Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter

Bacterial genome-wide association studies: exploring the genetic variation underlying bacterial phenotypes

With the continuous advancements in high-throughput genome sequencing technologies and the development of innovative bioinformatics tools, bacterial genome-wide association studies (BGWAS) have emerged as a transformative approach for investigating the genetic variations underlying diverse bacterial phenotypes at the population genome level. This review provides a comprehensive overview of the application of BGWAS in elucidating genetic determinants of bacterial drug resistance, pathogenicity, host specificity, biofilm formation, and probiotic fermentation characteristics. We systematically summarize the BGWAS workflow, including study design, data analysis pipelines, and the bioinformatics software employed at various stages. Furthermore, we highlight specialized tools tailored for BGWAS and discuss their unique features and applications. We also discuss confounding factors that can influence the accuracy and reliability of BGWAS results, including population structure, linkage disequilibrium, and multiple testing. By incorporating recent advancements, this review serves as a comprehensive reference for researchers utilizing BGWAS to uncover the genetic basis of bacterial phenotypes.

Genome-Wide association study to identify genetic markers associated with Campylobacter jejuni motility

The ability of Campylobacter jejuni to survive and persist under harsh conditions is linked to the presence of flagella. This structure promotes the motility of the bacteria towards their optimum environment. The aim of this study was to examine the genetic basis for motility within 136 C. jejuni isolates through two different Genome-Wide Association Studies, gene presence/absence and Single Nucleotide Polymorphisms (SNPs). The motility phenotype was widely distributed across the phylogeny with large intra-lineage swarming performance variabilities. Accessory genes significantly associated with motility were found in four key genomic regions. One of these regions affected the Cj0727-Cj0733 operon, that encodes a putative ABC transporter system for phosphate uptake, while other influenced the capsule biosynthesis locus. Multiple SNPs mostly linked to increased motility were also discovered in clusters of genes, with special relevance to transport and membrane proteins. Therefore, the capsule and membrane composition might influence nutrient transfer, further impacting the protonmotive force that drives flagellar motor rotation in C. jejuni. The study provides novel genetic markers with a potential role in the motility phenotype of the pathogen.

Strain diversity and host specificity of the gut symbiont Gilliamella in Apis mellifera, Apis cerana and Bombus terrestris

Social bees, with their specialized gut microbiota and societal transmission between individuals, provide an ideal model for studying host-gut microbiota interactions. While the functional disparities arising from strain-level diversity of gut symbionts and their effects on host health have been studied in Apis mellifera and bumblebees, studies focusing on host-specific investigations of individual strains across different honeybee hosts remain relatively unexplored. In this study, the complete genomic sequences of 17 strains of Gilliamella from A. mellifera, Apis cerana and Bombus terrestris were analyzed. The analysis revealed that the strains of A. mellifera display a more expansive genomic and functional content compared to the strains of A. cerana and B. terrestris. Phylogenetic analysis showed a deep divergence among the Gilliamella strains from different hosts. Additionally, biochemistry tests and antibiotic susceptibility tests revealed that gut strains from A. mellifera exhibited a more extensive pathway for carbohydrate metabolism and a greater resistance to antibiotics than gut strains from A. cerana and B. terrestris. Strains from A. mellifera and A. cerana showed higher colonization efficiency and competitive ability whithin their respective host species, indicating a higher degree of host-specific adaptation of local gut microbiota. In addition, colonization by A. mellifera-derived strain triggers a stronger transcriptional response in the host than A. cerana-derived strain. The variation in the number of differentially expressed genes and the involvement of distinct signaling pathways across these two host species suggest species-specific adaptations to Gilliamella strains. These findings suggest that despite occupying similar niches in the bee gut, strain-level variations can influence microbial functions, and their impact on host physiological functions may vary across different strains.

microGWAS: a computational pipeline to perform large-scale bacterial genome-wide association studies

