Biology subjects

Tonkin-Hill, G.

Publications and source records attributed to Tonkin-Hill, G..

At least 19 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

MxSure: a mixture model for inferring within-host substitution rates and transmission SNP thresholds

Quantifying short-term evolutionary rates of microbial genomes is essential for understanding the processes that shape within-host evolution and for establishing thresholds needed to track transmission. In studies of short-term evolutionary rates, samples are often collected from closely related clusters (e.g. longitudinally from the same host or from transmission pairs), with substantial time intervals separating genomes between clusters. Distinguishing strain replacement from persistence presents is also difficult in these studies. In addition, many public health and metagenomic bacterial strain tracking pipelines output pairwise SNP distances rather than the multiple sequence alignments required by common substitution rate estimation pipelines. This makes it hard to estimate within-host evolutionary rates in many commensal bacterial species that are difficult to culture and isolate. To address these challenges, we introduce MxSure, a tool for estimating substitution rates and transmission thresholds while accounting for strain replacement from pairwise SNP distance data, as commonly generated by transmission tracking and metagenomic analysis pipelines. We demonstrate the accuracy of MxSure through extensive simulations and by analysing species with previously estimated substitution rates from longitudinal metagenomic datasets. Using MxSure, we estimated within-host substitution rates and transmission SNP thresholds for multiple commensal bacterial species including Bifidobacterium longum and Bifidobacterium bifidum from a longitudinal study of the infant gut microbiome.

bioinformatics↗

bioRxiv2026

Rapid gene exchange explains differences in bacterial pangenome structure

The size and diversity of bacterial gene repertoires, known as pangenomes, vary widely across species. The evolutionary forces driving the maintenance of pangenomes is an open topic of debate, with contradictory theories suggesting that pangenomes exist as a result of neutral evolution, with all genes gained and lost at random, or that all genes provide a fitness benefit to the host and are maintained by positive selection. Modelling of pangenome dynamics has provided insight into how gene exchange explains observed gene frequency distributions, and stands as the only means of jointly inferring contributions of individual gene selection effects and mobility on the maintenance of pangenomes. However, previous modelling studies have not included both gene-level selection and mobility, and do not consider broadly sampled genome datasets for many species. To differentiate neutral and selective forces maintaining pangenomes, we developed a mechanistic model of gene-level evolution, Pansim, and a scalable model fitting framework, PopPUNK-mod. Together, these tools leverage rapid genome distance calculation to fit models of pangenome dynamics to datasets containing hundreds of thousands of genomes. We used this framework to compare the pangenome dynamics of over 400 different bacterial species, using over 600,000 genomes. We find that diversity in pangenome characteristics between species is driven predominantly by variation in the number of rapidly exchanged genes, while the rate of exchange of remaining genes is conserved. We find that bacterial phylogeny, rather than ecology, correlates with pangenome dynamics. We express that pan-species gene-level analyses are now needed to understand selection across accessory genes. Our work highlights the importance of gene exchange rate differences in governing differences in pangenome characteristics between species.

bioinformatics↗

bioRxiv2025

Mapping the evolutionary path towards multi-drug resistance in the pandemic Escherichia coli ST131 lineage

Escherichia coli sequence type (ST) 131 is the most widely studied genetic lineage of the species to date, originally identified in the early 2000s as an increasingly common cause of human urinary tract and bloodstream infections worldwide. This lineage is subdivided into four extant main subclades A, B, C1 and C2 that exhibit distinct features in terms of invasiveness, antibiotic resistance and transmissibility. However, the evolutionary pathway from the generally susceptible ST131-B to the drug-resistant ST131-C clades remains poorly mapped. To fill this knowledge gap, we analysed in detail human clinical isolates obtained in Vietnam, designated as belonging to the generally neglected minor clade ST131-B0. We sequenced them using both short- and long-read technology, and combined these data with a recently published high-resolution genomic collection to provide further insight into the evolutionary process and its timeline. Extensive genomic analyses established ST131-B0 as an intermediary progenitor in the evolutionary path leading from ST131-B to the ST131-C clades, associated with multiple type I pili switches, as well as the loss and gain of specific chromosomal genes representing diverse core functions such as metabolism, transcription, DNA binding and type II toxin-antitoxin systems. Furthermore, all Vietnamese isolates of ST131-B0 unprecedentedly harboured blaCTX-M genes encoding extended-spectrum {beta}-lactamases, a trait dominant in ST131-C clades and not previously observed in ST131-B0. Our study supports the hypothesis that the ST131-C clades have gradually evolved from ST131-B by reducing the host range with better adaptation to colonising humans under selective conditions.

