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Feil, E. J.

Publications and source records attributed to Feil, E. J..

17 recordsLinked to original sources
bioRxiv2025

Ciprofloxacin resistance in Klebsiella pneumoniae: phenotype prediction from genotype and global distribution of resistance determinants

BACKGROUNDCiprofloxacin 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. METHODSThe KlebNET-GSP AMR Genotype-Phenotype Group aggregated a matched genotype-phenotype dataset of n=12,167 K. pneumoniae species complex (KpSC) 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(6')-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 KpSC isolates). RESULTSThe rules-based classifier predicted R vs S/I with categorical agreement, sensitivity, and specificity >96%, and major/very major error rates <4%. Performance was similar across diverse KpSC 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 KpSC 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 (R2=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.

genomics↗
bioRxiv2025

Capsular specificity in temperate phages of Klebsiella pneumoniae is driven by diverse receptor-binding enzymes

In bacteriophages infecting Klebsiella pneumoniae, capsule specificity is a major determinant of host range due to the presence of capsule-specific depolymerases. Yet for temperate phages, the genetic and functional basis of this specificity remains less well understood. Depolymerases appear unexpectedly rare in prophage genomes, raising unresolved questions about which prophage genes mediate capsule tropism, whether this apparent scarcity reflects biological or ecological differences versus annotation limits, and whether prophage-encoded receptor-binding proteins (RBPs) are functionally active. To address these questions, we analysed 3,900 Klebsiella genomes from diverse ecological niches to identify prophage-encoded proteins mediating capsule tropism. We conducted a genome-wide association study (GWAS) correlating prophage protein clusters (from 8,105 prophages) with confidently assigned bacterial K-loci. GWAS identified high-confidence predictors for 16 out of 35 most diverse K-loci, of which 14 were receptor-binding proteins (RBPs) belonging to classical depolymerases (n = 6), SGNH hydrolases which deacetylate polysaccharides (n = 6), and structurally novel RBPs (n = 2). Overall, we predicted K-locus specificity for 26 putative depolymerases, of which 12 were deemed as strong predictions against 10 K-loci. In parallel, we attempted recombinant production of 50 putative depolymerases selected from 469 candidate proteins identified in prophages from a representative subset of 99 bacterial isolates, together with an additional 10 depolymerases selected based on GWAS predictions. All recombinant proteins were tested against a Klebsiella reference panel of 119 K-types. Of the 50 manually chosen putative depolymerases, 34 failed to yield detectable recombinant expression, a pattern unlikely to be explained by degraded or defective prophages. Of the 14 active enzymes, 5 targeted a K-locus different from that of their bacterial host, and enzyme specificity could not always be reliably inferred from sequence similarity or structural homology. Comparison of GWAS predictions with experimental validation results revealed that 10 of the 12 strongest GWAS predictors were confirmed experimentally, while 2 produced soluble protein but showed no detectable activity against the tested K-types. Together, these results highlight the intrinsic difficulty of predicting activity and capsule specificity of prophage-encoded RBPs from genomic information alone. Finally, analysis of 4,598 high-completeness prophages revealed that SGNH-domain hydrolases are among the most prevalent enzymatic domains in prophage RBPs. Two SGNH-domain RBPs identified by GWAS were experimentally confirmed as active esterases, supporting capsule deacetylation as a widespread alternative to polysaccharide depolymerisation in temperate phages. Our findings reveal that Klebsiella prophages encode structurally diverse RBPs, suggesting temperate phages may rely not only on depolymerisation but also on capsule modification--such as deacetylation--for infection. This also implies that capsule diversity in K. pneumoniae may be substantially underestimated, with implications for phage specificity, competition and vaccine design.

