Enterobacterales plasmid sharing amongst human bloodstream infections, livestock, wastewater, and waterway niches in Oxfordshire, UK

Enterobacterales are found both in human niches (e.g. hospital patients [Linh et al., 2021; Kraftova et al., 2021] and wastewater [Cahill et al., 2019]) and in non-human niches (e.g. livestock-associated [Subramanya et al., 2021; AbuOun et al., 2021] and waterways [Díaz-Gavidia et al., 2021]). In recent decades, widespread carriage of antimicrobial resistance (AMR) genes has complicated the treatment of Enterobacterales infections (Buchy et al., 2020; Ruppé et al., 2020). The dissemination of AMR genes between Enterobacterales occurs in a ‘Russian-doll’-style hierarchy of nested, mobilisable genetic structures (Sheppard et al., 2016): genes not only move between bacterial hosts on mobilisable or conjugative plasmids but can also be transferred within and between plasmids and chromosomes by smaller mobile genetic elements (MGEs) such as insertion sequences (Che et al., 2021; Shaw et al., 2021). Despite gene gain/loss events, many plasmids have been shown to have a persistent structure encoding replication and transfer machinery (Orlek et al., 2017b; Matlock et al., 2021a).

Many plasmids can transfer between species and are seen across different niches (Redondo-Salvo et al., 2020) but the extent to which they are shared between human and non-human niches remains poorly understood. Previous studies investigating this topic have often been limited in size given the genetic diversity in these niches (Mounsey et al., 2021), and/or restricted to single species (Ludden et al., 2019) or drug-resistant isolates (Shen et al., 2020), or are systematic studies, pooling geographically/temporally disparate samples (Cherak et al., 2021; Bastidas-Caldes et al., 2022). Further, fragmented genome assemblies in many cases make recovering complete plasmids, and other MGEs, impossible (Hilpert et al., 2021).

Instances of cross-niche transfer of plasmids are well described, but the frequency of such events is poorly characterised. There are multiple instances where AMR genes have emerged from non-human niches and subsequently become major clinical problems in human Enterobacterales infections, highlighting the relevance of inter-niche transfer in AMR gene dissemination (e.g. bla_CTX-M, mcr-1 [Wang et al., 2018] and bla_NDM-1 [Sekizuka et al., 2011]). In general, environmental bacteria are believed to be the original source of AMR genes that eventually become prevalent in clinical settings after transfer into clinical pathogens. However, we know little about natural rates of inter-niche transfer beyond these high-profile examples. It remains unclear how plasmids evolve within natural populations, meaning we understand little about the wider context in which AMR genes emerge and disseminate.

To explore Enterobacterales plasmid diversity and sharing across niches in a geographically and temporally restricted context, we studied hybrid assemblies (i.e. using both long and short reads) of large Enterobacterales isolate collections in Oxfordshire, UK, from (i) human bloodstream infections (BSIs; 2008–2018), (ii) livestock-associated sources (faeces from cattle, pigs, poultry, sheep; surrounding environmental soils; all 2017 except poultry 2019–2020), and (iii) wastewater treatment work (WwTW)-associated sources (influent, effluent, waterways upstream/downstream of effluent outlets; Oxfordshire, 2017).

Our dataset of n=3697 plasmids from n=1458 isolates (Figure 1a, Table 1) contained bacteria from human BSIs (n=1880 plasmids from n=738 isolates), livestock-associated sources (cattle, pig, poultry, and sheep faeces, soils surrounding livestock farms; n=1155 plasmids from n=512 isolates), and from wastewater treatment works (WwTW)-associated sources (influent, effluent, waterways upstream/downstream of effluent outlets; n=662 plasmids from n=208 isolates). All sampling sites were <60 km apart (Figure 1b) and timeframes overlapped (2008–2020; Figure 1c). Isolates had a median 2 plasmids (IQR = 1–4, range = 0–16). Major Enterobacterales genera represented included: n=1044 Escherichia, n=212 Klebsiella, n=125 Citrobacter, and n=63 Enterobacter.