Identifying genetic variants associated with bacterial phenotypes, such as virulence, host preference and antimicrobial resistance, has great potential for a better understanding of the mechanisms involved in these traits. The availability of large collections of bacterial genomes has made genome-wide association studies (GWAS) a common approach for this purpose. The need to employ multiple software tools for data pre- and postprocessing limits the application of these methods by experienced bioinformaticians. To address this issue, we have developed a pipeline to perform bacterial GWAS from a set of assemblies and annotations, with multiple phenotypes as targets. The associations are run using five sets of genetic variants: unitigs, gene presence/absence, rare variants (i.e. gene burden test), gene-cluster-specific k-mers and all unitigs jointly. All variants passing the association threshold are further annotated to identify overrepresented biological processes and pathways. The results can be further augmented by generating a phylogenetic tree and predicting the presence of antimicrobial resistance and virulence-associated genes. We tested the microGWAS pipeline on a previously reported dataset on Escherichia coli virulence, successfully identifying the causal variants and providing further interpretation of the association results. The microGWAS pipeline integrates state-of-the-art tools to perform bacterial GWAS into a single, user-friendly and reproducible pipeline, allowing for the democratization of these analyses. The pipeline, together with its documentation, can be accessed at https://github.com/microbial-pangenomes-lab/microGWAS.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Unraveling genetic mysteries: A comprehensive review of GWAS and DNA insights in animal and plant pathosystems

DNA serves as the carrier of genetic information, with sequence variations playing a pivotal role in defining hereditary traits. Genome-Wide Association Studies (GWAS) facilitate the investigation of the links between genetic variations and phenotypes, significantly influencing biological research, particularly in animal and plant pathology. By identifying genetic markers associated with specific traits or diseases, GWAS enhances our understanding of host-pathogen interactions and improves disease-resistant breeding strategies. It has been vital in revealing the genetic basis of disease resistance, pinpointing key genes and DNA loci, which enrich genetic resources for breeding programs and deepen our knowledge of disease resistance mechanisms at the DNA level. Additionally, GWAS contributes to pathogen population genetics, facilitating a thorough exploration of pathogen virulence. Integrating GWAS with marker-assisted selection enhances breeding efficiency and precision in selecting for disease-resistant traits. While previous research has largely focused on host genetics, the genetic variation of pathogens is equally significant. Notably, reports integrating animal and plant pathosystems are still lacking. Given the importance of these systems, this review summarizes key advancements in this field, addresses current challenges, and proposes future directions, thereby offering a vital reference for ongoing research.

New Campylobacter Lineages in New Zealand Freshwater: Pathogenesis and Public Health Implications

This study investigated the diversity of thermophilic Campylobacter species isolated from three New Zealand freshwater catchments affected by pastoral and urban activities. Utilising matrix-assisted laser desorption ionisation-time of flight and whole genome sequence analysis, the study identified Campylobacter jejuni (n = 46, 46.0%), C. coli (n = 39, 39%), C. lari (n = 4, 4.0%), and two novel Campylobacter species lineages (n = 11, 11%). Core genome sequence analysis provided evidence of prolonged persistence or continuous faecal shedding of closely related strains. The C. jejuni isolates displayed distinct sequence types (STs) associated with human, ruminant, and environmental sources, whereas the C. coli STs included waterborne ST3302 and ST7774. Recombination events affecting loci implicated in human pathogenesis and environmental persistence were observed, particularly in the cdtABC operon (encoding the cytolethal distending toxin) of non-human C. jejuni STs. A low diversity of antimicrobial resistance genes (aadE-Cc in C. coli), with genotype/phenotype concordance for tetracycline resistance (tetO) in three ST177 isolates, was noted. The data suggest the existence of two types of naturalised waterborne Campylobacter: environmentally persistent strains originating from waterbirds and new environmental species not linked to human campylobacteriosis. Identifying and understanding naturalised Campylobacter species is crucial for accurate waterborne public health risk assessments and the effective allocation of resources for water quality management.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Simple and accurate genomic classification model for distinguishing between human and pig Staphylococcus aureus

Staphylococcus aureus (S. aureus) can cause various infections in humans and animals, contributing to high morbidity and mortality. To prevent and control cross-species transmission of S. aureus, it is necessary to understand the host-associated genetic variants. We performed a two-stage genome-wide association study (GWAS) including initial screening and further validation to compare genomic differences between human and pig S. aureus, aiming to identify host-associated determinants. Our multiple GWAS analyses found six consensus significant k-mers associated with host species, providing novel genetic evidence for distinguishing human from pig S. aureus. The best k-mer predictor achieved a high classification accuracy of 98.12% on its own and had extremely high resolution similar to the SNPs-based phylogeny, offering a very simple target for predicting the cross-species transmission risk of S. aureus. The final k-mer model revealed that 90% of S. aureus isolates from farm workers were predicted as livestock origin, suggesting a high risk of cross-species transmission. Bayesian inference revealed different cross-species transmission directions, with the human-to-pig transmission for ST5 and the pig-to-human transmission for ST398. Our findings provide a simple and accurate k-mer model for identifying and predicting the cross-species transmission risk of S. aureus.