microbiology↗
bioRxiv2025

amr.watch - monitoring antimicrobial resistance trends from global genomics data

BackgroundWhole genome sequencing (WGS) is increasingly supporting routine pathogen surveillance at local and national levels, providing comparable data that can inform on the emergence and spread of antimicrobial resistance (AMR) globally. However, the potential for shared WGS data to guide interventions around AMR remains under-exploited, in part due to challenges in collating and transforming the growing volumes of data into timely insights. We present an interactive platform, amr.watch (https://amr.watch), that enables interrogation of AMR trends from public WGS data on an ongoing basis to support research and policy. MethodsThe amr.watch platform incorporates, analyses and visualises high-quality WGS data from WHO-defined priority bacterial pathogens. Analytics are performed using community-standard methods with bespoke species-specific curation of AMR mechanisms. FindingsBy 31 March 2025, amr.watch included data from 620,700 pathogen genomes with geotemporal information, with highly variable representation of different species and geographic regions. By integrating WGS data with sampling information, amr.watch enables users to assess geotemporal trends among genotypic variants (e.g. sequence types) and AMR mechanisms, with implications for interventions including antimicrobial prescribing and drug and vaccine development. Interpretationamr.watch is an information platform for scientists and policy-makers delivering ongoing situational awareness of AMR trends from genomic data. As broad adoption of WGS continues, amr.watch is positioned to monitor both pathogen populations and our global efforts in genomic surveillance, guiding control strategies tailored to each pathogens characteristics. FundingUK National Institute for Health Research & Gates Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWhole genome sequencing (WGS) approaches enable us to track the spread of bacterial pathogens and antimicrobial resistance (AMR) with high resolution at local, national and global levels. To date, genomic studies assessing AMR dynamics have largely used retrospective data collected for specific research agendas. However, the growing volumes of publicly-shared WGS data, generated increasingly from routine surveillance, provide improved power to detect novel trends and guide interventions. Efforts to collate public bacterial genome data exist, such as AllTheBacteria, although these are aimed at the research community and do not facilitate data usage and interpretation by non-genomics experts, particularly those in public health. To our knowledge, no platforms exist for readily interrogating AMR dynamics from continuously updated public genome data across the array of different WHO priority bacterial pathogens. Of note, however, TyphiNet provides an interactive online dashboard for examining AMR trends exclusively from Salmonella Typhi using a periodically-updated data set from the Pathogenwatch platform. These findings are based on searching PubMed without language restrictions from Jan 1 2000 to December 31 2024, using terms related to "genomic surveillance" and "antimicrobial resistance". Added value of this studyWe have developed amr.watch which, to our knowledge, is the first platform that enables ongoing analysis and visualisation of AMR trends from public genome data across the spectrum of WHO priority bacterial pathogens via an accessible interface. Crucially, the platform incorporates processed genome data via a live always-on stream, enabling insights that are delayed only by the time to data deposition. We reviewed public genomes with available geotemporal information up until 31 March 2025, providing a contemporary global landscape of pathogen genome sequencing. While the number of genomes available annually grew over five-fold globally between 2010 and 2018, we also revealed the extent of differences in geographic representation, with 89.6% of genomes originating from high-income countries and 89 countries contributing no genome data from the priority pathogens. Implications of all the available evidenceRapid generation and sharing of global genome data enables us to more precisely track the spread and define the characteristics of contemporary circulating resistant pathogens. The amr.watch platform provides a solution for retrieving, curating and translating shared genomic data into relevant insights that are accessible and actionable by diverse stakeholders. It thereby forms a basis for monitoring progress in surveillance efforts, alerting on ongoing population changes and guiding enhanced precision for surveillance and interventional development. Additionally, our review of available bacterial genome data highlights the need for additional efforts to increase and sustain the implementation of genomic surveillance of AMR globally, and improve the timely sharing of WGS data and its associated metadata.

genomics↗
bioRxiv2024

Diversity, functional classification and genotyping of SHV β-lactamases in Klebsiella pneumoniae

