Biology subjects

Ingle, D. J.

Publications and source records attributed to Ingle, D. J..

16 recordsLinked to original sources

bioRxiv2026

Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy

Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a species has been used to identify associations between the microbiome and disease. However, this approach overlooks intra-species genetic variation and is susceptible to spurious correlations arising from the compositional nature of abundance data and microbial load. Fast, k-mer-based algorithms can now accurately estimate strain-level Average Nucleotide Identity (ANI) in metagenomes. Despite its value as an orthogonal metric for strain-level analysis, methods for conducting ANI-based association studies remain limited. To address this, we developed StrainSpy, a statistical algorithm that identifies associations between containment ANI and variables of interest across a wide range of study designs, including longitudinal and multi-cohort designs. Re-analysis of a study examining gut microbiota recovery in 12 healthy adults following antibiotic exposure revealed novel strain-level associations, including a reduction in strain-level diversity despite species persistence. Applying StrainSpy to a multi-cohort analysis of 3,414 colorectal cancer metagenomes identified novel strain-level associations with colorectal cancer. However, in a separate collection of microbiome-immunotherapy studies, no individual strain was consistently associated across cohorts. Importantly, across both datasets, StrainSpy informed containment ANI-based machine learning models achieved comparable accuracy to traditional abundance-based methods. StrainSpy is publicly available as an R package github.com/gtonkinhill/strainspy.

microbiology↗

bioRxiv2026

Incremental AMR acquisition driving successive genotype replacements and the rise of extensively drug resistant (XDR) Shigella sonnei in Australia over 20 years.

BackgroundIn Australia, the burden of shigellosis is predominantly in returning travellers or in men who have sex with men (MSM). Here, we combine genomic data with comprehensive epidemiological data on sexual exposure and international travel to explore population dynamics of Shigella sonnei and the expansion of multi-drug resistant (MDR) and extensively drug-resistant (XDR) sub-lineages. MethodsA population-level study of all cultured Shigella sonnei isolates in the state of Victoria, Australia, was undertaken between January 2002 and December 2024. Antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatic analyses of 1,305 Shigella sonnei isolates were performed at the Microbiological Diagnostic Unit Public Health Laboratory. Enhanced metadata on source attribution including travel and sexual exposure were collected through surveillance forms or by interviews. ResultsThis study highlights significant shifts in Shigella sonnei cases in Victoria from sensitive strains to MDR and then XDR, particularly in the MSM-associated groups but also associated with a large point source outbreak. We describe an historical pattern of shifting genotype prevalence, and replacement to more varied and higher proportions of antimicrobial resistance over the last decade, resulting in the establishment of two distinct but highly concerning XDR sub-lineages within Victoria. ConclusionsOur genomic-epidemiological analyses highlight that drug-resistant Shigella sonnei remains an ongoing public health threat, and the importance of ongoing surveillance. We determined local evolutionary trajectories and identified expanding sub-lineages that informed shifts in clinical management and antimicrobial recommendations over time, including the use of azithromycin and carbapenems. Placing these local dynamics within the broader global epidemiology, we link how regional evolution interconnects with international dissemination, proving valuable context for guiding local, national and global strategies for prevent outbreaks and antimicrobial resistance.

microbiology↗

bioRxiv2026

Benchmarking long-read simulators against Oxford Nanopore whole-genome sequencing data

BackgroundOxford Nanopore Technologies (ONT) sequencing is increasingly used for whole-genome sequencing (WGS) across a wide range of applications. However, the platform has evolved rapidly through updates to flow cell chemistry and basecalling algorithms, altering the characteristics of the resulting sequencing data. Read simulators provide synthetic datasets with known ground truth, enabling controlled development and evaluation of methods. However, many existing simulators were developed for earlier versions of ONT sequencing or use generic long-read assumptions, and their realism for contemporary ONT data is unclear. ResultsWe benchmarked six ONT-compatible read simulators (Badread, LongISLND, lrsim, NanoSim, PBSIM3 and SimLoRD) using a microbial genome reference and ONT R10.4.1 reads as the empirical standard. Each tool was configured to maximise realism, including training on empirical reads when supported. We compared simulated and real datasets with respect to read length, read accuracy, FASTQ quality scores and sequence error profiles. No simulator reproduced all metrics of the real data well. PBSIM3 most closely reproduced read length, read accuracy and FASTQ quality scores, making it a strong simulator for broad read-level realism. However, it did not capture important features of the real error profile, including context-dependent substitution rates and homopolymer-length errors. Badread and LongISLND better reproduced some aspects of the error profile, but showed other departures from the real data. ConclusionPBSIM3 is a good general-purpose choice for many ONT WGS simulation tasks because it reproduced several key read-level properties well. However, Badread or LongISLND may be preferable for applications where error structure is more important. No evaluated tool was realistic across all tested metrics, highlighting a gap for improved long-read simulators.

