Biology subjects

Lees, J. A.

Publications and source records attributed to Lees, J. A..

33 records · Page 2Linked to original sources

bioRxiv2023

Accurate and fast graph-based pangenome annotation and clustering with ggCaller

Bacterial genomes differ in both gene content and sequence mutations, which can cause important clinical phenotypic differences such as vaccine escape or antimicrobial resistance. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated and clustered, representing the pangenome. Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller; https://github.com/samhorsfield96/ggCaller). ggCaller combines gene prediction, functional annotation and clustering into a single step using population-wide de Bruijn Graphs, removing redundancy in gene annotation, and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial genome datasets, comparing it to current state-of-the-art tools. ggCaller is ~50x faster with equivalent or greater accuracy, particularly in datasets with complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial epidemiology and evolutionary study.

bioinformatics↗

bioRxiv2022

Genealogical inference and more flexible sequence clustering using iterative PopPUNK

Bacterial genome data are accumulating at an unprecedented speed due the routine use of sequencing in clinical diagnoses, public health surveillance and population genetics studies. Genealogical reconstruction is fundamental to many of these uses, however, inferring genealogy from large-scale genome datasets quickly, accurately, and flexibly is still a challenge. Here, we extend an alignment- and annotation-free method, PopPUNK, to increase its flexibility and interpretability across datasets. Our method, iterative-PopPUNK, rapidly produces multiple consistent cluster assignments across a range of sequence identities. By constructing a partially resolved genealogical tree with respect to these clusters, users can select a resolution most appropriate for their needs. We demonstrated the accuracy of clusters at all levels of similarity and genealogical inference of iterative-PopPUNK based on simulated data and obtained phylogenetically-concordant results in real datasets from seven bacterial species. Using two example sets of Escherichia/Shigella genomes and Vibrio parahaemolyticus genomes we show that iterative-PopPUNK can achieve cluster resolutions ranging from phylogroup down to sequence typing (ST). The iterative-PopPUNK algorithm is implemented in the PopPUNK_iterate program, available as part of PopPUNK package.

bioinformatics↗

bioRxiv2022

BlpC-mediated selfish program leads to rapid loss of Streptococcus pneumoniae clonal diversity during infection

Chromosomal barcoding and high-throughput sequencing were used to investigate the population dynamics of Streptococcus pneumoniae. During infant mouse colonization, >35-fold reduction in diversity and expansion of a single clonal lineage was observed within 1 day post-inoculation. This loss of diversity was not due to immune factors, host microbiota or exclusively because of genetic drift. Rather, it required the expression of blp bacteriocins induced by the BlpC-quorum sensing pheromone. This points towards the role of intra-strain competition whereby the subpopulation reaching a quorum eliminates others that have yet to activate the blp locus. We show that this loss of diversity also restricts the number of unique clones that could establish colonization during transmission between hosts. Moreover, we show that genetic variation in the blp locus is associated with transmissibility in the human population. We posit this is due to its importance in clonal selection and its role as a selfish element.

microbiology↗

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

Pneumococcal genetic variability influences age-dependent bacterial carriage

The pneumococcal conjugate vaccine (PCV) primarily reduces disease burden in adults through a reduction in carriage prevalence of invasive serotypes in children. Current vaccine formulations are the same for both adults and children, but tailoring these formulations to age category could optimize vaccine efficacy. Identification of specific pneumococcal genetic factors associated with carriage in younger or older age groups may suggest alternative formulations and contribute to a better mechanistic understanding of immunity. Here, we used whole genome sequencing to dissect pneumococcal variation associated with age. We performed genome sequencing in a large carriage cohort, and conducted a meta-analysis with an existing carriage study. We compiled a dictionary of pathogen genetic variation including serotype, sequence cluster, sequence elements, SNPs, burden combined rare variants, and clusters of orthologous genes (COGs) for each cohort - all of which used in a genome-wide association with host age. Age-dependent colonization had some heritability, though this varied between cohorts (h2 = 0.10, 0.00 - 0.69 95% CI in the first; h2 = 0.46, 0.33 - 0.60 95% CI in the second cohort). We found that serotypes and genetic background (strain) explained most of the heritability in each cohort (h2serotype = 0.06 and h2GPSC = 0.04 in the first; h2 serotype = 0.20 and h2 GPSC = 0.23 in the second cohort). We found one candidate association (p = 1.2x10-9) upstream of an accessory Sec-dependent serine-rich glycoprotein adhesin. Overall, association with age was highly cohort and strain dependent, supporting proposals for a future vaccination strategy which is primarily targeted using serotypes rather than proteins, and is tailored towards specific pathogen populations.