Figure 1

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Sampling niche was strongly associated with isolate genus (Fisher’s test, p-value <0.001; Table 2). Klebsiella isolates were disproportionately derived from BSI versus other niches (22% [161/738] Klebsiella from BSI versus 8% [51/669] from other niches). Citrobacter and Enterobacter were disproportionately derived from WwTW-associated versus other niches (51% [107/208] Citrobacter and Enterobacter from WwTW versus 6% [81/1250] Citrobacter and Enterobacter from other niches). Chromosomal Mash trees (see Materials and methods) for the two most common species in the dataset, Escherichia coli (72% [1,044/1,458]; see Appendix 1—figure 1) and Klebsiella pneumoniae (11% [163/1458]; Appendix 1—figure 2), demonstrated intermixing of human and non-human isolates within clades, consistent with species lineages not being structured by niche.

We contextualised our plasmids within known plasmid diversity using ‘plasmid taxonomic units’ (PTUs; using COPLA, see Materials and methods), designed to be equivalent to a plasmid ‘species’. We found 32% (1193/3697) of plasmids were unclassified, highlighting the substantial plasmid diversity within this geographically restricted dataset, whilst the remaining 68% (2,504/3,697) were assigned a PTU. In total, we found n=67 known PTUs, containing a median 9 plasmids (IQR = 4–30, range = 1–556), with the largest PTU-F_E (556/2,504), corresponding to F-type Escherichia plasmids.

Near-identical plasmid sharing observed between human and livestock-associated Enterobacterales

We screened for near-identical plasmids shared across isolates by grouping those with a low Mash distance (d<0.0001) and highly similar lengths (longest plasmid ≤1% longer than shorter plasmids; note that this near-identical threshold becomes an identical threshold for extremely small plasmids; see Materials and methods). We found n=225 near-identical groups of ≥2 members, recruiting 19% (712/3697) plasmids. Bootstrapping accumulation curves for near-identical plasmid groups and singletons per the number of isolates (ACs; see Materials and methods), we revealed a highly ‘open’ accumulation (Heap’s parameter γ=0.97, Appendix 1—figure 3), suggesting further isolate sampling would detect more unique plasmids approximately linearly. Restricted to BSI/livestock-associated isolates alone, we found similar curves for both niches (BSI γ=0.98, livestock-associated γ=0.94), suggesting they had similar levels of plasmid diversity.

The most common group size of near-identical plasmids were pairs, representing 71% (159/225) of groups (group size IQR = 2–3, range = 2–32). Plasmid members of near-identical groups represented multiple bacterial host STs (25% [56/225]), species (4% [9/225]), and genera (4% [9/225]), consistent with plasmids capable of inter-lineage/species/genus transfer. Further, 8% (17/225) of near-identical groups contained plasmids found across human BSIs and at least one other sampling niche (livestock-associated/WwTW-associated), suggesting inter-niche transfer (i.e. ‘cross-niche groups’; Figure 2a). Within cross-niche groups, n=3/17 contained plasmids from multiple bacterial species (Figure 2b), and most consisted of conjugative plasmids (n=5/17 conjugative, n=9/17 mobilisable, n=3/17 non-mobilisable; Figure 2c). AMR genes were carried by plasmids in n=6/17 cross-niche groups (Figure 2d), with n=5/6 of these groups containing at least one beta-lactamase protein encoding gene.

