Bacterial Genome-wide Association Studies Research Articles

Clinical and in vitro models identify distinct adaptations enhancing Staphylococcus aureus pathogenesis in human macrophages.

Staphylococcus aureus is a facultative intracellular pathogen of human macrophages, which facilitates chronic infection. The genotypes, pathways, and mutations influencing that phenotype remain incompletely explored. Here, we used two distinct strategies to ascertain S. aureus gene mutations affecting pathogenesis in macrophages. First, we analyzed isolates collected serially from chronic cystic fibrosis (CF) respiratory infections. We found that S. aureus strains evolved greater macrophage invasion capacity during chronic human infection. Bacterial genome-wide association studies (GWAS) identified 127 candidate genes for which mutation was significantly associated with macrophage pathogenesis in vivo. In parallel, we passaged laboratory S. aureus strains in vitro to select for increased infection of human THP-1 derived macrophages, which identified 15 candidate genes by whole-genome sequencing. Functional validation of candidate genes using isogenic transposon mutant knockouts and CRISPR interference (CRISPRi) knockdowns confirmed virulence contributions from 37 of 39 tested genes (95%) implicated by in vivo studies and 7 of 10 genes (70%) ascertained from in vitro selection, with one gene in common to the two strategies. Validated genes included 17 known virulence factors (39%) and 27 newly identified by our study (61%), some encoding functions not previously associated with macrophage pathogenesis. Most genes (80%) positively impacted macrophage invasion when disrupted, consistent with the phenotype readily arising from loss-of-function mutations in vivo. This work reveals genes and mechanisms that contribute to S. aureus infection of macrophages, highlights differences in mutations underlying convergent phenotypes arising from in vivo and in vitro systems, and supports the relevance of S. aureus macrophage pathogenesis during chronic respiratory infection in CF. Additional studies will be needed to illuminate the exact mechanisms by which implicated mutations affect their phenotypes.

GWarrange: a pre- and post- genome-wide association studies pipeline for detecting phenotype-associated genome rearrangement events.

The use of k-mers to capture genetic variation in bacterial genome-wide association studies (bGWAS) has demonstrated its effectiveness in overcoming the plasticity of bacterial genomes by providing a comprehensive array of genetic variants in a genome set that is not confined to a single reference genome. However, little attempt has been made to interpret k-mers in the context of genome rearrangements, partly due to challenges in the exhaustive and high-throughput identification of genome structure and individual rearrangement events. Here, we present GWarrange, a pre- and post-bGWAS processing methodology that leverages the unique properties of k-mers to facilitate bGWAS for genome rearrangements. Repeat sequences are common instigators of genome rearrangements through intragenomic homologous recombination, and they are commonly found at rearrangement boundaries. Using whole-genome sequences, repeat sequences are replaced by short placeholder sequences, allowing the regions flanking repeats to be incorporated into relatively short k-mers. Then, locations of flanking regions in significant k-mers are mapped back to complete genome sequences to visualise genome rearrangements. Four case studies based on two bacterial species (Bordetella pertussis and Enterococcus faecium) and a simulated genome set are presented to demonstrate the ability to identify phenotype-associated rearrangements. GWarrange is available at https://github.com/DorothyTamYiLing/GWarrange.

Abstract 789: GrafGen: Distance-Based Inference of Population Ancestry for Helicobacter pylori Genomes

Abstract BACKGROUND: The 1.67 megabase H. pylori genome contains ~143,000 biallelic single-nucleotide polymorphisms (SNP) with minor allele frequency > 1%. Confounding by population stratification is a major source of bias requiring adjustment in genome-wide association studies (GWAS). Previous model- and distance-based methods for bacterial genomes yield varying results depending upon which strains are included for comparison in analyses. We therefore developed a robust classification of H. pylori ancestry to facilitate generalizable inferences about disease associations. METHODS: GrafGen is an R software package adapted from the GrafPop tool for human ancestry (Jin et al., 2017; Jin et al., 2019). The underlying classification algorithm compares SNPs of an individual genome with frequencies in reference populations to estimate subject ancestry and ancestral proportions based on calculated genetic distances. Outputs incorporate visualization tools that provide natural geometric interpretation of population structure. RESULTS: Training data were obtained from the H. pylori Genome Project (HpGP), a global survey of 1011 H. pylori genomes collected across 51 countries (Thorell et al., 2023). Based on genetic distances, HpGP sequences clustered into nine mutually exclusive populations designated by their predominant geographic source. Previously published population assignments for a test set of 255 sequences obtained from GenBank mapped to specific GrafGen classifications (Table). GrafGen assignments based on randomly selected sets of 14,300 and 1430 SNPs were >97% and >90% identical, respectively, to those based on all 143,000 SNPs. CONCLUSIONS: GrafGen's universal categorization of H. pylori ancestry has utility for bacterial GWAS. The software code is publicly available for research on this important pathogen. Theoretically, the same algorithm can be implemented to infer ancestry of any single chromosome haploid species that has sufficient sequence data for references. Citation Format: William Wheeler, Difei Wang, Isaac Zhao, Yumi Jin, Charles S. Rabkin. GrafGen: Distance-Based Inference of Population Ancestry for Helicobacter pylori Genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 789.

