicon; however, larger multiple-replicon plasmids contain several replicons and can accumulate multiple genes (especially AMR genes). As observed in ST8881, there is a plasmid with three replicons, that contains five different AMR-causing genes. The multiple-replicon plasmid contains many MGEs, including IS and transposases, which could facilitate the fusion of multiple plasmids into one large one. Although not thoroughly studied in E. coli, this phenomenon has been shown in K. pneumoniae [77]. The presence of this large multiple-replicon plasmid is concerning for the dissemination of AMR. This implies it may be possible for more AMR genes to be accumulated onto a single plasmid through recombination events, and thus a bacterium carrying this plasmid would have increased fitness but at an insignificant adaptive cost increase. Similarly, the presence of three AMR markers in close proximity present on the IS6 MGE is also of particular concern. A single HGT event could lead to a drastic change in AMR phenotype, given that a single IS contains markers for the resistance of a variety of antimicrobials. The results of the identification of putative AMR mutations using machine learning are generally inconclusive, mainly due to the small dataset size. However, as a proof of concept for E. coli, it is still possible to extract potentially important information. Firstly, LR can extract more putatively informative variants than RF, as there are more repeated mutations for drugs of the same class. For example, LR identified mdtC as putatively important for fluoroquinolones. mdtC encodes for a multidrug efflux pump, which is a mechanism for multidrug resistance [78]. This gene was not identified by AMRFinder. Another example is mgtA and yjjX, which the LR model identified as important for trimethoprim resistance. mgtA is a magnesium-translocating P-type ATPase while yjjX is an inosine/xanthosine triphosphatase. However, neither of these genes 52 have been previously identified as being correlated with AMR. Given that the model identified multiple mutations on these genes to be important, they may be putative genes of interest requiring further validation in vitro and in silico. There were also two examples of genes that were identified as important by both models. This increases the association confidence of the gene with its associated antimicrobial. lptG was identified as important for fluroquinolones by both models. This gene encodes an LPS export ABC transporter, and decreased lptG has been shown to increase the outer membrane permeability [79]. This may be significant in the efflux of antimicrobial products, but has yet to be strongly correlated with AMR in previous studies. The gss gene was also identified as important for cephalosporin resistance by the LR model, and in ciprofloxacin by the RF model. This gene encodes a bifunctional glutathionylspermidine amidase/synthase. One study has investigated the link between the glutathione pathway and AMR, revealing that more resistant E. coli exhibited fewer mutations within gss [80]. The model may be correlating the lack of the gss mutation to be important for AMR. However, the genes that were identified as important by both models are of particular interest and may be important to study in greater depth in the future studies. However, one major limitation is the fact that neither models identified known genes associated with AMR. As such, the results must be taken with caution, and the reliability of the models on a small dataset is ambiguous at best. A major limitation of the genomic characterization of Qatari isolates relates to the quality of the genomes. The sequence and assembly quality control metrics show that there may be underlying issues with the short-read sequencing of the isolates. The quality and coverage of the raw sequences are less than ideal. This may contribute to the increase in number of MGEs called by MGEFinder. Decreased sequence quality may results in more clipped reads during 53 alignments, which is one method of MGE identification used by MGEFinder. Moreover, the biggest concern is the genome assemblies, most of which contain a large number of contigs, with many short contigs. Although all contigs with lengths shorter than 1000bp were discarded, there are still many that range between 1000bp to 5000bp. With most of the analysis based on the genome assemblies, it is therefore imperative to keep in mind the lower assembly quality as a major limitation of this work. K. pneumoniae contamination is another major issue in the sequences. Although most of the reads are attributed to Enterobacteriaceae, on a genus and species level, only a fraction of the reads corresponds to Escherichia or even more specifically E. coli. Aside from K. pneumoniae, the other frequent contaminant is Salmonella enterica. However, the majority of reads are classified broadly at the family level as Enterobacteriaceae. It may be possible that there are a lot of similarities between the different Enterobacteriaceae species, but this broad classification may also be due to the Kraken database used. Since the standard database was used, which contains sequences from a wide range of species (containing archaea, bacterial, viral, and eukaryotic references), it may be less specific than using a bacterial database. Moreover, manually curating an Enterobacteriaceae would likely provide the best classification. For this work, one major assumption is that isolates that have high percentage of reads classified broadly as Enterobacteriaceae but low percentages of non-E. coli species are considered to not have significant contamination. 54 Chapter 3: Global contextualization of extended-spectrum beta-lactamase producing E. coli from pediatric patients in Qatar 3.1 Introduction Although there have been several recent studies characterizing ESBL-producing E. coli in Qatar or the Middle East [37, 39, 41, 81], there have not been any studies performing comparative genomics or global contextualization of Middle Eastern ESBL-producing Enterobacteriaceae. Moreover, there is a lack of studies globally contextualizing ESBL-producing E. coli from any region. One study focused on comparing E. coli from Thailand against the global isolates [82], but only compared 149 Thai isolates against 514 global genomes. Furthermore, the study focused only on SNPs within core genes. This work serves as the first study to compare the Qatari genetic landscape of ESBL-producing E. coli against the global genetic landscape of E. coli. Rather than comparing to a small sample of global isolates, this work will include thousands of global isolates to have a more representative view of how the Qatari lineages insert alongside the global lineages. 3.2 Methods 3.2.1 Acquisition of global bacterial isolates A superset of 37 405 E. coli isolates from the Pathosystems Resource Integration Center (PATRIC) [83] were downloaded from the Bacterial and Viral Bioinformatics Resource Center. These isolates come in the form of assembled contigs. However, because of runtime and computational resource limitations, subsets of the PATRIC dataset were used for analysis. 55 The PATRIC dataset was reduced into a 9803 isolates subset, named the T100 dataset. To produce this dataset, the pairwise distance between each Qatari isolate and each isolate within the PATRIC dataset was computed using Mash [49]. Then, for each Qatari isolate, the top 100 most closely related isolates based on the Mash distance were added to the T100 dataset. This dataset comprises of 9803 isolates. Two ST specific subsets were also curated in order to have a more refined analysis with more sensitivity for STs of interest. The first subset comprises of ST4981, ST10997 and ST617, for a total of 151 isolates. The second subset comprises of ST8881 alongside ST648 and ST3177, for a total of 295 isolates. 3.2.2 Clustering of Qatari isolates alongside global isolates PopPUNK (v2.4.0) [67] was used to cluster the Qatari isolates alongside the T100 dataset. A database was built from all assembled genomes using pp-sketchlib (v1.7.0) [68] with k-mer lengths 13-29, a k-mer step of 2, and a sketch size of 100000. A DBScan model was fitted and refined (details in Appendix C ) for the core genome, accessory genome, and both in conjunction. Cluster assignments were provided by PopPUNK for each isolate, along with a t-SNE of the accessory genome and a neighbor-joining tree from the core-genome distance. Using identical methods, two more sketch databases were produced, and a DBScan model fitted to it. One database is comprised of all ST4981, ST10997 and ST617 isolates. The other contained all ST8881 alongside ST648 and ST3177. The idea behind these unique models is that they would be more sensitive to the changes within the genomes of these isolates, as to when all 10 000 isolates are clustered at the same time. 56 3.3 Results 3.3.1 Clustering alongside global isolates The phylogenetic tree generated from the core-genome distances show that, despite some outliers, the major STs are overall very well separated by their core genome (Figure 3.1). There is also some consensus between the STs and the cluster assignments provided by DBScan. DBScan assigns ST131, and ST1193 their own unique clusters. Moreover, many of the ST73, ST38, ST648, and ST10 isolates were assigned to their own respective clusters. ST10, however, is the most diverse ST, as it includes a multitude of different clusters that were assigned by DBScan. ST10997 and ST4981 are also assigned the same cluster by DBScan. Based on the core-genome distances, these STs are nestled between ST10 isolates, and thus are very closely related to ST10. ST8881 also appears to be within a large cluster of ST648 isolates. Moreover, DBScan assigned all ST8881 and ST648 isolates to the same cluster. 