Figure 2

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Sharing between BSI and livestock-associated isolates was supported by 8/17 cross-niche groups (n=45 plasmids). Of these, n=3/8 groups contained BSI/sheep plasmids: one group contained mobilisable Col-type plasmids, the remaining two groups contained conjugative FIB-type plasmids, of which one group contained plasmids carrying the AMR genes aph(3'')-Ib, aph(6)-Id, bla_TEM-1, dfrA5, sul2, and the other group contained plasmids carrying the MDR efflux pump protein robA (see Materials and methods). A further n=2/8 groups contained BSI/pig mobilisable Col-type plasmids, of which one group other carried the AMR genes aph(3'')-Ib, aph(6)-Id, dfrA14, and sul2. Lastly, n=1/8 groups contained BSI/poultry non-mobilisable Col-type plasmids, n=1/8 contained BSI/pig/poultry/influent non-mobilisable Col-type plasmids, and n=1/8 contained BSI/cattle/pig/poultry/influent mobilisable Col-type plasmids.

Plasmid clustering reveals a diverse but intertwined population structure across niches

Near-identical plasmids shared across niches are a likely signature of recent transfer events, but we also wanted to examine the wider plasmid population structure. We therefore agnostically clustered all plasmids based on alignment-free sequence similarity (clusters were groups of n≥3 plasmids; see Materials and methods and Appendix 1—figures 4 and 5). We defined n=247 plasmid clusters with median 5 members (IQR = 3–10, range = 3–123) recruiting 71% (2627/3697) of the plasmids. The remainder were either singletons (i.e. single, unconnected plasmids; 19% [718/3697]) or doubletons (i.e. pairs of connected plasmids; 10% [352/3697]). By bootstrapping b=1000 ACs for plasmid clusters, doubletons, and singletons found against number of isolates sampled (Appendix 1—figure 6; see Materials and methods), we estimated that the rarefaction curve had a Heap’s parameter γ=0.75, suggesting further isolate sampling would likely detect more plasmid diversity and clusters.

Of the plasmid clusters, n=69/247 (28%) had at least 10 members, representing 50% (1832/3697) of all plasmids (Figure 3a). 122/247 (49%) clusters contained BSI plasmids and plasmids from at least one other niche. This included 73/247 (30%) of clusters with both BSI and livestock-associated plasmids, representing n=38 unique plasmid replicon haplotypes (i.e. combinations of replication proteins) of which only 24% (9/38) were Col-type plasmids, which are often well conserved and carry few genes (Rozwandowicz et al., 2018). 72/247 (29%) of clusters contained both BSI and influent/effluent/downstream plasmids, reflecting a route of Enterobacterales dissemination into waterways. In contrast, only 18/247 (7%) of clusters contained both BSI and upstream waterway plasmids, of which most (13/18 [72%]) also contained influent/effluent/downstream plasmids.

Figure 3

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Overall, plasmid clusters scored high homogeneity (h) but low completeness (c) with respect to biological and ecological characteristics (non-putative PTUs [h=0.99, c=0.66]; replicon haplotype [h=0.92, c=0.69]; bacterial host sequence type (ST) [h=0.84, c=0.14] in Figure 3b; predicted mobility [h=0.93, c=0.20] in Figure 3c). This indicated that clustered plasmids often had similar characteristics, but the same characteristics were often observed in multiple clusters. When scoring plasmid clusters against broad sampling niche (BSI, livestock-associated, or WwTW-associated; Figure 3a), homogeneity was low (h=0.12, c=0.61), indicating mixed clusters. The imperfect homogeneity is to be anticipated as replicon haplotypes and mobilities can vary within plasmid families, and plasmid families can have diverse host ranges (Redondo-Salvo et al., 2020).

Plasmids carrying AMR genes were found in 21% (52/247) of the plasmid clusters (i.e. ‘antimicrobial resistance gene [ARG]-carrying clusters’), representing n=550 plasmids (Figure 3d). Of the ARG-carrying clusters, 92% (48/52) contained at least one beta-lactamase-carrying plasmid (n=437 plasmids in total). AMR genes were present in a median 67% of ARG-carrying cluster members (IQR = 28–100%, range = 3–100%). This highlights that AMR genes are not necessarily widespread on genetically similar plasmids and can be potentially acquired multiple different times through the activity of smaller MGEs (e.g. transposons) or recombination. For example, cluster 12 was a group of n=42 conjugative, PTU-F_E plasmids found in BSI, wastewater, and waterways. Of these, 31% (13/42) carried the AMR gene bla_TEM-1, and in a range of genetic contexts: n=9/13 bla_TEM-1 genes were found within Tn3 and n=4/13 were carried without a transposase, of which n=2/4 were found with the additional AMR genes aph(6)-Id, aph(3’’)-Ib, and sul2. F-type plasmids were the most common AMR gene carriers (61% [337/550] of all ARG-carrying cluster plasmids), underlining the known role of F-type plasmids in AMR gene dissemination (Matlock et al., 2021a).

