Samuel Lipworth

Enterobacterales pathogens that encode multiple carbapenemase genes are an emerging public health threat. The genomic and plasmid context of the multi-carbapenemase producers has not yet been explored at scale. We analysed 1501 publicly-available global genomes that encoded two or more genes from the five prevalent carbapenemase families (IMP, KPC, NDM, OXA-48-like, and VIM). We observed 241 distinct allelic combinations, with NDM/OXA-48-like (25.6%), multiple KPC (25.3%), KPC/NDM (18.8%), and multiple NDM (10.9%) being the most common groups. We found that many genomes were part of high-risk lineages (48.0%). Most genomes carried carbapenemase genes on two plasmids (59.2%) or one plasmid (23.5%), and most genomes encoded different carbapenemase families (56.2%) rather than copies of the same allele (36.2%). We observed multiple mechanisms of multi-carbapenemase evolution including plasmid co-integration, convergence with virulence, and generation of novel allele diversity through duplication. Multi-carbapenemase plasmids had more AMR genes that conferred resistance to more AMR classes compared to plasmids encoding a single carbapenemase. The proportion of cross-class combinations (KPC/NDM and NDM/OXA-48-like) increased significantly over time, with NDM-5/OXA-181 (3.3-fold) and NDM-4/OXA-181 (8.9-fold) combinations enriched. These findings provide valuable insights into the genomic features of multi-carbapenemase Enterobacterales to support the development of effective surveillance programmes.

Background: Optimising sampling frames for genomic surveillance of E. coli and Klebsiella may support interventions to mitigate bloodstream infections (BSIs), but approaches to estimating sample size and how these relate to bacterial population diversity at multiple genetic levels (strain/plasmid/antimicrobial resistance genes [ARGs]) are lacking. Methods: Using systematically collected, regionally-stratified, whole genome sequencing data from a 6-month genomic survey of English E. coli / Klebsiella BSI isolates (n=1,939; NEKSUS), we used Bayesian approaches to estimate the sample sizes required to capture genetic diversity at strain- (MLST, fastBAPS clusters), plasmid- and ARG-levels, using two diversity measures: ‘coverage’ (i.e. proportion of the bacterial population/plasmids/ARGs represented by features observed in the sample), and the number/proportion of unique features observed at varying surveillance sample sizes. Our method is available as a user-friendly R Shiny application, EpiSENTRY ( https://01a0c3dd-90d2-e597-58a5-51391a468387.share.connect.posit.cloud/ ). Findings: Randomly sampling 1,400 E. coli / Klebsiella BSI isolates each achieved ≥80% coverage of bacterial lineages and ARGs (i.e. capture features constituting ≥80% of the population) at 95% certainty, but captured lower proportions of unique features (28% MLSTs, 59% fastBAPS clusters, 19% plasmid subcommunities, and 57% ARGs for E. coli; 49%, 87%, 50% and 83% for Klebsiella , respectively). Sample sizes required for 80% coverage of E. coli plasmid populations were higher than MLSTs (1,928[95% CrI:1,837–2,025] vs 1,351[1,230–1,472]), while for Klebsiella , estimates were lower for plasmids (1,115[1,052–1,181] vs 1,422[1,331–1,515] for MLSTs). Lower sample sizes adequately captured 80% coverage of fastBAPS clusters and ARGs (649[575 – 727] and 27[23–30], respectively, for E. coli ; 113[84–145] and 83[72-96] for Klebsiella ), due to feature-specific frequency distributions. Klebsiella BSIs were more diverse than E. coli , with sampling 10.9% vs 3.2% of total annual BSIs in England needed to reach 80% MLST coverage. Plasmid populations and Klebsiella MLSTs were region-specific, emphasising the need for regionally-representative sampling. Interpretation: Bayesian analysis facilitates estimates of context-specific genomic surveillance sample sizes. Cost-effective targets for coverage require further optimisation. Funding: National Institute for Health and Social Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (NIHR207397); UKHSA and NIHR Oxford Biomedical Research Centre (BRC); UKHSA