microbiology↗

bioRxiv2020

Negative frequency-dependent selection and asymmetrical transformation stabilise multi-strain bacterial population structures

Streptococcus pneumoniae can be split into multiple strains, each with a characteristic combination of core and accessory genome variation, able to co-circulate and compete within the same hosts. Previous analyses of epidemiological datasets suggested the short-term vaccine-associated dynamics of S. pneumoniae strains may be mediated through multi-locus negative frequency-dependent selection (NFDS), acting to maintain accessory loci at equilibrium frequencies. To test whether this model could explain how such multi-strain populations were generated, it was modified to incorporate recombination. The outputs of simulations featuring symmetrical recombination were compared with genomic data on locus frequencies and distributions between genotypes, pairwise genetic distances and tree shape. These demonstrated NFDS prevented the loss of variation through neutral drift, but generated unstructured populations of diverse isolates. Making recombination asymmetrical, favouring deletion of accessory loci over insertion, alongside multi-locus NFDS significantly improved the fit to genomic data. In a population at equilibrium, structuring into multiple strains was stable due to outbreeding depression, resulting from recombinants with reduced accessory genomes having lower fitness than their parental genotypes. As many bacteria inhibit the integration of insertions into their chromosomes, this combination of asymmetrical recombination and multi-locus NFDS may underlie the co-existence of strains within a single ecological niche.

evolutionary biology↗

bioRxiv2020

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content, resulting from frequent horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here we introduce Panaroo, a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. We verified our approach through extensive simulations of de novo assemblies using the infinitely many genes model and by analysing a number of publicly available large bacterial genome datasets. Using a highly clonal Mycobacterium tuberculosis dataset as a negative control case, we show that failing to account for annotation errors can lead to pangenome estimates that are dominated by error. We additionally demonstrate the utility of the improved graphical output provided by Panaroo by performing a pan-genome wide association study in Neisseria gonorrhoeae and by analysing gene gain and loss rates across 51 of the major global pneumococcal sequence clusters. Panaroo is freely available under an open source MIT licence at https://github.com/gtonkinhill/panaroo.

genomics↗

bioRxiv2019

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

Discovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.

microbiology↗

bioRxiv2019

Contrasting approaches to genome-wide association studies impact the detection of resistance mechanisms in Staphylococcus aureus

Rapid detection of antibiotic resistance using whole-genome sequencing (WGS) could improve clinical outcomes and limit the spread of resistance. For this to succeed, we need an accurate way of linking genotype to phenotype, that identifies new resistance mechanisms as they appear. To assess how close we are to this goal, we characterized antimicrobial resistance determinants in >4,000 Staphylococcus aureus genomes of isolates associated with bloodstream infection in the United Kingdom and Ireland. We sought to answer three questions: 1) how well did known resistance mechanisms explain phenotypic resistance in our collection, 2) how many previously identified resistance mechanisms appeared in our collection, and 3) how many of these were detectable using four contrasting genome-wide association study (GWAS) methods. Resistance prediction based on the detection of known resistance determinants was 98.8% accurate. We identified challenges in correcting for population structure, clustering orthologous genes, and identifying causal mechanisms in rare or common phenotypes, which reduced the recovery of known mechanisms. Limited sensitivity and specificity of these methods made prediction using GWAS-discovered hits alone less accurate than using literature-derived genetic determinants. However, GWAS methods identified novel mutations associated with resistance, including five mutations in rpsJ, which improved tetracycline resistance prediction for 28 isolates, and a T118I substitution in fusA which resulted in better fusidic acid resistance prediction for 5 isolates. Thus, GWAS approaches in conjunction with phenotypic testing data can support the development of comprehensive databases to enable real-time use of WGS for patient management.