evolutionary biology↗

bioRxiv2025

Quantifying the effects of antibiotic resistance and within-host competition on strain fitness in Streptococcus pneumoniae

Competition plays a key role in shaping the structure and diversity of bacterial populations. In many clinically important bacterial species, strains compete at multiple scales: at the between-host scale for new hosts to colonise, and at the within-host scale during co-colonisation. Characterising these multiple facets of competition plays an important role in understanding bacterial ecology. This is particularly relevant for antibiotic resistance, where competition between antibiotic-susceptible and resistant strains determines resistance dynamics. In this work, we perform survival analyses on a large longitudinal dataset of Streptococcus pneumoniae carriage to quantify how within-host competition affects the rates of clearance and establishment of pneumococcal strains. We find that the presence of a within-host competitor is associated with a 33% increase in clearance and a 54% reduction in establishment. Priority effects and serotype differences partially predict the outcomes of this within-host competition. Further, we quantify the effects of antibiotic resistance on betweenand within-host components of fitness. Antibiotic consumption is associated with increased clearance rate for both susceptible and resistant strains, albeit to a higher extent in susceptible strains. In the absence of antibiotics, we find some evidence that resistance is associated with increased susceptibility to within-host competition, suggesting a fitness cost of resistance. Overall, our work provides quantitative insights into pneumococcal competition across scales and the role of this competition in shaping pneumococcal epidemiology.

ecology↗

bioRxiv2024

Enhanced metagenomics-enabled transmission inference with TRACS

Coexisting strains of the same species within the human microbiota pose a substantial challenge to inferring the host-to-host transmission of both pathogenic and commensal microbes. Here, we present TRACS, a highly accurate algorithm for estimating genetic distances between strains at the level of individual SNPs, which is robust to intra-species diversity within the host. Analysis of well-characterised Faecal Microbiota Transplantation datasets, along with extensive simulations, demonstrates that TRACS substantially outperforms existing strain aware transmission inference methods. We use TRACS to infer transmission networks in patients colonised with multiple strains, including SARS-CoV-2 amplicon sequencing data from UK hospitals, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from malaria patients infected with Plasmodium falciparum. Applying TRACS to gut metagenomic samples from a large cohort of 176 mothers and 1,288 infants born in UK hospitals revealed species-specific transmission rates between mothers and their infants. Notably, TRACS identified increased persistence of Bifidobacterium breve in infants, a finding missed by previous analyses due to the presence of multiple strains.

bioinformatics↗

bioRxiv2024

Basic reproduction number for pandemic Escherichia coli clones is comparable to typical pandemic viruses