The beta-lactamase bla_TEM-1 was the most common AMR gene detected (8% of total AMR gene annotations [424/5402]; see Materials and methods). In terms of sequence length (bp), plasmids made up 3.1% of the overall dataset but 13.8% of the bla_TEM-1 -carrying proportion. Of the plasmid clusters, 16% (39/247) carried bla_TEM-1, and of these nine clusters were seen in human BSI and at least one other niche. Plasmid clusters either variably or always carrying bla_TEM-1 were strongly associated with BSI (p<0.01, Chi-squared test X²=8.19, 33/161 of BSI clusters containing bla_TEM-1 vs. 5/86 for non-BSI clusters) and carried a higher number of other AMR genes (p<0.01, Wilcoxon text of bla_TEM-1-plasmid clusters vs. others; see Appendix 1—figure 7).

An intertwined ecology of plasmids across human and livestock-associated niches

Plasmids can change their genetic content, particularly when subject to new selective pressures (Rodríguez-Beltrán et al., 2021; Pesesky et al., 2019). Many plasmids have a structure with a ‘backbone’ of conserved core genes and a ‘cargo’ of variable accessory genes (Orlek et al., 2017b; Matlock et al., 2021a; Coluzzi et al., 2022). We wanted to explore evidence for cross-niche plasmids with minimal mutational evolution in a shared backbone (compatible withapproximately years of evolutionary separation) but variable accessory gene repertoires.

We first conducted a pangenome-style analysis (see Materials and methods) on the n=69/247 plasmid clusters with at least 10 members. For each cluster, we determined ‘core’ (genes found in ≥95% of plasmids) and ‘accessory’ gene repertoires (found in <95% of plasmids). Within clusters, we found median 9 core genes (IQR = 4–53, range = 0–219), and median 9 accessory genes (IQR = 3–145, range = 0–801) (Figure 3e). Core genes comprised a median proportion 42.2% of the total pangenome sizes (IQR = 20.9–66.7%). At an individual plasmid level, core genes shared by a cluster comprised a median 62.5% of each plasmid’s gene repertoire (IQR = 37.4–83.3%; Figure 3e). Putatively conjugative plasmids carried a significantly higher proportion of accessory genes in their repertoires than mobilisable/non-mobilisable plasmids (Kruskal-Wallis test followed by Dunn’s test [H(2)=193.01, p-value <0.001] ).

Using multiple sequence alignments of the core genes within each cluster, we produced maximum likelihood phylogenies (see Supplementary file 1 and Materials and methods). For this step, we only considered the n=62/69 clusters where each plasmid had ≥1 core gene. With the n=27/62 clusters that contained both BSI and livestock-associated plasmids, we measured the phylogenetic signal for plasmid sampling niche using Fritz and Purvis’ D (see Supplementary file 2 and Materials and methods). The analysis indicated that the evolutionary history of plasmid clusters is neither strictly segregated by sampling niche nor completely intermixed, but something intermediate.

Alongside the core-gene phylogenies, we generated gene repertoire heatmaps (example cluster 2 in Figure 4a–b; all clusters and heatmaps in Supplementary file 1). By visualising the genes in a consensus synteny order (see Materials and methods), the putative backbone within each plasmid cluster is shown alongside its accessory gene and transposase repertoire. This highlights how plasmids might gain/lose accessory functions within a persistent backbone. Log-transformed linear regression revealed a significant relationship between Jaccard distance of accessory genes presence against core-gene cophenetic distance (y=0.080log(x)+0.978, R²=0.47, F(1,52988)=4.75e4, p-value <0.001; see Appendix 1—figure 8 and Materials and methods).