Reduced ambiguity and improved interpretability of bacterial genome-wide associations using gene-cluster-centric k-mers.

The wide adoption of bacterial genome sequencing and encoding both core and accessory genome variation using k-mers has allowed bacterial genome-wide association studies (GWAS) to identify genetic variants associated with relevant phenotypes such as those linked to infection. Significant limitations still remain because of k-mers being duplicated across gene clusters and as far as the interpretation of association results is concerned, which affects the wider adoption of GWAS methods on microbial data sets. We have developed a simple computational method (panfeed) that explicitly links each k-mer to their gene cluster at base-resolution level, which allows us to avoid biases introduced by a global de Bruijn graph as well as more easily map and annotate associated variants. We tested panfeed on two independent data sets, correctly identifying previously characterized causal variants, which demonstrates the precision of the method, as well as its scalable performance. panfeed is a command line tool written in the python programming language and is available at https://github.com/microbial-pangenomes-lab/panfeed.

Bacterial genome-wide association study substantiates papGII of Escherichia coli as a major risk factor for urosepsis

BackgroundUrinary tract infections (UTIs) are among the most common bacterial infections worldwide, often caused by uropathogenic Escherichia coli. Multiple bacterial virulence factors or patient characteristics have been linked separately to progressive, more invasive infections. In this study, we aim to identify pathogen- and patient-specific factors that drive the progression to urosepsis by jointly analysing bacterial and host characteristics.MethodsWe analysed 1076 E. coli strains isolated from 825 clinical cases with UTI and/or bacteraemia by whole-genome sequencing (Illumina). Sequence types (STs) were determined via srst2 and capsule loci via fastKaptive. We compared the isolates from urine and blood to confirm clonality. Furthermore, we performed a bacterial genome-wide association study (bGWAS) (pyseer) using bacteraemia as the primary clinical outcome. Clinical data were collected by an electronic patient chart review. We concurrently analysed the association of the most significant bGWAS hit and important patient characteristics with the clinical endpoint bacteraemia using a generalised linear model (GLM). Finally, we designed qPCR primers and probes to detect papGII-positive E. coli strains and prospectively screened E. coli from urine samples (n = 1657) at two healthcare centres.ResultsOur patient cohort had a median age of 75.3 years (range: 18.00–103.1) and was predominantly female (574/825, 69.6%). The bacterial phylogroups B2 (60.6%; 500/825) and D (16.6%; 137/825), which are associated with extraintestinal infections, represent the majority of the strains in our collection, many of which encode a polysaccharide capsule (63.4%; 525/825). The most frequently observed STs were ST131 (12.7%; 105/825), ST69 (11.0%; 91/825), and ST73 (10.2%; 84/825). Of interest, in 12.3% (13/106) of cases, the E. coli pairs in urine and blood were only distantly related. In line with previous bGWAS studies, we identified the gene papGII (p-value < 0.001), which encodes the adhesin subunit of the E. coli P-pilus, to be associated with ‘bacteraemia’ in our bGWAS. In our GLM, correcting for patient characteristics, papGII remained highly significant (odds ratio = 5.27, 95% confidence interval = [3.48, 7.97], p-value < 0.001). An independent cohort of cases which we screened for papGII-carrying E. coli at two healthcare centres further confirmed the increased relative frequency of papGII-positive strains causing invasive infection, compared to papGII-negative strains (p-value = 0.033, chi-squared test).ConclusionsThis study builds on previous work linking papGII with invasive infection by showing that it is a major risk factor for progression from UTI to bacteraemia that has diagnostic potential.

Genetic variants linked to the phenotypic outcome of invasive disease and carriage of Neisseria meningitidis.