Extra-intestinal pathogenic Escherichia coli (ExPEC) ubiquitously colonize the human gut and represent clinically the most significant bacterial species causing urinary tract infections and bacteremia in addition to contributing to meningitis in neonates. During the last two decades, new E. coli multi-drug resistant clones such as ST131, particularly its clades C1 and C2, have spread globally, as has their generally less resistant sister clade ST131-A. Phylodynamic coalescent modeling has indicated exponential growth in the populations corresponding to these clades during the early 2000s. However, it remains unknown how their transmission dynamics compare to viral epidemics and pandemics in terms of key epidemiological quantities such as the basic reproduction number (R0). Estimation of R0 for opportunistic pathogenic bacteria poses a difficult challenge compared to viruses causing acute infections, since data on E. coli infections accumulate with a much longer delay, even in the most advanced public health reporting systems. Here, we developed a compartmental model for asymptomatic gut colonization and onward transmission coupled with a stochastic epidemiological observation model for bacteremia and fitted the model to annual Norwegian national E. coli disease surveillance and bacterial population genomics data. Approximate Bayesian Computation leveraged by the ELFI software package was used to infer R0 for the pandemic ST131 clades. The resulting R0 estimates for ST131-A, ST131-C1 and ST131-C2 were 1.47 (1.31-1.60), 1.18 (1.12-1.20) and 1.13 (1.08-1.20), respectively, indicating that the ST131-A transmission potential can be even comparable to pandemic influenza viruses, such as H1N1. The significantly lower transmissibility of ST131-C1 and ST131-C2 suggests that their global dissemination has been aided by antibiotic selection pressure and that they may be more effectively transmitted through healthcare facilities instead of primarily community-driven transmission. In summary our results provide a fundamental advance in understanding the relative transmissibility of these opportunistic pathogens and that it can vary markedly even between very closely related E. coli.

microbiology↗

bioRxiv2023

Escherichia coli plasmidome maps the game of clones

Escherichia coli is the most widely studied microbe in history, but its extrachromosomal elements known as plasmids remain poorly delineated. Here we used long-read technology to high-resolution sequence the entire plasmidome and the corresponding host chromosomes from an unbiased longitudinal survey covering two decades and over 2,000 E. coli isolates. We find that some plasmids have persisted in lineages even for centuries, demonstrating strong plasmid-lineage associations. Our analysis provides a detailed map of recent vertical and horizontal evolutionary events involving plasmids with key antibiotic resistance, competition and virulence determinants. We present genomic evidence of both chromosomal and plasmid-driven success strategies that represent convergent phenotypic evolution in distant lineages, and use in vitro experiments to verify the importance of bacteriocin-producing plasmids for clone success. Our study has general implications for understanding plasmid biology and bacterial evolutionary strategies.

microbiology↗

bioRxiv2023

PAN-GWES: Pangenome-spanning epistasis and co-selection analysis via de Bruijn graphs

Studies of bacterial adaptation and evolution are hampered by the difficulty of measuring traits such as virulence, drug resistance and transmissibility in large populations. In contrast, it is now feasible to obtain high-quality complete assemblies of many bacterial genomes thanks to scalable and affordable long-read sequencing technology. To exploit this opportunity we introduce a phenotype- and alignment-free method for discovering co-selected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact coloured de Bruijn graph to approximate the intra-genome distances between pairs of loci for a collection of bacterial genomes to account for the impacts of linkage disequilibrium (LD). We demonstrate the versatility of our approach to efficiently identify associations between loci linked with drug resistance and adaptation to the hospital niche in the major human bacterial pathogens Streptococcus pneumoniae and Enterococcus faecalis.

bioinformatics↗

bioRxiv2023

Inter-species gene flow drives ongoing evolution of Streptococcus pyogenes and Streptococcus dysgalactiae subsp. equisimilis

Streptococcus dysgalactiae subsp. equisimilis (SDSE) is an emerging cause of human infection with invasive disease incidence and clinical manifestations comparable to the closely related species, Streptococcus pyogenes. Through systematic genomic analyses of 501 disseminated SDSE strains, we demonstrate extensive overlap between the genomes of SDSE and S. pyogenes. More than 75% of core genes are shared between the two species with one third demonstrating evidence of cross-species recombination. Twenty-five percent of mobile genetic element (MGE) clusters and 16 of 55 SDSE MGE insertion regions were found across species. Assessing potential cross-protection from leading S. pyogenes vaccine candidates on SDSE, 12/34 preclinical vaccine antigen genes were shown to be present in >99% of isolates of both species. Relevant to possible vaccine evasion, six vaccine candidate genes demonstrated evidence of inter-species recombination. These findings demonstrate previously unappreciated levels of genomic overlap between these closely related pathogens with implications for streptococcal pathobiology, disease surveillance and prevention.