Activation of the l-fucose utilization cluster in Campylobacter jejuni induces proteomic changes and enhances Caco-2 cell invasion and fibronectin binding

Most Campylobacter jejuni isolates carry the fucose utilization cluster (Cj0480c-Cj0489) that supports the metabolism of l-fucose and d-arabinose. In this study we quantified l-fucose and d-arabinose metabolism and metabolite production, and the impact on Caco-2 cell interaction and binding to fibronectin, using C. jejuni NCTC11168 and the closely related human isolate C. jejuni strain 286. When cultured with l-fucose and d-arabinose, both isolates showed increased survival and production of acetate, pyruvate and succinate, and the respective signature metabolites lactate and glycolic acid, in line with an overall upregulation of l-fucose cluster proteins. In vitro Caco-2 cell studies and fibronectin-binding experiments showed a trend towards higher invasion and a significantly higher fibronectin binding efficacy of C. jejuni NCTC11168 cells grown with l-fucose and d-arabinose, while no significant differences were found with C. jejuni 286. Both fibronectin binding proteins, CadF and FlpA, were detected in the two isolates, but were not significantly differentially expressed in l-fucose or d-arabinose grown cells. Comparative proteomics analysis linked the C. jejuni NCTC11168 phenotypes uniquely to the more than 135-fold upregulated protein Cj0608, putative TolC-like component MacC, which, together with the detected Cj0606 and Cj0607 proteins, forms the tripartite secretion system MacABC with putative functions in antibiotic resistance, cell envelope stress response and virulence in Gram negative pathogenic bacteria. Further studies are required to elucidate the role of the MacABC system in C. jejuni cell surface structure modulation and virulence.

Genomic and functional determinants of host spectrum in Group B Streptococcus

Group B Streptococcus (GBS) is a major human and animal pathogen that threatens public health and food security. Spill-over and spill-back between host species is possible due to adaptation and amplification of GBS in new niches but the evolutionary and functional mechanisms underpinning those phenomena are poorly known. Based on analysis of 1,254 curated genomes from all major GBS host species and six continents, we found that the global GBS population comprises host-generalist, host-adapted and host-restricted sublineages, which are found across host groups, preferentially within one host group, or exclusively within one host group, respectively, and show distinct levels of recombination. Strikingly, the association of GBS genomes with the three major host groups (humans, cattle, fish) is driven by a single accessory gene cluster per host, regardless of sublineage or the breadth of host spectrum. Moreover, those gene clusters are shared with other streptococcal species occupying the same niche and are functionally relevant for host tropism. Our findings demonstrate (1) the heterogeneity of genome plasticity within a bacterial species of public health importance, enabling the identification of high-risk clones; (2) the contribution of inter-species gene transmission to the evolution of GBS; and (3) the importance of considering the role of animal hosts, and the accessory gene pool associated with their microbiota, in the evolution of multi-host bacterial pathogens. Collectively, these phenomena may explain the adaptation and clonal expansion of GBS in animal reservoirs and the risk of spill-over and spill-back between animals and humans.

Pan-Genome-Wide Association Study reveals a key role of the salmochelin receptor IroN in the biofilm formation of Salmonella Typhimurium and its monophasic variant 4,[5],12:i:

