Biology subjects

Corander, J.

Publications and source records attributed to Corander, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2020

Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen Enterococcus faecium

Enterococcus faecium is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that E. faecium strains from hospitalized patients form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here we further explored the adaptive evolution of E. faecium using a genome-wide co-evolution study (GWES) to identify co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are not proximal to each other based on the completely assembled chromosome of clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data. Impact statementEnterococcus faecium has emerged as an important nosocomial pathogen around the world. Population genetics revealed that clinical E. faecium strains form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here, the adaptive evolution of E. faecium was further explored using an unsupervised machine learning method (SuperDCA) to identify genome-wide co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are separated by a large chromosomal distance in a clinical clade A1 reference isolate, but appeared adjacent to each other in non-clade A1 isolates. We identified four different types of large-scale genomic rearrangements and in all cases, we found insertion of two different genomic islands and an insertion element at the border. In contrast, no genomic rearrangement and insertions were identified among a group of clade A1 pet isolates, suggesting a distinct evolutionary trajectory. Based on the in silico predicted biological functions, we found a common link to a stress response for the genes encoded in three regions. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. Data summaryRaw core-genome alignment (1.1 MB, Harvest suite v1.1.2), including the 1,644 Clade A isolates and the complete E. faecium AUS0004 (accession number CP003351) as a reference is available under the following gitlab repository https://gitlab.com/sirarredondo/efm_gwes.

genomics↗

bioRxiv2020

A comprehensive and high-quality collection of E. coli genomes and their genes

Escherichia coli is a highly diverse organism which includes a range of commensal and pathogenic variants found across a range of niches and worldwide. In addition to causing severe intestinal and extraintestinal disease, E. coli is considered a priority pathogen due to high levels of observed drug resistance. The diversity in the E. coli population is driven by high genome plasticity and a very large gene pool. All these have made E. coli one of the most well-studied organisms, as well as a commonly used laboratory strain. Today, there are thousands of sequenced E. coli genomes stored in public databases. While data is widely available, accessing the information in order to perform analyses can still be a challenge. Collecting relevant available data requires accessing different sources, where data may be stored in a range of formats, and often requires further manipulation, and processing to apply various analyses and extract useful information. In this study, we collated and intensely curated a collection of over 10,000 E. coli and Shigella genomes to provide a single, uniform, high-quality dataset. Shigella were included as they are considered specialised pathovars of E. coli. We provide these data in a number of easily accessible formats which can be used as the foundation for future studies addressing the biological differences between E. coli lineages and the distribution and flow of genes in the E. coli population at a high resolution. The analysis we present emphasises our lack of understanding of the true diversity of the E. coli species, and the biased nature of our current understanding of the genetic diversity of such a key pathogen. Author NotesAll supporting data have been provided within the article or through supplementary data files. All supporting code is provided in the git repository https://github.com/ghoresh11/ecoli_genome_collection. Significance as a BioResource to the communityAs of today, there are more than 140,000 E. coli genomes available on public databases. While data is widely available, collating the data and extracting meaningful information from it often requires multiple steps, computational resources and expert knowledge. Here, we collate a high quality and comprehensive set of over 10,000 E. coli genomes, isolated from human hosts, into a set of manageable files that offer an accessible and usable snapshot of the currently available genome data, linked to a minimal data quality standard. The data provided includes a detailed synopsis of the main lineages present, including their antimicrobial and virulence profiles, their complete gene content, and all the associated metadata for each genome. This includes a database which enables the user to compare newly sequenced isolates against the assembled genomes. Additionally, we provide a searchable index which allows the user to query any DNA sequence against the assemblies of the collection. This collection paves the path for many future studies, including those investigating the differences between E. coli lineages, following the evolution of different genes in the E. coli pan-genome and exploring the dynamics of horizontal gene transfer in this important organism. Data SummaryO_LIThe complete aggregated metadata of 10,146 high quality genomes isolated from human hosts (doi.org/10.6084/m9.figshare.12514883, File F1). C_LIO_LIA PopPUNK database which can be used to query any genome and examine its context relative to this collection (Deposited to doi.org/10.6084/m9.figshare.12650834). C_LIO_LIA BIGSI index of all the genomes which can be used to easily and quickly query the genomes for any DNA sequence of 61 bp or longer (Deposited to doi.org/10.6084/m9.figshare.12666497). C_LIO_LIDescription and complete profiling the 50 largest lineages which represent the majority of publicly available human-isolated E. coli genomes (doi.org/10.6084/m9.figshare.12514883, File F2). Phylogenetic trees of representative genomes of these lineages, presented in this manuscript, are also provided (doi.org/10.6084/m9.figshare.12514883, Files tree_500.nwk and tree_50.nwk). C_LIO_LIThe complete pan-genome of the 50 largest lineages which includes: O_LIA FASTA file containing a single representative sequence of each gene of the gene pool (doi.org/10.6084/m9.figshare.12514883, File F3). C_LIO_LIComplete gene presence-absence across all isolates (doi.org/10.6084/m9.figshare.12514883, File F4). C_LIO_LIThe frequency of each gene within each of the lineages (doi.org/10.6084/m9.figshare.12514883, File F5). C_LIO_LIThe representative sequences from each lineage for all the genes (doi.org/10.6084/m9.figshare.12514883, File F6). C_LI C_LI