microbiology↗

bioRxiv2023

Detecting co-selection through excess linkage disequilibrium in bacterial genomes

Population genomics has revolutionised our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that the majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.

bioinformatics↗

bioRxiv2023

Calculating and applying pathogen mutational spectra using MutTui

Mutational spectra describe the pattern of mutations acquired during evolution and are driven by factors including mutagens, repair processes and selection. Calculating mutational spectra of pathogen genomic datasets may enable analysis of factors that influence these mutational processes, including replication niches, transmission routes and pathogen biology. Here, we introduce MutTui, which can leverage multiple types of sequence data to calculate and compare mutational spectra of DNA and RNA pathogens. MutTui is available at https://github.com/chrisruis/MutTui.

genomics↗

bioRxiv2022

Consistent typing of plasmids with the mge-cluster pipeline

Extrachromosomal elements of bacterial cells such as plasmids are notorious for their importance in evolution and adaptation to changing ecology. However, high-resolution population-wide analysis of plasmids has only become accessible recently with the advent of scalable long-read sequencing technology. Current typing methods for the classification of plasmids remain limited in their scope which motivated us to develop a computationally efficient approach to simultaneously recognize novel types and classify plasmids into previously identified groups. Our method can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. We provide an intuitive visualization, classification and clustering scheme that users can explore interactively. This provides a framework that can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We illustrate the attractive features of our approach by the analysis of population-wide plasmid data from the opportunistic pathogen Escherichia coli and the distribution of the colistin resistance gene mcr-1.1 in the plasmid population.

bioinformatics↗

bioRxiv2022

Mutational spectra analysis reveals bacterial niche and transmission routes

As observed in cancers, individual mutagens and defects in DNA repair create distinctive mutational signatures that combine to form context-specific spectra within cells. We reasoned that similar processes must occur in bacterial lineages, potentially allowing decomposition analysis to identify disrupted DNA repair processes and niche-specific mutagen exposure. Here we reconstructed mutational spectra for 84 clades from 31 diverse bacterial species, assigned signatures to specific DNA repair pathways using hypermutator lineages, and, by comparing mutational spectra of clades from different environmental and biological locations, extracted reproducible niche-associated mutational signatures. We show that mutational spectra can predict general and specific bacterial niches and therefore reveal the site of infection and types of transmission routes for established and emergent human bacterial pathogens. One sentence summaryVariable mutagen exposure and DNA repair drive differential mutational spectra between bacteria and enable niche inference

microbiology↗

bioRxiv2022

Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe

Horizontal gene transfer (HGT) plays a critical role in the evolution and diversification of many microbial species. The resulting dynamics of gene gain and loss can have important implications for the development of antibiotic resistance and the design of vaccine and drug interventions. Methods for the analysis of gene presence/absence patterns typically do not account for errors introduced in the automated annotation and clustering of gene sequences. In particular, methods adapted from ecological studies, including the pangenome gene accumulation curve, can be misleading as they may reflect the underlying diversity in the temporal sampling of genomes rather than a difference in the dynamics of HGT. Here, we introduce Panstripe, a method based on Generalised Linear Regression that is robust to population structure, sampling bias and errors in the predicted presence/absence of genes. We demonstrate using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analysing several diverse bacterial genome datasets representing major human pathogens. Panstripe is freely available as an R package at https://github.com/gtonkinhill/panstripe.

evolutionary biology↗

bioRxiv2022

Pneumococcal within-host diversity during colonisation, transmission and treatment