PhD Funding Competition; Robertson Fellowship; cloud compute infrastructure for this work was donated by Oracle Corporation Infrastructure. Research in context: Evidence before this study: A MEDLINE (Ovid) search for genomic surveillance sample size estimation (“sample size estimation/calculation” AND “surveillance”; no restrictions on publication type, date or language) retrieved 62 records (22 included). Most studies (68% [15/22]) used calculations based on frequentist formulae, which require a user-specified arbitrary minimum frequency threshold for the feature/disease under surveillance. Surveillance outcomes were mostly prevalence/incidence (59% [13/22]) and presence/absence (54% [12/22]) of diseases/genomic features, and all studies considered diseases/features individually. Re-analyses of COVID-19 variant surveillance in high-income settings suggested that one third of the sequencing effort would have sufficed. Several publications highlight estimates should be tailored to specific surveillance goals. No studies extended surveillance sample size calculations to capturing population diversity of multiple genomic features simultaneously (e.g. plasmids, antimicrobial resistance [AMR] genes [ARGs]), although these may be important for bacterial genomic surveillance. Bayesian estimation is a powerful method of statistical inference where prior beliefs about the distribution of a population parameter (e.g.: the frequency of a certain genomic feature) can be updated to a more realistic posterior distribution using the likelihood of the available data, thus incorporating uncertainty from potentially noisy data. Added value of this study: We applied Bayesian analysis to large-scale genomic data from a 6-month nationally-representative survey of English bloodstream infection-associated E. coli and Klebsiella to estimate the posterior frequency distribution, with uncertainty, of four genomic features (multi-locus sequence type [MLST], fastBAPS cluster, plasmid subcommunities, ARGs). We used estimates to inform meaningful minimum feature frequency thresholds for conventional sample size calculations, by relating frequency to proportion of population diversity captured (i.e.: coverage – the proportion of the population represented by the features detected at least once in the sample), as well as number/proportion of unique features captured. This method overcomes limitations of ecological estimators when scaling to large populations by using all survey data to inform estimates, while placing more weight on epidemiologically important common features. We have made our method available as a user-friendly R Shiny application, EpiSENTRY ( https://01a0c3dd-90d2-e597-58a5-51391a468387.share.connect.posit.cloud/ ), allowing users to easily estimate sample sizes from their own data. Implications of all the available evidence: Randomly sampling 1,400 E. coli and Klebsiella bloodstream (BSI) isolates, each, in England sufficiently captured strains and ARGs representing 80% of the estimated total bacterial population (80% coverage) at 95% certainty. While this captured the most common BSI-associated bacterial genomic features, >51% of unique MLSTs, >50% of plasmid subcommunities, >13% of fastBAPS clusters, and >17% of ARGs may remain undetected even at high coverages because there are so many unique variants at all genomic levels. Target coverage thresholds may need to be adjusted, or alternative targeted sequencing methods used (e.g. selection based on phenotype) for rare features of critical public health importance (e.g. carbapenemases). Klebsiella BSIs were more diverse than E. coli , relative to incidence, requiring 10.9% vs 3.2% of BSIs to be sequenced to capture comparable diversity. Plasmid populations and Klebsiella MLSTs were also region-specific, emphasising the need for regionally-representative sampling. Sample size estimates should be context-specific to best support national, regional, or local surveillance, and potentially inform infection prevention and control (IPC) efforts. Cost-effectiveness analyses are required to optimise sampling strategies.