bioinformatics↗

bioRxiv2026

Flex-It: A global standardised genotyping framework for Shigella flexneri

Shigella flexneri is the leading causative agent of shigellosis globally. The public health threat posed by S. flexneri is compounded by its emergence as a sexually transmissible infection, importance of international travel in driving dissemination, and the increasing prevalence of antimicrobial resistance (AMR). A rapid and robust computational method is needed to enhance genomic surveillance and systematically explore features of the population structure of this WHO priority pathogen, which is scalable and readily implementable across jurisdictions, particularly as vaccine development efforts are underway. Here, we present Flex-It, a genomic framework and genotyping scheme implemented in Mykrobe for S. flexneri serotypes 1-5, X & Y, compatible with previous approaches used to describe S. flexneris population structure. To develop Flex-It, we curated a retrospective dataset of 5,819 publicly available S. flexneri genomes. We characterised the global population structure for S. flexneri, exploring geographical and temporal traits, and showed the granular diversity of AMR and serotype profiles. We applied Flex-It to >13,000 genomes routinely generated by public health laboratories from Australia, the UK and the USA across a ten-year period. We found significant genotype diversity in all three locations, with the emergence of genotypes with converged resistance to all major drugs currently used for treatment. Flex-It provides an open-source, novel genotyping method that rapidly characterises S. flexneri and its ciprofloxacin resistance determinants in <1 minute from both short and long whole-genome sequencing reads. Flex-It provides the community with a standardised nomenclature to monitor the emergence and spread of S. flexneri lineages.

microbiology↗

bioRxiv2025

Benchmarking of tools for resolving plasmids from short-read assemblies for Klebsiella pneumoniae

Plasmids play a critical role in the dissemination of antimicrobial resistance genes and virulence factors in healthcare associated pathogens, such as Klebsiella pneumoniae. Surveillance of these plasmids relies on whole genome sequencing data often generated in clinical and public health settings which frequently use short-read platforms. Therefore, there is a need for robust, scalable tools that can identify and/or reconstruct plasmid sequences from short read data. A myriad of tools already exist to address this problem, however the optimum tool for plasmid identification in K. pneumoniae remains unclear. From a comprehensive search of the literature and code repositories we identified 44 plasmid identification tools, highlighting the uncertainty around best practices. Here, we sought to evaluate these 44 tools to determine which is best suited for reconstructing the plasmidome of K. pneumoniae and related species from the species complex (KpSC). We used a publicly available dataset of 568 diverse KpSC isolates that had both short-read Illumina data and closed hybrid assemblies available. This allowed us to investigate which tools perform best at recovering plasmid sequences when only short-read data is available, whilst knowing the ground truth. From the 44 tools, 34 were excluded as they: were intended for plasmid typing / characterisation (n=3), were not intended for KpSC (n=1), required metagenomic data (n=7), required long read data (n=1), could not be installed (n=13) or could not be run on the command line (n=10). The remaining nine tools had their precision and recall metrics calculated and combined into an overall F1 score. Each individual tool displayed the full range of F1 scores (0 to 1) across our collection of genomes, overall, the best performing was PlaScope followed closely by MOB-suite. Future tools developed in this crowded space should offer meaningful advancements over existing tools and be rigorously benchmarked using standardised datasets that reflect plasmid diversity.

genomics↗

bioRxiv2024

The complete genome sequence of the crayfish pathogen Candidatus Paracoxiella cheracis n.g. n.sp. provides insight into pathogenesis and the phylogeny of the Coxiellaceae family