bioinformatics↗

bioRxiv2019

Mesenchymal expression of activated K-ras yields Noonan Syndrome-like bone defects that are rescued by mid-gestational MEK inhibition.

Activating germline K-ras mutations cause Noonan syndrome (NS), which is characterized by several developmental deficits including cardiac defects, cognitive delays and skeletal abnormalities. NS patients have increased signaling through the MAPK pathway. To model NS skeletal defects and understand the effect of hyperactive K-ras signaling on normal limb development, we generated a mouse model in which activated K-rasG12D was expressed specifically in mesenchymal progenitors of the limb bud. These mice display short, abnormally mineralized long bones that phenocopy those of NS patients. This defect was first apparent at E14.5, and was characterized by a delay in bone collar formation. Coincident mutation of p53 had no effect on the K-rasG12D induced bone defect, arguing that it is does not result from senescence or apoptosis. Instead, our data revealed profound defects in the development of the committed osteoblasts; their appearance is delayed, concordant with the delay in bone collar formation, and they display an aberrant localization outside of the bone shaft. Additionally, we see growth plate defects including a reduction in the hypertrophic chondrocyte layer. Most importantly, we found that in utero delivery of a MEK inhibitor between E10.5 and E14.5 is sufficient to completely suppress the ability of activated K-ras to induce NS-like long bone defects in embryogenesis. These data define a critical point in mid-gestation in which elevated MAPK signaling impairs growth plate and bone collar formation and yield NS-like limb defects. Moreover, they offer insight into possible therapeutic strategies for skeletal defects in patients with Noonan Syndrome.\n\nSIGNIFICANCE STATEMENTNoonan syndrome is a genetic condition that is characterized by various developmental defects including skeletal abnormalities that lead to short stature. These patients carry mutations that activate Ras/MAPK signaling. We have generated a mouse model that recapitulates these Noonan Syndrome-like bone defects. Analysis of these animals establishes the developmental window in which bone formation goes awry, and reveals disruption of an early event that is critical for the longitudinal growth of bones. Additionally, we show that treatment with an inhibitor of Ras/MAPK signaling during this key developmental window is sufficient to completely suppress these Noonan Syndrome-like bone defects. This offers possible therapeutic strategies for skeletal defects in patients with Noonan Syndrome.

developmental biology↗

bioRxiv2019

Genome-wide epistasis and co-selection study using mutual information

Discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level co-variation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which is demonstrated to maintain a very low rate of false positive findings among those SNP pairs highlighted to deviate significantly from the null hypothesis of neutral co-evolution in simulated data. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Application of the method to large population genomic data sets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

genomics↗

bioRxiv2018

Fast Hierarchical Bayesian Analysis of Population Structure

We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet Process Mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analysing an alignment of over 110,000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximise the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and subclades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while generally being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.