57 Figure 3.1 Phylogenetic tree generated from the core-genome distances using PopPUNK. The node colors correspond to cluster assignments from DBScan, while the outside band colors correspond to STs shown in the legend. Only the isolates from STs of interest have colored leaf nodes. Un-generated leaf nodes correspond to isolates of other STs. Overall, DBScan clusters correlated well with STs. However, ST8881 is clustered together with global ST648 isolates and CC4981 is clustered together with ST617 isolates. Looking at the t-SNE generated from the accessory-genome distances (Figure 3.2), the STs can also be separated based on their accessory genome. It is, however, interesting to note 58 that there are two groups of ST131 isolates that can be separated based on their accessory genome. However, unlike clustering solely within the Qatari isolates, all ST131 isolates are assigned the same DBScan cluster. Figure 3.2 t-SNE of the accessory-genome distances. The node colors correspond to the cluster assigned by DBScan. The colors are the same as in Figure 3.1. ST131 59 3.3.2 Clustering of Qatari sequence types of interest 3.3.2.1 Comparing Qatari CC4981 to global ST4981 and ST617 isolates Within the PATRIC, there are only six global ST4981 isolates and no ST10997 isolates. The ST4981 isolates from PATRIC include one from China, two from Laos, two from Canada and 1 from unknown origins. By clustering ST4981 and ST10997 isolates alongside their most closely related global ST617 isolates and global ST4981 isolates, the Qatari isolates are not assigned a unique cluster by DBScan when compared to the global context. There is also no monophyletic group consisting of only Qatari ST4981 or ST10997 isolates. Figure 3.4 shows that overall, the accessory genome of Qatari ST4981 is not separable from other global isolates. However, it is interesting to note that ST10997 is somewhat more separable from other isolates. 60 Figure 3.3 Phylogenetic tree (generated from core-genome distances) of Qatari ST4981 and ST10997 isolates alongside global ST4981 and ST617 isolates downloaded from the PATRIC database. The tree was generated from the core-genome distances obtained from PopPUNK. The node colors correspond to cluster assignments by DBScan, and the outside band colors correspond to STs, with yellow being ST4981 and light blue corresponding to ST10997. Un-generated leaf nodes correspond to global ST4981 and ST617 isolates. Qatari CC4981 isolates are not observed to be genetically distinct within the global context. Qatari ST4981 clusters alongside global ST4981 isolates. CC4981 are not assigned a unique DBScan cluster, nor do they form monophyletic clade. 61 Figure 3.4 t-SNE generated from the accessory-genome distances obtained from PopPUNK of Qatari ST4981 and ST10997 alongside global ST4981 and ST617 isolates downloaded from the PATRIC database. Light blue dots indicate Qatari isolates, while black dots correspond to isolates from PATRIC. The dots with an outline indicate Qatari ST10997 isolates. ST10997 is more separable from global ST4981 and ST617 isolates, compared to the Qatari ST4981 isolates. 3.3.2.2 Comparing Qatari ST8881 to global ST648 isolates Within the PATRIC database, there are no examples of ST8881 isolates. The core-genome distance phylogenetic tree in Figure 3.5 shows that all Qatari ST8881 are on a monophyletic group. Moreover, DBScan assigned all these isolates to a unique Qatari-only cluster. This maybe be an indication that ST8881 is genetically distinct from its closest global isolates, especially those of closely related ST648. However, looking at the accessory genome (Figure 3.6), the t-SNE plot shows that ST8881 is not easily separable from the other global isolates. However, the Qatari isolates are still grouped together rather than being dispersed. Since the DBScan clustering suggests that ST8881 is an independent cluster when compared against global ST648 isolates, only ST8881 will be used for further comparative analysis. 62 Figure 3.5 Phylogenetic tree (generated from core-genome distances) of Qatari ST8881 isolates alongside global ST648 and ST3177 isolates downloaded from the PATRIC database. The tree was generated from the core-genome distances obtained from PopPUNK. Only ST8881 is colored in this figure. A zoomed-in version of the ST8881 monophyletic group is shown in the center. Un-generated leaf nodes correspond to global ST648 isolates. Qatari ST8881 forms a monophyletic clade, while also being assigned a unique DBScan cluster. This demonstrates a gain in resolution and sensitivity when clustering Qatari ST8881 only alongside its most closely related global lineages, rather than all global lineages. The tree and cluster also show the unique genetic nature of ST8881 compared to all other globally circulating lineages. 63 Figure 3.6 t-SNE generated from the accessory-genome distances obtained from PopPUNK of Qatari ST8881 alongside global ST648 and ST3177 isolates downloaded from the PATRIC database. Light blue dots indicate Qatari isolates, while black dots correspond to isolates from PATRIC. ST8881 is shown to have a somewhat separable accessory genome from other global ST648 isolates. 3.3.3 Comparative genomics of ST8881 against globally circulating ST648 isolates 3.3.3.1 Mobile genetic elements A heatmap of the MGE profile of ST8881 alongside ST648 shows that the ST8881 isolates are clustered together (Figure 2.1). As seen when comparing ST8881 against the other Qatari isolates, ST8881 contains a set of seven MGEs that is unseen in any other ST648 isolate: 64 IS609, IS6100, ISEc30, ISEc40, ISEc9, ISEch14, and MITEEc1. However, ISEc40 is only found in two other ST648 isolates. Figure 3.7 Heatmap for the MGE profile of ST8881 and ST648. The dendrogram on the left was produced through hierarchical clustering of the rows. The colored bars show the ST8881 and Qatari ST648 isolates in relation to the global ST648 isolates. Only a subset of MGEs is labelled. ST8881 isolates are clustered together according to their MGE profile. 65 3.3.3.2 Plasmids As shown in Figure 3.8, although the ST8881 isolates cluster together, there is no consensus plasmid profile between all ST8881 isolates. The only replicons found in all ST8881 isolates were: IncFIA, IncFIB(AP001918), and IncFII. However, there is a single replicon that is unique to only one ST8881 and unseen in the rest of the ST648 isolates: FIA(pBK30683) Figure 3.8 Heatmap for the plasmid profile of ST8881 and ST648. The dendrogram on the left was produced through hierarchical clustering of the rows. The colored bars show the ST8881 and Qatari ST648 isolates. Only a subset of plasmids is labelled. ST8881 is clustered together according to its plasmid profile. 66 3.3.3.3 Virulence factors The heatmap in Figure 3.9 shows that the ST8881 isolates cluster together. There is a set of 180 virulence factors that is present in all ST8881 isolates. This complete set is not found in any other isolate. Figure 3.9 Heatmap for the virulence factor profile of ST8881 and ST648. The dendrogram on the left was produced through hierarchical clustering of the rows. The colored bars show the ST8881 and Qatari ST648 isolates. Only a subset of virulence factors is labelled. ST8881 is clustered together according to its virulence factor profile. 67 3.3.3.4 Antibiotic resistance markers Shown in Figure 3.10, all ST8881 isolates have a unique set of 18 AMR markers, exactly as seen when compared among Qatari isolates. Additionally, blaOXA is found in all ST8881 isolates, but only seen in three other ST648 isolates, none of which are Qatari isolates. Figure 3.10 Heatmap for the AMR profile of ST8881 and ST648. The dendrogram on the left was produced through hierarchical clustering of the rows. The colored bars show the ST8881 and Qatari ST648 isolates. Only a subset of AMR markers is labelled. ST8881 is clustered together according to its AMR marker profile. 68 3.4 Discussion Clustering the Qatari isolates alongside global isolates reveals that there are no unique Qatari clusters, in the form of cluster assignments by PopPUNK. The STs are very well separated both according to their core- and accessory-genomes. However, within a ST, there is usually no Qatari monophyletic group. For example, Qatari ST131 isolates span the range of the global ST131 genetic landscape. As such, within a ST, there is much diversity, indicating that ESBL-producing E. coli in Qatar may have been introduced into the region multiple times by a wide range of sources. The most diverse of the predominant STs is ST10, whose Qatari isolates are partitioned into a large number of different clusters. While all Qatari ST131 isolates were assigned the same cluster, ST10 isolates were assigned into one of over 50 clusters. Therefore, a wide range of different E. coli AMR phenotypes and genotypes makes it difficult to efficiently treat infections, and this is especially an issue within Qatar as it is a travel hub, and its population includes a large number of expatriates. The Qatari diversity is driven by both the breadth of STs observed within a single hospital, and both the diverse genetic landscape within particular STs. Because of the compute power and time limitations, the entire PATRIC dataset could not be used for global contextualization of the Qatari isolates. As a secondary method, the clustering of the Qatari isolates against the pre-built E. coli sketching database from PopPUNK was also performed (details in Appendix F ). The pre-built E. coli database comprises of 10 287 E. coli genome isolated from human hosts [84]. This database contains the 50 largest E. coli lineages and thus is a representation of the breadth in the global E. coli genetic landscape. Once again, the STs are observed to be very well separated, however there is no observed Qatari-only clusters. There are also major gaps in the global genetic landscape where there are no Qatari isolates. However, since the database contains both AMR and antimicrobial-susceptible E. coli, it may be 69 the case that these lineages represent susceptible bacteria, while the Qatari dataset was based off specifically ESBL-producing E. coli and only represents a fraction of the E. coli lineages. Consistent within both sets of cluster assignments is the presence of two monophyletic groups that only contain Qatari isolates: one containing ST8881 isolates, and one containing the CC4981 complex. However, in both cases, the Qatari isolates are assigned a cluster with other global isolates. In the case of ST8881, it is clustered with ST648 isolates. The CC4981 complex is clustered with ST617 and ST3177. Since STs numbers are assigned in incremental order in order of discovery, this means that ST8881 was first identified after ST648 and ST4981 alongside ST10997 was first