genomics↗

bioRxiv2020

Population analysis of Legionella pneumophila reveals the basis of resistance to complement-mediated killing

Legionella pneumophila is the most common cause of the severe respiratory infection known as Legionnaires disease. L. pneumophila is typically a symbiont of free-living amoeba, and our understanding of the bacterial factors that determine human pathogenicity is limited. Here we carried out a population genomic study of 900 L. pneumophila isolates from human clinical and environmental samples to examine their genetic diversity, global distribution and the basis for human pathogenicity. We found that although some clones are more commonly associated with clinical infections, the capacity for human disease is representative of the breadth of species diversity. To investigate the bacterial genetic basis for human disease potential, we carried out a genome-wide association study that identified a single gene (lag-1), to be most strongly associated with clinical isolates. Molecular evolutionary analysis showed that lag-1, which encodes an O-acetyltransferase responsible for lipopolysaccharide modification, has been distributed horizontally across all major phylogenetic clades of L. pneumophila by frequent recent recombination events. Functional analysis revealed a correlation between the presence of a functional lag-1 gene and resistance to killing in human serum and bovine broncho-alveolar lavage. In addition, L. pneumophila strains that express lag-1 escaped complement-mediated phagocytosis by neutrophils. Importantly, we discovered that the expression of lag-1 confers the capacity to evade complement-mediated killing by inhibiting deposition of classical pathway molecules on the bacterial surface. In summary, our combined population and functional analyses identified L. pneumophila genetic traits linked to human disease and revealed the molecular basis for resistance to complement-mediated killing, a previously elusive trait of direct relevance to human disease pathogenicity. SignificanceLegionella pneumophila is an environmental bacterium associated with a severe pneumonia known as Legionnaires disease. A small number of L. pneumophila clones are responsible for a large proportion of human infections suggesting they have enhanced pathogenicity. Here, we employed a large-scale population analysis to investigate the evolution of human pathogenicity and identified a single gene (lag-1) that was more frequently found in clinical isolates. Functional analysis revealed that the lag-1-encoded O-acetyltransferase, involved in modification of lipopolysaccharide, conferred resistance to the classical pathway of complement in human serum. These findings solve a long-standing mystery in the field regarding L. pneumophila resistance to serum killing, revealing a novel mechanism by which L. pneumophila may avoid immune defences during infection.

microbiology↗

bioRxiv2020

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

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

microbiology↗

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

The binding mechanism of Streptococcus suis accessory virulence factor and adhesin SadP to globotetraosylceramide