Characterising the genetic diversity of pathogens within the host promises to greatly improve surveillance and reconstruction of transmission chains. For bacteria, it also informs our understanding of inter-strain competition, and how this shapes the distribution of resistant and sensitive bacteria. Here we study the genetic diversity of Streptococcus pneumoniae within individual infants and their mothers by deep sequencing whole pneumococcal populations from longitudinal nasopharyngeal samples. We demonstrate deep sequencing has unsurpassed sensitivity for detecting multiple colonisation, doubling the rate at which highly invasive serotype 1 bacteria were detected in carriage compared to gold-standard methods. The greater resolution identified an elevated rate of transmission from mothers to their children in the first year of the childs life. Comprehensive treatment data demonstrated infants were at an elevated risk of both the acquisition, and persistent colonisation, of a multidrug resistant bacterium following antimicrobial treatment. Some alleles were enriched after antimicrobial treatment, suggesting they aided persistence, but generally purifying selection dominated within-host evolution. Rates of co-colonisation imply that in the absence of treatment, susceptible lineages outcompeted resistant lineages within the host. These results demonstrate the many benefits of deep sequencing for the genomic surveillance of bacterial pathogens.

genomics↗

bioRxiv2021

Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation

In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species and the number of genomes is expected to continue to increase at an accelerated pace given the advances in sequencing technology and widespread genomic surveillance initiatives. This explosion of data calls for innovative methods to enable rapid exploration of the structure of a population based on different data modalities, such as multiple sequence alignments, assemblies and estimates of gene content across different genomes. Here we present Mandrake, an efficient implementation of a dimensional reduction method tailored for the needs of large-scale population genomics. Mandrake is capable of visualising population structure from millions of whole genomes and we illustrate its usefulness with several data sets representing major pathogens. Our method is freely available both as an analysis pipeline (https://github.com/johnlees/mandrake) and as a browser-based interactive application (https://gtonkinhill.github.io/mandrake-web/).

genomics↗

bioRxiv2021

Genome-wide association, prediction and heritability in bacteria

Advances in whole-genome genotyping and sequencing have allowed genome-wide analyses of association, prediction and heritability in many organisms. However, the application of such analyses to bacteria is still in its infancy, being limited by difficulties including the plasticity of bacterial genomes and their strong population structure. Here we propose, and validate using simulations, a suite of genome-wide analyses for bacteria. We combine methods from human genetics and previous bacterial studies, including linear mixed models, elastic net and LD-score regression, and introduce innovations such as frequency-based allele coding, testing for both insertion/deletion and nucleotide effects and partitioning heritability by genome region. We then analyse three phenotypes of a major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for each of two antibiotics, penicillin and ceftriaxone. We show that these are highly heritable leading to high prediction accuracy, which is explained by many genetic associations identified under good control of population structure effects. In the case of ceftriaxone MIC, these results are surprising because none of the isolates was resistant according to the inhibition zone diameter threshold. We estimate that just over half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes around a quarter of the heritability of ceftriaxone MIC. For the within-host survival phenotype carriage duration, no reliable associations were found but we observed moderate heritability and prediction accuracy, indicating a polygenic trait. While generating important new results for S. pneumoniae, we have critically assessed existing methods and introduced innovations that will be useful for future large-scale population genomics studies to help decipher the genetic architecture of bacterial traits. Author summaryGenome-wide association, prediction and heritability analyses in bacteria are beginning to help unravel the genetic underpinnings of traits such as antimicrobial resistance, virulence, within-host survival and transmissibility. Progress to date is limited by challenges including the effects of strong population structure and variable recombination, and the many gaps in sequence alignments including the absence of entire genes in many isolates. More work is required to critically asses and develop methods for bacterial genomics. We address this task here, using a range of existing methods from bacterial and human genetics, such as linear mixed models, elastic net and LD-score regression. Using simulations, we first validate and then adapt these methods to introduce new analyses, including separate assessment of gap and nucleotide effects, a new allele coding for association analyses and a method to partition heritability into genome regions. We analyse within-host survival and two antimicrobial response traits of Streptococcus pneumoniae, identifying many novel associations while demonstrating good control of population structure and accurate prediction. We present both new results for an important pathogen and methodological advances that will be useful in guiding future studies in bacterial population genomics.

genomics↗