identified after ST617 and ST3177. This suggests that ST8881 could be a descendant of ST648, while the CC4981 may be a descendant of ST617. This is of particular importance, because ST648 is a globally-circulating high-risk clone, while ST617 is a clonal complex of globally-circulating ST10. When clustering CC4981 alongside global ST617 and ST4981 isolates, the Qatari CC4981 isolates are not separable from global ST4981 isolates, meaning that the Qatari isolates are not genetically distinct. Isolates within CC4981 are also quite heterogenous, as even within ST4981 or ST10997, there is no homogeneity within the MGE, plasmid, virulence factor, or AMR marker profiles. Moreover, with only a sample size of three for ST4981 and two for ST10997, it is difficult to determine whether any putative trend is representative of the Qatari ST, or whether it is due to chance. Clustering ST8881 isolates alongside global ST648 isolates reveals that ST8881 remains in its own monophyletic group, but is also assigned a unique cluster by DBScan, comprising only of itself. Compared to clustering Qatari isolates against a global genetic landscape of isolates, this is an increase in sensitivity. An increase in sensitivity can be explained by the heterogeneity 70 of the sketching database. Given a more heterogeneous database with more distantly related isolates, the clustering algorithm could easily group together isolates that otherwise could be in different clusters. Therefore, during clustering it is important to keep in mind the composition of the dataset, especially if there are outliers that are extremely distantly related that can skew the results. One benefit of DBScan is that it is robust against outliers, which is especially important in this work when the isolates in the PATRIC dataset may not all be of high quality, and may not even have correct metadata. The observation that ST8881 is its own cluster is also important to note because it supports the hypothesis that ST8881 may be an endemic strain to the Arabian Peninsula, as it has not been extensively documented around the world and is likely a descendant of ST648. Comparing the ST8881 genomes to those of ST648, there aren’t many genomic features that are unique to ST8881 that are not found in ST648 isolates. Rather, ST8881 is differentiated by the set of genomic features it contains differing from ST648. For example, ST8881 continues to have a unique AMR marker profile compared to global ST648 isolates. ST8881 contains the partial blaOXA gene, which is only found in three other ST648 isolates. Most other isolates contain the full blaOXA-1 gene, or none at all. Moreover, ST8881 also has a unique MGE profile and a unique virulence factors profile. From the genomic characterization of Qatari isolates in Chapter 1:, ST8881 is shown to be unique within the Qatari context. With the addition of the global contextualization and comparisons of ST8881 against global ST648 isolates, the conclusion that ST8881 is a putative emerging high-risk clone of ST648 is strengthened. Since ST8881 is genetically distinct from all other isolates within Qatar and around the world, it is possible that it is an endemic lineage to Qatar. No previous studies have characterized ST8881, and it is not found within the entire 71 PATRIC dataset. However, it was sampled eight times within the Qatari dataset with no currently known epidemiological link between them, as they were sampled with large temporal gaps between them. ST8881’s status as a putative emerging high-risk clone is characterized by the potential stability of its AMR markers compared to other lineages. This is due to its chromosomal integration of bla genes and the presence of a multiple-replicon plasmid containing five genes encoding for AMR. This may lead to further propagation of these genes through recombination or MGE activity and produce future high-risk clones. ST8881 isolates also show a resistance phenotype to aztreonam, but a known AMR marker for that antibiotic could not be identified in any of the genomes. This could indicate the presence of a novel resistance mechanism, which could be instrumental for the development of newer antibiotics to treat AMR E. coli. This work has shown the importance of further studies on ST8881 as it may be an important lineage that may be easily exported out of Qatar. 72 Chapter 4: Conclusion 4.1 Analysis of the work within the context of current research The findings presented in this work has deepened the understanding of the Qatari genomic landscape of ESBL-producing E. coli. Expanding on previous high-level WGS analysis [41], the work presented here has investigated two groups of isolates of particular interest due to their unique positions among other Qatari isolates. ST8881 and CC4981 presented in Chapter 2, have also not been previously studied and thus this work represents the first study to characterize these STs in detail. These STs have been linked to globally circulating high-risk clones and may be important to keep track of for AMR transmission monitoring. Due to the demographics of other countries within the Arabian Peninsula being very similar to one another, the results of this work could be generalized to other countries of the region. This work is also the first study to globally contextualize Qatari ESBL-producing E. coli. Not only is it the first in Qatar, but there is also a lack of studies comparing Middle Eastern Enterobacteriaceae against global isolates. The results in Chapter 3 demonstrate the utility, but also the drawbacks of performing a clustering-based approach for global contextualization on a large dataset. Though the results obtained from clustering Qatari isolates alongside large numbers of global isolates were helpful, they were not as informative as the results obtained from clustering specific Qatari STs alongside a set of closely related global STs. The loss of resolution when performing clustering on a large diverse dataset is a major limitation, as otherwise, it would be extremely informative to be able to obtain well-defined clusters without having to subset isolates into more closely related groups prior to clustering. 73 4.2 Significance and contribution To reiterate, the two major hypotheses on the Qatari ESBL-producing E. coli genetic landscape were that two groups of lineages would be found: one group containing globally circulating lineages, while the other group would contain lineages not previously observed globally that may be endemic within Qatar. Both hypotheses can be supported based on the findings in this work. Of the globally circulating STs outlined in [29–31], only two were not identified in Qatar. Although this sample size is quite small and specific to pediatric patients, it is remarkable that such a wide range of STs are identified. However, there are also a number of isolates belonging to STs that have not been previously studied. Many of these came as singletons, however ST8881 along with the CC4981 complex are examples of lineages that occur multiple times within the dataset but have not been characterized previously. However, although the findings in this work suggest that these lineages are likely direct descendants of globally circulating high-risk clones, it is unclear whether they are endemic to Qatar or not. If they indeed have low prevalence in the population, it is noteworthy that these lineages were sampled multiple times within a modestly sized dataset. The results presented in this work provide a more in-depth understanding of the underlying genetics behind ESBL-producing E. coli in Qatar. Moreover, the presence of ST8881 and CC4981 as putative endemic Qatari lineages could be of use in AMR surveillance. As these lineages are multi-drug resistant and are descendants (or clonal complexes) of globally circulating high-risk clones, it is possible that they become emerging clones themselves. Given ST8881 also contains plasmids and MGEs that carry multiple genes for AMR, they could also easily disseminate these genotypic features to other lineages. Therefore, this work serves as a foundation to further investigate these possible lineages of interest. 74 This work also presented several putative AMR-associated genes that have not been extensively studied in the past. Genes such as gss and lptG may be of interest for future in silico and in vitro studies, to determine whether they are truly associated with AMR. This study is also presented as a proof-of-concept for the use of interpretable machine learning to determine putative AMR-associated genes. With more available training data, this could be used to identify drug resistance directly from WGS as well as to infer possible mutations and mechanisms of resistance. 4.3 Strengths and limitations The major strength of this work lies in its rigor of the analyses and novelty of the samples. Both the genomic characterization and global contextualization are novel in the context of Qatar and the Middle East. Moreover, the bioinformatics analyses were thorough, by investigating a large number of genotypic features ranging in size from SNPs to entire plasmids, which helps elucidate the evolutionary patterns of Qatari E. coli lineages. A baseline, a k-mer based method that does not contain any genetic context information, was also used to compare against the results obtained from genotypic features. Therefore, this work is valuable in providing a more thorough analysis of the genetics of ESBL-producing E. coli in Qatar. The limitations of this study lie in the dataset and to some extent data quality. The assembled genomes are of relatively low coverage and quality. Moreover, the sample size is small, with only 251 isolates to work with. Therefore, due to both sample size and sequence quality, many of the results are not as strongly supported. Given higher quality sequences, a larger dataset, or ideally both, the strength of the results would be significantly increased. Moreover, the sequences were collected in 2018, so the results presented in this work are five 75 years late, thus the prevalence in these lineages in Qatar could have shifted. Finally, the lack of epidemiological information for each isolate means that it is difficult to determine whether two related isolates are because of epidemiological proximity. The small and unbalanced dataset (in terms of AMR phenotypes) also resulted in relatively inconclusive results for the finding of putative AMR-associated genes. Although some putative genes were found, genes that were known to be present were not. Moreover, the limited available training data meant that only 5-fold cross validation could be done, and the machine learning model’s performance could not be accurately assessed. 