Salmonella enterica subsp. enterica serovar Typhimurium variant 4,[5],12:i:- (so called S. 4,[5],12:i:-) has rapidly become one of the most prevalent serovars in humans in Europe, with clinical cases associated with foodborne from pork products. The mechanisms, genetic basis and biofilms relevance by which S. 4,[5],12:i:- maintains and spreads its presence in pigs remain unclear. In this study, we examined the genetic basis of biofilm production in 78 strains of S. 4,[5],12:i:- (n = 57) and S. Typhimurium (n = 21), from human gastroenteritis, food products and asymptomatic pigs. The former showed a lower Specific Biofilm Formation index (SBF) and distant phylogenetic clades, suggesting that the ability to form biofilms is not a crucial adaptation for the S. 4,[5],12:i:- emerging success in pigs. However, using a pan-Genome-Wide Association Study (pan-GWAS) we identified genetic determinants of biofilm formation, revealing 167 common orthologous groups and genes associated with the SBF. The analysis of annotated sequences highlighted specific genetic deletions in three chromosomal regions of S. 4,[5],12:i:- correlating with SBF values: i) the complete fimbrial operon stbABCDE widely recognized as the most critical factor involved in Salmonella adherence; ii) the hxlA, hlxB, and pgiA genes, which expression in S. Typhimurium is induced in the tonsils during swine infection, and iii) the entire iroA locus related to the characteristic deletion of the second-phase flagellar genomic region in S. 4,[5],12:i:-. Consequently, we further investigated the role of the iro-genes on biofilm by constructing S. Typhimurium deletion mutants in iroBCDE and iroN. While iroBCDE showed no significant impact, iroN clearly contributed to S. Typhimurium biofilm formation. In conclusion, the pan-GWAS approach allowed us to uncover complex interactions between genetic and phenotypic factors influencing biofilm formation in S. 4,[5],12:i:- and S. Typhimurium.

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.

Mechanisms of host adaptation by bacterial pathogens

The emergence of new infectious diseases poses a major threat to humans, animals, and broader ecosystems. Defining factors that govern the ability of pathogens to adapt to new host species is therefore a crucial research imperative. Pathogenic bacteria are of particular concern, given dwindling treatment options amid the continued expansion of antimicrobial resistance. In this review, we summarize recent advancements in the understanding of bacterial host species adaptation, with an emphasis on pathogens of humans and related mammals. We focus particularly on molecular mechanisms underlying key steps of bacterial host adaptation including colonization, nutrient acquisition, and immune evasion, as well as suggest key areas for future investigation. By developing a greater understanding of the mechanisms of host adaptation in pathogenic bacteria, we may uncover new strategies to target these microbes for the treatment and prevention of infectious diseases in humans, animals, and the broader environment.

Epistasis, core-genome disharmony, and adaptation in recombining bacteria

Recombination of short DNA fragments via horizontal gene transfer (HGT) can introduce beneficial alleles, create genomic disharmony through negative epistasis, and create adaptive gene combinations through positive epistasis. For non-core (accessory) genes, the negative epistatic cost is likely to be minimal because the incoming genes have not co-evolved with the recipient genome and are frequently observed as tightly linked cassettes with major effects. By contrast, interspecific recombination in the core genome is expected to be rare because disruptive allelic replacement is likely to introduce negative epistasis. Why then is homologous recombination common in the core of bacterial genomes? To understand this enigma, we take advantage of an exceptional model system, the common enteric pathogens Campylobacter jejuni and C. coli that are known for very high magnitude interspecies gene flow in the core genome. As expected, HGT does indeed disrupt co-adapted allele pairings, indirect evidence of negative epistasis. However, multiple HGT events enable recovery of the genome's co-adaption between introgressing alleles, even in core metabolism genes (e.g., formate dehydrogenase). These findings demonstrate that, even for complex traits, genetic coalitions can be decoupled, transferred, and independently reinstated in a new genetic background-facilitating transition between fitness peaks. In this example, the two-step recombinational process is associated with C. coli that are adapted to the agricultural niche.IMPORTANCEGenetic exchange among bacteria shapes the microbial world. From the acquisition of antimicrobial resistance genes to fundamental questions about the nature of bacterial species, this powerful evolutionary force has preoccupied scientists for decades. However, the mixing of genes between species rests on a paradox: 0n one hand, promoting adaptation by conferring novel functionality; on the other, potentially introducing disharmonious gene combinations (negative epistasis) that will be selected against. Taking an interdisciplinary approach to analyze natural populations of the enteric bacteria Campylobacter, an ideal example of long-range admixture, we demonstrate that genes can independently transfer across species boundaries and rejoin in functional networks in a recipient genome. The positive impact of two-gene interactions appears to be adaptive by expanding metabolic capacity and facilitating niche shifts through interspecific hybridization. This challenges conventional ideas and highlights the possibility of multiple-step evolution of multi-gene traits by interspecific introgression.