Neisseria meningitidis can be a human commensal in the upper respiratory tract but is also capable of causing invasive diseases such as meningococcal meningitis and septicaemia. No specific genetic markers have been detected to distinguish carriage from disease isolates. The aim here was to find genetic traits that could be linked to phenotypic outcomes associated with carriage versus invasive N. meningitidis disease through a bacterial genome-wide association study (GWAS). In this study, invasive N. meningitidis isolates collected in Sweden (n=103) and carriage isolates collected at Örebro University, Sweden (n=213) 2018-2019 were analysed. The GWAS analysis, treeWAS, was applied to single-nucleotide polymorphisms (SNPs), genes and k-mers. One gene and one non-synonymous SNP were associated with invasive disease and seven genes and one non-synonymous SNP were associated with carriage isolates. The gene associated with invasive disease encodes a phage transposase (NEIS1048), and the associated invasive SNP glmU S373C encodes the enzyme N-acetylglucosamine 1-phosphate (GlcNAC 1-P) uridyltransferase. Of the genes associated with carriage isolates, a gene variant of porB encoding PorB class 3, the genes pilE/pilS and tspB have known functions. The SNP associated with carriage was fkbp D33N, encoding a FK506-binding protein (FKBP). K-mers from PilS, tbpB and tspB were found to be associated with carriage, while k-mers from mtrD and tbpA were associated with invasiveness. In the genes fkbp, glmU, PilC and pilE, k-mers were found that were associated with both carriage and invasive isolates, indicating that specific variations within these genes could play a role in invasiveness. The data presented here highlight genetic traits that are significantly associated with invasive or carriage N. meningitidis across the species population. These traits could prove essential to our understanding of the pathogenicity of N. meningitidis and could help to identify future vaccine targets.

Global-scale GWAS associates a subset of SNPs with animal-adapted variants in M. tuberculosis complex

BackgroundWhile Mycobacterium tuberculosis complex (MTBC) variants are clonal, variant tuberculosis is a human-adapted pathogen, and variant bovis infects many hosts. Despite nucleotide identity between MTBC variants exceeding 99.95%, it remains unclear what drives these differences. Markers of adaptation into variants were sought by bacterial genome-wide association study of single nucleotide polymorphisms extracted from 6,362 MTBC members from varied hosts and countries.ResultsThe search identified 120 genetic loci associated with MTBC variant classification and certain hosts. In many cases, these changes are uniformly fixed in certain variants while absent in others in this dataset, providing good discriminatory power in distinguishing variants by polymorphisms. Multiple changes were seen in genes for cholesterol and fatty acid metabolism, pathways previously proposed to be important for host adaptation, including Mce4F (part of the fundamental cholesterol intake Mce4 pathway), 4 FadD and FadE genes (playing roles in cholesterol and fatty acid utilization), and other targets like Rv3548c and PTPB, genes shown essential for growth on cholesterol by transposon studies.ConclusionsThese findings provide a robust set of genetic loci associated with the split of variant bovis and variant tuberculosis, and suggest that adaptation to new hosts could involve adjustments in uptake and catabolism of cholesterol and fatty acids, like the proposed specialization to different populations in MTB lineages by alterations to host lipid composition. Future studies are required to elucidate how the associations between cholesterol profiles and pathogen utilization differences between hosts and MTBC variants, as well as the investigation of uncharacterized genes discovered in this study. This information will likely provide an understanding on the diversification of MBO away from humans and specialization towards a broad host range.

Escherichia coli killing by epidemiologically successful sublineages of Shigella sonnei is mediated by colicins

Shigella sp. are enteric pathogens which causes >125 million cases of shigellosis annually. S.sonnei accounts for about a quarter of those cases and is increasingly prevalent in industrialising nations. Being an enteric pathogen, S.sonnei benefits from outcompeting gut commensals such as Escherichia coli to establish itself and cause disease. There are numerous mechanisms that bacterial pathogens use to outcompete its rivals including molecules called colicins. A Type 6 Secretion System (T6SS) was recently described as contributing to E.coli killing in S.sonnei. We used Bulk Phenotyping of Epidemiological Replicates (BPER) which combined bacterial Genome Wide Association Studies (bGWAS) and high throughput phenotyping on a collection of S.sonnei surveillance isolates to identify the genetic features associated with E.coli killing and explore their relationship with epidemiological behaviour. We further explored the presence of colicins and T6SS components in the isolates using genomics, laboratory experimentation, and proteomics. Our bGWAS analysis returned known and novel colicin and colicin related genes as significantly associated with E.coli killing. In silico analyses identified key colicin clusters responsible for the killing phenotype associated with epidemiologically successful sub-lineages. The killing phenotype was not associated with the presence of a T6SS. Laboratory analyses confirmed the presence of the key colicin clusters and that killing was contact-independent. Colicins are responsible for E.coli killing by S.sonnei, not a T6SS. This phenotype contributes to shaping the observed epidemiology of S.sonnei and may contribute to its increasing prevalence globally. BPER is an epidemiologically relevant approach to phenotypic testing that enables the rapid identification of genetic drivers of phenotypic changes, and assessment of their relevance to epidemiology in natural settings. Biotechnology and Biological Sciences Research Council, Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship, Wellcome Trust, Medical Research Council (UK), French National Research Agency.