genomics↗

bioRxiv2018

Fast and flexible bacterial genomic epidemiology with PopPUNK

The routine use of genomics for disease surveillance provides the opportunity for high-resolution bacterial epidemiology.\n\nHowever, current whole-genome clustering and multi-locus typing approaches do not fully exploit core and accessory genomic variation, and cannot both automatically identify, and subsequently expand, clusters of significantly-similar isolates in large datasets and across species.\n\nHere we describe PopPUNK (Population Partitioning Using Nucleotide K-mers; https://poppunk.readthedocs.io/en/latest/). software implementing scalable and expandable annotation- and alignment-free methods for population analysis and clustering.\n\nVariable-length k-mer comparisons are used to distinguish isolates divergence in shared sequence and gene content, which we demonstrate to be accurate over multiple orders of magnitude using both simulated data and real datasets from ten taxonomically-widespread species. Connections between closely-related isolates of the same strain are robustly identified, despite variation in the discontinuous pairwise distance distributions that reflects species diverse evolutionary patterns. PopPUNK can process 103-104 genomes as single batch, with minimal memory use and runtimes up to 200-fold faster than existing methods. Clusters of strains remain consistent as new batches of genomes are added, which is achieved without needing to re-analyse all genomes de novo.\n\nThis facilitates real-time surveillance with stable cluster naming and allows for outbreak detection using hundreds of genomes in minutes. Interactive visualisation and online publication is streamlined through automatic output of results to multiple platforms.\n\nPopPUNK has been designed as a flexible platform that addresses important issues with currently used whole-genome clustering and typing methods, and has potential uses across bacterial genetics and public health research.

genomics↗

bioRxiv2017

SuperDCA for genome-wide epistasis analysis

The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 104-105 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 105 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.\n\nAuthor SummaryRecent work has demonstrated the emerging potential in statistical genome-wide modeling to uncover co-selection and epistatic interactions between polymorphisms in bacterial chromosomes from densely sampled population data. Here we develop the Potts model based approach further into a fully mature computational method which can be applied to most existing bacterial population genomic data sets in a straightforward manner. Our advances are relying on more efficient parameter scoring, highly optimized and parallelized open source C++ code, which does not rely on the computation-intensive polymorphism subsampling approximations used earlier. By analyzing the two largest available population samples of Streptococcus pneumoniae (the pneumococcus), we highlight several biological discoveries related to the survival of the pneumococcus and co-evolution of penicillin-binding loci, which were not uncovered by the earlier analyses. Our method holds considerable potential for building understanding about numerous organisms at a systems biological level.

genomics↗

bioRxiv2017

Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration

Streptococcus pneumoniae is a leading cause of invasive disease in infants, especially in low-income settings. Asymptomatic carriage in the nasopharynx is a prerequisite for disease, and the duration of carriage is an important consideration in modelling transmission dynamics and vaccine response. Existing studies of carriage duration variability are based at the serotype level only, and do not probe variation within lineages or fully quantify interactions with other environmental factors.\n\nHere we developed a model to calculate the duration of carriage episodes from longitudinal swab data. By combining these results with whole genome sequence data we estimate that pneumococcal genomic variation accounted for 63% of the phenotype variation, whereas host traits accounted for less than 5%. We further partitioned this heritability into both lineage and locus effects, and quantified the amount attributable to the largest sources of variation in carriage duration: serotype (17%), drug-resistance (9%) and other significant locus effects (7%). For the locus effects, a genome-wide association study identified 16 loci which may have an effect on carriage duration independent of serotype. Hits at a genome-wide level of significance were to prophage sequences, suggesting infection by such viruses substantially affects carriage duration.\n\nThese results show that both serotype and non-serotype specific effects alter carriage duration in infants and young children and are more important than other environmental factors such as host genetics. This has implications for models of pneumococcal competition and antibiotic resistance, and leads the way for the analysis of heritability of complex bacterial traits.\n\nSignificance statementOther than serotype, the genetic determinants of pneumococcal carriage duration are unknown. In this study we used longitudinal sampling to measure the duration of carriage in infants, and searched for any associated variation in the pan-genome. While we found that the pathogen genome explains most of the variability in duration, serotype did not fully account for this. Recent theoretical work has proposed the existence of alleles which alter carriage duration to explain the puzzle of continued coexistence of antibiotic-resistant and sensitive strains. Here we have shown that these alleles do exist in a natural population, and also identified candidates for the loci which fulfil this role. Together these findings have implications for future modelling of pneumococcal epidemiology and resistance.

genomics↗