Interpreting phenotypes of blaSHV alleles in Klebsiella pneumoniae genomes is complex. While all strains are expected to carry a chromosomal copy conferring resistance to ampicillin, they may also carry mutations in chromosomal blaSHV alleles or additional plasmid-borne blaSHV alleles that have extended-spectrum {beta}-lactamase (ESBL) activity and/or {beta}-lactamase inhibitor (BLI) resistance activity. In addition, the role of individual mutations/amino acid changes is not completely documented or understood. This has led to confusion in the literature and in antimicrobial resistance (AMR) gene databases (e.g., NCBIs Reference Gene Catalog and the {beta}-lactamase database (BLDB)) over the specific functionality of individual SHV protein variants. Therefore, identification of ESBL-producing strains from K. pneumoniae genome data is complicated. Here, we reviewed the experimental evidence for the expansion of SHV enzyme function associated with specific amino-acid substitutions. We then systematically assigned SHV alleles to functional classes (wildtype, ESBL, BLI-resistant) based on the presence of these mutations. This resulted in the re-classification of 37 SHV alleles compared with current assignments in NCBIs Reference Gene Catalog and/or BLDB (21 to wildtype, 12 to ESBL, 4 to BLI-resistant). Phylogenetic and comparative genomic analyses support that; i) SHV-1 (encoded by blaSHV-1) is the ancestral chromosomal variant; ii) ESBL and BLI-resistant variants have evolved multiple times through parallel substitution mutations; iii) ESBL variants are mostly mobilised to plasmids; iv) BLI-resistant variants mostly result from mutations in chromosomal blaSHV. We used matched genome-phenotype data from the KlebNET-GSP Genotype-Phenotype Group to identify 3,999 K. pneumoniae isolates carrying one or more blaSHV alleles but no other acquired {beta}-lactamases, with which we assessed genotype-phenotype relationships for blaSHV. This collection includes human, animal, and environmental isolates collected between 2001 to 2021 from 24 countries across six continents. Our analysis supports that mutations at Ambler sites 238 and 179 confer ESBL activity, while most omega-loop substitutions do not. Our data also provide direct support for wildtype assignment of 67 protein variants, including eight that were noted in public databases as ESBL. We reclassified these eight variants as wildtype, because they lack ESBL-associated mutations, and our phenotype data support susceptibility to 3GCs (SHV-27, SHV-38, SHV-40, SHV-41, SHV-42, SHV-65, SHV-164, SHV-187). The approach and results outlined here have been implemented in Kleborate v2.4.1 (a software tool for genotyping K. pneumoniae from genome assemblies), whereby known and novel blaSHV alleles are classified based on causative mutations. Kleborate v2.4.1 was also updated to include ten novel protein variants from the KlebNET-GSP dataset and all alleles in public databases as of November 2023. This study demonstrates the power of sharing AMR phenotypes alongside genome data to improve understanding of resistance mechanisms. Impact statementSince every K. pneumoniae genome has an intrinsic SHV {beta}-lactamase and may also carry additional mobile forms, the correct interpretation of blaSHV genes detected in genome data can be challenging and can lead to K. pneumoniae being misclassified as ESBL-producing. Here, we use matched K. pneumoniae genome and drug susceptibility data contributed from dozens of studies, together with systematic literature review of experimental evidence, to improve our understanding of blaSHV allele variation and mapping of genotype to phenotype. This study shows the value of coordinated data sharing, in this case via the KlebNET-GSP Genotype-Phenotype Group, to improve our understanding of the evolutionary history and functionality of blaSHV genes. The results are captured in an open-source AMR dictionary utilised by the Kleborate genotyping tool, that could easily be incorporated into or used to update other tools and AMR gene databases. This work is part of the wider efforts of the KlebNET-GSP group to develop and support a unified platform tailored for the analysis and interpretation of K. pneumoniae genomes by a wide range of stakeholders. Data summaryBlaSHV allele sequences and class assignments are distributed with Kleborate, v2.4.1, DOI:10.5281/zenodo.10469001. Table S1 provides a summary of blaSHV alleles, including primary accessions, class-modifying mutations, and supporting evidence for class assignments that differ from NCBIs Reference Gene Catalog or BLDB. Whole genome sequence data are publicly available as reads and/or assemblies, individual accessions are given in Table S2; corresponding genotypes and antibiotic susceptibility phenotypes and measurements are available in Tables S3 and S4, respectively.

genomics↗
bioRxiv2024

Convergence and global epidemiology of Klebsiella pneumoniae plasmids harbouring the iuc3 virulence locus.