Cereulide production capacities and genetic properties of 31 emetic Bacillus cereus group strains

The highly potent toxin cereulide is a frequent cause of foodborne intoxications. This extremely resistant toxin is produced by Bacillus cereus group strains carrying the plasmid encoded cesHPTABCD gene cluster. It is known that the capacities to produce cereulide vary greatly between different strains but the genetic background of these variations is not clear. In this study, cereulide production capacities were associated with genetic characteristics. For this, cereulide levels in cultures of 31 strains were determined after incubation in tryptic soy broth for 24 h at 24 °C, 30 °C and 37 °C. Whole genome sequencing based data were used for an in-depth characterization of gene sequences related to cereulide production. The taxonomy, population structure and phylogenetic relationships of the strains were evaluated based on average nucleotide identity, multi-locus sequence typing (MLST), core genome MLST and single nucleotide polymorphism analyses. Despite a limited strain number, the approach of a genome wide association study (GWAS) was tested to link genetic variation with cereulide quantities. Our study confirms strain-dependent differences in cereulide production. For most strains, these differences were not explainable by sequence variations in the cesHPTABCD gene cluster or the regulatory genes abrB, spo0A, codY and pagRBc. Likewise, the population structure and phylogeny of the tested strains did not comprehensively reflect the cereulide production capacities. GWAS yielded first hints for associated proteins, while their possible effect on cereulide synthesis remains to be further investigated.

Host genetic regulation of human gut microbial structural variation

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance

Quinolone resistance presents a growing global health threat. We employed word-based GWAS to explore genomic data, aiming to enhance our understanding of this phenomenon. Unlike traditional variant-based GWAS analyses, this approach simultaneously captures multiple genomic factors, including single and interacting resistance mutations and genes. Analyzing a dataset of 92 genomic E. coli samples from a wastewater treatment plant in Dresden, we identified 54 DNA unitigs significantly associated with quinolone resistance. Remarkably, our analysis not only validated known mutations in gyrA and parC genes and the results of our variant-based GWAS but also revealed new (mutated) genes such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI, implicated in antibiotic resistance. Furthermore, our study identified joint mutations in 14 genes including the known gyrA gene, providing insights into potential synergistic effects contributing to quinolone resistance. These findings showcase the exceptional capabilities of word-based GWAS in unraveling the intricate genomic foundations of quinolone resistance.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

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.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Contrasting genes conferring short- and long-term biofilm adaptation in Listeria

Listeria monocytogenes is an opportunistic food-borne bacterium that is capable of infecting humans with high rates of hospitalization and mortality. Natural populations are genotypically and phenotypically variable, with some lineages being responsible for most human infections. The success of L. monocytogenes is linked to its capacity to persist on food and in the environment. Biofilms are an important feature that allow these bacteria to persist and infect humans, so understanding the genetic basis of biofilm formation is key to understanding transmission. We sought to investigate the biofilm-forming ability of L. monocytogenes by identifying genetic variation that underlies biofilm formation in natural populations using genome-wide association studies (GWAS). Changes in gene expression of specific strains during biofilm formation were then investigated using RNA sequencing (RNA-seq). Genetic variation associated with enhanced biofilm formation was identified in 273 genes by GWAS and differential expression in 220 genes by RNA-seq. Statistical analyses show that the number of overlapping genes flagged by either type of experiment is less than expected by random sampling. This novel finding is consistent with an evolutionary scenario where rapid adaptation is driven by variation in gene expression of pioneer genes, and this is followed by slower adaptation driven by nucleotide changes within the core genome.

Evidence of potential Campylobacter jejuni zooanthroponosis in captive macaque populations

Non-human primates share recent common ancestry with humans and exhibit comparable disease symptoms. Here, we explored the transmission potential of enteric bacterial pathogens in monkeys exhibiting symptoms of recurrent diarrhoea in a biomedical research facility in China. The common zoonotic bacterium Campylobacter jejuni was isolated from macaques (Macaca mulatta and Macaca fascicularis) and compared to isolates from humans and agricultural animals in Asia. Among the monkeys sampled, 5 % (44/973) tested positive for C. jejuni, 11 % (5/44) of which displayed diarrhoeal symptoms. Genomic analysis of monkey isolates, and 1254 genomes from various sources in Asia, were used to identify the most likely source of human infection. Monkey and human isolates shared high average nucleotide identity, common MLST clonal complexes and clustered together on a phylogeny. Furthermore, the profiles of putative antimicrobial resistance genes were similar between monkeys and humans. Taken together these findings suggest that housed macaques became infected with C. jejuni either directly from humans or via a common contamination source.