The Coxiellaceae bacterial family, within the order Legionellales, is defined by a collection of poorly characterized obligate intracellular bacteria. The zoonotic pathogen and causative agent of human Q fever, Coxiella burnetii, represents the best characterized member of this family. Coxiellaceae establish replicative niches within diverse host cells and rely on their host for survival, making them challenging to isolate and cultivate within a laboratory setting. Here we describe a new genus within the Coxiellaceae family that has been previously shown to infect economically significant freshwater crayfish. Using culture-independent long-read metagenomics, we reconstructed the complete genome of this novel organism and demonstrate that the previously referred to as Candidatus Coxiella cheraxi represents a novel genus within this family, herein denoted Candidatus Paracoxiella cheracis. Interestingly, we demonstrate that Candidatus P. cheracis encodes for a complete, putatively functional Dot/Icm type 4 secretion system that likely mediates the intracellular success of this pathogen. In silico analysis defined a unique repertoire of Dot/Icm effector proteins and highlighted homologues of several important C. burnetii effectors including a homologue of CpeB that was demonstrated to be a Dot/Icm substrate in C. burnetii. IMPORTANCEUsing long-read sequencing technology we have uncovered the full genome sequence of Candidatus Paracoxiella cheracis, a pathogen of economic importance in aquaculture. Analysis of this sequence has revealed new insight into this novel member of the Coxiellaceae family, demonstrating that it represents a new genus within this poorly characterized family of intracellular organisms. Importantly, the genome sequence reveals invaluable information that will support diagnostics and potentially both preventative and treatment strategies within crayfish breeding facilities. Candidatus P. cheracis also represents a new member of Dot/Icm pathogens that rely on this system to establish an intracellular niche. Candidatus P. cheracis possesses a unique cohort of putative Dot/Icm substrates that constitute a collection of new eukaryotic cell biology manipulating effector proteins.

microbiology↗

bioRxiv2024

Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi

BackgroundTyphoid fever results from systemic infection with Salmonella enterica serovar Typhi (Typhi) and causes 10 million illnesses annually. Disease control relies on prevention (water, sanitation, and hygiene interventions or vaccination) and effective antimicrobial treatment. Antimicrobial resistant (AMR) Typhi lineages have emerged and become established in many parts of the world. Knowledge of local pathogen populations informed by genomic surveillance, including of lineages (defined by the GenoTyphi scheme) and AMR determinants, is increasingly used to inform local treatment guidelines and to inform vaccination strategy. Current tools for genotyping Typhi require multiple read alignment or assembly steps and have not been validated for analysis of data generated with Oxford Nanopore Technologies (ONT) long-read sequencing devices. Here, we introduce Typhi Mykrobe, a command line software tool for rapid genotyping of Typhi lineages, AMR determinants, and plasmid replicons direct from sequencing reads. ResultsWe validated Typhi Mykrobe lineage genotyping by comparison with the current standard read mapping-based approach and demonstrated 99.8% concordance across nearly 13,000 genomes sequenced with Illumina platforms. For the few isolates with discordant calls, we show that Typhi Mykrobe results are better supported by the evidence from raw sequence read data than the results generated using the mapping-based approach. We also demonstrate 99.9% concordance for detection of AMR determinants compared with the current standard assembly-based approach, with similar results for plasmid marker detection. Typhi Mykrobe predicts clinical resistance categorisation (S/I/R) for eight drug classes, and we show strong agreement with phenotypic categorisations generated from reference laboratory minimum inhibitory concentration (MIC) data for n=1,572 Illumina-sequenced isolates (>99% agreement within one doubling dilution). We show strong concordance (>96% for genotype and >98% for AMR and plasmid) between calls made from ONT reads and those made from Illumina reads for isolates sequenced on both platforms (n =93 genomes). Typhi Mykrobe takes less than a minute per sample and is available at https://github.com/typhoidgenomics/genotyphi. ConclusionsTyphi Mykrobe provides rapid and sensitive genotyping of Typhi genomes direct from Illumina and ONT reads, although lower accuracy was observed for R9 ONT data. It demonstrated accurate assignment of GenoTyphi lineage, detection of AMR determinants and prediction of corresponding AMR phenotypes, and identification of plasmid replicons.