Streptococcus suis is part of the pig commensal microbiome and a major pathogen causing pneumonia and meningitis in pigs and occasionally also zoonotic meningitis. According to genomic analysis, S. suis is divided into asymptomatic carriage, respiratory and systemic strains with distinct genomic signatures. The virulence factor S. suis adhesin P (SadP) recognizes the galabiose Gal1-4Gal-oligosaccharide. Based on its oligosaccharide fine specificity, SadP can be divided into subtypes PN and PO. We show here that subtype PN is distributed in the systemic strains that cause meningitis, whereas type PO is found in asymptomatic carriage and respiratory strains. Both types of SadP are shown to predominantly bind to pig lung globotriaosylceramide (Gb3). However, SadP adhesin from systemic subtype PN strain also binds to globotetraosylceramide (Gb4). Mutagenesis studies of the galabiose-binding domain of type PN SadP adhesin showed that the amino acid asparagine-285, which is replaced by an aspartate residue in type Po SadP, was required for binding to Gb4 and, strikingly, it was also required for interaction with the glycomimetic inhibitor phenylurea-galabiose. Molecular dynamics simulations provided further insight into the role of Asn-285 for Gb4 and phenylurea-galabiose binding, suggesting additional hydrogen bonding to terminal GalNAc of Gb4 and urea-group. Thus, the Asn-285-mediated molecular mechanism of type PN SadP binding to Gb4 could be used as a candidate to selectively target S. suis in invasive systemic disease without interfering with commensal strains, which may open up new venues for developing intervention strategies against this pathogen.

microbiology↗

bioRxiv2020

The effect of recombination on the evolution of a population of Neisseria meningitidis

Neisseria meningitidis (the meningococcus) is a major human pathogen with a history of high invasive disease burden, particularly in sub-Saharan Africa. Our current understanding of the evolution of meningococcal genomes is limited by the rarity of large-scale genomic population studies and lack of in-depth investigation of the genomic events associated with routine pathogen transmission. Here we fill this knowledge gap by a detailed analysis of 2,839 meningococcal genomes obtained through a carriage study of over 50,000 samples collected systematically in Burkina Faso, West Africa, before, during, and after the serogroup A vaccine rollout, 2009-2012. Our findings indicate that the meningococcal genome is highly dynamic, with recombination hotspots and frequent gene sharing across deeply separated lineages in a structured population. Furthermore, our findings illustrate the profound effect of population structure on genome flexibility, with some lineages in Burkina Faso being orders of magnitude more recombinant than others. We also examine the effect of selection on the population, in particular how it is correlated with recombination. We find that recombination principally acts to prevent the accumulation of deleterious mutations, although we do also find an example of recombination acting to speed the adaptation of a gene. In general, we show the importance of recombination in the evolution of a geographically expansive population with deep population structure in a short timescale. This has important consequences for our ability to both foresee the outcomes of vaccination programmes and, using surveillance data, predict when lineages of the meningococcus are likely to become a public health concern.

evolutionary biology↗

bioRxiv2020

Genomic Epidemiology with Mixed Samples

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, skipping the colony pick step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

bioinformatics↗

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

gplas: a comprehensive tool for plasmid analysis using short-read graphs

SummaryPlasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data is often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and clustering based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short read sequence data.\n\nAvailability and implementationGplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git\n\nContacta.c.schurch@umcutrecht.nl

bioinformatics↗

bioRxiv2019

Real-time sampling of travelers shows intestinal colonization by multidrug-resistant bacteria to be a dynamic process with multiple transient acquisitions