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.

Meningococcal factor H-binding protein: implications for disease susceptibility, virulence, and vaccines.

Neisseria meningitidis is a human-adapted pathogen that causes meningitis and sepsis worldwide. N. meningitidis factor H-binding protein (fHbp) provides a mechanism for immune evasion by binding human complement factor H (CFH) to protect it from complement-mediated killing. Here, we discuss features of fHbp which enable it to engage human CFH (hCFH), and the regulation of fHbp expression. Studies of host susceptibility and bacterial genome-wide association studies (GWAS) highlight the importance of the interaction between fHbp and CFH and other complement factors, such as CFHR3, on the development of invasive meningococcal disease (IMD). Understanding the basis of fHbp:CFH interactions has also informed the design of next-generation vaccines as fHbp is a protective antigen. Structure-informed refinement of fHbp vaccines will help to combat the threat posed by the meningococcus, and accelerate the elimination of IMD.

Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing

In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in Streptococus pneumoniae. This application not only validates the effectiveness of our method but also highlights its potential in real-world applications.

Genomic analysis of diversity, biogeography, and drug resistance in Mycobacterium bovis.

Mycobacterium bovis is the cause of bovine tuberculosis, and it can also cause disease in humans, with symptoms similar to those caused by M. tuberculosis. However, our understanding of its genomic diversity, biogeography, and drug resistance remains incomplete. We performed a comparative and phylogenetic analysis of 3228 M. bovis genomes from 24 countries. Following drug susceptibility testing, we applied a bacterial genome-wide association study to capture associations between genomic variation and drug resistance in 74 newly isolated strains from China. The data show that the cattle-adapted M. bovis were divided into six lineages with a strong phylogeographical population structure. Lineages 1 and 6 are the most widespread globally, while others show a strong geographical restriction. Note that 17.39% of M. bovis isolates were resistant to at least one drug in China. Furthermore, we identify genomic variations associated with an increased risk of resistance acquisition. This study furthers our knowledge of M. bovis diversity, biogeography, and drug resistance and will facilitate more deeply informed genomic tracking and surveillance to minimize its threat to human health, as a cause of zoonotic tuberculosis.

CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS.

MotivationGenome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects.ResultsHere, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation.Availability and implementationWe provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB.Supplementary information Supplementary data are available at Bioinformatics online.

Strain Variation in Clostridioides difficile Cytotoxicity Associated with Genomic Variation at Both Pathogenic and Nonpathogenic Loci.

ABSTRACTClinical disease from Clostridioides difficile infection can be mediated by two toxins and their neighboring regulatory genes located within the five-gene pathogenicity locus (PaLoc). We provide several lines of evidence that the cytotoxicity of C. difficile may be modulated by genomic variants outside the PaLoc. We used a phylogenetic tree-based approach to demonstrate discordance between cytotoxicity and PaLoc evolutionary history, an elastic net method to show the insufficiency of PaLoc variants alone to model cytotoxicity, and a convergence-based bacterial genome-wide association study (GWAS) to identify correlations between non-PaLoc loci and changes in cytotoxicity. Combined, these data support a model of C. difficile disease wherein cytotoxicity may be strongly affected by many non-PaLoc loci. Additionally, we characterize multiple other in vitro phenotypes relevant to human infections, including germination and sporulation. These phenotypes vary greatly in their clonality, variability, convergence, and concordance with genomic variation. Finally, we highlight the intersection of loci identified by the GWAS for different phenotypes and clinical severity. This strategy to identify overlapping loci can facilitate the identification of genetic variation linking phenotypic variation to clinical outcomes.IMPORTANCE Clostridioides difficile has two major disease-mediating toxins, A and B, encoded within the pathogenicity locus (PaLoc). In this study, we demonstrate via multiple approaches that genomic variants outside the PaLoc are associated with changes in cytotoxicity. These genomic variants may provide new avenues of exploration in the hunt for novel disease-modifying interventions. Additionally, we provide insight into the evolution of several additional phenotypes also critical for clinical infection, such as sporulation, germination, and growth rate. These in vitro phenotypes display a range of responses to evolutionary pressures and, as such, vary in their appropriateness for certain bacterial genome-wide association study approaches. We used a convergence-based association method to identify the genomic variants most correlated with both changes in these phenotypes and disease severity. These overlapping loci may be important for both bacterial function and human clinical disease.

PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies

Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing collections of bacterial genomes. First, a sub-sampling approach was undertaken to reduce the allele frequency and effect size of a known and detectable genotype-phenotype relationship by modifying phenotype labels. Second, a phenotype-simulation approach was conducted to simulate phenotypes from existing genetic variants. We implemented both approaches into a computational pipeline (PowerBacGWAS) that supports power calculations for burden testing, pan-genome and variant GWAS; and applied it to collections of Enterococcus faecium, Klebsiella pneumoniae and Mycobacterium tuberculosis. We used this pipeline to determine sample sizes required to detect causal variants of different minor allele frequencies (MAF), effect sizes and phenotype heritability, and studied the effect of homoplasy and population diversity on the power to detect causal variants. Our pipeline and user documentation are made available and can be applied to other bacterial populations. PowerBacGWAS can be used to determine sample sizes required to find statistically significant associations, or the associations detectable with a given sample size. We recommend to perform power calculations using existing genomes of the bacterial species and population of study.

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome.

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand.

Burkholderia pseudomallei is a soil-dwelling bacterium endemic to Southeast Asia and northern Australia that causes the disease, melioidosis. Although the global genomic diversity of clinical B. pseudomallei isolates has been investigated, there is limited understanding of its genomic diversity across small geographic scales, especially in soil. In this study, we obtained 288 B. pseudomallei isolates from a single soil sample (~100g; intensive site 2, INT2) collected at a depth of 30cm from a site in Ubon Ratchathani Province, Thailand. We sequenced the genomes of 169 of these isolates that represent 7 distinct sequence types (STs), including a new ST (ST1820), based on multi-locus sequence typing (MLST) analysis. A core genome SNP phylogeny demonstrated that all identified STs share a recent common ancestor that diverged an estimated 796-1260 years ago. A pan-genomics analysis demonstrated recombination between clades and intra-MLST phylogenetic and gene differences. To identify potential differential virulence between STs, groups of BALB/c mice (5 mice/isolate) were challenged via subcutaneous injection (500 CFUs) with 30 INT2 isolates representing 5 different STs; over the 21-day experiment, eight isolates killed all mice, 2 isolates killed an intermediate number of mice (1-2), and 20 isolates killed no mice. Although the virulence results were largely stratified by ST, one virulent isolate and six attenuated isolates were from the same ST (ST1005), suggesting that variably conserved genomic regions may contribute to virulence. Genomes from the animal-challenged isolates were subjected to a bacterial genome-wide association study to identify genomic regions associated with differential virulence. One associated region is a unique variant of Hcp1, a component of the type VI secretion system, which may result in attenuation. The results of this study have implications for comprehensive sampling strategies, environmental exposure risk assessment, and understanding recombination and differential virulence in B. pseudomallei.

Antimicrobial resistance determinants are associated with Staphylococcus aureus bacteraemia and adaptation to the healthcare environment: a bacterial genome-wide association study.