BackgroundKlebsiella pneumoniae (Kp) is an important pathogen of humans and animals, and recent reports of convergent strains that carry both virulence and antimicrobial resistance genes (ARGs) have raised serious public health concern. The plasmid-borne iuc locus, encoding the siderophore aerobactin, is a key virulence factor in this species. The variant iuc3 is associated with porcine and human clinical isolates and is carried by mostly uncharacterised IncF plasmids. MethodsWe used a combination of short-read and long-read sequencing to characterise IncFIB(K)/IncFII iuc3-carrying plasmids harboured by 79 Kp isolates and one K. oxytoca isolate recovered as part of two large One-Health studies in Italy (SpARK) and Thailand (OH-DART). Adding data from public repositories gave a combined dataset of 517 iuc3 isolates, and the plasmids were analysed using both clustering and phylogenetic methods. FindingsWe note seven large, convergent, plasmids from Thailand that have emerged through the hybridisation of co-circulating plasmids harbouring iuc3 and antimicrobial resistance genes (ARGs) encoding extended-spectrum beta-lactamases (ESBLs). We were also able to identify putative parental plasmids which were mostly associated with two neighbouring meat markets, as were the hybrid plasmids. Clustering and global phylogenetic analyses resolved an iuc3 plasmid sub-group circulating throughout Asia, with occasional examples in Europe and elsewhere. This variant carries multiple ARGs and is commonly harboured by clinical isolates, thus warranting targeted plasmid surveillance. InterpretationOur study reveals that plasmid hybridisation leading to the convergence of resistance and virulence traits may be very common, even in non-clinical ( One-Health) settings. Population-scale plasmid genomics makes it possible to identify putative parental plasmids, which will help to identify plasmid types that are most likely to hybridise, and what the selective consequences may be for the plasmid and host. A distinct iuc3 plasmid sub-variant is associated with clinical isolates in Asia which requires close monitoring. Research In ContextMultiple reports of convergent clones of Klebsiella pneumoniae that combine both hypervirulence and multidrug resistance (MDR-hvKp) have been published recently; a PubMed search in November 2023 using the key words convergence Klebsiella pneumoniae returned 143 papers, 99 of which were published from 2020 onwards. Our study demonstrates that the hybridisation of plasmids carrying AMR and virulence genes is a frequent, ongoing, process in natural populations. The subsequent transfer of plasmids conferring both traits is thus likely to be a key driver behind the spread of convergent strains. Our study also provides an exemplar of how hybrid assemblies can facilitate large-scale global genomic plasmid epidemiology. Evidence before the studyAlthough multiple recent reports highlight the emergence and spread of convergent Kp strains, the confluence of resistance and virulence genes within the same plasmid has not been studied at a population level, and putative parental plasmids are rarely identified. Moreover, there have been few high-resolution genomic epidemiology studies on closely related plasmids using both long and short-read data on a global scale. Added valueWe more than double the number of complete sequences available for plasmids harbouring iuc3 from 58 to 139 and provide evidence on the host lineages most likely to harbour these plasmids (e.g., ST35), and epidemiological source (e.g., pig, wild animal, human). Our comparative analysis of phylogenetic and clustering approaches will help to inform future plasmid epidemiological studies. ImplicationsThe hybridisation of plasmids harbouring virulence and resistance genes occurs frequently in natural populations, even within One-Health settings. However, the selective drivers (if any) and evolutionary consequences of this phenomenon are unclear. There is clear utility in generating closed plasmid genomes on a population scale, and targeted plasmid surveillance on a clinical sub-variant of iuc3 plasmids is warranted.

genomics↗
bioRxiv2023

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in-silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics. Data SummaryThe scripts used to run and fit the models can be found at https://github.com/gbatbiff/Kpneu_MIC_prediction. The Illumina sequences from Thorpe et al. are available from the European Nucleotide Archive under accession PRJEB27342. All the other genomes are available on https://www.bv-brc.org/ database. Impact statementKlebsiella pneumoniae is a leading cause of hospital and community acquired infections worldwide, highly contributing to the global burden of antimicrobial resistance (AMR). Ordinary methods to assess antibiotic resistance are not always satisfactory, and may not be effective in terms of costs and delays, so robust methods able to accurately predict AMR are increasingly needed. Genome-based prediction of minimum inhibitory concentrations (MICs) through machine learning methods is a promising tool to assist clinical diagnosis, also offsetting phenotypic MIC discordance between the different culture-based assays. However, benchmarking predictive models against phenotypic data is problematic due to inconsistencies in the way these data are generated and how they should be handled remains unclear. In this work, we focused on genome-based prediction of MIC and evaluated the performance of interpretable machine learning models across different genetic architectures and data encodings. Our workflow highlighted how MICs need to be treated as different types of data depending on the method used to measure them, in particular considering each antibiotic separately. Our findings shed further light on the factors affecting model performance, paving the way to future improvements of antibiotic resistance prediction.

bioinformatics↗
bioRxiv2023

Vibrio aestuarianus Clade A and Clade B isolates are associated with Pacific oyster (Crassostrea gigas) disease outbreaks across Ireland