4.4 Future work This study lays the groundwork for future studies on ST8881 and CC4981. Given the prevalence of these lineages in Qatar compared to the rest of the world, it is important to further determine whether these are just low-prevalence lineages, or whether they are endemic to Qatar. This would require a broader Qatari dataset, including more genomes isolated from more than just pediatric patients, and from different regions of Qatar. Moreover, a study that combines this work with epidemiological data would be invaluable in determining and correlating the genetic links to epidemiological links. This would be helpful for AMR surveillance, and for understanding the possible transmission of plasmids and MGEs across and within lineages. Finally, further studies on the identification of putative AMR-associated genes can be performed to increase the confidence levels of the current work. With a larger training dataset, more convincing results can be obtained, which can then be validated in vitro. The identification of new mechanisms of resistance will be crucial in the development of novel antimicrobials. 76 References 1. Janda JM, Abbott SL. The Changing Face of the Family Enterobacteriaceae (Order: “Enterobacterales”): New Members, Taxonomic Issues, Geographic Expansion, and New Diseases and Disease Syndromes. Clin Microbiol Rev. 2021;34:e00174-20. 2. Paterson DL. Resistance in gram-negative bacteria: Enterobacteriaceae. American Journal of Infection Control. 2006;34:S20–8. 3. Gharavi MJ, Zarei J, Roshani-Asl P, Yazdanyar Z, Sharif M, Rashidi N. Comprehensive study of antimicrobial susceptibility pattern and extended spectrum beta-lactamase (ESBL) prevalence in bacteria isolated from urine samples. Sci Rep. 2021;11:578. 4. Jain P, Bepari AK, Sen PK, Rafe T, Imtiaz R, Hossain M, et al. High prevalence of multiple antibiotic resistance in clinical E. coli isolates from Bangladesh and prediction of molecular resistance determinants using WGS of an XDR isolate. Sci Rep. 2021;11:22859. 5. Li Y, Kumar S, Zhang L, Wu H. Klebsiella pneumonia and Its Antibiotic Resistance: A Bibliometric Analysis. Biomed Res Int. 2022;2022:1668789. 6. Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Aguilar GR, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet. 2022;399:629–55. 7. Zorc JJ, Kiddoo DA, Shaw KN. Diagnosis and Management of Pediatric Urinary Tract Infections. Clin Microbiol Rev. 2005;18:417–22. 8. Teklu DS, Negeri AA, Legese MH, Bedada TL, Woldemariam HK, Tullu KD. Extended-spectrum beta-lactamase production and multi-drug resistance among Enterobacteriaceae isolated in Addis Ababa, Ethiopia. Antimicrobial Resistance & Infection Control. 2019;8:39. 9. Pandey N, Cascella M. Beta Lactam Antibiotics. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2022. 10. Tooke CL, Hinchliffe P, Bragginton EC, Colenso CK, Hirvonen VHA, Takebayashi Y, et al. β-Lactamases and β-Lactamase Inhibitors in the 21st Century. J Mol Biol. 2019;431:3472–500. 11. Pitout JD, Laupland KB. Extended-spectrum β-lactamase-producing Enterobacteriaceae: an emerging public-health concern. The Lancet Infectious Diseases. 2008;8:159–66. 12. Peirano G, Pitout JDD. Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae: Update on Molecular Epidemiology and Treatment Options. Drugs. 2019;79:1529–41. 13. Paterson DL, Bonomo RA. Extended-Spectrum β-Lactamases: a Clinical Update. 2005. https://journals.asm.org/doi/epdf/10.1128/CMR.18.4.657-686.2005?src=getftr. Accessed 7 Feb 2023. 77 14. Talbot GH, Bradley J, Edwards JE, Gilbert D, Scheld M, Bartlett JG. Bad Bugs Need Drugs: An Update on the Development Pipeline from the Antimicrobial Availability Task Force of the Infectious Diseases Society of America. Clinical Infectious Diseases. 2006;42:657–68. 15. Bevan ER, Jones AM, Hawkey PM. Global epidemiology of CTX-M β-lactamases: temporal and geographical shifts in genotype. Journal of Antimicrobial Chemotherapy. 2017;72:2145–55. 16. Cantón R, González-Alba JM, Galán JC. CTX-M Enzymes: Origin and Diffusion. Front Microbiol. 2012;3:110. 17. Carattoli A. Plasmids and the spread of resistance. Int J Med Microbiol. 2013;303:298–304. 18. Gonullu N, Aktas Z, Kayacan CB, Salcioglu M, Carattoli A, Yong DE, et al. Dissemination of CTX-M-15 beta-lactamase genes carried on Inc FI and FII plasmids among clinical isolates of Escherichia coli in a university hospital in Istanbul, Turkey. J Clin Microbiol. 2008;46:1110–2. 19. Ho PL, Yeung MK, Lo WU, Tse H, Li Z, Lai EL, et al. Predominance of pHK01-like incompatibility group FII plasmids encoding CTX-M-14 among extended-spectrum beta-lactamase-producing Escherichia coli in Hong Kong, 1996-2008. Diagn Microbiol Infect Dis. 2012;73:182–6. 20. Cottell JL, Webber MA, Coldham NG, Taylor DL, Cerdeño-Tárraga AM, Hauser H, et al. Complete sequence and molecular epidemiology of IncK epidemic plasmid encoding blaCTX-M-14. Emerg Infect Dis. 2011;17:645–52. 21. Carattoli A. Resistance plasmid families in Enterobacteriaceae. Antimicrob Agents Chemother. 2009;53:2227–38. 22. Ho PL, Lo WU, Yeung MK, Li Z, Chan J, Chow KH, et al. Dissemination of pHK01-like incompatibility group IncFII plasmids encoding CTX-M-14 in Escherichia coli from human and animal sources. Vet Microbiol. 2012;158:172–9. 23. Woodford N, Carattoli A, Karisik E, Underwood A, Ellington MJ, Livermore DM. Complete nucleotide sequences of plasmids pEK204, pEK499, and pEK516, encoding CTX-M enzymes in three major Escherichia coli lineages from the United Kingdom, all belonging to the international O25:H4-ST131 clone. Antimicrob Agents Chemother. 2009;53:4472–82. 24. Poirel L, Decousser J-W, Nordmann P. Insertion sequence ISEcp1B is involved in expression and mobilization of a bla(CTX-M) beta-lactamase gene. Antimicrob Agents Chemother. 2003;47:2938–45. 25. Cantón R, Coque TM. The CTX-M beta-lactamase pandemic. Curr Opin Microbiol. 2006;9:466–75. 26. Fagerström A, Mölling P, Khan FA, Sundqvist M, Jass J, Söderquist B. Comparative distribution of extended-spectrum beta-lactamase–producing Escherichia coli from urine infections and environmental waters. PLoS One. 2019;14:e0224861. 78 27. Ny S, Löfmark S, Börjesson S, Englund S, Ringman M, Bergström J, et al. Community carriage of ESBL-producing Escherichia coli is associated with strains of low pathogenicity: a Swedish nationwide study. J Antimicrob Chemother. 2017;72:582–8. 28. Mathers AJ, Peirano G, Pitout JDD. The Role of Epidemic Resistance Plasmids and International High-Risk Clones in the Spread of Multidrug-Resistant Enterobacteriaceae. Clin Microbiol Rev. 2015;28:565–91. 29. Baquero F, Tedim AP, Coque TM. Antibiotic resistance shaping multi-level population biology of bacteria. Front Microbiol. 2013;4:15. 30. Riley LW. Pandemic lineages of extraintestinal pathogenic Escherichia coli. Clinical Microbiology and Infection. 2014;20:380–90. 31. Shaik S, Ranjan A, Tiwari SK, Hussain A, Nandanwar N, Kumar N, et al. Comparative Genomic Analysis of Globally Dominant ST131 Clone with Other Epidemiologically Successful Extraintestinal Pathogenic Escherichia coli (ExPEC) Lineages. mBio. 2017;8:e01596-17. 32. Nicolas-Chanoine M-H, Bertrand X, Madec J-Y. Escherichia coli ST131, an Intriguing Clonal Group. Clin Microbiol Rev. 2014;27:543–74. 33. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Current Opinion in Genetics & Development. 2005;15:589–94. 34. Rasko DA, Rosovitz MJ, Myers GSA, Mongodin EF, Fricke WF, Gajer P, et al. The Pangenome Structure of Escherichia coli: Comparative Genomic Analysis of E. coli Commensal and Pathogenic Isolates. J Bacteriol. 2008;190:6881–93. 35. Tantoso E, Eisenhaber B, Kirsch M, Shitov V, Zhao Z, Eisenhaber F. To kill or to be killed: pangenome analysis of Escherichia coli strains reveals a tailocin specific for pandemic ST131. BMC Biology. 2022;20:146. 36. Bertelli C, Gray KL, Woods N, Lim AC, Tilley KE, Winsor GL, et al. Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks. Microbial Genomics. 2022;8:000818. 37. Dandachi I, Chaddad A, Hanna J, Matta J, Daoud Z. Understanding the Epidemiology of Multi-Drug Resistant Gram-Negative Bacilli in the Middle East Using a One Health Approach. Frontiers in Microbiology. 2019;10. 38. Eltai NO, Al Thani AA, Al-Ansari K, Deshmukh AS, Wehedy E, Al-Hadidi SH, et al. Molecular characterization of extended spectrum β -lactamases enterobacteriaceae causing lower urinary tract infection among pediatric population. Antimicrob Resist Infect Control. 2018;7:90. 39. Eltai NO, Al Thani AA, Al Hadidi SH, Al Ansari K, Yassine HM. Antibiotic resistance and virulence patterns of pathogenic Escherichia coli strains associated with acute gastroenteritis among children in Qatar. BMC Microbiology. 2020;20:54. 79 40. Hendriksen RS, Bortolaia V, Tate H, Tyson GH, Aarestrup FM, McDermott PF. Using Genomics to Track Global Antimicrobial Resistance. Frontiers in Public Health. 2019;7. 41. Perez-Lopez A, Sundararaju S, Al-Mana H, Tsui KM, Hasan MR, Suleiman M, et al. Molecular Characterization of Extended-Spectrum β-Lactamase–Producing Escherichia coli and Klebsiella pneumoniae Among the Pediatric Population in Qatar. Frontiers in Microbiology. 2020;11. 42. Seemann T. mlst - Scan contig files against traditional PubMLST typing schemes. 2022. 43. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. FastQC: a quality control tool for high throughput sequence data. 2012. 44. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biology. 2019;20:257. 45. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5. 46. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8. 47. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17:10–2. 48. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. J Comput Biol. 2012;19:455–77. 49. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biology. 2016;17:132. 50. Seemann T. Snippy - Rapid haploid variant calling and core genome alignment. 2020. 