A genome-wide association study reveals that epistasis underlies the pathogenicity of Pectobacterium

Pectobacterium spp. are important bacterial pathogens that cause soft rot symptoms in various crops. However, their mechanism of pathogenicity requires clarity to help control their infections. Here, genome-wide association studies (GWAS) were conducted by integrating genomic data and measurements of two phenotypes (virulence and cellulase activity) for 120 various Pectobacterium strains in order to identify the genetic basis of their pathogenicity. An artificial intelligence-based software program was developed to automatically measure lesion areas on Chinese cabbage, thereby facilitating accurate and rapid data collection for virulence phenotypes for use in GWAS analysis. The analysis discovered 428 and 158 loci significantly associated with Pectobacterium virulence (lesion area) and cellulase activity, respectively. In addition, 1,229 and 586 epistasis loci pairs were identified for the virulence and cellulase activity phenotypes, respectively. Among them, the AraC transcriptional regulator exerted epistasis effects with another three nutrient transport-related genes in pairs contributing to the virulence phenotype, and their epistatic effects were experimentally confirmed for one pair with knockout mutants of each single gene and double gene. This study consequently provides valuable insights into the genetic mechanism underlying Pectobacterium spp. pathogenicity. IMPORTANCE Plant diseases and pests are responsible for the loss of up to 40% of food crops, and annual economic losses caused by plant diseases reach more than $220 billion. Fighting against plant diseases requires an understanding of the pathogenic mechanisms of pathogens. This study adopted an advanced approach using population genomics integrated with virulence-related phenotype data to investigate the genetic basis of Pectobacterium spp., which causes serious crop losses worldwide. An automated software program based on artificial intelligence was developed to measure the virulence phenotype (lesion area), which greatly facilitated this research. The analysis predicted key genomic loci that were highly associated with virulence phenotypes, exhibited epistasis effects, and were further confirmed as critical for virulence with mutant gene deletion experiments. The present study provides new insights into the genetic determinants associated with Pectobacterium pathogenicity and provides a valuable new software resource that can be adapted to improve plant infection measurements.

Reactive oxygen species are regulated by immune deficiency and Toll pathways in determining the host specificity of honeybee gut bacteria

Host specificity is observed in gut symbionts of diverse animal lineages. But how hosts maintain symbionts while rejecting their close relatives remains elusive. We use eusocial bees and their codiversified gut bacteria to understand host regulation driving symbiotic specificity. The cross-inoculation of bumblebee Gilliamella induced higher prostaglandin in the honeybee gut, promoting a pronounced host response through immune deficiency (IMD) and Toll pathways. Gene silencing and vitamin C treatments indicate that reactive oxygen species (ROS), not antimicrobial peptides, acts as the effector in inhibiting the non-native strain. Quantitative PCR and RNAi further reveal a regulatory function of the IMD and Toll pathways, in which Relish and dorsal-1 may regulate Dual Oxidase (Duox) for ROS production. Therefore, the honeybee maintains symbiotic specificity by creating a hostile gut environment to exotic bacteria, through differential regulation of its immune system, reflecting a co-opting of existing machinery evolved to combat pathogens.

Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes

Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method.

Host Association and Spatial Proximity Shape but Do Not Constrain Population Structure in the Mutualistic Symbiont Xenorhabdus bovienii

