Pan-pathogen deep sequencing of nosocomial bacterial pathogens in Italy in spring 2020: a prospective cohort study.

BACKGROUND: Nosocomial infections pose a considerable risk to patients who are susceptible, and this is particularly acute in intensive care units when hospital-associated bacteria are endemic. During the first wave of the COVID-19 pandemic, the surge of patients presented a significant obstacle to the effectiveness of infection control measures. We aimed to assess the risks and extent of nosocomial pathogen transmission under a high patient burden by designing a novel bacterial pan-pathogen deep-sequencing approach that could be integrated with standard clinical surveillance and diagnostics workflows. METHODS: We did a prospective cohort study in a region of northern Italy that was severely affected by the first wave of the COVID-19 pandemic. Inpatients on both ordinary and intensive care unit (ICU) wards at the San Matteo hospital, Pavia were sampled on multiple occasions to identify bacterial pathogens from respiratory, nasal, and rectal samples. Diagnostic samples collected between April 7 and May 10, 2020 were cultured on six different selective media designed to enrich for Acinetobacter baumannii, Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, and DNA from each plate with positive growth was deep sequenced en masse. We used mSWEEP and mGEMS to bin sequencing reads by sequence cluster for each species, followed by mapping with snippy to generate high quality alignments. Antimicrobial resistance genes were detected by use of ARIBA and CARD. Estimates of hospital transmission were obtained from pairwise bacterial single nucleotide polymorphism distances, partitioned by within-patient and between-patient samples. Finally, we compared the accuracy of our binned Acinetobacter baumannii genomes with those obtained by single colony whole-genome sequencing of isolates from the same hospital. FINDINGS: We recruited patients from March 1 to May 7, 2020. The pathogen population among the patients was large and diverse, with 2148 species detections overall among the 2418 sequenced samples from the 256 patients. In total, 55 sequence clusters from key pathogen species were detected at least five times. The antimicrobial resistance gene prevalence was correspondingly high, with key carbapenemase and extended spectrum ß-lactamase genes detected in at least 50 (40%) of 125 patients in ICUs. Using high-resolution mapping to infer transmission, we established that hospital transmission was likely to be a significant mode of acquisition for each of the pathogen species. Finally, comparison with single colony Acinetobacter baumannii genomes showed that the resolution offered by deep sequencing was equivalent to single-colony sequencing, with the additional benefit of detection of co-colonisation of highly similar strains. INTERPRETATION: Our study shows that a culture-based deep-sequencing approach is a possible route towards improving future pathogen surveillance and infection control at hospitals. Future studies should be designed to directly compare the accuracy, cost, and feasibility of culture-based deep sequencing with single colony whole-genome sequencing on a range of bacterial species. FUNDING: Wellcome Trust, European Research Council, Academy of Finland Flagship program, Trond Mohn Foundation, and Research Council of Norway.

Pangenome-spanning epistasis and coselection 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 high-accuracy long-read sequencing technologies. To exploit this opportunity, we introduce a phenotype- and alignment-free method for discovering coselected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact colored de Bruijn graph to approximate the intragenome 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.

Detecting co-selection through excess linkage disequilibrium in bacterial genomes.

Population genomics has revolutionized 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 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.

Overlapping Streptococcus pyogenes and Streptococcus dysgalactiae subspecies equisimilis household transmission and mobile genetic element exchange.

Streptococcus dysgalactiae subspecies equisimilis (SDSE) and Streptococcus pyogenes share skin and throat niches with extensive genomic homology and horizontal gene transfer (HGT) possibly underlying shared disease phenotypes. It is unknown if cross-species transmission interaction occurs. Here, we conduct a genomic analysis of a longitudinal household survey in remote Australian First Nations communities for patterns of cross-species transmission interaction and HGT. Collected from 4547 person-consultations, we analyse 294 SDSE and 315 S. pyogenes genomes. We find SDSE and S. pyogenes transmission intersects extensively among households and show that patterns of co-occurrence and transmission links are consistent with independent transmission without inter-species interference. We identify at least one of three near-identical cross-species mobile genetic elements (MGEs) carrying antimicrobial resistance or streptodornase virulence genes in 55 (19%) SDSE and 23 (7%) S. pyogenes isolates. These findings demonstrate co-circulation of both pathogens and HGT in communities with a high burden of streptococcal disease, supporting a need to integrate SDSE and S. pyogenes surveillance and control efforts.

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 shared 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.

Mutational spectra are associated with bacterial niche.