51. Forde BM, Zakour NLB, Stanton-Cook M, Phan M-D, Totsika M, Peters KM, et al. The Complete Genome Sequence of Escherichia coli EC958: A High Quality Reference Sequence for the Globally Disseminated Multidrug Resistant E. coli O25b:H4-ST131 Clone. PLOS ONE. 2014;9:e104400. 52. Feijao P, Yao H-T, Fornika D, Gardy J, Hsiao W, Chauve C, et al. MentaLiST - A fast MLST caller for large MLST schemes. Microb Genom. 2018;4:e000146. 53. Zhou Z, Alikhan N-F, Mohamed K, Fan Y, Group the AS, Achtman M, et al. The EnteroBase user’s guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity. Genome Res. 2020;30:138–52. 80 54. Johansson MHK, Bortolaia V, Tansirichaiya S, Aarestrup FM, Roberts AP, Petersen TN. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. Journal of Antimicrobial Chemotherapy. 2021;76:101–9. 55. Durrant MG, Li MM, Siranosian BA, Montgomery SB, Bhatt AS. A Bioinformatic Analysis of Integrative Mobile Genetic Elements Highlights Their Role in Bacterial Adaptation. Cell Host & Microbe. 2020;27:140-153.e9. 56. Florensa AF, Kaas RS, Clausen PTLC, Aytan-Aktug D, Aarestrup FM. ResFinder – an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb Genom. 2022;8:000748. 57. Xie Z, Tang H. ISEScan: automated identification of insertion sequence elements in prokaryotic genomes. Bioinformatics. 2017;33:3340–7. 58. Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44 Web Server issue:W16–21. 59. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O, Villa L, et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58:3895–903. 60. Robertson J, Nash JHE. MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microb Genom. 2018;4:e000206. 61. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, Haft DH, et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021;11:12728. 62. Seemann T. ABRicate - Mass screening of contigs for antimicrobial resistance or virulence genes. 2020. 63. Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33 Database issue:D325-328. 64. Schwengers O, Jelonek L, Dieckmann MA, Beyvers S, Blom J, Goesmann A. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microbial Genomics. 2021;7:000685. 65. Howe K, Bateman A, Durbin R. QuickTree: building huge Neighbour-Joining trees of protein sequences. Bioinformatics. 2002;18:1546–7. 66. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J, Glasner C, et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom. 2016;2:e000093. 81 67. Lees JA, Harris SR, Tonkin-Hill G, Gladstone RA, Lo SW, Weiser JN, et al. Fast and flexible bacterial genomic epidemiology with PopPUNK. Genome Res. 2019;29:304–16. 68. Lees J, nickjcroucher. johnlees/pp-sketchlib: Sketchlib v1.6.5. 2021. 69. Rode CK, Melkerson-Watson LJ, Johnson AT, Bloch CA. Type-Specific Contributions to Chromosome Size Differences in Escherichia coli. Infect Immun. 1999;67:230–6. 70. Schaufler K, Semmler T, Wieler LH, Trott DJ, Pitout J, Peirano G, et al. Genomic and Functional Analysis of Emerging Virulent and Multidrug-Resistant Escherichia coli Lineage Sequence Type 648. Antimicrob Agents Chemother. 2019;63:e00243-19. 71. Chung YJ, Saier MH. Overexpression of the Escherichia coli sugE Gene Confers Resistance to a Narrow Range of Quaternary Ammonium Compounds. J Bacteriol. 2002;184:2543–5. 72. Pitout JDD, Peirano G, Chen L, DeVinney R, Matsumura Y. Escherichia coli ST1193: Following in the Footsteps of E. coli ST131. Antimicrobial Agents and Chemotherapy. 2022;66:e00511-22. 73. Liu Z, Liu Y, Xi W, Liu S, Liu J, Mu H, et al. Genetic Features of Plasmid- and Chromosome-Mediated mcr-1 in Escherichia coli Isolates From Animal Organs With Lesions. Frontiers in Microbiology. 2021;12. 74. Farzana R. A genomic approach to understanding the molecular epidemiology and clinical burden of multi-drug resistant Enterobacterale Infections in Bangladesh. 2020. 75. Wirth T, Falush D, Lan R, Colles F, Mensa P, Wieler LH, et al. Sex and virulence in Escherichia coli: an evolutionary perspective. Mol Microbiol. 2006;60:1136–51. 76. van Aartsen JJ, Moore CE, Parry CM, Turner P, Phot N, Mao S, et al. Epidemiology of paediatric gastrointestinal colonisation by extended spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae isolates in north-west Cambodia. BMC Microbiology. 2019;19:59. 77. Wang X, Zhao J, Ji F, Chang H, Qin J, Zhang C, et al. Multiple-Replicon Resistance Plasmids of Klebsiella Mediate Extensive Dissemination of Antimicrobial Genes. Frontiers in Microbiology. 2021;12. 78. Kim H-S, Nagore D, Nikaido H. Multidrug Efflux Pump MdtBC of Escherichia coli Is Active Only as a B2C Heterotrimer. J Bacteriol. 2010;192:1377–86. 79. Ruiz N, Gronenberg LS, Kahne D, Silhavy TJ. Identification of two inner-membrane proteins required for the transport of lipopolysaccharide to the outer membrane of Escherichia coli. Proc Natl Acad Sci U S A. 2008;105:5537–42. 82 80. Kominkova M, Michalek P, Cihalova K, Guran R, Cernei N, Nejdl L, et al. Study of Linkage between Glutathione Pathway and the Antibiotic Resistance of Escherichia coli from Patients’ Swabs. Int J Mol Sci. 2015;16:7210–29. 81. Hays JP, Safain KS, Almogbel MS, Habib I, Khan MA. Extended Spectrum- and Carbapenemase-Based β-Lactam Resistance in the Arabian Peninsula—A Descriptive Review of Recent Years. Antibiotics (Basel). 2022;11:1354. 82. Runcharoen C, Raven KE, Reuter S, Kallonen T, Paksanont S, Thammachote J, et al. Whole genome sequencing of ESBL-producing Escherichia coli isolated from patients, farm waste and canals in Thailand. Genome Med. 2017;9:81. 83. Davis JJ, Wattam AR, Aziz RK, Brettin T, Butler R, Butler RM, et al. The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. Nucleic Acids Research. 2020;48:D606–12. 84. Horesh G, Blackwell GA, Tonkin-Hill G, Corander J, Heinz E, Thomson NR. A comprehensive and high-quality collection of Escherichia coli genomes and their genes. Microbial Genomics. 2021;7:000499. 83 Appendices Appendix A Isolate metadata and QC metrics Sample Name Date Specimen UNIT ST N50 (Kbp) Length (Mbp) Misassemblies Mismatches/100kbp Genome Fraction CP2 2018-05-06 Rectal swab GPED 38 72.4Kbp 5.2Mbp 162 2524.44 0.777 CP4 2018-06-15 Rectal swab GPED 127 83.1Kbp 5.4Mbp 85 1205.58 0.824 E6 2018-03-22 Rectal swab GPED 421 22.8Kbp 5.3Mbp 78 1195.27 0.832 E8 2018-03-05 Sputum GPED 361 28.6Kbp 5.1Mbp 116 2519.51 0.736 CP8 2018-07-03 Rectal swab GPED 354 74.2Kbp 5.2Mbp 145 2247.82 0.801 CP9 2018-07-14 Rectal swab PICU 540 22.4Kbp 5.2Mbp 114 2503.03 0.734 E10 2018-03-15 Rectal swab GPED 9269 3.1Kbp 6.5Mbp 92 2884.48 0.746 84 E12 2018-03-22 Urine PICU 127 31.7Kbp 5.4Mbp 78 1189.82 0.825 E13 2018-03-25 Abscess fluid GPED 10 46.3Kbp 5.3Mbp 145 2446.98 0.778 E14 2018-03-01 Rectal swab GPED 10995 39.6Kbp 5.0Mbp 138 2548.84 0.742 E15 2018-01-30 Urine GPED 131 120.3Kbp 5.1Mbp 59 340.4 0.898 E17 2018-02-13 Rectal swab GPED 58 59.9Kbp 5.1Mbp 165 2515.1 0.761 E19 2018-02-22 Rectal swab GPED 73 115.5Kbp 5.4Mbp 92 1177.27 0.855 E20 2018-02-26 Rectal swab NICU 278 83.5Kbp 5.4Mbp 155 2479.84 0.772 E21 2018-03-04 Rectal swab GPED 2141 75.1Kbp 5.4Mbp 140 2210.61 0.805 E22 2018-03-04 Urine GPED 131 105.2Kbp 5.0Mbp 48 328.91 0.886 85 CP25 2018-09-18 Rectal swab GPED 3541 114.1Kbp 4.8Mbp 155 2524.82 0.744 CP26 2018-10-01 Rectal swab GPED 410 108.3Kbp 5.1Mbp 167 2493.4 0.766 CP29 2018-12-14 Rectal swab PICU 448 87.6Kbp 5.2Mbp 157 2502.01 0.745 E34 2018-04-13 Rectal swab GPED 2076 25.9Kbp 5.3Mbp 127 2534.63 0.759 E35 2018-04-14 Urine PICU 1193 33.2Kbp 5.1Mbp 100 1184.49 0.836 E39 2018-04-15 Rectal swab GPED 10 125.6Kbp 4.8Mbp 151 2512.21 0.736 E40 2018-04-15 Rectal swab GPED 162 119.3Kbp 4.8Mbp 158 2517.64 0.741 E41 2018-04-29 Blood GPED 38 70.6Kbp 5.5Mbp 164 2474.09 0.801 E42 2018-05-02 Urine GPED 141 129.1Kbp 5.2Mbp 80 1181.95 0.825 86 E43 2018-05-05 Rectal swab GPED 10 75.9Kbp 4.6Mbp 138 2520.4 0.728 E44 2018-05-06 Rectal swab GPED 501 81.4Kbp 5.1Mbp 156 2438.8 0.774 E46 2018-05-06 Rectal swab GPED 1193 101.6Kbp 5.1Mbp 110 1188.4 0.837 E47 2018-05-06 Rectal swab GPED 120 36.3Kbp 4.9Mbp 137 2496.39 0.726 E56 2018-05-20 Rectal swab PICU 2076 116.3Kbp 5.5Mbp 163 2534.55 0.776 E57 2018-05-20 Rectal swab PICU 500 42.7Kbp 5.1Mbp 140 2480.9 0.772 E59 2018-05-23 Rectal swab PICU 131 155.3Kbp 5.1Mbp 46 335.13 0.911 E60 2018-05-23 Rectal swab NICU 28 100.5Kbp 5.2Mbp 88 1239.02 0.822 E61 2018-05-24 Rectal swab GPED 1193 91.3Kbp 5.2Mbp 108 1189.14 0.838 87 E62 2018-05-25 Rectal swab GPED 1312 46.0Kbp 4.6Mbp 129 2493.54 0.717 E64 2018-05-25 Rectal swab PICU 131 26.8Kbp 5.2Mbp 42 320.35 0.91 E77 2018-06-05 Rectal swab PICU 101 23.2Kbp 4.7Mbp 131 2517.32 0.739 E78 2018-06-05 Rectal swab GPED 10 19.7Kbp 4.8Mbp 118 2513 0.732 E79 2018-06-15 Rectal swab NICU 90 17.0Kbp 5.0Mbp 115 2519.04 0.746 E80 2018-06-16 Rectal swab PICU 38 25.2Kbp 5.6Mbp 122 2438.38 0.802 E81 2018-06-16 Rectal swab GPED 10 11.3Kbp 4.7Mbp 105 2526.8 0.728 E82 2018-06-16 Rectal swab PICU 131 19.7Kbp 5.3Mbp 24 36.91 0.946 E85 2018-07-10 Urine GPED 131 23.4Kbp 5.4Mbp 30 61.87 0.947 88 E96 2018-07-26 Urine GPED 1163 146.1Kbp 4.7Mbp 141 2145.02 0.777 E97 2018-07-28 Abscess GPED 131 46.5Kbp 5.1Mbp 21 59.07 0.935 E98 2018-07-31 Peritoneal fluid GPED 224 172.5Kbp 5.2Mbp 165 2580.62 0.769 E99 2018-07-31 Peritoneal fluid GPED 48 31.7Kbp 4.9Mbp 135 2430.91 0.757 E101 2018-08-01 Peritoneal fluid GPED 10 38.8Kbp 5.3Mbp 148 2467.26 0.771 E102 2018-08-02 Ileostomy site GPED 10992 70.9Kbp 4.7Mbp 125 2519.03 0.715 E103 2018-08-04 Rectal swab GPED 95 92.0Kbp 6.0Mbp 83 1211.44 0.836 E117 2018-08-28 Respiratory NICU 10 11.1Kbp 5.1Mbp 109 2489.81 0.758 E121 2018-08-30 Rectal swab PICU 131 57.2Kbp 5.2Mbp 51 320.87 0.913 89 E122 2018-08-30 Rectal swab PICU 2186 47.1Kbp 5.2Mbp 139 2537.12 0.778 E128 2018-09-02 Rectal swab GPED 131 65.2Kbp 5.3Mbp 46 354.13 0.907 E129 2018-09-02 Urine GPED 73 94.2Kbp 5.3Mbp 85 1132.04 0.857 C14 2018-12-20 Blood GPED 421 185.9Kbp 5.1Mbp 89 1194.97 0.835 E144 2018-09-09 Rectal swab GPED 405 55.4Kbp 5.3Mbp 156 2421.27 0.799 E146 2018-09-10 Rectal swab GPED 219 56.5Kbp 5.3Mbp 142 2503.77 0.753 E147 2018-09-10 Rectal swab PICU 224 25.9Kbp 5.3Mbp 142 2569.55 0.753 CP15 2018-07-21 Rectal swab GPED 167 71.0Kbp 5.0Mbp 143 2481.77 0.755 E151 2018-09-10 Rectal swab PICU 315 53.6Kbp 5.3Mbp 125 2520.32 0.768 90 E152 2018-09-10 Rectal swab GPED 450 41.5Kbp 5.0Mbp 152 2430.29 0.774 E171 2018-09-16 Rectal swab PICU 229 69.1Kbp 5.1Mbp 155 2493.77 0.758 E173 2018-09-16 