2.Bacteria from the Vibrionaceae family have been implicated in mass mortalities of farmed Pacific oysters (Crassostrea gigas) in multiple countries, leading to substantial impairment for growth in the sector. In Ireland there has been concern that Vibrio have been involved in serious summer outbreaks. There is evidence that Vibrio aestuarianus is increasingly becoming the main pathogen of concern for the Pacific Oyster industry in Ireland. While bacteria belonging to the Vibrio splendidus clade are also detected frequently in mortality episodes, their role in the outbreaks of summer mortality are not well understood. To identify and characterise strains involved in these outbreaks, 43 Vibrio isolates were recovered from Pacific oyster summer mass mortality episodes in Ireland from 2008-2015 and these were whole genome sequenced. Among these, 25 were found to be V. aestuarianus (implicated in disease) and 18 V. splendidus sensu lato (role in disease undetermined). Two distinct clades of V. aestuarianus - Clade A and Clade B - were found that had previously been described as circulating within French oyster culture. The high degree of similarity between the Irish and French V. aestuarianus isolates points to translocation of the pathogen between Europes two major oyster producing countries, probably via trade in spat and other age classes. V. splendidus isolates were more diverse, but the data reveal a single clone of this species that has spread across oyster farms in Ireland. This underscores that Vibrio could be transmitted readily across oyster farms. The presence of V. aestuarianus Clades A and B in not only France but also Ireland adds weight to growing concern that this pathogen is spreading and impacting Pacific oyster production within Europe. 3. OutcomePacific oyster culture in Ireland has increasingly suffered from summer mass mortality events. Many of these mortalities in recent years have been associated with Vibrio aestuarianus; the role of another pathogen, Vibrio splendidus has, so far, remained inconclusive. Here we show that two clades of V. aestuarianus are circulating in Ireland, and that these are members of two clades that have previously caused extensive oyster die offs in France. Their discovery in Ireland is consistent with transport of infected oyster stock between the two countries. Although V. splendidus-like strains in Ireland were highly diverse, a small clonal group was detected that appears to have spread rapidly from a single source to disparate locations in Ireland. Combined, these findings highlight the appearance of a highly pathogenic Vibrio in Ireland, and the risk of transmission between interconnected oyster production industries in Europe. 4. Data summarySequences generated in this study were deposited on the NCBI. Accession number: PRJNA797364. Publicly accessed genomes are listed in Table S2. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗
bioRxiv2021

Polymorphism at four-fold degenerate site, but not at the intergenic regions, explains nucleotide compositional strand asymmetry in bacteria

We investigated single nucleotide polymorphism in intergenic regions (IRs) and four-fold degenerate sites (FFS) in genomes of three {gamma}-Proteobacteria and two Firmicutes to understand the mechanism of nucleotide compositional asymmetry between the leading and the lagging strands. Pattern of the polymorphism spectra were alike regarding transitions but variable regarding transversions in the IRs of these bacteria. Contrasting trends of complementary polymorphisms such as C[->]T vs G[->]A as well as A[->]G vs T[->]C in the IRs vindicated similar replication-associated strand asymmetry regarding cytosine and adenine deamination, respectively, across these bacteria. Surprisingly, the polymorphism pattern at FFS was different from that of the IRs and its frequency was always more than the IRs in these bacteria. Further, the polymorphism patterns within a bacterium were inconsistent across the five amino acids, which neither the replication nor the transcription-associated mutations could explain. However, the polymorphism at FFS coincided with amino acid specific codon usage bias in the five bacteria. Further, strand asymmetry in nucleotide composition could be explained by the polymorphism at FFS, not at the IRs. Therefore, polymorphisms at FFS might not be treated as nearly neutral unlike that in IRs in these bacteria.

molecular biology↗
bioRxiv2021

One Health or Three? Transmission modelling of Klebsiella isolates reveals ecological barriers to transmission between humans, animals and the environment