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 detect both disruption of DNA repair processes and exposure to niche-specific mutagens. Here we reconstruct mutational spectra for 84 clades from 31 diverse bacterial species and find distinct mutational patterns. We extract signatures driven by specific DNA repair defects using hypermutator lineages, and further deconvolute the spectra into multiple signatures operating within different clades. We show that these signatures are explained by both bacterial phylogeny and replication niche. By comparing mutational spectra of clades from different environmental and biological locations, we identify niche-associated mutational signatures, and then employ these signatures to infer the predominant replication niches for several clades where this was previously obscure. Our results show that mutational spectra may be associated with sites of bacterial replication when mutagen exposures differ, and can be used in these cases to infer transmission routes for established and emergent human bacterial pathogens.

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

Bacterial genomes differ in both gene content and sequence mutations, which underlie extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. 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). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow 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 data sets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with data sets containing 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 evolution and epidemiology.

Mge-cluster: a reference-free approach for typing bacterial plasmids.

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. Here, we introduce mge-cluster that can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. Our approach offers a faster runtime than existing algorithms, with moderate memory usage, and enables an intuitive visualization, classification and clustering scheme that users can explore interactively within a single framework. Mge-cluster platform for plasmid analysis can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We underscore the advantages of our approach by analysing a population-wide plasmid data set obtained from the opportunistic pathogen Escherichia coli, studying the prevalence of the colistin resistance gene mcr-1.1 within the plasmid population, and describing an instance of resistance plasmid transmission within a hospital environment.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions.

Here we introduce a new endpoint "census population size" to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOIvar), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOIvar from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOIvar in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOIvar, and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Challenges in prokaryote pangenomics.

Horizontal gene transfer (HGT) and the resulting patterns of gene gain and loss are a fundamental part of bacterial evolution. Investigating these patterns can help us to understand the role of selection in the evolution of bacterial pangenomes and how bacteria adapt to a new niche. Predicting the presence or absence of genes can be a highly error-prone process that can confound efforts to understand the dynamics of horizontal gene transfer. This review discusses both the challenges in accurately constructing a pangenome and the potential consequences errors can have on downstream analyses. We hope that by summarizing these issues researchers will be able to avoid potential pitfalls, leading to improved bacterial pangenome analyses.

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 generalized linear regression that is robust to population structure, sampling bias, and errors in the predicted presence/absence of genes. We show using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analyzing several diverse bacterial genome data sets representing major human pathogens.

Pneumococcal within-host diversity during colonization, transmission and treatment.

Characterizing 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 468 infants and 145 of their mothers by deep sequencing whole pneumococcal populations from 3,761 longitudinal nasopharyngeal samples. We demonstrate that deep sequencing has unsurpassed sensitivity for detecting multiple colonization, doubling the rate at which highly invasive serotype 1 bacteria were detected in carriage compared with gold-standard methods. The greater resolution identified an elevated rate of transmission from mothers to their children in the first year of the child's life. Comprehensive treatment data demonstrated that infants were at an elevated risk of both the acquisition and persistent colonization of a multidrug-resistant bacterium following antimicrobial treatment. Some alleles were enriched after antimicrobial treatment, suggesting that they aided persistence, but generally purifying selection dominated within-host evolution. Rates of co-colonization 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.

Mandrake: visualizing 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 visualizing population structure from millions of whole genomes, and we illustrate its usefulness with several datasets 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/). This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Comparative Genomics of Disease and Carriage Serotype 1 Pneumococci.

The isolation of Streptococcus pneumoniae serotypes in systemic tissues of patients with invasive disease versus the nasopharynx of healthy individuals with asymptomatic carriage varies widely. Some serotypes are hyper-invasive, particularly serotype 1, but the underlying genetics remain poorly understood due to the rarity of carriage isolates, reducing the power of comparison with invasive isolates. Here, we use a well-controlled genome-wide association study to search for genetic variation associated with invasiveness of serotype 1 pneumococci from a serotype 1 endemic setting in Africa. We found no consensus evidence that certain genomic variation is overrepresented among isolates from patients with invasive disease than asymptomatic carriage. Overall, the genomic variation explained negligible phenotypic variability, suggesting a minimal effect on the disease status. Furthermore, changes in lineage distribution were seen with lineages replacing each other over time, highlighting the importance of continued pathogen surveillance. Our findings suggest that the hyper-invasiveness is an intrinsic property of the serotype 1 strains, not specific for a "disease-associated" subpopulation disproportionately harboring unique genomic variation.