Staphylococcus aureus is a major bacterial pathogen in humans, and a dominant cause of severe bloodstream infections. Globally, antimicrobial resistance (AMR) in S. aureus remains challenging. While human risk factors for infection have been defined, contradictory evidence exists for the role of bacterial genomic variation in S. aureus disease. To investigate the contribution of bacterial lineage and genomic variation to the development of bloodstream infection, we undertook a genome-wide association study comparing bacteria from 1017 individuals with bacteraemia to 984 adults with asymptomatic S. aureus nasal carriage. Within 984 carriage isolates, we also compared healthcare-associated (HA) carriage with community-associated (CA) carriage. All major global lineages were represented in both bacteraemia and carriage, with no evidence for different infection rates. However, kmers tagging trimethoprim resistance-conferring mutation F99Y in dfrB were significantly associated with bacteraemia-vs-carriage (P=10-8.9-10-9.3). Pooling variation within genes, bacteraemia-vs-carriage was associated with the presence of mecA (HMP=10-5.3) as well as the presence of SCCmec (HMP=10-4.4). Among S. aureus carriers, no lineages were associated with HA-vs-CA carriage. However, we found a novel signal of HA-vs-CA carriage in the foldase protein prsA, where kmers representing conserved sequence allele were associated with CA carriage (P=10-7.1-10-19.4), while in gyrA, a ciprofloxacin resistance-conferring mutation, L84S, was associated with HA carriage (P=10-7.2). In an extensive study of S. aureus bacteraemia and nasal carriage in the UK, we found strong evidence that all S. aureus lineages are equally capable of causing bloodstream infection, and of being carried in the healthcare environment. Genomic variation in the foldase protein prsA is a novel genomic marker of healthcare origin in S. aureus but was not associated with bacteraemia. AMR determinants were associated with both bacteraemia and healthcare-associated carriage, suggesting that AMR increases the propensity not only to survive in healthcare environments, but also to cause invasive disease.

Multiple-Replicon Resistance Plasmids of Klebsiella Mediate Extensive Dissemination of Antimicrobial Genes.

Multiple-replicon resistance plasmids have become important carriers of resistance genes in Gram-negative bacteria, and the evolution of multiple-replicon plasmids is still not clear. Here, 56 isolates of Klebsiella isolated from different wild animals and environments between 2018 and 2020 were identified by phenotyping via the micro-broth dilution method and were sequenced and analyzed for bacterial genome-wide association study. Our results revealed that the isolates from non-human sources showed more extensive drug resistance and especially strong resistance to ampicillin (up to 80.36%). The isolates from Malayan pangolin were particularly highly resistant to cephalosporins, chloramphenicol, levofloxacin, and sulfamethoxazole. Genomic analysis showed that the resistance plasmids in these isolates carried many antibiotic resistance genes. Further analysis of 69 plasmids demonstrated that 28 plasmids were multiple-replicon plasmids, mainly carrying beta-lactamase genes such as blaCTX–M–15, blaCTX–M–14, blaCTX–M–55, blaOXA–1, and blaTEM–1. The analysis of plasmids carried by different isolates showed that Klebsiella pneumoniae might be an important multiple-replicon plasmid host. Plasmid skeleton and structure analyses showed that a multiple-replicon plasmid was formed by the fusion of two or more single plasmids, conferring strong adaptability to the antibiotic environment and continuously increasing the ability of drug-resistant isolates to spread around the world. In conclusion, multiple-replicon plasmids are better able to carry resistance genes than non-multiple-replicon plasmids, which may be an important mechanism underlying bacterial responses to environments with high-antibiotic pressure. This phenomenon will be highly significant for exploring bacterial resistance gene transmission and diffusion mechanisms in the future.

Genome-wide association studies reveal the role of polymorphisms affecting factor H binding protein expression in host invasion by Neisseria meningitidis.

Many invasive bacterial diseases are caused by organisms that are ordinarily harmless components of the human microbiome. Effective interventions against these microbes require an understanding of the processes whereby symbiotic or commensal relationships transition into pathology. Here, we describe bacterial genome-wide association studies (GWAS) of Neisseria meningitidis, a common commensal of the human respiratory tract that is nevertheless a leading cause of meningitis and sepsis. An initial GWAS discovered bacterial genetic variants, including single nucleotide polymorphisms (SNPs), associated with invasive meningococcal disease (IMD) versus carriage in several loci across the meningococcal genome, encoding antigens and other extracellular components, confirming the polygenic nature of the invasive phenotype. In particular, there was a significant peak of association around the fHbp locus, encoding factor H binding protein (fHbp), which promotes bacterial immune evasion of human complement by recruiting complement factor H (CFH) to the meningococcal surface. The association around fHbp with IMD was confirmed by a validation GWAS, and we found that the SNPs identified in the validation affected the 5' region of fHbp mRNA, altering secondary RNA structures, thereby increasing fHbp expression and enhancing bacterial escape from complement-mediated killing. This finding is consistent with the known link between complement deficiencies and CFH variation with human susceptibility to IMD. These observations demonstrate the importance of human and bacterial genetic variation across the fHbp:CFH interface in determining IMD susceptibility, the transition from carriage to disease.