The Klebsiella group is highly diverse both genetically and ecologically, being commonly recovered from humans, livestock, plants, soil, water, and wild animals. Many species are opportunistic pathogens, and can harbour diverse classes of antimicrobial resistance (AMR) genes. K. pneumoniae is responsible for a high public-health burden, due in part to the rapid spread of health-care associated clones that are non-susceptible to carbapenems. Klebsiella thus represents a highly pertinent taxon for assessing the risk to public health posed by animal and environmental reservoirs. Here we report an analysis of 6548 samples and 3,482 genome sequences representing 15 Klebsiella species sampled over a 15-month period from a wide range of clinical, community, animal and environmental settings in and around the city of Pavia, in the northern Italian region of Lombardy. Despite carbapenem-resistant clones circulating at a high frequency in the hospitals, we find no genotypic or phenotypic evidence for non-susceptibility to carbapenems outside of the clinical environment. The non-random distribution of species and strains across sources point to ecological barriers that are likely to limit AMR transmission. Although we find evidence for occasional transmission between settings, hierarchical modelling and intervention analysis suggests that direct transmission from the multiple non-human (animal and environmental) sources included in our sample accounts for less than 1% of hospital disease, with the vast majority of clinical cases originating from other humans.

microbiology↗
bioRxiv2021

Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch

BackgroundKlebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting. MethodsWe developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16,537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1,636 genomes from four low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR. ResultsUsing Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (e.g. ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that three O-types (O1-O3) represented 88.9% of all genomes, whereas capsule types were much more diverse. ConclusionsPathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance. summaryPathogenwatch is a free web-application for analysis of Klebsiella genomes to aid surveillance at local, national and global levels. We improved the representation of genomes from middle-income countries through the Global Health Research Unit on AMR, further facilitating ongoing surveillance.

genomics↗
bioRxiv2020

Quantifying bacterial evolution in the wild: a birthday problem for Campylobacter lineages

Measuring molecular evolution in bacteria typically requires estimation of the rate at which nucleotide changes accumulate in strains sampled at different times that share a common ancestor. This approach has been useful for dating ecological and evolutionary events that coincide with the emergence of important lineages, such as outbreak strains and obligate human pathogens. However, in multi-host (niche) transmission scenarios, where the pathogen is essentially an opportunistic environmental organism, sampling is often sporadic and rarely reflects the overall population, particularly when concentrated on clinical isolates. This means that approaches that assume recent common ancestry are not applicable. Here we present a new approach to estimate the molecular clock rate in Campylobacter that draws on the popular probability conundrum known as the birthday problem. Using large genomic datasets and comparative genomic approaches, we use isolate pairs that share recent common ancestry to estimate the rate of nucleotide change for the population. Identifying synonymous and non-synonymous nucleotide changes, both within and outside of recombined regions of the genome, we quantify clock-like diversification to estimate synonymous rates of nucleotide change for the common pathogenic bacteria Campylobacter coli (2.4 x 10-6 s/s/y) and Campylobacter jejuni (3.4 x 10-6 s/s/y). Finally, using estimated total rates of nucleotide change, we infer the number of effective lineages within the sample time-frame - analogous to a shared birthdays - and assess the rate of turnover of lineages in our sample set over short evolutionary timescales. This provides a generalizable approach to calibrating rates in populations of environmental bacteria and shows that multiple lineages are maintained, implying that large-scale clonal sweeps may take hundreds of years or more in these species. Author SummaryGrowth and reproduction in living organisms require DNA replication but this process is error prone. Along with variation introduced by horizontal gene transfer, it can lead to alterations in the nucleotide sequence. These nucleotide changes accumulate over time in successive generations at an approximately constant rate termed the molecular clock. Therefore, if this rate is known, one can estimate the date when two or more lineages diverged. In bacteria, this can be informative for understanding the time-scale of emergence and spread of pathogenic strains. Such analyses are robust when the ancestral population is known, such as for obligate pathogens that only infect humans. However, when the bacterium inhabits multiple hosts or niches it is difficult to infer direct ancestry from one strain to another, reducing the accuracy of molecular clock estimates. Here we focus on one such multi-host organism, Campylobacter, a leading cause of food-borne gastroenteritis. Reconstructing the population history by estimating empirical nucleotide change rates from carefully selected isolate pairs, and evaluating the maintenance of multiple lineages over time, we provide information about strain diversification. Our method is a new addition to the bacterial genomics toolkit that will help in understanding the spread of opportunistic pathogens.

genomics↗
bioRxiv2020

Co-evolutionary signals from Burkholderia pseudomallei population genomics highlight its survival strategy in a hostile environment