Figure 4

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Sharing of plasmids between different niches is normally focused on those carrying AMR genes that are of particular current clinical concern, such as extended-spectrum beta-lactamase (ESBL) or carbapenemase genes, meaning we lack information on the vast ‘denominator’ of background plasmid sharing, and on the dissemination of other AMR genes which are now widespread in clinical isolates and from which important insights might be gained. By analysing a dataset of n=3697 systematically collected Enterobacterales plasmids sampled from human BSI, livestock- and WwTW-associated sources in a geographically and temporally restricted context, we found evidence supporting significant plasmid dissemination across niches, putting those which carry AMR genes of current major clinical concern into context. We found 225 instances of shared, near-identical plasmid groups, 25% of which were found across multiple bacterial STs, 4% across multiple bacterial species, and 8% in both human BSI and ≥1 non-BSI niche. Beyond this near-identical sharing, we analysed ‘clusters’ of plasmids and found that 73/247 clusters contained plasmids seen in both human BSIs and other contexts. Approximately a fifth (52/247) of plasmid clusters contained plasmids carrying AMR genes (n=550 plasmids). Our results suggest the need for broad, unselected, and detailed sampling frames to fully understand plasmid diversity and evolution, and to evaluate the ‘One Health’ risk of AMR associated with plasmid sharing across niches.

Whilst many plasmid clusters were strongly structured by host phylogeny and isolate source, some plasmids from human BSIs were highly genetically related to those in other niches, including livestock. However, not all of these carried AMR genes. Our results highlight the potential routes for transfer that exist through similar plasmids. However, recovering these instances of putative sharing is a sampling challenge. Accumulation curve analyses suggested increasing the size of our dataset would have led to further near-identical matches at an approximately linear rate, meaning even a dataset of this size captures only a small fraction of the true extent of plasmid sharing between human clinical and other non-human/clinical niches. This presents a challenge for designing appropriately powered studies. Had we only sampled n=100 livestock-associated isolates (i.e. around 20% of our actual sample), there was only a 39% chance that we would have detected ≥5 matches with BSI plasmids (Appendix 1—figure 9).

Understanding the evolutionary history, distribution, and epidemiology of well-known genes in environmental plasmids may offer insights into the future trajectories of more recently emerged genes. For example, the first plasmid-encoded beta-lactamase to be described was bla_TEM-1, identified in 1965 in an E. coli isolate in Greece (Datta and Kontomichalou, 1965) and now widely prevalent in Enterobacterales (Bush and Bradford, 2020). bla_TEM-1 has a narrow spectrum of activity and is now less clinically concerning than newer genes which mediate broad-spectrum resistance, but in our dataset bla_TEM-1 was strongly associated with plasmid clusters seen in BSI and with the carriage of other AMR genes. bla_TEM-1 may continue to play an important role in the spread of AMR-carrying plasmids which can transfer recently emerged genes, and similarities in its association with plasmids and other smaller transposable MGEs may reflect the future trajectory of other AMR genes of more recent clinical concern such as ESBLs and carbapenemases.

Given that plasmids observed in BSI isolates represent small proportion of human Enterobacterales diversity, many more sharing events may occur in the human gut (Forster et al., 2019) which we only sampled incompletely using wastewater influent as a proxy. The human colon contains around 10¹⁴ bacteria (Sender et al., 2016), with large ranges of Enterobacteriaceae abundance. Further, even small numbers of across-niche sharing events, such as transfer events of important AMR genes from species-to-species or niche-to-niche, may have significant clinical implications, as has been seen with several important AMR genes globally. Future studies need to carefully consider the limitations of sampling frames in detecting any genetic overlap, given both substantial diversity and the effects of niches and geography (Shaw et al., 2021; Hanage, 2019).