microbiology↗

bioRxiv2024

Exploring SNP Filtering Strategies: The Influence of Strict vs Soft Core

Phylogenetic analyses are crucial for understanding microbial evolution and infectious disease transmission. Bacterial phylogenies are often inferred from single nucleotide polymorphism (SNP) alignments, with SNPs as the fundamental signal within these data. SNP alignments can be reduced to a strict core by removing those sites which do not have data present in every sample. However, as sample size and genome diversity increase, a strict core can shrink markedly, discarding potentially informative data. Here, we propose and provide evidence to support the use of a soft core that tolerates some missing data, preserving more information for phylogenetic analysis. Using large datasets of Neisseria gonorrhoeae and Salmonella enterica serovar Typhi, we assess different core thresholds. Our results show that strict cores can drastically reduce informative sites compared to soft cores. In a 10,000-genome alignment of Salmonella enterica serovar Typhi, a 95% soft core yielded 10 times more informative sites than a 100% strict core. Similar patterns were observed in Neisseria gonorrhoeae. We further evaluated the accuracy of phylogenies built from strict and soft-core alignments using datasets with strong temporal signals. Soft-core alignments generally outperformed strict cores in producing trees displaying clock-like behaviour; for instance, the Neisseria gonorrhoeae 95% soft core phylogeny had a root-to-tip regression R2 of 0.50 compared to 0.21 for the strict-core phylogeny. This study suggests that soft-core strategies are preferable for large, diverse microbial datasets. To facilitate this, we developed Core-SNP-filter (github.com/rrwick/Core-SNP-filter), an open-source software tool for generating soft-core alignments from whole-genome alignments based on user-defined thresholds. IMPACT STATEMENTThis study addresses a major limitation in modern bacterial genomics - the significant data loss observed in large datasets for phylogenetic analyses, often due to strict-core SNP alignment approaches. As bacterial genome sequence datasets grow and diversity increases, a strict-core approach can greatly reduce the number of informative sites, compromising phylogenetic resolution. Our research highlights the advantages of soft-core alignment methods which tolerate some missing data and retain more genetic information. To streamline the processing of alignments, we developed Core-SNP-filter (github.com/rrwick/Core-SNP-filter), a publicly available resource-efficient tool that filters alignments to informative and core sites. DATA SUMMARYAll genomic sequence reads used in this study were already publicly available and accessions can be found in Supplementary Dataset 1. Supplementary methods and all code can be found in the accompanying GitHub repository: (github.com/mtaouk/Core-SNP-filter-methods).

genomics↗

bioRxiv2024

Dynamics of antimicrobial resistance and virulence of Salmonella enterica serovar Dublin

Salmonella Dublin is a host-adapted, invasive non-typhoidal Salmonella (iNTS) serovar that causes bloodstream infections in humans and demonstrates increasing prevalence of antimicrobial resistance (AMR). Using a global dataset of 1,303 genomes, coupled with in vitro assays, we examined the evolutionary, resistance, and virulence characteristics of S. Dublin. Our analysis revealed strong geographic associations between AMR profiles and plasmid types, with highly resistant isolates confined predominantly to North America, linked to IncC plasmids co-encoding AMR and heavy metal resistance. By contrast, Australian isolates were largely antimicrobial-susceptible, reflecting differing AMR pressures. We identified two phylogenetically distinct Australian lineages, ST10 and ST74, with a small number of ST10 isolates harbouring a novel hybrid plasmid encoding both AMR and mercuric resistance. Whereas the ST10 lineage remains globally dominant, the ST74 lineage was less prevalent. ST74 exhibited unique genomic features including a larger pan genome compared to ST10 and the absence of key virulence loci including SPI-19 which encodes a type VI secretion system (T6SS). Despite these genomic differences, the ST74 lineage displayed enhanced intracellular replication in human macrophages and induced less pro-inflammatory responses compared with ST10, suggesting alternative virulence strategies that may support systemic dissemination of ST74. The Vi antigen was absent in all ST10 and ST74 genomes, highlighting challenges for serotyping and vaccine development, and has implications for current diagnostic and control strategies for S. Dublin infections. Collectively, this study represents the most comprehensive investigation of S. Dublin to date and importantly, has revealed distinct adaptations of two genotypes within the same serovar, leading to different epidemiological success. The regional emergence and evolution of distinct S. Dublin lineages highlights the need to understand the divergence of intra-serovar virulence mechanisms which may impact the development of effective control measures against this important global pathogen.