To what extent are generalist species cohesive evolutionary units rather than a compilation of recently diverged lineages? We examine this question in the context of host specificity and geographic structure in the insect pathogen and nematode mutualist Xenorhabdus bovienii. This bacterial species partners with multiple nematode species across two clades in the genus Steinernema. We sequenced the genomes of 42 X. bovienii strains isolated from four different nematode species and three field sites within a 240-km² region and compared them to globally available reference genomes. We hypothesized that X. bovienii would comprise several host-specific lineages, such that bacterial and nematode phylogenies would be largely congruent. Alternatively, we hypothesized that spatial proximity might be a dominant signal, as increasing geographic distance might lower shared selective pressures and opportunities for gene flow. We found partial support for both hypotheses. Isolates clustered largely by nematode host species but did not strictly match the nematode phylogeny, indicating that shifts in symbiont associations across nematode species and clades have occurred. Furthermore, both genetic similarity and gene flow decreased with geographic distance across nematode species, suggesting differentiation and constraints on gene flow across both factors, although no absolute barriers to gene flow were observed across the regional isolates. Several genes associated with biotic interactions were found to be undergoing selective sweeps within this regional population. The interactions included several insect toxins and genes implicated in microbial competition. Thus, gene flow maintains cohesiveness across host associations in this symbiont and may facilitate adaptive responses to a multipartite selective environment. IMPORTANCE Microbial populations and species are notoriously hard to delineate. We used a population genomics approach to examine the population structure and the spatial scale of gene flow in Xenorhabdus bovienii, an intriguing species that is both a specialized mutualistic symbiont of nematodes and a broadly virulent insect pathogen. We found a strong signature of nematode host association, as well as evidence for gene flow connecting isolates associated with different nematode host species and collected from distinct study sites. Furthermore, we saw signatures of selective sweeps for genes involved with nematode host associations, insect pathogenicity, and microbial competition. Thus, X. bovienii exemplifies the growing consensus that recombination not only maintains cohesion but can also allow the spread of niche-beneficial alleles.

Genome-wide association reveals host-specific genomic traits in Escherichia coli

BACKGROUND

Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood.

RESULTS

We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type.

CONCLUSIONS

This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli.

Exploring the sorghum race level diversity utilizing 272 sorghum accessions genomic resources

Due to evolutionary divergence, sorghum race populations exhibit significant genetic and morphological variation. A k-mer-based sorghum race sequence comparison identified the conserved k-mers of all 272 accessions from sorghum and the race-specific genetic signatures identified the gene variability in 10,321 genes (PAVs). To understand sorghum race structure, diversity and domestication, a deep learning-based variant calling approach was employed in a set of genotypic data derived from a diverse panel of 272 sorghum accessions. The data resulted in 1.7 million high-quality genome-wide SNPs and identified selective signature (both positive and negative) regions through a genome-wide scan with different (iHS and XP-EHH) statistical methods. We discovered 2,370 genes associated with selection signatures including 179 selective sweep regions distributed over 10 chromosomes. Co-localization of these regions undergoing selective pressure with previously reported QTLs and genes revealed that the signatures of selection could be related to the domestication of important agronomic traits such as biomass and plant height. The developed k-mer signatures will be useful in the future to identify the sorghum race and for trait and SNP markers for assisting in plant breeding programs.

Genome-Wide Association to Study the Host-Specificity Determinants of Xanthomonas perforans

Xanthomonas perforans and X. euvesicatoria are the causal agents of bacterial spot disease of tomato and pepper, endemic to the Southeastern United States. Although very closely related, the two bacterial species differ in host specificity, where X. perforans is the dominant pathogen of tomato and X. euvesicatoria that of pepper. This is in part due to the activity of avirulence proteins that are secreted by X. perforans strains and elicit effector-triggered immunity in pepper leaves, thereby restricting pathogen growth. In recent years, the emergence of several pepper-pathogenic X. perforans lineages has revealed variability within the bacterial species to multiply and cause disease in pepper, even in the absence of avirulence gene activity. Here, we investigated the basal evolutionary processes underlying the host range of this species using multiple genome-wide association analyses. Surprisingly, we identified two novel gene candidates that were significantly associated with pepper-pathogenic X. perforans and X. euvesicatoria. Both candidates were predicted to be involved in the transport/acquisition of nutrients common to the plant cell wall or apoplast and included a TonB-dependent receptor, which was disrupted through independent mutations within the X. perforans lineage. The other included a symporter of protons/glutamate, gltP, enriched with pepper-associated mutations near the promoter and start codon of the gene. Functional analysis of these candidates revealed that only the TonB-dependent receptor had a minor effect on the symptom development and growth of X. perforans in pepper leaves, indicating that pathogenicity to this host might have evolved independently within the bacterial species and is likely a complex, multigenic trait.