Background: Optimising sampling frames for genomic surveillance of E. coli and Klebsiella may support interventions to mitigate bloodstream infections (BSIs), but approaches to estimating sample size and how these relate to bacterial population diversity at multiple genetic levels (strain/plasmid/antimicrobial resistance genes[ARGs]) are lacking.Methods: Using systematically collected, regionally-stratified, whole genome sequencing data from a 6-month genomic survey of English E. coli/Klebsiella BSI isolates (n=1,939; NEKSUS), we used Bayesian approaches to estimate sample sizes required to capture genetic diversity at strain- (MLST, fastBAPS clusters), plasmid- and ARG-levels, using two diversity measures: ‘coverage’ (i.e. proportion of the bacterial population/plasmids/ARGs represented by features observed in the sample), and the number/proportion of unique features observed at varying surveillance sample sizes. Our method, EpiSENTRY, is available as an RShiny application.Findings: Randomly sampling 1,400 E. coli/Klebsiella BSI isolates achieved ≥80% coverage of bacterial lineages and ARGs (i.e. capture features constituting ≥80% of the population) at 95% certainty, but captured lower proportions of unique features (28% MLSTs, 59% fastBAPS clusters, 19% plasmid subcommunities, and 57% ARGs for E. coli; 49%, 87%, 50% and 83% for Klebsiella, respectively). Sample sizes required for 80% coverage of E. coli plasmid populations were higher than MLSTs (1,928[95% CrI:1,837–2,025] vs 1,351[1,230–1,472]), while for Klebsiella, estimates were lower for plasmids (1,115[1,052–1,181] vs 1,422[1,331–1,515] for MLSTs). Lower sample sizes reached 80% coverage of fastBAPS clusters and ARGs (649[575 – 727] and 27[23–30], respectively, for E. coli; 113[84–145] and 83[72-96] for Klebsiella), due to feature-specific frequency distributions. Klebsiella BSIs were more diverse than E. coli, with sampling 10.9% vs 3.2% of annual BSIs in England needed to reach 80% MLST coverage. Plasmid populations and Klebsiella MLSTs were region-specific, emphasising the need for regionally-representative sampling.Interpretation: Bayesian analysis facilitates estimates of context-specific genomic surveillance sample sizes. Cost-effective targets for coverage require further optimisation.Funding: National Institute for Health and Social Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (NIHR207397); UKHSA and NIHR Oxford Biomedical Research Centre (BRC); UKHSA PhD Funding Competition; Robertson Fellowship; cloud compute infrastructure for this work was donated by Oracle Corporation Infrastructure.

2. Metagenomes facilitate Escherichia coli strain-level profiling in human gut microbiomes, informing our understanding of colonisation diversity and the genetic features distinguishing infection from carriage. Among numerous reference-based tools for short-read metagenomic strain-level profiling, the best approach remains unclear. Here, we benchmarked six publicly available tools—PanTax, PathoScope 2.0, StrainGE, Strainify, StrainR2 and StrainScan—for their ability to detect co-existing strains of E. coli and estimate their relative abundance across real and simulated metagenomes of increasing complexity with varying reference database composition. We show that the tools varied substantially in both the number and relative abundance of strains predicted, indicating that despite being designed for the same broad purpose, they produce markedly different representations of strain composition from the same metagenomic samples. StrainR2 achieved the highest F1 score across our experiments (0.995) and successfully distinguished all strains within a complex, 10-strain spike-in mixture containing strains sharing up to 99.9% average nucleotide identity and included in a 304-strain E. coli reference database. However, when ground-truth strains were removed from the reference database to mimic the profiling of samples of unknown strain content PathoScope 2.0 and StrainGE performed strongest: both estimated phylogroup abundances accurately and frequently correctly assigned strain identities to the closest available genomic neighbours to the ground truth. Computational requirements also varied substantially (median runtime: 0.11– 68.45 h; peak memory: 2.08–351.53 GiB). Collectively, these results demonstrate that tool selection should reflect the intended application, and we provide practical guidance on the relative strengths and use cases of each approach. These findings will facilitate characterisation of E. coli strain-level diversity within short-read gut metagenomes with greater accuracy than previously possible. 