microbiology↗

bioRxiv2024

Genomic epidemiology and phenotypic characterisation of Salmonella entericaserovar Panama in Victoria, Australia

Salmonella enterica serovar Panama, a causative agent of non-typhoidal salmonellosis (NTS), is one of several serovars that causes invasive NTS disease (iNTS) in humans. S. Panama is an understudied pathogen, with its pathobiology poorly understood. It is a predominant iNTS serovar in Australia, a high-income country with high rates of salmonellosis, where S. Panama has been documented to have a high odds ratio for causing iNTS. This study investigates the genomic epidemiology and antimicrobial resistance profiles of all S. Panama isolates recovered in Victoria, Australia, between 2000 and 2020. We examined the infection dynamics of S. Panama in seven isolates, representing the genetic diversity of the study population. Two sub-lineages, encompassed within a previously described Asian lineage, were identified. Multi-drug resistance (resistance to [≥]3 drug classes) was detected in 46 (51.7%) Australian isolates. The plasmid-mediated colistin resistance gene, mcr1.1, was detected in one Australian S. Panama isolate, carried by an IncI plasmid previously reported in Salmonella and Escherichia coli isolates collected from poultry in South-East Asia. Examination of the intracellular replication dynamics of S. Panama isolates demonstrated diverse phenotypes. In THP-1 derived macrophages, despite low host cell uptake, S. Panama showed higher replication rates over time compared to S. enterica serovar Typhimurium. However, a causative genotype could not be identified to explain this observed phenotype. This study provides insights into the S. Panama isolates imported into Australia over two-decades, showing MDR was common in this iNTS serovar, and colistin resistance reported for the first time. It provides the first data on the host-pathogen interactions of S. Panama in Australia, which will aid our collective understanding of the pathobiology of S. Panama and iNTS serovars more broadly. Author SummaryIn Australia, non-typhoidal Salmonella (NTS) cases have been on the rise since the 1970s; characterised by self-limiting enteritis, some NTS infections can result in systemic infections called invasive NTS disease. Salmonella enterica serovar Panama is a leading iNTS serovar in Australia. This study characterised the genomic epidemiology of S. Panama, identifying two lineages circulating in Australia over two decades and placing them within a global context. It also investigated the antimicrobial resistance (AMR) mechanisms of S. Panama, with multi-drug resistance commonly observed. Further, it identified the first plasmid-mediated colistin-resistant S. Panama in Australia. We additionally examined the characteristics of S. Panama-mediated host-pathogen interactions in both epithelial and macrophage cells lines, providing the first insight into the infection dynamics of this understudied pathogen. Thus, this study combines genomics and in vitro infection experiments to understand the pathogenic behaviour of the neglected iNTS S. Panama.

microbiology↗

bioRxiv2024

Global diversity and evolution of Salmonella Panama, an understudied serovar causing gastrointestinal and invasive disease worldwide: a genomic epidemiology study