BackgroundThe soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis. It kills up to 40% of cases and contributes to human morbidity and mortality in many tropical and sub-tropical countries. As no vaccines are currently available, prevention is the key health policy and is achieved by avoiding direct contact with soil and standing water. The pathogen notoriously persists in ranges of environmental conditions which make disease prevention difficult. We aimed to scan B. pseudomallei genomes for signals of evolutionary adaptations that allow it to thrive across environmental conditions, which should ultimately inform prevention policy. MethodsWe conducted three layers of analyses: a genome-wide epistasis and co-selection study (GWES) on 2,011 B. pseudomallei genomes to detect signals of co-selection; gene expression analyses across 82 diverse physical, chemical, biological and infectious conditions to identify specific conditions in which such selection might have acted; and gene knockout assays to confirm the function of the co-selection hotspot. FindingsWe uncovered 13,061 mutation pairs in distinct genes and non-coding RNA that have been repeatedly co-selected through B. pseudomallei evolution. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions including temperature stress, osmotic stress, UV radiation, and nutrient deprivation; highlighting these conditions as the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed the role of BPSL1661 under nutrient deprivation, and explored the functional basis of the co-selection gene network surrounding BPSL1661 in facilitating bacterial survival under nutrient depletion. InterpretationOur findings suggest that B. pseudomallei has a selective advantage to survive nutrient-limited conditions. Anthropogenic activities such as shifting cultivation systems with more frequent rotations of cropping and shortened fallow periods or continuous cultivation of cash crops could directly or indirectly contribute to loss of soil nutrient; these may lead to the preferential survival of B. pseudomallei and a subsequent rise of melioidosis. Successful disease control for melioidosis needs to consider improving environmental health in addition to current preventive efforts. FundingWellcome Trust, European Research Council, UK Department of Health, Thailand Research Fund and Khon Kaen University Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed with terms (co-selection AND bacteria AND population) with no date or language restrictions from database inception until April 11, 2021. We identified 44 publications of which four were conducted at a genome-wide scale. These four studies were performed on human-restricted pathogens, detected co-selection of antibiotic resistance gene networks which highlight the use of antibiotics as major selection pressures and further inform treatment options. However, none of these studies were performed on Burkholderia pseudomallei or other opportunistic pathogens that have been adapted to both natural and host environments. The selection pressures exerted on these pathogens and the genetic determinants allowed for their adaptations remain unclear, which limit our understanding on the bacterial biology and the information used for disease control. Added value of this studyBased on genomes of 2,011 B. pseudomallei collected from melioidosis endemic areas, we identified and confirmed genetic signals for co-selection. Using transcriptome profiling covering a broad spectrum of conditions and exposures, we showed that genes under co-selection displayed marked expression correlation under physical stress conditions with the gene at the co-selection hotspot conditionally expressed under nutrient starvation. Furthermore, we experimentally validated the function of the hotspot gene and demonstrated that unlike host-restricted pathogens, the B. pseudomallei co-selection network does not facilitate host infection but is focused on bacterial survival in a harsh environment, particularly under nutrient depletion. Aside from providing a data resource, the study also showcases the power of combined genetics, transcriptomics and functional analysis as a tool for biology discovery. Implications of all available evidenceOur findings provide evolutionary and biological evidence for preferential survival of B. pseudomallei under nutrient starvation. Agricultural practice that induces soil loss, which is not uncommon in melioidosis endemic areas has been linked to soil nutrient depletion and may contribute to the prevalence of B. pseudomallei and a consequent rise of melioidosis in these regions. Successful melioidosis control has to consider environmental health in addition to existing prevention policy.

microbiology↗
bioRxiv2019

Genomic analysis of carbapenemase-encoding plasmids from Klebsiella pneumoniae across Europe highlights three major patterns of dissemination

The incidence of Klebsiella pneumoniae infections that are resistant to carbapenems, a last-line class of antibiotics, has been rapidly increasing. The primary mechanism of carbapenem resistance is production of carbapenemase enzymes, which are most frequently encoded on plasmids by blaOXA-48-like, blaVIM, blaNDM and blaKPC genes. Using short-read sequence data, we previously analysed genomes of 1717 isolates from the K. pneumoniae species complex submitted during the European survey of carbapenemase-producing Enterobacteriaceae (EuSCAPE). Here, we investigated the diversity, prevalence and transmission dynamics of carbapenemase-encoding plasmids using long-read sequencing of representative isolates (n=79) from this collection in combination with short-read data from all isolates. We highlight three major patterns by which carbapenemase genes have disseminated via plasmids. First, blaOXA-48-like genes have spread across diverse lineages primarily via a highly conserved, epidemic pOXA-48-like plasmid. Second, blaVIM and blaNDM genes have spread via transient associations of diverse plasmids with numerous lineages. Third, blaKPC genes have transmitted predominantly by stable association with one clonal lineage (ST258/512) despite frequent mobilisation between pre-existing yet diverse plasmids within the lineage. Despite contrasts in these three modes of carbapenemase gene spread, which can be summarised as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage, all are underpinned by significant propagation along high-risk clonal lineages.

microbiology↗
bioRxiv2019

Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov.