3. Impact statement: Strain-level diversity within the human gut microbiome can be important for human health, with species such as Escherichia coli existing as both commensal and pathogenic strains. Most gut microbiome datasets are from short-read i.e. , Illumina, sequencing, and numerous bioinformatic tools have been developed to profile strain-level variation from these data. However, available tools have been benchmarked in various ways and are subject to author bias. This is, to our knowledge, the first independent benchmarking of six publicly available tools for E. coli strain profiling in short-read metagenomes. Using both real and simulated datasets of increasing complexity, we demonstrate tool performance varies in terms of strain detection and relative abundance estimation, highlighting that tool choice should be guided by the specific research question, as no single method performs optimally across all scenarios. This work provides an unbiased framework for tool selection, supporting more accurate and reproducible E. coli strain-level analyses in gut microbiome research from short-metagenomic data. 4. Data summary: The authors confirm all supporting data, code and protocols have been provided. Supplementary methods, tables and figures are available in the online Supplementary Material. Code for simulating metagenomes and generating E. coli spike-ins using InSilicoSeq, running tools on the simulated gut metagenomes and real-world spike-in datasets, inclusive of computational benchmarking, and R visualization and statistical analysis are available within a dedicated GitHub repository ( https://github.com/mattgal11/benchmarking_short_read_strain_profilers ). The following supplementary data files required for reproducible analyses are available on FigShare ( https://doi.org/10.6084/m9.figshare.32125474 ): ZymoBIOMICS® D6331 gut microbiome standard dataset predicted relative abundances (Zymobiomics_D6331_raw_predicted_abundance.csv) SRR13355226 mock community paired-end reads with human reads depleted (SRR13355226_depleted_R1.fastq.gz & SRR13355226_depleted_R2.fastq.gz) SRR13355226 mock community dataset predicted abundance data, with and without human read removal (SRR13355226_raw_predicted_abundance_with_and_without_human_read_r emoval.csv) Normalised per-contig relative abundances for 98 species used to construct the baseline gut microbiome profile for InSilicoSeq metagenome simulation (Normalised_relative_abundance_for_InSilicoSeq_simulated_metagenomes_ gut_microbiome_profile.csv) Simulated metagenomes dataset call types and detection metric across detection thresholds (Simulated_metagenomes_raw_call_type_assingments_and_detection_thres holds.csv) Simulated metagenomes dataset (all references) predicted relative abundances (Simulated_metagenomes_all_references_raw_predicted_abundances.csv) Simulated metagenomes dataset (all references) mapped reads for PathoScope and Strainify (all_refs_pathoscope_reads_mapped.csv & all_refs_strainify_reads_mapped.csv) Simulated metagenomes dataset (reduced reference database) predicted relative abundances (Simulated_metagenomes_K12_and_Sakai_removed_from_reference_datab ase_raw_predicted_abundance.csv) E. coli reference genomes used in real-world spike ins dataset (304_ecoli_strains_reference_genomes.csv) NCBI datasets assembly stats data frame for 304 E. coli reference genomes (abram_et_al_assembly_stats.xlsx) FastANI all-vs-all whole-genome ANI for 304 E. coli reference database (fastANI_dRep_genomes_all_vs_all_real_world_spike_ins.tsv) Real world spike-ins dataset call types and detection metric across detection thresholds (real_world_spike_ins_detection_metrics_all_thresholds.csv) Real world spike-ins dataset predicted relative abundances (real_world_spike_ins_benchmarking_results.csv) Real world spike-ins dataset (reduced reference database) predicted relative abundances (real_world_spike_ins_benchmarking_no_ground_truth_results.csv) Real world spike-ins dataset computational benchmarking results (real_world_spike_ins_comp_benchmark_DB_step.csv & real_world_spike_ins_comp_benchmark_profiling.csv & strainR2_memory_comparisons.csv) Real world spike-ins dataset paired metagenome file sizes (real_world_spike_ins_metagenome_file_sizes.csv)