BackgroundNontyphoidal Salmonella (NTS) is a globally important bacterial pathogen, typically associated with foodborne gastrointestinal infection. Some NTS serovars can also colonise normally sterile sites in humans to cause invasive NTS (iNTS) disease. One understudied Salmonella enterica serovar which is responsible for a significant number of cases of iNTS disease is Panama. Despite global dissemination, numerous outbreaks, and a reported association with iNTS disease, S. enterica serovar Panama (S. Panama) has not been investigated in detail. MethodsUsing combined epidemiological and whole genome sequencing data we analysed 836 S. Panama genomes derived from historical collections, national surveillance datasets, and publicly available data. The collection represents all inhabited continents and includes isolates collected between 1931 and 2019. Maximum likelihood and Bayesian phylodynamic approaches were used to determine population structure & evolutionary history, and to infer geo-temporal dissemination. A combination of different bioinformatic approaches utilising short-read and long-read data were used to characterise geographic and clade-specific trends in antimicrobial resistance (AMR), and genetic markers for invasiveness. FindingsWe identified the presence of multiple geographically linked S. Panama clades, and regional trends in antimicrobial resistance profiles. Most isolates were pan-susceptible to antibiotics and belonged to clades circulating in the United States of America, Latin America, and the Caribbean. Multidrug resistant (MDR) isolates in our collection belonged to two phylogenetic clades circulating in Europe and Asia/Oceania, which exhibited the highest invasiveness indices based on the conservation of 196 extra-intestinal predictor genes. InterpretationThis first large-scale phylogenetic analysis of S. Panama revealed important information about population structure, AMR, global ecology, and genetic markers of invasiveness of the identified genomic subtypes. Our findings provide an important baseline for understanding S. Panama infection in the future. The presence of MDR clades with an elevated invasiveness index should be monitored by ongoing surveillance as such clades may pose an increased public health risk. FundingThe project was supported by both a Global Challenges Research Fund (GCRF) data & resources grant BBS/OS/GC/000009D and the BBSRC Core Capability Grant to the Earlham Institute BB/CCG1720/1. JCDH received a Wellcome Trust Investigator award (grant number 222528/Z/21/Z). CVP was supported by the John Lennon Memorial Scholarship from the University of Liverpool and a Fee Bursary Award from the Institute of Integrative Biology at the University of Liverpool. FXW was awarded the Institut Pasteur; Sante publique France; the Fondation Le Roch-Les Mousquetaires; and the French governments Investissement dAvenir programme, Laboratoire dExcellence "Integrative Biology of Emerging Infectious Diseases" (grant number ANR-10-LABX-62-IBEID). MDS was supported by a BBSRC grant (KSB: BB/V009184/1). RJBengtsson was supported by an MRC grant (KSB: MR/R020787/1). RJBennett was supported by a BBSRC DTP (BB/M011186/1). DJI was supported by a National Health and Medical Research Council (NHMRC) Emerging Leadership Fellowship (GNT1195210). BPH was supported by NHMRC Leadership Fellowship (GNT1196103). KSB and EVR are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with the United Kingdom Health Security Agency, in collaboration with University of Warwick. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or UKHSA. MAC is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) (NIHR200892) in Genomics and Enabling Data at University of Warwick in partnership with the UK Health Security Agency (UKHSA), in collaboration with Universities of Cambridge and Oxford. MAC is based at UKHSA. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. No payment was received by any pharmaceutical company or other agency to write this article. The authors were not precluded from accessing data in the study, and accept responsibility to submit for publication. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSalmonella Panama has consistently been reported as a frequently isolated Salmonella serovar in national surveillance datasets, causing both sporadic cases and larger outbreaks. However, this picture has been masked due to most of the focus being placed on the top two serovars associated with nontyphoidal Salmonella (NTS) and invasive NTS disease: Typhimurium and Enteritidis. Previous works on S. Panama have determined transmission to include human faeces and breast milk, as well as non-human sources such as environmental reservoirs and animals (reptiles and pigs used for food). In contrast to most of the Salmonella serovars causing gastroenteritis, S. Panama has also been associated with a range of systemic infections including septicaemia, meningitis, and osteomyelitis, mostly affecting infants. The patterns of antimicrobial resistance of this pathogen were not well known, with studies reporting a mixture of different antimicrobial resistance profiles. Added value of this studyHere we conducted a large-scale study of 836 globally relevant S. Panama isolates to understand global population structure and disease ecology. The S. Panama genomes used in this study were sourced from a combination of historical collections (including the first ever isolated strain of this serovar dating from 1931), national surveillance datasets, and publicly available data. Some of these isolates had been linked to travel, allowing an inferred location for understanding population structure. Overall, this assembled S. Panama collection spanned a range of 88 years and covered 45 countries and regions of all inhabited continents. We applied bacterial phylodynamic approaches to determine population structure, evolutionary history, and clade-specific trends of invasiveness and antimicrobial resistance correlated with geo-temporal parameters. Implications of all the available evidenceThis study revealed the population structure and global ecology of S. Panama, indicating that multidrug resistant S. Panama circulates in European and Asian regions, and presents an increased number of genetic markers for extra-intestinal invasiveness. The transmission and expansion of antimicrobial-resistant S. Panama strains presented in this study highlight the significance of supporting control and monitoring efforts from an international perspective.