Rectal swab PICU 88 90.8Kbp 5.2Mbp 151 2491.12 0.774 E178 2018-09-17 Rectal swab GPED 73 101.1Kbp 5.3Mbp 96 1195.28 0.839 CP18 2018-08-28 Rectal swab GPED 410 66.9Kbp 4.9Mbp 150 2513.6 0.75 E181 2018-09-17 Rectal swab GPED 131 102.5Kbp 5.3Mbp 20 44.6 0.941 E196 2018-09-24 Rectal swab GPED 295 24.9Kbp 5.3Mbp 130 2547.6 0.758 C20 2018-12-28 Blood PICU 10 73.7Kbp 4.9Mbp 146 2468.48 0.748 CP20 2018-09-03 Rectal swab PICU 46 62.2Kbp 4.7Mbp 163 2443.61 0.735 91 E202 2018-09-22 Rectal swab GPED 648 28.8Kbp 5.4Mbp 130 2160.62 0.806 E203 2018-09-26 Peritoneal fluid GPED 10 20.1Kbp 5.1Mbp 131 2427.99 0.777 E204 2018-09-24 Rectal swab GPED 131 20.1Kbp 5.3Mbp 27 71.61 0.956 E205 2018-09-24 Rectal swab PICU 10 23.5Kbp 4.6Mbp 131 2482.97 0.722 E207 2018-09-24 Rectal swab PICU 73 27.2Kbp 5.3Mbp 71 1121.22 0.852 CP21 2018-09-09 Rectal swab GPED 46 52.7Kbp 4.8Mbp 163 2434.53 0.739 E223 2018-10-03 Rectal swab GPED 540 31.0Kbp 4.9Mbp 118 2503.45 0.732 E228 2018-10-04 Rectal swab PICU 131 64.9Kbp 5.2Mbp 49 317.6 0.907 E229 2018-10-07 Rectal swab PICU 131 35.9Kbp 5.1Mbp 20 51.75 0.94 92 E230 2018-10-07 Rectal swab GPED 10 30.2Kbp 4.8Mbp 126 2481.89 0.734 E231 2018-10-07 Rectal swab GPED 10 37.0Kbp 4.9Mbp 139 2512.03 0.743 E24 2018-03-31 Rectal swab PICU 10997 68.7Kbp 4.9Mbp 147 2451.41 0.736 E248 2018-10-07 Rectal swab NICU 8881 17.2Kbp 5.3Mbp 126 2152.35 0.803 E249 2018-10-10 Rectal swab GPED 10997 18.3Kbp 5.1Mbp 137 2453.6 0.732 E25 2018-03-31 Rectal swab PICU 131 105.2Kbp 5.0Mbp 46 307.4 0.906 E251 2018-10-07 Rectal swab GPED 1294 14.3Kbp 4.8Mbp 112 2501.99 0.716 E253 2018-10-07 Abscess GPED 93 22.5Kbp 4.8Mbp 134 2470.21 0.735 E254 2018-10-08 Rectal swab GPED 1147 39.0Kbp 5.1Mbp 150 2549.25 0.761 93 E26 2018-03-31 Rectal swab GPED 3268 92.1Kbp 5.0Mbp 147 2523.52 0.755 E273 2018-10-08 Rectal swab GPED ND 2.7Kbp 8.3Mbp 111 1969.75 0.941 E279 2018-10-10 Rectal swab GPED 349 129.4Kbp 5.1Mbp 156 2519.44 0.769 E295 2018-10-18 Rectal swab GPED 1193 56.5Kbp 5.2Mbp 103 1190.81 0.839 E298 2018-10-18 Urine GPED 410 49.8Kbp 4.9Mbp 154 2490.8 0.764 E299 2018-10-19 Rectal swab GPED 69 64.6Kbp 5.4Mbp 158 2449.27 0.791 E300 2018-10-19 Rectal swab GPED 38 122.7Kbp 5.1Mbp 157 2525.84 0.762 E301 2018-10-20 Rectal swab PICU 1727 33.0Kbp 4.7Mbp 137 2527.09 0.733 E302 2018-10-20 Rectal swab GPED 73 65.1Kbp 5.3Mbp 76 1124.82 0.854 94 E321 2018-10-25 Respiratory PICU 155 64.5Kbp 4.9Mbp 157 2503.79 0.741 E322 2018-10-26 Peritoneal fluid GPED 131 87.4Kbp 5.2Mbp 65 346.38 0.905 E324 2018-10-25 Rectal swab PICU 131 11.4Kbp 8.7Mbp 25 84.8 0.942 E325 2018-10-25 Rectal swab GPED 616 67.5Kbp 5.0Mbp 153 2568.2 0.75 E327 2018-10-24 Rectal swab GPED 131 165.9Kbp 5.1Mbp 50 330.21 0.897 E328 2018-10-27 Ascitic fluid GPED 1193 108.0Kbp 5.0Mbp 112 1183.04 0.836 E343 2018-10-29 Rectal swab GPED 131 29.7Kbp 5.1Mbp 49 338.22 0.897 E345 2018-10-29 Rectal swab GPED 1193 120.7Kbp 5.1Mbp 110 1182.96 0.837 E346 2018-10-29 Rectal swab PICU 131 157.8Kbp 5.3Mbp 24 57.5 0.951 95 E347 2018-10-29 Rectal swab PICU 38 110.5Kbp 5.2Mbp 142 2509.02 0.766 E348 2018-10-29 Rectal swab GPED 38 93.6Kbp 5.4Mbp 154 2475.83 0.791 E350 2018-10-29 Rectal swab PICU 10 109.5Kbp 5.0Mbp 143 2510.41 0.765 E357 2018-11-02 Rectal swab GPED 131 168.9Kbp 5.3Mbp 65 334.72 0.907 E360 2018-11-04 Urine GPED 73 174.9Kbp 5.2Mbp 81 1191.4 0.824 E36 2018-04-14 Rectal swab PICU 131 32.2Kbp 5.1Mbp 19 39.21 0.938 E364 2018-11-04 Rectal swab PICU 1642 74.2Kbp 4.8Mbp 135 2585.92 0.74 E402 2018-11-11 Rectal swab GPED 515 107.8Kbp 4.9Mbp 174 2541.99 0.743 E406 2018-11-11 Rectal swab GPED 131 134.1Kbp 5.5Mbp 22 101.98 0.959 96 E409 2018-11-11 Rectal swab PICU 501 67.7Kbp 5.3Mbp 165 2453.14 0.782 E421 2018-11-13 Rectal swab GPED 442 46.3Kbp 5.0Mbp 146 2520.8 0.765 E422 2018-11-14 Rectal swab PICU 5614 47.8Kbp 5.1Mbp 137 2541.44 0.76 E424 2018-11-14 Rectal swab GPED 34 22.3Kbp 6.0Mbp 148 2551.43 0.803 E425 2018-11-14 Rectal swab GPED 10 73.3Kbp 5.3Mbp 163 2481.77 0.759 E427 2018-11-15 Rectal swab PICU 10994 64.8Kbp 4.8Mbp 136 2470.21 0.749 E447 2018-11-22 Rectal swab GPED 34 80.6Kbp 5.0Mbp 151 2469.63 0.762 E448 2018-11-21 Rectal swab PICU 38 137.0Kbp 5.3Mbp 156 2462.9 0.791 E451 2018-11-20 Rectal swab GPED 3873 98.1Kbp 5.2Mbp 156 2537.45 0.762 97 E452 2018-11-20 Rectal swab GPED 38 125.3Kbp 5.3Mbp 152 2473.16 0.791 E454 2018-11-20 Rectal swab GPED 130 118.2Kbp 5.2Mbp 163 2529.14 0.782 E472 2018-11-29 Rectal swab GPED 1193 118.5Kbp 5.1Mbp 113 1183.85 0.836 E474 2018-12-03 Urine GPED 38 111.5Kbp 5.4Mbp 156 2456.54 0.8 E475 2018-12-03 Urine GPED 131 123.9Kbp 5.2Mbp 18 56.33 0.942 E477 2018-12-01 Rectal swab GPED 4981 118.9Kbp 4.9Mbp 156 2462.91 0.746 E478 2018-11-30 Rectal swab GPED 46 66.6Kbp 4.8Mbp 136 2485.55 0.731 E48 2018-05-09 Rectal swab PICU 442 94.5Kbp 5.0Mbp 143 2561.84 0.743 E49 2018-05-09 Rectal swab GPED 349 87.4Kbp 5.1Mbp 149 2448.43 0.788 98 E495 2018-12-05 Rectal swab PICU 131 54.9Kbp 5.1Mbp 51 316.95 0.905 E496 2018-12-04 Rectal swab GPED 517 59.8Kbp 5.2Mbp 147 2546.65 0.748 E497 2018-12-04 Rectal swab GPED 131 137.7Kbp 5.2Mbp 60 340.35 0.9 E498 2018-12-04 Rectal swab GPED 10 130.1Kbp 4.7Mbp 144 2511.96 0.733 E500 2018-12-06 Rectal swab PICU 10 43.9Kbp 5.3Mbp 164 2448.99 0.783 E502 2018-12-06 Rectal swab GPED 155 47.9Kbp 4.9Mbp 146 2570.61 0.743 E503 2018-12-03 Urine GPED 141 61.3Kbp 5.2Mbp 77 1185.35 0.823 E51 2018-05-11 Rectal swab GPED 2141 47.6Kbp 5.5Mbp 133 2183.87 0.817 E53 2018-05-13 Rectal swab GPED 5708 1.6Kbp 8.9Mbp 171 2156.8 0.907 99 E54 2018-05-13 Abscess fluid GPED 131 45.1Kbp 5.3Mbp 20 54.28 0.951 E55 2018-05-20 Rectal swab PICU 131 172.3Kbp 5.1Mbp 52 320.9 0.894 E66 2018-05-29 Rectal swab GPED 69 30.5Kbp 5.6Mbp 157 2452.69 0.801 E68 2018-05-30 Respiratory PICU 648 72.3Kbp 5.2Mbp 132 2165.25 0.814 E70 2018-06-03 Peritoneal fluid GPED 131 69.8Kbp 5.2Mbp 19 60.68 0.946 E71 2018-06-03 Ascitic fluid GPED 550 61.4Kbp 5.3Mbp 87 1102.59 0.85 E72 2018-06-05 Rectal swab NICU 68 139.0Kbp 5.2Mbp 160 2505.18 0.777 E73 2018-06-05 Rectal swab NICU 68 47.2Kbp 5.1Mbp 142 2497.98 0.775 E87 2018-07-11 Rectal swab PICU 131 25.3Kbp 5.4Mbp 25 73.73 0.952 100 E89 2018-07-11 Rectal swab PICU 62 33.1Kbp 5.8Mbp 146 2209.89 0.796 E91 2018-07-17 Urine GPED 73 55.6Kbp 5.3Mbp 80 1189.11 0.86 E92 2018-07-18 Urine GPED 156 25.9Kbp 4.8Mbp 119 2548.72 0.731 E105 2018-08-04 Rectal swab GPED 6496 112.0Kbp 5.0Mbp 171 2538.57 0.745 E106 2018-08-06 Rectal swab PICU 5584 261.2Kbp 4.9Mbp 161 3349.95 0.684 E111 2018-08-17 Urine GPED 131 18.3Kbp 5.2Mbp 22 69.45 0.942 E112 2018-08-24 Rectal swab GPED 38 114.2Kbp 5.5Mbp 161 2475.98 0.803 E116 2018-08-29 Rectal swab GPED 48 90.4Kbp 4.8Mbp 141 2506.28 0.726 E131 2018-09-04 Urine GPED 998 48.3Kbp 5.4Mbp 80 1209.89 0.842 101 E132 2018-09-03 Rectal swab NICU 131 103.9Kbp 5.1Mbp 53 347.39 0.9 E133 2018-09-03 Rectal swab GPED 48 38.9Kbp 4.8Mbp 132 2497.9 0.728 E136 2018-09-06 Rectal swab GPED 542 80.6Kbp 4.8Mbp 143 2548.54 0.741 E137 2018-09-07 Rectal swab GPED 69 74.1Kbp 5.3Mbp 160 2361.04 0.802 E138 2018-09-07 Rectal swab PICU 10 62.0Kbp 4.9Mbp 142 2489.74 0.753 E141 2018-09-09 Rectal swab PICU 131 84.5Kbp 5.4Mbp 17 91.97 0.971 E153 2018-09-10 Rectal swab GPED 131 27.6Kbp 5.2Mbp 12 13.43 0.963 E155 2018-09-13 Rectal swab NICU 8881 17.7Kbp 5.3Mbp 130 2151.66 0.802 E158 2018-09-11 Rectal swab GPED 38 76.5Kbp 4.9Mbp 142 2517.35 0.761 102 E160 2018-09-11 Rectal swab PICU 131 55.2Kbp 5.2Mbp 19 53.86 0.936 E161 2018-09-11 Rectal swab PICU 2040 87.9Kbp 5.0Mbp 153 2571.81 0.74 E162 2018-09-12 Rectal swab GPED 405 44.4Kbp 5.1Mbp 154 2451.39 0.777 E165 2018-09-17 Urine GPED 1193 19.2Kbp 5.2Mbp 94 1190.91 0.828 E166 2018-09-14 Rectal swab NICU 181 20.5Kbp 4.8Mbp 104 2503.71 0.728 E167 2018-09-14 Rectal swab GPED 10993 48.9Kbp 5.1Mbp 86 1199.31 0.795 E169 2018-09-16 Rectal swab NICU 4981 18.2Kbp 4.8Mbp 134 2460.96 0.731 E180 2018-09-17 Rectal swab NICU 450 14.4Kbp 5.0Mbp 130 2438.74 0.753 E183 2018-09-18 Urine GPED 69 41.6Kbp 5.4Mbp 155 2370.33 0.797 103 E185 2018-09-19 Rectal swab GPED 38 28.8Kbp 5.4Mbp 146 2454.05 0.799 E186 2018-09-20 Rectal swab GPED 443 40.6Kbp 5.1Mbp 151 2536.91 0.751 E189 2018-09-20 Rectal swab PICU 10994 21.2Kbp 4.8Mbp 122 2466.09 0.741 E191 2018-09-20 Rectal swab GPED 1727 23.4Kbp 4.8Mbp 126 2533.84 0.732 E192 2018-09-23 Rectal swab GPED 115 19.6Kbp 5.7Mbp 127 2515.21 0.769 E195 2018-09-24 Rectal swab GPED 648 24.3Kbp 5.4Mbp 123 2175.47 0.8 E197 2018-09-24 Rectal swab NICU 4981 5.8Kbp 4.7Mbp 115 2451.74 0.718 E200 2018-09-26 Rectal swab NICU 8881 32.0Kbp 5.3Mbp 139 2161.32 0.806 E211 2018-09-27 Rectal swab GPED 69 18.6Kbp 5.5Mbp 146 2464.7 0.778 104 E215 2018-09-30 Rectal swab GPED 1611 25.1Kbp 4.9Mbp 122 2537.33 0.736 E217 2018-09-30 Rectal swab GPED 484 14.5Kbp 4.9Mbp 107 2507.38 0.74 E219 2018-09-30 Rectal swab PICU 38 20.6Kbp 5.4Mbp 151 2498.77 0.791 E220 2018-09-30 Rectal swab PICU 3346 22.4Kbp 5.3Mbp 104 2092.09 0.788 E233 2018-10-05 Rectal swab GPED 517 30.1Kbp 5.1Mbp 138 2489.55 0.742 E234 2018-10-05 Rectal swab PICU 155 42.6Kbp 4.8Mbp 150 2554.01 0.733 E236 2018-10-06 Rectal swab NICU 156 65.9Kbp 4.9Mbp 144 2532.3 0.735 E258 2018-10-08 Rectal swab GPED 10 33.5Kbp 5.1Mbp 159 2463.8 0.776 E262 2018-10-08 Rectal swab GPED 38 31.3Kbp 5.3Mbp 156 2464.29 0.783 105 E263 2018-10-08 Rectal swab PICU 73 32.8Kbp 5.3Mbp 86 1185.75 0.837 E264 2018-10-08 Rectal swab PICU 131 28.6Kbp 5.1Mbp 39 334.27 0.895 E265 2018-10-08 Rectal swab GPED 394 25.4Kbp 5.1Mbp 122 2483.78 0.752 E266 2018-10-08 Rectal swab GPED 38 26.0Kbp 5.4Mbp 130 2455.52 0.794 E269 2018-10-09 Rectal swab GPED 1695 24.1Kbp 5.1Mbp 126 2462.13 0.74 E280 2018-10-09 Rectal swab GPED 131 169.9Kbp 5.1Mbp 53 314.17 0.907 E284 2018-10-12 Rectal swab GPED 38 114.6Kbp 5.5Mbp 157 2464.03 0.808 E286 2018-10-12 Rectal swab GPED 328 64.1Kbp 5.3Mbp 151 2532.29 0.763 E287 2018-10-13 Urine GPED 181 106.9Kbp 4.8Mbp 139 2530.35 0.74 106 E288 2018-10-16 Urine GPED 752 21.8Kbp 6.1Mbp 138 2571.17 0.767 E294 2018-10-16 Urine GPED 405 81.6Kbp 5.5Mbp 158 2420.25 0.804 E308 2018-10-21 Rectal swab PICU 38 125.1Kbp 5.2Mbp 139 2507.34 0.766 E310 2018-10-21 Rectal swab GPED 155 48.4Kbp 4.9Mbp 158 2513.55 0.74 E312 2018-10-23 Urine GPED 131 45.8Kbp 5.3Mbp 21 54.64 0.953 E313 2018-10-22 Rectal swab GPED 1193 31.8Kbp 5.1Mbp 94 1183.55 0.836 E318 2018-10-22 Rectal swab PICU 8881 23.9Kbp 5.3Mbp 137 2135.37 0.804 E330 2018-10-28 Rectal swab GPED 131 138.5Kbp 5.1Mbp 50 308.64 0.911 E334 