Klebsiella oxytoca causes opportunistic human infections and post-antibiotic haemorrhagic diarrhoea. This Enterobacteriaceae species is genetically heterogeneous and is currently subdivided into seven phylogroups (Ko1 to Ko4, Ko6 to Ko8). Here we investigated the taxonomic status of phylogroups Ko3 and Ko4. Genomic sequence-based phylogenetic analyses demonstrate that Ko3 and Ko4 formed well-defined sequence clusters related to, but distinct from, Klebsiella michiganensis (Ko1), Klebsiella oxytoca (Ko2), K. huaxiensis (Ko8) and K. grimontii (Ko6). The average nucleotide identity of Ko3 and Ko4 were 90.7% with K. huaxiensis and 95.5% with K. grimontii, respectively. In addition, three strains of K. huaxiensis, a species so far described based on a single strain from a urinary tract infection patient in China, were isolated from cattle and human faeces. Biochemical and MALDI-ToF mass spectrometry analysis allowed differentiating Ko3, Ko4 and Ko8 from the other K. oxytoca species. Based on these results, we propose the names Klebsiella spallanzanii for the Ko3 phylogroup, with SPARK_775_C1T (CIP 111695T, DSM 109531T) as type strain, and Klebsiella pasteurii for Ko4, with SPARK_836_C1T (CIP 111696T, DSM 109530T) as type strain. Strains of K. spallanzanii were isolated from human urine, cow faeces and farm surfaces, while strains of K. pasteurii were found in faecal carriage from humans, cows and turtles.\n\nAccession numbersThe nucleotide sequences generated in this study were deposited in ENA and are available through the INSDC databases under accession numbers MN091365 (SB6411T = SPARK775C1T), MN091366 (SB6412 T = SPARK836C1T) and MN104661 to MN104677 (16S rRNA), MN076606 to MN076643 (gyrA and rpoB), and MN030558 to MN030567 (blaOXY). Complete genomic sequences were submitted to European Nucleotide Archive under the BioProject number PRJEB15325.\n\nAbbreviationsANI, average nucleotide identity; HCCA, a-cyano-4-hydroxycinnamic acid; isDDH, in silico DNA-DNA hybridization; SCAI, Simmons citrate agar with inositol; MALDI57 ToF MS: Matrix-assisted laser desorption/ionization time of flight mass spectrometry

microbiology↗
bioRxiv2019

PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria

Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement And Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino-acid or nucleotide identity thresholds and uses the clustering information to rapidly classify paralogous gene families into either putative fission/fusion events or gene duplications. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation and estimates sequence divergence for each gene family. We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.\n\nAvailabilityPIRATE is implemented in Perl and is freely available under an GNU GPL 3 open source license from https://github.com/SionBayliss/PIRATE.\n\nSupplementary InformationSupplementary data is available online.

bioinformatics↗
bioRxiv2018

High-resolution sweep metagenomics using ultrafast read mapping and inference

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

bioinformatics↗
bioRxiv2017

Piggy: A Rapid, Large-Scale Pan-Genome Analysis Tool for Intergenic Regions in Bacteria

Despite overwhelming evidence that variation in intergenic regions (IGRs) in bacteria impacts on phenotypes, most current approaches for analysing pan-genomes focus exclusively on protein-coding sequences. To address this we present Piggy, a novel pipeline that emulates Roary except that it is based only on IGRs. We demonstrate the use of Piggy for pan-genome analyses of Staphylococcus aureus and Escherichia coli using large genome datasets. For S. aureus, we show that highly divergent (\"switched\") IGRs are associated with differences in gene expression, and we establish a multi-locus reference database of IGR alleles (igMLST; implemented in BIGSdb). Piggy is available at https://github.com/harry-thorpe/piggy.

genomics↗