genomics↗

bioRxiv2022

Tracking the horizontal transfer of plasmids in Shigella sonnei and Shigella flexneri using phylogenetics

The silent pandemic of antimicrobial resistance (AMR) represents a significant global public health threat. AMR genes in bacteria are often carried on mobile elements, such as plasmids. The horizontal movement of plasmids allows AMR genes and resistance to key therapeutics to disseminate in a population. However, the quantification of the movement of plasmids remains challenging with existing computational approaches. Here, we introduce a novel method that allows us to reconstruct and quantify the movement of plasmids in bacterial populations over time. To do so, we model chromosomal and plasmid DNA co-evolution using a joint coalescent and plasmid transfer process in a Bayesian phylogenetic network approach. This approach reconstructs differences in the evolutionary history of plasmids and chromosomes to reconstruct instances where plasmids likely move between bacterial lineages while accounting for parameter uncertainty. We apply this new approach to a five-year dataset of Shigella, exploring the plasmid transfer rates of five different plasmids with different AMR and virulence profiles. In doing so, we reconstruct the co-evolution of the large Shigella virulence plasmid with the chromosome DNA. We quantify higher plasmid transfer rates of three small plasmids that move between lineages of Shigella sonnei. Finally, we determine the recent dissemination of a multidrug-resistant plasmid between S. sonnei and S. flexneri lineages in multiple independent events and through steady growth in prevalence since 2010. This approach has a strong potential to improve our understanding of the evolutionary dynamics of AMR-carrying plasmids as they are introduced, circulate, and are maintained in bacterial populations.

evolutionary biology↗

bioRxiv2020

Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, Shigella sonnei

Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for S. sonnei to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000 S. sonnei isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant S. sonnei clones and demonstrating the genomic framework can facilitate monitoring of the emergence and spread of resistant clones at local and global scales.

microbiology↗

bioRxiv2020

Systematic analysis of key parameters for genomics-based real-time detection and tracking of multidrug-resistant bacteria

BackgroundPairwise single nucleotide polymorphisms (SNPs) are a cornerstone for genomic approaches to multidrug-resistant organisms (MDROs) transmission inference in hospitals. However, the impact of key analysis parameters on these inferences has not been systematically analysed. MethodsWe conducted a multi-hospital 15-month prospective study, sequencing 1537 MDRO genomes for comparison; methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus faecium, and extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae. We systematically assessed the impact of sample and reference genome diversity, masking of prophage and regions of recombination, cumulative genome analysis compared to a three-month sliding-window, and the comparative effects each of these had when applying a SNP threshold for inferring likely transmission ([≤]15 SNPs for S. aureus, [≤]25 for other species). FindingsAcross the species, using a reference genome of the same sequence type provided a greater degree of pairwise SNP resolution, compared to species and outgroup-reference alignments that typically resulted in inflated SNP distances and the possibility of missed transmission events. Omitting prophage regions had minimal impacts, however, omitting recombination regions a highly variable effect, often inflating the number of closely related pairs. Estimating pairwise SNP distances was more consistent using a sliding-window than a cumulative approach. InterpretationThe use of a closely-related reference genome, without masking of prophage or recombination regions, and a sliding-window for isolate inclusion is best for accurate and consistent MDRO transmission inference. The increased stability and resolution provided by these approaches means SNP thresholds for putative transmission inference can be more reliably applied among diverse MDROs. FundingThis work was supported by the Melbourne Genomics Health Alliance (funded by the State Government of Victoria, Department of Health and Human Services, and the ten member organizations); an National Health and Medical Research Council (Australia) Partnership grant (GNT1149991) and individual grants from National Health and Medical Research Council (Australia) to NLS (GNT1093468), JCK (GNT1008549) and BPH (GNT1105905).