Genome-wide association, prediction and heritability in bacteria with application to Streptococcus pneumoniae.

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

Age-specific patterns of DBLα var diversity can explain why residents of high malaria transmission areas remain susceptible to Plasmodium falciparum blood stage infection throughout life.

Immunity to Plasmodium falciparum is non-sterilising, thus individuals residing in malaria-endemic areas are at risk of infection throughout their lifetime. Here we seek to find a genomic epidemiological explanation for why residents of all ages harbour blood stage infections despite lifelong exposure to P. falciparum in areas of high transmission. We do this by exploring, for the first known time, the age-specific patterns of diversity of variant antigen encoding (var) genes in the reservoir of infection. Microscopic and submicroscopic P. falciparum infections were analysed at the end of the wet and dry seasons in 2012-2013 for a cohort of 1541 residents aged from 1 to 91 years in an area characterised by high seasonal malaria transmission in Ghana. By sequencing the near ubiquitous Duffy-binding-like alpha domain (DBLα) that encodes immunogenic domains, we defined var gene diversity in an estimated 1096 genomes detected in sequential wet and dry season sampling of this cohort. Unprecedented var (DBLα) diversity was observed in all ages with 42,399 unique var types detected. There was a high degree of maintenance of types between seasons (>40% seen more than once), with many of the same types, especially upsA, appearing multiple times in isolates from different individuals. Children and adolescents were found to be significant reservoirs of var DBLα diversity compared with adults. Var repertoires within individuals were highly variable, with children having more related var repertoires compared to adolescents and adults. Individuals of all ages harboured multiple genomes with var repertoires unrelated to those infecting other hosts. High turnover of parasites with diverse isolate var repertoires was also observed in all ages. These age-specific patterns are best explained by variant-specific immune selection. The observed level of var diversity for the population was then used to simulate the development of variant-specific immunity to the diverse var types under conservative assumptions. Simulations showed that the extent of observed var diversity with limited repertoire relatedness was sufficient to explain why adolescents and adults in this community remain susceptible to blood stage infection, even with multiple genomes.

An accurate method for identifying recent recombinants from unaligned sequences.

MOTIVATION: Recombination is a fundamental process in molecular evolution, and the identification of recombinant sequences is thus of major interest. However, current methods for detecting recombinants are primarily designed for aligned sequences. Thus, they struggle with analyses of highly diverse genes, such as the var genes of the malaria parasite Plasmodium falciparum, which are known to diversify primarily through recombination. RESULTS: We introduce an algorithm to detect recent recombinant sequences from a dataset without a full multiple alignment. Our algorithm can handle thousands of gene-length sequences without the need for a reference panel. We demonstrate the accuracy of our algorithm through extensive numerical simulations; in particular, it maintains its effectiveness in the presence of insertions and deletions. We apply our algorithm to a dataset of 17 335 DBLα types in var genes from Ghana, observing that sequences belonging to the same ups group or domain subclass recombine amongst themselves more frequently, and that non-recombinant DBLα types are more conserved than recombinant ones. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/qianfeng2/detREC_program. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying Targets of Protective Antibodies against Severe Malaria in Papua, Indonesia, Using Locally Expressed Domains of Plasmodium falciparum Erythrocyte Membrane Protein 1.

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), a diverse family of multidomain proteins expressed on the surface of malaria-infected erythrocytes, is an important target of protective immunity against malaria. Our group recently studied transcription of the var genes encoding PfEMP1 in individuals from Papua, Indonesia, with severe or uncomplicated malaria. We cloned and expressed domains from 32 PfEMP1s, including 22 that were upregulated in severe malaria and 10 that were upregulated in uncomplicated malaria, using a wheat germ cell-free expression system. We used Luminex technology to measure IgG antibodies to these 32 domains and control proteins in 63 individuals (11 children). At presentation to hospital, levels of antibodies to PfEMP1 domains were either higher in uncomplicated malaria or were not significantly different between groups. Using principal component analysis, antibodies to 3 of 32 domains were highly discriminatory between groups. These included two domains upregulated in severe malaria, a DBLß13 domain and a CIDRα1.6 domain (which has been previously implicated in severe malaria pathogenesis), and a DBLδ domain that was upregulated in uncomplicated malaria. Antibody to control non-PfEMP1 antigens did not differ with disease severity. Antibodies to PfEMP1 domains differ with malaria severity. Lack of antibodies to locally expressed PfEMP1 types, including both domains previously associated with severe malaria and newly identified targets, may in part explain malaria severity in Papuan adults.