2018-10-28 Rectal swab NICU 10 47.4Kbp 5.0Mbp 137 2498.24 0.769 107 E336 2018-10-27 Rectal swab NICU 8881 103.9Kbp 5.2Mbp 154 2173.34 0.811 E339 2018-10-30 Urine GPED 131 94.5Kbp 5.1Mbp 18 58.76 0.94 E352 2018-10-29 Rectal swab GPED 501 19.9Kbp 5.3Mbp 149 2446.4 0.777 E355 2018-10-29 Rectal swab PICU 1193 29.2Kbp 5.0Mbp 96 1188.13 0.828 E378 2018-11-05 Rectal swab PICU 515 91.3Kbp 4.9Mbp 172 2542.7 0.745 E381 2018-11-05 Rectal swab GPED 38 105.1Kbp 5.3Mbp 144 2433.58 0.798 E382 2018-11-05 Rectal swab PICU 155 61.7Kbp 4.9Mbp 160 2508.48 0.743 E384 2018-11-09 Rectal swab GPED 58 77.6Kbp 4.8Mbp 158 2533.46 0.74 E385 2018-11-06 Rectal swab PICU 394 106.9Kbp 5.2Mbp 152 2522.75 0.762 108 E387 2018-11-07 Urine GPED 636 51.7Kbp 5.0Mbp 79 1194.9 0.823 E388 2018-11-06 CSF GPED 131 72.2Kbp 5.4Mbp 29 70.3 0.947 E412 2018-11-12 Rectal swab PICU 58 136.0Kbp 4.9Mbp 168 2541.24 0.753 E413 2018-11-12 Rectal swab GPED 48 54.6Kbp 4.8Mbp 153 2517.5 0.745 E414 2018-11-12 Rectal swab PICU 1722 186.8Kbp 4.9Mbp 138 2299.27 0.77 E417 2018-11-12 Rectal swab PICU 38 74.2Kbp 5.4Mbp 149 2451.39 0.796 E419 2018-11-13 Rectal swab GPED 4684 179.7Kbp 4.9Mbp 157 2550.28 0.739 E420 2018-11-13 Rectal swab PICU 2562 212.8Kbp 5.4Mbp 153 3313.85 0.706 E431 2018-11-16 Rectal swab GPED 38 92.7Kbp 5.4Mbp 167 2462.85 0.798 109 E432 2018-11-16 Rectal swab GPED 10 83.7Kbp 5.1Mbp 155 2486.88 0.746 E433 2018-11-15 Rectal swab GPED 131 124.7Kbp 5.4Mbp 17 51.9 0.959 E435 2018-11-19 Rectal swab GPED 10998 99.8Kbp 4.9Mbp 131 2541.86 0.749 E436 2018-11-19 Rectal swab GPED 2089 18.1Kbp 5.1Mbp 124 2486.85 0.761 E437 2018-11-19 Rectal swab PICU 34 109.7Kbp 4.9Mbp 149 2481.07 0.764 E442 2018-11-18 Rectal swab PICU 206 53.0Kbp 5.1Mbp 149 2532.21 0.761 E443 2018-11-18 Rectal swab GPED 773 46.8Kbp 5.0Mbp 136 2451.63 0.754 E444 2018-11-18 Rectal swab NICU 8881 105.5Kbp 5.2Mbp 157 2174.91 0.811 E456 2018-11-23 Urine GPED 10 93.8Kbp 5.2Mbp 178 2476.69 0.779 110 E457 2018-11-23 Skin swab GPED 2076 16.2Kbp 5.3Mbp 143 2531.59 0.759 E460 2018-11-26 Urine GPED 38 123.3Kbp 5.4Mbp 166 2503.69 0.791 E461 2018-11-22 Urine GPED 6355 167.3Kbp 5.3Mbp 95 1158.99 0.84 E466 2018-11-27 Rectal swab GPED 515 56.0Kbp 4.7Mbp 148 2519.42 0.723 E469 2018-11-28 Rectal swab GPED 131 57.7Kbp 5.1Mbp 52 333.71 0.894 E480 2018-12-02 Rectal swab GPED 90 128.7Kbp 5.1Mbp 143 2508.35 0.766 E481 2018-12-02 Rectal swab NICU 130 78.1Kbp 5.2Mbp 165 2537.71 0.786 E483 2018-12-02 Rectal swab GPED 442 32.8Kbp 5.0Mbp 141 2549.83 0.749 E488 2018-12-03 Rectal swab GPED 8149 45.0Kbp 4.6Mbp 118 2491.99 0.724 111 E489 2018-12-03 Rectal swab GPED 450 55.5Kbp 4.9Mbp 147 2427.96 0.775 E504 2018-12-06 Rectal swab GPED 1312 60.3Kbp 4.8Mbp 134 2529.75 0.736 E506 2018-12-19 Urine GPED 648 105.7Kbp 5.4Mbp 144 2178.04 0.809 E507 2018-09-22 Blood GPED 131 71.3Kbp 5.1Mbp 13 19.35 0.965 Table 4.1 Complete table of isolates along with their associated metadata and quality control metrics. 112 Appendix B Isolate antimicrobial resistance phenotypes Sample Name ST CRO CAZ FEP AMC TZP AZT ETP MEM GEN AMK CIP LVX SXT NIT E13 10 R S S S S S S S S S S S R S E39 10 R R R S S R S S S S S S R S E43 10 R I R S S R S S S S S S S S E78 10 R S S S S I S S S S S R S E81 10 R S R S S S S S S S S R S E101 10 R S R S S R S S S S S S R S E117 10 R S I S S I S S S S R R R S C20 10 R R R S S R S S S S R R S S E203 10 R S S S S S S S S S I I R S E205 10 R R R S S R S S S S S S R I E230 10 R S S S S S S S S S R R R S E231 10 R R R S S R S S S S S S R S E350 10 R S S R S S S S S R I R S 113 E425 10 R S S R S S S S S I I R S E498 10 R R R S S R S S S S S R S E500 10 R S S S S S S S S S S R S E138 10 R R R I S R S S S S R R R S E258 10 R R R R S S S R S S S R S E334 10 R S S I S S S S S R R R S E432 10 R S S R S S S S S S S S S E456 10 I S S S S S S S S S S S R S E60 28 R R R S S R S S S S R R S S E424 34 R R S R S S S R S S S R S E447 34 R S S S S I S S S S R I R S E437 34 R S S S S I S S S S S S R S CP2 38 R S I R I R R S R S S S R S E41 38 R I R I S R S S S S S S R S E80 38 R S S S S S S S S S S R S 114 E300 38 R R S R S S S S S S I S S E347 38 R R R R I S S S S S S R S E348 38 R R R R I S S S S R R R I E448 38 R R R R S S S S S S S S R E452 38 R S R I S R S S S S S S R S E474 38 R I R S S R S S S S S S S S E112 38 R R R S S R S S S S S S R S E158 38 R R R S S R S S S S S S S S E185 38 R R I R S R S S R S R R R S E219 38 R R S R S S S S S S S R I E262 38 R S R R S S S S S S S S S E266 38 R S S S S S S S S R R R S E284 38 R S R R S S S S S S S R I E308 38 R R R I S S S S S S S R S E381 38 R S S I S S S S S S S R I 115 E417 38 R R S R S S S S S R R R S E431 38 R I R R R S S S S S S R S E460 38 R S R I S R S S S S S S R S CP20 46 R R R R R R R R S S R R R S CP21 46 R R R R R R R R R R R R R S E478 46 R R R R I R S S S S S S S E99 48 R R R I S R S S R S S S R S E116 48 R S S S S S S S S S S S R S E133 48 S R S S S I S S S S S S S S E413 48 R S S S S S S S S R R S S E17 58 R I R S S R S S S S S S R S E384 58 R R R R S S S S S S S R S E412 58 R S S R S S S S S S S R S E89 62 R I R S S R S S R S S S S S E72 68 R R R S S R S S S S S S S 116 E73 68 R R R S S R S S S S S S S E299 69 R S S R S S S S S S S R S E66 69 R S S S S S S S S S S S R S E137 69 R S R S S R S S S S S S S S E183 69 R R R S S R S S S S S S S S E211 69 R S R S S I S S S S S S S S E19 73 R S S S S S S S S S S S S S E129 73 R R R S S R S S S S S S S S E178 73 R R R S S R S S S S S S S S E207 73 R S I S S R S S S S R R S S E302 73 R R S I S S S R S S S R S E360 73 R S I S S R S S S S S S S S E91 73 R S R S S I S S S S S S R S E263 73 R S S S S S S S S S S S S E173 88 R S S S S S S S S S S S R S 117 E79 90 R R R I S R S S S S R R S E480 90 R S I R S R S S S S R R S E253 93 R S R S S S S S R S S S R S E103 95 R I R S S R S S S S S S R S E77 101 R R R S S R S S S S S R S E192 115 R S R S S R S S I S R R R S E47 120 R R R S S R S S S S S S R S CP4 127 R R R R R R R R R S R R S E12 127 R R R S S R S S R S S S R S E454 130 R S S S S I S S S S S S R S E481 130 R I R S S R S S S S S S S E15 131 R I R S S R S S S S I S R S E22 131 R R R S S R S S S S R R R S E59 131 R R R S S R S S S S R R R S E64 131 R R R S S R S S S S S S R S 118 E82 131 R I R I S R S S R S R R S E85 131 R R R I S R S S R S R R R S E97 131 R S I S S R S S S S R R R S E121 131 R R R S S R S S S S S S S S E128 131 R R R I S R S S S S S S S S E181 131 R R R S S R S S S S R R R S E204 131 R R R R I R S S S S S S R S E228 131 R R R S S R S S S S S S R S E229 131 R I R S S R S S S S R R R S E25 131 R R R S S R S S S S S S R S E322 131 R R R S S R S S S S S S R S E324 131 R S S I S S S S S R R R S E327 131 R S R S S S S S S R R R S E343 131 R R I I S S S S S R R R S E346 131 R R S R S S S R S R R S S 119 E357 131 R S S S S S S S S S S R S S E36 131 R S I S S I S S S S R R S S E406 131 R R R R R S S S S S S R S E475 131 R S I S S S S S S S R R R S E495 131 R R S I S S S S S R R R S E497 131 R I R S S R S S R S S R S E54 131 R R R I S S S S R S R R S S E55 131 R R R S S R S S R S R R R S E70 131 R S I S S I S S S S R R S S E87 131 R R R S S R S S S S R R R S E111 131 R S S S S S S S S S R R S S E132 131 R R R S S R S S S S R R R S E141 131 R R R I S R S S R S R R R S E153 131 R I I R I R S S S S R R R S E160 131 R S R S S R S S S S R R R S 120 E264 131 R S S S S S S S S R R S S E280 131 R R R I S S S S S S S R S E312 131 R R I R I S S S S R R R S E330 131 R R R R S S S S S S S R S E339 131 R S R S S S S S S R R S S E388 131 R R R I S R S S R S R R R S E433 131 R R R R S S S S S R R R S E469 131 R S S S S S S S S S S R S E507 131 R R R R R R S S S S R R R S E42 141 R S R S S I S S S S S S S S E503 141 R R R S S R S S S S S S S S E321 155 R S I S S I S S S S S S R S E502 155 R R S R S S S S S R R R I E234 155 R S S S S S S S S S S S R S E310 155 R S S R S S S S S S S R S 121 E382 155 R S S I S S S S S S S R S E92 156 R R R I S R S S S S S S R S E236 156 R R I R S S S S S R R R S E40 162 R S R S S S S S S S S S R S CP15 167 R R R R R R R R S S R R R R E166 181 R S I S S S S S S S S S R S E287 181 R R R R R R S S S S S S R S E442 206 R S S S S S S S S S S S S E146 219 R S S S S S S S S S S S R S E98 224 R R R S S R S S R S R R R S E147 224 R S R S S I S S R S R R R S E171 229 R R R S S R S S S S S S R R E20 278 R R I S S R S S S S S S R S E196 295 R I I S S S S S S S S S S S E151 315 R I R S S R S S S S S S R S 122 E286 328 R S S R S S S S S S S S S E279 349 S R S R S S S S S S S R S E49 349 R I R S S R S S S S S S S S CP8 354 R R R R R R R S S S R R R S E8 361 R R R R R R S S R S R R R S E265 394 R S S S S S S S S S S S S E385 394 R S S S S S S S S S S S S E144 405 R R R I S R S S R S R R R S E162 405 R R R R R R R S S S S S S R E294 405 R R R R S R I S S S R R R S CP26 410 R R R R R R R R S S R R R S CP18 410 R R R R R R R R R S R R R I E298 410 R R R R I S S S S R R R S E6 421 R I R R S R S S S S R R S S C14 421 R R R R S R S S S S S S S S 123 E421 442 R S S S S S S S S S S S S E48 442 R R R S S R S S S S S S S S E483 442 R R R S S R S S S S S S S E186 443 R R S S S S S S S S R S S CP29 448 R R R R R R R R R S R R R S E152 450 R S R S S I S S S S R R R S E180 450 R S S S S S S S S S R R R S E489 450 R S R I S R S S S S R R S E217 484 R S S S S S S S S S S S S E57 500 R I I I S R S S R S R R R I E44 501 R S R S S I S S S S R R R S E409 501 R R S R R S S S S R R R I E352 501 R R S R S S S S S R R R I E402 515 R S S R S S S S S S S R S E378 515 R S S I S S S S S S S R S 124 E466 515 R S I S S I S S S S S S R S E496 517 R R R S S R S S S S S S S E233 517 R R R S S R S S S S R S R S CP9 540 R R R R R R R R S S S S R S E223 540 R R S R S S S S S I R R S E136 542 R R I R S S S S S S R R S S E71 550 R R R S S R S S S S S S S S E325 616 R S S I S S S S S S S R S E387 636 R R R I S R S S R S S S R S E202 648 R R R I S R S S R S R R R S E68 648 R R R I I R S S S S R R R S E195 648 R R R I S R S S R S R R R S E506 648 R R R R R R S S R I R R R S E288 752 R S I S S R S S S S S S S S E443 773 R S S S S S S S S R R S I 125 E131 998 R I R S S R S S R S S S S S E254 1147 R S S S S S S S S S S S S E96 1163 R I R S S R S S S S I S S S E35 1193 R R R S S R S S S S R R S S E46 1193 R S R S S R S S R S R R R S E61 1193 R I R S S R S S R S R R R R E295 1193 R I R S S R S S R S R R R S E328 1193 R S R S S R S S R S R R R S E345 1193 R S S R I S S R S R R S S E472 1193 R S S R S S S R S R R S S E165 