By examining plasmid relatedness compared to bacterial host relatedness in E. coli, we demonstrated that plasmids seen across different niches are not necessarily associated with clonal lineages. Using a pangenome-style analysis, we showed that plasmids can share sets of near-identical core genes alongside diverse accessory gene repertoires. While plasmids with more distantly related core genes tended to have dissimilar accessory gene content, plasmids with more closely related core genes shared a wide range of accessory gene content. This would be consistent with a hypothesis of persistent ‘backbone’ structures gaining and losing accessory functions as they move between hosts and niches. We suggest that this mode of transfer might be worth considering. Evolutionary models for plasmids which can accommodate well-conserved backbone evolution alongside accessory structural changes and gain/loss events are urgently needed. Estimating plasmid evolutionary rates remains a challenge, with little known about appropriate values for mutation rates in plasmids, and even less for non-mutational processes such as gene gain/loss.

Our study had several limitations. Our non-BSI isolates were not as temporally varied as the BSI isolates, meaning we could not fully explore temporal evolution. Although we evaluated four bacterial genera, 72% (1044/1458) of our sequenced isolates were E. coli, and so our analyses and findings are particularly focused on this species. Additionally, we did not sample livestock-associated niches densely enough to explore individual livestock types (cattle/pig/poultry/sheep) sharing plasmids with BSI isolates (see Appendix 1—figure 9). Isolate-based methodologies are limited in evaluating the true diversity of the niches sampled; composite approaches including metagenomics might shed additional insight in future studies. Further, the exact source of an isolate is poorly defined for wastewater/waterway isolates as they act as a confluence of multiple sources, although they represent important niches in their own right. We only analysed plasmids from complete genomes, i.e., where the chromosome and all plasmids were circularised, meaning we disregarded ~23% and ~33% of BSI and non-BSI assemblies, respectively. The exclusive use of complete assemblies was to ensure full plasmid sequences could be examined in their full genomic context. We only focused on plasmids as horizontally transmissible elements here; detailed study of other smaller MGEs across-niches would represent interesting future work. We have also investigated a limited subset of Enterobacterales: plasmid sharing likely extends to other bacterial hosts not investigated here. Lastly, our isolate culture methods for livestock-associated samples may not have been as sensitive for the identification of Klebsiella spp. as for other Enterobacterales such as Escherichia, as we did not use enrichment and selective culture on Simmons citrate agar with inositol (Rodrigues et al., 2022). This may have limited our ability to study the epidemiology of livestock Klebsiella plasmids.

In conclusion, this study presents to our knowledge the largest evaluation of systematically collected Enterobacterales plasmids across human and non-human niches within a geographically and temporally restricted context. Near-identical plasmids can be found in different niches, pointing to putative dissemination, although this dynamic likely varies by plasmid cluster; the proportion of near-identical plasmid groups that were found across niches was 8% (17/225) and influenced by sample size. We demonstrate a likely intertwined ecology of plasmids across human and non-human niches, where different plasmid clusters are variably but incompletely structured 1475 and putative ‘backbone’ plasmid structures can rapidly gain and lose accessory genes following cross-niche spread. Future ‘One Health’ studies require dense and unselected sampling, and complete/near-complete plasmid reconstruction, to appropriately understand plasmid epidemiology across niches.

Livestock-associated isolates n=247 Enterobacterales isolates from farm-proximate soils and poultry faeces (n=19 farms; n=5 cattle, n=4 pig, n=5 poultry, n=5 sheep) were collected and sequenced for this study in 2017–2020. DNA extraction and sequencing was performed as in Shaw et al., 2021. Genomes were hybrid assemblies reconstructed using Unicycler (Wick et al., 2017) (v. 0.4.4; default hybrid assembly parameters except --min_component_size 500 --min_dead_end_size 500). Only complete assemblies (plasmids and chromosomes) were considered (n=162/247).