Whole bacterial genome sequence reconstruction using Oxford Nanopore Technologies (‘Nanopore’) long-read-only sequencing may offer a lower-cost, higher-throughput alternative for pathogen surveillance to ‘hybrid’ assembly with recent improvements in Nanopore sequencing accuracy. We evaluated the accuracy, including plasmid reconstruction, of Nanopore long-read-only genome assemblies of Enterobacterales. We sequenced 92 genomes from clinical Enterobacterales isolates, collected in England under a national surveillance programme, with long-read Nanopore (R10.4.1, Dorado v5.0.0 super-high-accuracy basecalled) and short-read Illumina (NovaSeq) sequencing approaches. Genomes were assembled using three long-read-only (Flye, Hybracter long and Autocycler) and three hybrid assemblers (Hybracter hybrid, Unicycler normal and bold). Three polishing modalities (Medaka v2 with subsampled or un-subsampled long-reads; Polypolish+Pypolca with short-reads) were investigated. Autocycler circularised the most chromosomes [87/92 (95%)]. Plasmid sequence reconstruction was comparable among all assemblers except Flye, all recovering 90–96% of plasmids, although the ‘ground truth’ was uncertain. Flye performed worse than other assemblers on almost all metrics. Autocycler+Medaka (un-subsampled long-reads) was the most accurate long-read-only assembler/polisher combination, comparable to hybrid assemblies [median 0 (IQR: 0–0) single nucleotide variants (SNVs) and 0 (IQR: 0–1) insertions/deletions (indels) per genome; median quality value/ Q score 100 (IQR: 64–100)], with only 4/92 genome sequences having >10 SNVs/indels. Medaka polishing with un-subsampled long-reads resulted in small improvements in indels, but not SNVs for both Flye and Autocycler assemblies. Seven-locus multi-locus sequence type, antimicrobial resistance, virulence and stress gene annotation was equivalent across assembler/polisher combinations. Nanopore long-read-only bacterial genome assembly with Autocycler combined with Medaka polishing (using un-subsampled reads) is similarly accurate and possibly more complete than hybrid assemblies, representing a viable alternative for incorporating high-quality genomic data, including plasmids, into Enterobacterales surveillance.

Background: Healthcare-associated carbapenemase-producing Enterobacterales (CPE) outbreaks are a major healthcare challenge. Epidemiological studies have identified patient-level risk factors for CPE transmission, and genomic studies have highlighted high-risk’ lineages or mobile genetic elements (MGEs); however, a unified dissemination risk-prediction framework is lacking. Objectives: To synthesise available data on epidemiological, microbiological and genomic risk factors to quantify healthcare-associated CPE outbreak potential. Methods: Data: Six bibliographic databases and other sources were searched (‘carbapenemase’ AND ‘outbreak’ AND ‘MGE’; ≤31/01/24). Data were extracted on primary (patients infected/colonised) and secondary (outbreak duration/resolution, mortality) outcomes, and risk/protective factors including epidemiological, microbiological/genomic and infection control measures. Study eligibility: Studies reporting healthcare-associated CPE outbreaks involving MGE-associated IMP/KPC/NDM/OXA-48-like/VIM carbapenemases confirmed by whole-genome sequencing. Study quality: Reporting quality was assessed against the ORION checklist (random subset). Data synthesis: After descriptive summaries, multivariable linear mixed effect modelling was used to estimate associations between risk/protective factors and outbreak size. Results: 179 records (272 outbreaks) were included from 3,188 screened (41 countries, 2004-2023), affecting median 10 patients (IQR=5-27, range=2-223), and lasting 12 months (IQR=5-30, range=1 day-16 years). Data on outbreak size (primary outcome) was 99.6% complete (271/272) but more limited for secondary outcomes (29-97% complete) and risk/protective factors (70/91 factors had ≥10% missingness). 39% (107/272) of outbreaks involved MGE-mediated transmission, which is a potential underestimate as 66% (104/157) of reports used clonal outbreak definitions. The involvement of more institutions (adjusted relative outbreak size: 1.10 per institution [95% CI: 1.04-1.16]; p=0.001 ), and more Enterobacterales sequence types (1.04 per sequence type [1.01-1.08]; p=0.011 ), were associated with larger outbreaks. Reporting quality assessment (n=98 studies) revealed adequate reporting on median 11/19 relevant ORION items (IQR=8-13; range=1-18). Conclusions: Heterogenous/incomplete reporting of CPE outbreaks precludes integrated risk evaluation based on epidemiological, microbiological, and genomic factors. Systematic sampling, sequencing and epidemiological metadata reporting may strengthen data quality for quantifying healthcare-associated CPE dissemination risk.