1193 R S R S S S S S S S R R R S E313 1193 R R I R S S S S S R R S S E355 1193 R S S S S S S S S R R S S E251 1294 R S S I S S S S S S S R S E62 1312 R R R S S R S S S S R R R S 126 E504 1312 R S S S S S S S S S S S S E215 1611 R R R I S S S S S S S S S E364 1642 R S S S S S S S S S S S S E269 1695 R S S I S S S S S S S R S E414 1722 R I R S S S S S S S S S S E301 1727 R R S I S S S S S S S S S E191 1727 R R R I S S S S S S S S S E161 2040 R S I I S R S S S S S S S S E34 2076 R R R S S R S S S S S S S S E56 2076 R S R S S R S S S S S S R S E457 2076 R R R I I R S S S S S S S S E436 2089 R I I I I I S S S S R R R S E21 2141 R R R S S R S S S S S S R I E51 2141 R R R S S R S S S S S S R S E122 2186 R S S S S S S S S S S S R S 127 E420 2562 R S S R S S S S S S S R S E26 3268 R R R I S R S S S S S S S S E220 3346 R S S R S S S R S S S R S CP25 3541 R R R R R R R S S S S S S S E451 3873 R S I S S I S S S S S S R S E419 4684 R S S R S S S S S S S R S E477 4981 R I R S S R S S S S R R R S E169 4981 R I R S S R S S S S R R R S E197 4981 R R R I S R S S S S R R R S E106 5584 R S S S S I S S S S S S S S E422 5614 R S S S S S S S S S S S S E53 5708 R R R S S R S S S S S S R S E461 6355 R R R I S R S S R S S S S S E105 6496 R R R I S R S S S S S S S S E488 8149 R R R S S R S S S S S S S 128 E248 8881 R R R I S S S R S R R R S E155 8881 R R R I S R S S R S R R R S E200 8881 R R R I S R S S R S R R R S E318 8881 R R R R S S S R S R R R I E336 8881 R R R R S S S R S R R R I E444 8881 R R R R S S S R S R R R I E10 9269 R I I S S R S S S S S S R S E102 10992 R R R S S R S S S S S S S S E167 10993 R S S S S S S S S S S S S S E427 10994 R S S R R S S S S S S S S E189 10994 R R S R S S S S S S S S S E14 10995 R S S S S S S S S S S S S S E24 10997 R R S R R R S S S S S S S S E249 10997 R R R R S S S S S R R R S E435 10998 R S S S S S S S S S S S S S 129 E273 ND R S S R S S S S S R R R S Table 4.2 The antimicrobial resistance phenotypes (obtained through antimicrobial susceptibility testing) of each isolate. Isolates have been ordered by ST. Drug abbreviations are as follows: Ceftriaxone (CRO), Ceftazidime (CAZ), Cefepime (FEP), Aztreonam (AZT), Amoxicillin-clavulanate (AMC), Piperacillin-tazobactam (TZP), Ertapenem (ERT), Meropenem (MEM), Gentamicin (GEN), Amikacin (AMK), Ciprofloxacin (CIP), Levofloxacin (LVX), Trimethoprim-sulfamethoxazole (SXT), Nitrofurantoin (NIT). 130 Appendix C Building a sketch database for PopPUNK, clustering model selection, and hyperparameter optimization C.1 Building a sketch database for PopPUNK A database was built from the assembled genomes using pp-sketchlib (v1.7.0) [68] with k-mer lengths 13-29, a k-mer step of 2, and a sketch size of 100000. K-mer lengths were confirmed to be sufficient by looking at the included plots of the relation between k-mer distances and core and accessory fits from randomly chosen sample pairs (Figure 4.1 and Figure 4.2). For a good k-mer range, a general straight line fit between the proportion of matches and the k-mer length should be observed. Here, we observe the general straight line fit, indicating our k-mer range is sufficient for E. coli. 131 Figure 4.1 The example fit between two Qatari isolates to determine an optimal k-mer range. 132 Figure 4.2 The example fit between two global isolates to determine an optimal k-mer range. 133 C.2 Clustering model selection and hyperparameter optimization For both the characterization of Qatari isolates and the global contextualization, a Bayesian Gaussian Mixture Model (BGMM) and a DBScan model were fitted to the data. Both model selection and hyperparameter optimization were performed by qualitative observation of the clusters superimposed on a t-SNE of the accessory-genome distances and a phylogenetic tree of the core-genome distances, as well as quantitative measures using the network summary scores output by PopPUNK. The network score synthesizes the density (the proportion of distances assigned as “within strain”) and the transitivity (the measure of whether every member of each strain is connected to every other member) [67]. 134 Appendix D Predicting antimicrobial resistance mutations using machine learning D.1 Logistic regression model performance Figure 4.3 ROC AUC and F1 score of the LR model for each drug. The drugs are ceftazidime (CAZ), cefepime (FEP), amoxicillin-clavulanate (AMC), gentamicin (GEN), ciprofloxacin (CIP), levofloxacin (LVX), and trimethoprim-sulfamethoxazole (SXT). 135 D.2 Annotated feature importance results (random forests) Drug Position Importance Gene Function AMC 2656057 0.0036 yfdC Formate/nitrite transporter family 879950 0.0029 gsiB Glutathione ABC transporter substrate-binding 2835278 0.0029 hscA Fe-S protein assembly chaperone 4405121 0.0029 ubiD 4-hydroxy-3-polyprenylbenzoate decarboxylase 907574 0.0027 ybjO Inner membrane protein CAZ 879950 0.0034 gsiB Glutathione ABC transporter substrate-binding 2940033 0.0027 nrdI Class Ib ribonucleoside-disphosphate reductase 2878095 0.0027 lepA Translation elongation factor 89986 0.0025 leuB Isopropylmalate dehydrogenase 783659 0.0024 N/A Unannotated CIP 3454459 0.0044 ftsP Cell division protein 3426449 0.0043 hybC Hydrogenase 2 large subunit 3435160 0.0037 yghA NADP(+)-dependent aldehyde reductase 3465338 0.0036 mdaB NADPH:quinone oxidoreductase 3422638 0.0032 gss Bifunctional glutathionylspermidine amidase FEP 2462367 0.0032 fruK 1-phosphofructokinase 3274430 0.0024 ansB L-asparaginase 2 2869397 0.0023 N/A Unannotated 660839 0.0023 citF Citrate lyase subunit alpha 1053069 0.0022 kdsB 3-deoxy-octulosonate citidylyltransferase 136 GEN 2496834 0.0053 napH Quinol dehydrogenase ferredoxin subunit 1595851 0.0048 ydbH Hydrocarboxylate dehydrogenase a 3156943 0.0045 lplT Lysophospholipid transporter 3525976 0.0038 ebgA Beta-galactosidase subunit alpha 3703645 0.0034 panF Sodium/pantothenate symporter LVX 4910311 0.0061 lptG LPS export ABC transporter permease 3454459 0.0060 ftsP Cell division protein 3465338 0.0040 mdaB NADPH:quinone oxidoreductase 3746987 0.0035 rplD 50S ribosomal protein 645747 0.0031 hcxA Hydroxycarboxylate dehydrogenase SXT 660839 0.0035 citF Citrate lyase subunit alpha 4791898 0.0031 sugE Quaternary ammonium compound efflux 1889176 0.0028 astA Arginine N-succinyltransferase 1249126 0.0022 phoQ Two-component system sensor histidine kinase 3746987 0.0022 rplD 50S ribosomal protein Table 4.3 Top five features obtained from the feature importance values of the trained random forests model for each drug. 137 D.3 Annotated feature importance results (logistic regression) Drug Position Importance Gene Function AMC 2041673 0.024 tyrP Tyrosine transporter 2102418 0.020 yedE Selenium metabolism membrane protein 1922060 0.017 gapA Glyceraldehyde-3-phosphate dehydrogenase 3265650 0.017 yggR Type IV pilus twitching motility protein 5096366 0.016 nadR Multifunctional transcriptional regulator CAZ 3422638 0.027 gss Bifunctional glutathionylspermidine amidase 3426449 0.027 hybC Hydrogenase 2 large subunit 3430165 0.024 hybO Hydrogenase 2 small subunit 3422587 0.022 gss Bifunctional glutathionylspermidine amidase 4908051 0.022 N/A Unannotated CIP 4910625 0.026 lptG LPS export ABC transporter permease 3207071 0.025 N/A Unannotated 3424864 0.018 hybG Hydrogenase maturation factor 2359716 0.016 mdtC Multidrug efflux RND transporter permease 2359752 0.016 mdtC Multidrug efflux RND transporter permease FEP 3426449 0.030 hybC Hydrogenase 2 large subunit 3430165 0.028 hybO Hydrogenase 2 small subunit 3422638 0.027 gss Bifunctional glutathionylspermidine amidase 3456513 0.019 parC DNA topoisomerase IV subunit 3437723 0.019 N/A Unannotated 138 GEN 4893183 0.033 ridA 2-iminobutanoate/2-iminopropanoate deaminase 3445737 0.031 ygiQ Radical SAM family protein 3441685 0.031 dkgA 2,5-didehydrogluconate reductase 3439448 0.031 N/A Unannotated 3436835 0.030 N/A Intergenic region LVX 320707 0.024 N/A Unannotated 4910625 0.019 lptG LPS export ABC transporter permease 3424864 0.017 hybG Hydrogenase maturation factor 2359752 0.016 mdtC Multidrug efflux RND transporter permease 2359716 0.016 mdtC Multidrug efflux RND transporter permease SXT 4891069 0.026 mgtA Magnesium-translocating P-type ATPase 4891015 0.026 mgtA Magnesium-translocating P-type ATPase 5101729 0.023 yjjX Inosine/xanthosine triphosphatase 4891039 0.023 mgtA Magnesium-translocating P-type ATPase 5101510 0.023 yjjX Inosine/xanthosine triphosphatase Table 4.4 Top five features obtained from the feature importance values of the trained logistic regression model for each drug. 139 Appendix E SNP calling within reference genome plasmids E.1 SNP calling within the alignment to pEC958 Figure 4.4 Heatmap produced by the presence/absence matrix of SNPs within the pEC958 plasmid. The dendrogram is generated through hierarchical clustering along the rows. Only the ST1193 isolates are clustered together and differentiable from other STs. The other STs have much more variance in their SNP profile. 140 E.2 SNP calling within the alignment to pEC958B Figure 4.5 Heatmap produced by the presence/absence matrix of SNPs within the pEC958B plasmid. The dendrogram is generated through hierarchical clustering along the rows. SNPs in this small plasmid cannot separate the STs. There are patterns and SNPs that reoccur between isolates, but they are not consistent within specific STs. 141 Appendix F Clustering Qatari isolates alongside the pre-built E. coli PopPUNK database Figure 4.6 Phylogenetic tree of the Qatari isolates alongside the PopPUNK included E. coli database. The red nodes indicate Qatari isolates. The outside band colors corresponds to STs shown in the legend. Un-generated leaf nodes correspond to isolates of other STs. The STs somewhat cluster together, but not perfectly as there are gaps between some groups of isolates. We still observe ST8881 and ST648 to be clustered together, as well as ST10997 and ST4981. However, for many STs, there are gaps between groups of isolates of the same STs. However, these groups of isolates usually do not form a monophyletic clade containing only Qatari isolates. The only exception is ST8881, and the complex of ST4981 and ST10997.
Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hsiao, Will
Genomic characterization and global contextualization of ESBL-producing E. coli from pediatric patients in Qatar
Text