BackgroundAntimicrobial resistance (AMR) is highly prevalent in low- and middle-income countries (LMIC). International travel contributes substantially to the global spread of intestinal multidrug-resistant gram-negative (MDR-GN) bacteria. Of the 100 million annual visitors to LMIC, 30-70% become colonized by MDR-GN bacteria. The phenomenon has been well documented, but since sampling has only been conducted after travelers return home, data on the actual colonization process are scarce. We aimed to characterize colonization dynamics by exploring stool samples abroad on a daily basis while visiting LMIC. MethodsA group of 20 European volunteers visiting Lao Peoples Democratic Republic for three weeks provided daily stool samples and filled in daily questionnaires. Acquisition of extended-spectrum beta-lactamase-producing gram-negative bacteria (ESBL-GN) was examined by selective stool cultures followed by whole-genome sequencing (WGS) of isolates. ResultsWhile colonization rates were 70% at the end of the study, daily sampling revealed that all participants had acquired ESBL-GN at some time point during their overseas stay, the colonization status varying day by day. WGS analysis ascribed the transient pattern of colonization to sequential acquisition of new strains, resulting in a loss of detectable colonization by the initial MDR-GN strains. All but one participant acquired multiple strains (n=2-7). Of the total of 83 unique strains identified (53 E. coli, 10 Klebsiella, 20 other ESBL-GN species), some were shared by as many as four subjects. ConclusionsThis is the first study to characterize in real time the dynamics of acquiring MDR-GN during travel. Our data show multiple transient colonization events indicative of constant microbial competition.

microbiology↗

bioRxiv2019

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

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

microbiology↗

bioRxiv2019

Designing ecologically-optimised vaccines using population genomics

Streptococcus pneumoniae (the pneumococcus) is a common nasopharyngeal commensal capable of infecting normally sterile anatomical sites, resulting in invasive pneumococcal disease (IPD). Effective vaccines preventing IPD exist, but each of the antigens they contain typically induces protective immunity against only one of the approximately 100 pneumococcal serotypes, which are differentiated by immunogenically-distinct polysaccharide capsules. Serotypes vary in their propensity to cause IPD, quantified as their invasiveness. Vaccines are designed to include serotypes commonly isolated from IPD, but the immunity they induce is sufficiently strong to also eliminate vaccine serotypes from carriage. This enables their replacement by non-vaccine serotypes in the nasopharynx. The emergence of invasive non-vaccine serotypes has undermined some vaccination programmes benefits. Recent advances in genomics and modeling have enabled forecasting of which non-vaccine serotypes will be successful post-vaccination. Here, we demonstrate that vaccines optimised using this framework can minimise IPD and antibiotic-resistant disease more effectively than existing formulations in the model, through mitigating the consequences of serotype replacement. The simulations also demonstrate that tailoring vaccines to the pre-vaccine bacterial population is likely to have a substantial impact on reducing IPD, highlighting the importance of epidemiological data, genomics and ecological models as tools for vaccine design and evaluation.

evolutionary biology↗

bioRxiv2019

Fine-scale haplotype structure reveals strong signatures of positive selection in a recombining bacterial pathogen

Identifying the forces that create and shape ecologically meaningful variation in bacteria remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show derived alleles less than 300bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles compared to synonymous alleles. While linkage is shaped by many evolutionary processes, extensive simulations show only two distinct forms of positive selection can drive an excess of coupling linkage between neighboring nonsynonymous alleles: directional selection on introgressed alleles or selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.

evolutionary biology↗

bioRxiv2019

Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome

Enterococcus faecium is a gut commensal of many mammals but is also recognized as a major nosocomial human pathogen, as it is listed on the WHO global priority list of multi-drug resistant organisms. Previous research has suggested that nosocomial strains have multiple zoonotic origins and are only distantly related to those involved in human commensal colonization. Here we present the first comprehensive population-wide joint genomic analysis of hospital, commensal and animal isolates using both short- and long-read sequencing techniques. This enabled us to investigate the population plasmidome, core genome variation and genome architecture in detail, using a combination of machine learning, population genomics and genome-wide co-evolution analysis. We observed a high level of genome plasticity with large-scale inversions and heterogeneous chromosome sizes, collectively painting a high-resolution picture of the adaptive landscape of E. faecium, and identified plasmids as the main indicator for host-specificity. Given the increasing availability of long-read sequencing technologies, our approach could be widely applied to other human and animal pathogen populations to unravel fine-scale mechanisms of their evolution.

genomics↗

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↗