Co-amoxiclav (amoxicillin and clavulanate) is a commonly used combination antibiotic, with resistance in Escherichia coli associated with increased mortality. The class A beta-lactamase bla _TEM-1 is often carried by resistant E. coli but exhibits high phenotypic heterogeneity, complicating genotype-phenotype predictions. We curated a dataset of n = 377 diverse E. coli isolates where the only acquired beta-lactamase was bla _TEM-1. We generated hybrid assemblies and co-amoxiclav minimum inhibitory concentrations (MICs), and bla _TEM-1 qPCR expression data for a subset (n = 67/377). We first tested whether intrinsic expression of bla _TEM-1 varied between E. coli lineages, for example, from regulatory system differences, which are challenging to genomically quantify. Using genotypic features, we built a hierarchical Bayesian model for bla _TEM-1 expression, controlling for phylogeny. Expression varied across the phylogeny, with some lineages (phylogroups B1 and C, ST12) expressing bla _TEM-1 more than others (phylogroups E and F, ST372). Next, we built a second model to predict isolate MIC from genotypic features, again controlling for phylogeny. Phylogeny alone shifted MIC past the clinical breakpoint in 19% (55/292) of isolates with greater-than-chance probability, mostly representing ST12, ST69 and ST127. A third causal model confirmed that phylogenetic influence on bla _TEM-1 expression drove variation in MIC. We speculate that intergenic variation underlies this effect.

BACKGROUND: Ciprofloxacin resistant Klebsiella pneumoniae is common or emerging in many geographies, and knowledge of local resistance rates is important for empirical therapy. Whilst there are known K. pneumoniae ciprofloxacin resistance determinants, there is a lack of systematic data on the effect of determinants, alone and in combination, and there are no publicly accessible tools for predicting resistance from whole genome sequence data. METHODS: The KlebNET-GSP AMR Genotype-Phenotype Group aggregated a matched genotype-phenotype dataset of n=12,167 K. pneumoniae species complex ( Kp SC) isolates from 27 countries between 2001-2021. We developed a rules-based classifier to predict ciprofloxacin resistance by categorizing the number of quinolone resistance determining regions mutations in gyrA and parC , the number of plasmid-mediated quinolone resistance genes, and the presence/absence of aac(6ʹ)-Ib-cr (which can acetylate ciprofloxacin). Predictive performance was assessed using the discovery dataset, for which we re-phenotyped discrepant isolates; and validated using externally contributed datasets (n=7,030 Kp SC isolates). RESULTS: The rules-based classifier predicted R vs S/I with categorical agreement, sensitivity, and specificity >96%, and major/very major error rates Kp SC sources (human, animal, other), species, and intra-species lineages. External validation of the classifier yielded overall 93.12% categorical agreement [95% confidence interval (CI), 92.50-93.74%], 8.65% major errors [95% CI, 7.34-9.97%], and 6.20% very major errors [95% CI, 5.51-6.90%]. We implemented the classifier in Kleborate, a command-line tool that is integrated into the Pathogenwatch web platform. Using this to assess the global distribution of ciprofloxacin resistance determinants in Kp SC genomes available in Pathogenwatch (n=31,319, from 109 countries between years 2000-2023), we observed a significant positive association between national quinolone consumption rates and predicted ciprofloxacin resistance (R ² =0.20, p=0.004). CONCLUSIONS Ciprofloxacin resistance phenotypes can be reasonably predicted from genotypes, which is sufficient for informing surveillance. However, unexplained resistance remains and accuracy is insufficient for clinical applications. We demonstrate the value of aggregating genotype-phenotype data to explore resistance mechanisms and develop predictors, but highlight complexities in combining phenotype data from different assays and standards.