REHAB reads and assemblies and assemblies can be found in the NCBI BioProject PRJNA605147. BSI reads and assemblies can be found at https://doi.org/10.25452/figshare.plus.24573268. Analysis scripts can be found in the GitHub repository https://github.com/wtmatlock/oxfordshire-overlap (copy archived at Matlock, 2023).

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Author details

1. William Matlock

   Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Samuel Lipworth

   For correspondence

   william.matlock@ndm.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5608-0423

2. Samuel Lipworth

   1. Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
   2. Oxford University Hospitals NHS Trust, Oxford, United Kingdom

   Contribution

   Data curation, Formal analysis

   Contributed equally with

   William Matlock

   Competing interests

   No competing interests declared

3. Kevin K Chau

   Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Manal AbuOun

   Animal and Plant Health Agency, Addlestone, United Kingdom

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

5. Leanne Barker

   Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

6. James Kavanagh

   Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Monique Andersson

   Oxford University Hospitals NHS Trust, Oxford, United Kingdom

   Contribution

   Project administration

   Competing interests

   No competing interests declared

8. Sarah Oakley

   Oxford University Hospitals NHS Trust, Oxford, United Kingdom

   Contribution

   Project administration

   Competing interests

   No competing interests declared

9. Marcus Morgan

   Oxford University Hospitals NHS Trust, Oxford, United Kingdom

   Contribution

   Project administration

   Competing interests

   No competing interests declared

10. Derrick W Crook

    1. Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    2. Oxford University Hospitals NHS Trust, Oxford, United Kingdom
    3. NIHR Biomedical Research Centre, Oxford, United Kingdom

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0590-2850

11. Daniel S Read

    Centre for Ecology and Hydrology, Wallingford, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Supervision

    Competing interests

    No competing interests declared

12. Muna Anjum

    Animal and Plant Health Agency, Addlestone, United Kingdom

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

13. Liam P Shaw

    Department of Biology, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Writing – original draft, Writing – review and editing

    Contributed equally with

    Nicole Stoesser

    Competing interests

    No competing interests declared

14. Nicole Stoesser

    1. Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    2. Oxford University Hospitals NHS Trust, Oxford, United Kingdom
    3. NIHR Biomedical Research Centre, Oxford, United Kingdom

    Contribution

    Conceptualization, Supervision, Writing – original draft, Writing – review and editing

    Contributed equally with

    Liam P Shaw

    For correspondence

    nicole.stoesser@ndm.ox.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4508-7969

15. REHAB Consortium

    Contribution

    Conceptualization, Funding acquisition, Project administration, Resources, Supervision

    Competing interests

    No competing interests declared

    1. Manal AbuOun, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    2. Muna F Anjum, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    3. Mark J Bailey, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom
    4. Brett H, Thames Water Utilities, Reading, United Kingdom
    5. Mike J Bowes, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom
    6. Kevin K Chau, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    7. Derrick W Crook, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    8. Nicola de Maio, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    9. Nicholas Duggett, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    10. Daniel J Wilson, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    11. Daniel Gilson, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    12. H Soon Gweon, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom
    13. Alasdair Hubbard, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
    14. Sarah J Hoosdally, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    15. William Matlock, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    16. James Kavanagh, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    17. Hannah Jones, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    18. Timothy EA Peto, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    19. Daniel S Read, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom
    20. Robert Sebra, Icahn Institute of Data Science and Genomic Technology, Mt Sinai, New York, United States
    21. Liam P Shaw, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    22. Anna E Sheppard, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    23. Richard P Smith, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    24. Emma Stubberfield, Animal and Plant Health Agency,Weybridge, Addlestone, United Kingdom
    25. Nicole Stoesser, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    26. Jeremy Swann, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    27. A Sarah Walker, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    28. Neil Woodford, Antimicrobial Resistance and Healthcare Associated Infections (AMRHAI) Reference Unit, National Infection Service, Public Health England, London, United Kingdom

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