genomics↗

bioRxiv2020

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Antimicrobial resistance (AMR) in bacteria has been a global threat to public health for decades. A well-known driving force for the emergence, evolution and dissemination of genetic AMR determinants in bacterial populations is horizontal gene transfer, which is frequently mediated by mobile genetic elements (MGEs). Some MGEs can capture, maintain, and rearrange multiple AMR genes in a donor bacterium before moving them into recipients, giving rise to a phenomenon called horizontal gene co-transfer (HGcoT). This physical linkage or co-localisation between mobile AMR genes is of particular concern because it facilitates rapid dissemination of multidrug resistance within and across bacterial populations, providing opportunities for co-selection of AMR genes and limiting our therapeutic options. The study of HGcoT can be benefited from large-scale whole-genome sequencing (WGS) data, however, by far most published studies of HGcoT only consider simple co-occurrence measures, which can be confounded by strong bacterial population structure due to clonal reproduction, leading to spurious associations. To address this issue, we present GeneMates, an R package implementing a network approach to identification of HGcoT using WGS data. The package enables users to test for associations between presence-absence of bacterial genes using univariate linear mixed models controlling for population structure based on core-genome variation. Furthermore, when physical distances between genes of interest are measurable in bacterial genomes, users can evaluate distance consistency to further support their inference of putative horizontally co-transferred genes, whose co-occurrence cannot be completely explained by the population structure. We demonstrate how this package can be used to identify co-transferred AMR genes and recover known MGEs from Escherichia coli and Salmonella Typhimurium WGS data. GeneMates is accessible at github.com/wanyuac/GeneMates.

genomics↗

bioRxiv2019

Informal genomic surveillance of regional distribution of Salmonella Typhi genotypes and antimicrobial resistance via returning travellers

Salmonella enterica serovar Typhi (S. Typhi) is the causative agent of typhoid fever, a systemic human infection with a burden exceeding 20 million cases each year that occur disproportionately among children in low and middle income countries. Antimicrobial therapy is the mainstay for treatment, but resistance to multiple agents is common. Here we report genotypes and antimicrobial resistance (AMR) determinants detected from routine whole-genome sequencing (WGS) of 533 S. Typhi isolates referred to Public Health England between April 2014 and April 2017, 488 (92%) of which had accompanying patient travel information obtained via an enhanced surveillance questionnaire. The majority of cases involved S. Typhi 4.3.1 (H58) linked with travel to South Asia (58%). Travel to East and West Africa were associated with genotypes 4.3.1 and 3.3.1, respectively. Point mutations in the quinolone resistance determining region (QRDR), associated with reduced susceptibility to fluoroquinolones, were very common (85% of all cases) but the frequency varied significantly by region of travel: 95% in South Asia, 43% in East Africa, 27% in West Africa. QRDR triple mutants, resistant to ciprofloxacin, were restricted to 4.3.1 lineage II and associated with travel to India, accounting for 23% of cases reporting travel to the country. Overall 24% of isolates were MDR, however the frequency varied significantly by region and country of travel: 27% in West Africa, 52% in East Africa, 55% in Pakistan, 24% in Bangladesh, 3% in India. MDR determinants were plasmid-borne (IncHI1 PST2 plasmids) in S. Typhi 3.1.1 linked to West Africa, but in all other regions MDR was chromosomally integrated in 4.3.1 lineage I. We propose that routine WGS data from travel-associated cases in industrialised countries could serve as informal sentinel AMR genomic surveillance data for countries where WGS is not available or routinely performed.\n\nAuthor SummaryOur data demonstrate how routine WGS data produced by Public Health England can be further mined for informal passive surveillance of Salmonella Typhi circulating in different geographical regions where typhoid is endemic. We have shown the public health utility of a simplified approach to WGS reporting based on the GenoTyphi genotyping framework and nomenclature, which doesnt require the generation of a phylogenetic tree or other phylogenetic analysis. These approaches yielded results consistent with previously reported antimicrobial resistance (AMR) patterns of S. Typhi, including prevalence of multi-drug resistant (MDR) and fluoroquinolone resistance in different regions in association with different pathogen variants. These data provide a rationale and framework for the extraction and reporting of geographically stratified genotype and AMR data from public health labs in non-endemic countries. Prospective analysis and reporting of such data could potentially detect shifts in regional S. Typhi populations, such as replacement or spread of different subclades and the emergence and dissemination of MDR, fluoroquinolone resistant and/or extensively drug resistant S. Typhi, providing valuable data to inform typhoid control measures in low and middle income countries that are still building their genomics capacity.

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