Whole bacterial genome sequence reconstruction using Oxford Nanopore Technologies (“Nanopore”) long-read only sequencing may offer a lower-cost, higher-throughput alternative for pathogen surveillance to ‘hybrid’ assembly with recent improvements in Nanopore sequencing accuracy. We evaluated the accuracy, including plasmid reconstruction, of Nanopore long-read only genome assemblies of Enterobacterales. We sequenced 92 genomes from clinical Enterobacterales isolates, collected in England under a national surveillance program, with long-read Nanopore (R10.4.1, Dorado v5.0.0 super-high-accuracy basecalled) and short-read Illumina (NovaSeq) sequencing approaches. Genomes were assembled using three long-read only (Flye; Hybracter long; Autocycler), and three hybrid assemblers (Hybracter hybrid; Unicycler normal; bold). Three polishing modalities (Medaka v2 with subsampled or un-subsampled long-reads; Polypolish + Pypolca with short-reads) were investigated. Autocycler circularised the most chromosomes (87/92 [95%]). Plasmid sequence reconstruction was comparable between all assemblers except Flye, all recovering 90-96% of plasmids, although the ‘ground truth’ was uncertain. Flye performed worse than other assemblers on almost all metrics. Autocycler + Medaka (un-subsampled long-reads) was the most accurate long-read only assembler/polisher combination, comparable to hybrid assemblies (median 0 [IQR:0-0] SNPs and 0 [IQR:0-1] indels per genome; quality value/Q score, 100 [IQR: 64-100]), with only 4/92 genome sequences having >10 SNPs/indels. Medaka polishing with un-subsampled long-reads resulted in small improvements in indels but not SNPs for both Flye and Autocycler assemblies. Seven-locus MLST, antimicrobial resistance, virulence, and stress gene annotation was equivalent across assembler/polisher combinations. Nanopore long-read only bacterial genome assembly with Autocycler combined with Medaka polishing (using un-subsampled reads) is similarly accurate and possibly more complete than hybrid assemblies, representing a viable alternative for incorporating high-quality genomic data, including plasmids, into Enterobacterales surveillance. Data Summary: Nanopore long-reads and Illumina short-reads from the 92 Enterobacterales isolates from this study have been uploaded to ENA (BioProject accession: PRJEB93885). Code for the Nextflow assembly pipeline, downstream analysis scripts, and R statistical analysis scripts are available on GitHub ( https://github.com/oxfordmmm/NEKSUS_ont_hybrid_assembly_comparison ). The following supplementary data tables are available on FigShare ( https://figshare.com/account/home#/projects/253775 ): ENA Sample accessions and sample metadata (accessions_and_metadata.csv) Seqkit stats summaries of the Illumina and Nanopore reads (raw_qc_sup.cav) Summary of assembly contig features (contigs_summary_sup_cleaned.csv) Pairwise mash distances between contigs (mash_cleaned.csv) Plasmids matching across different assemblers compared to the Hybracter (hybrid) and manually-curated reference sets (plasmids_match_hybracter_mash.csv; plasmids_match_manual_mash.csv, respectively) Seven-locus multi-locus sequence type annotation (mlst_cleaned.csv) CheckM2 summaries of assemblies (checkm2_cleaned.csv) Nucleotide-level accuracy of assemblies (SNP, Indels, and Quality value compared to short-read mapping; assembly_nucleotide_accuracy_cleaned.csv) Bakta annotation (bakta_by_contig_cleaned.csv) AMRFinderPlus annotations of contigs (amrfinder_plus_cleaned.csv) MOB-suite annotation summaries of contigs (mobsuite_cleaned.csv) Impact Statement: Nanopore long-reads have historically been too error-prone to use alone for accurate bacterial genome assembly, necessitating additional Illumina short-reads to achieve structurally complete and accurate ‘hybrid’ genome assemblies for public health surveillance. This increases cost and complexity. Previous studies have shown that recent improvements in Nanopore chemistry (R10.4.1 flowcell) and basecalling (super-high accuracy) allow high-quality long-read only assemblies on a small number of laboratory reference strains. This is the first evaluation, to our knowledge, to assess Nanopore long-read only genome assembly compared with hybrid assembly on a large number of clinical isolates. In addition, this is the first large-scale evaluation of the recently released automated consensus long-read assembly tool, Autocycler. We show that Autocycler long-read only assemblies are more structurally complete for chromosomal sequences, while reconstructing a similar number of plasmids to other long-read and hybrid assemblers. Most long-read polished, Autocycler-assembled genome sequences have 0 errors (median: 0 SNPs/indels) relative to a short-read polished (hybrid) Autocycler assemblies, enabling accurate annotation of key genes.