Genome-wide association and dissociation studies in Pantoea ananatis reveal potential virulence factors affecting Allium porrum and Allium fistulosum × Allium cepa hybrid

Pantoea ananatis is a member of a Pantoea species complex that causes center rot of bulb onions (A. cepa) and also infects other Allium crops like leeks (Allium porrum), chives (Allium schoenoprasum), bunching onion or Welsh onion (Allium fistulosum), and garlic (Allium sativum). This pathogen relies on a chromosomal phosphonate biosynthetic gene cluster (HiVir) and a plasmid-borne thiosulfinate tolerance cluster (alt) for onion pathogenicity and virulence, respectively. However, pathogenicity and virulence factors associated with other Allium species remain unknown. We used phenotype-dependent genome-wide association (GWAS) and phenotype-independent gene-pair coincidence (GPC) analyses on a panel of diverse 92 P. ananatis strains, which were inoculated on A. porrum and A. fistulosum × A. cepa under greenhouse conditions. Phenotypic assays showed that, in general, these strains were more aggressive on A. fistulosum × A. cepa as opposed to A. porrum. Of the 92 strains, only six showed highly aggressive foliar lesions on A. porrum compared to A. fistulosum × A. cepa. Conversely, nine strains showed highly aggressive foliar lesions on A. fistulosum × A. cepa compared to A. porrum. These results indicate that there are underlying genetic components in P. ananatis that may drive pathogenicity in these two Allium spp. Based on GWAS for foliar pathogenicity, 835 genes were associated with P. ananatis' pathogenicity on A. fistulosum × A. cepa whereas 243 genes were associated with bacterial pathogenicity on A. porrum. The Hivir as well as the alt gene clusters were identified among these genes. Besides the 'HiVir' and the alt gene clusters that are known to contribute to pathogenicity and virulence from previous studies, genes annotated with functions related to stress responses, a potential toxin-antitoxin system, flagellar-motility, quorum sensing, and a previously described phosphonoglycan biosynthesis (pgb) cluster were identified. The GPC analysis resulted in the identification of 165 individual genes sorted into 39 significant gene-pair association components and 255 genes sorted into 50 significant gene-pair dissociation components. Within the coincident gene clusters, several genes that occurred on the GWAS outputs were associated with each other but dissociated with genes that did not appear in their respective GWAS output. To focus on candidate genes that could explain the difference in virulence between hosts, a comparative genomics analysis was performed on five P. ananatis strains that were differentially pathogenic on A. porrum or A. fistulosum × A. cepa. Here, we found a putative type III secretion system, and several other genes that occurred on both GWAS outputs of both Allium hosts. Further, we also demonstrated utilizing mutational analysis that the pepM gene in the HiVir cluster is important than the pepM gene in the pgb cluster for P. ananatis pathogenicity in A. fistulosum × A. cepa and A. porrum. Overall, our results support that P. ananatis may utilize a common set of genes or gene clusters to induce symptoms on A. fistulosum × A. cepa foliar tissue as well as A. cepa but implicates additional genes for infection on A. porrum.

Differential carbon utilization enables co-existence of recently speciated Campylobacteraceae in the cow rumen epithelial microbiome

The activities of different microbes in the cow rumen have been shown to modulate the host's ability to utilize plant biomass, while the host-rumen interface has received little attention. As datasets collected worldwide have pointed to Campylobacteraceae as particularly abundant members of the rumen epithelial microbiome, we targeted this group in a subset of seven cows with meta- and isolate genome analysis. We show that the dominant Campylobacteraceae lineage has recently speciated into two populations that were structured by genome-wide selective sweeps followed by population-specific gene import and recombination. These processes led to differences in gene expression and enzyme domain composition that correspond to the ability to utilize acetate, the main carbon source for the host, at the cost of inhibition by propionate. This trade-off in competitive ability further manifests itself in differential dynamics of the two populations in vivo. By exploring population-level adaptations that otherwise remain cryptic in culture-independent analyses, our results highlight how recent evolutionary dynamics can shape key functional roles in the rumen microbiome.

Whole genome characterization of thermophilic Campylobacter species isolated from dairy manure in small specialty crop farms of Northeast Ohio

Introduction

With more public interest in consuming locally grown produce, small specialty crop farms (SSCF) are a viable and growing segment of the food production chain in the United States.

Methods

The goal of this study was to investigate the genomic diversity of Campylobacter isolated from dairy manure (n = 69) collected from 10 SSCF in Northeast Ohio between 2018 and 2020.

Results

A total of 56 C. jejuni and 13 C. coli isolates were sequenced. Multi-locus sequence typing (MLST) identified 22 sequence types (STs), with ST-922 (18%) and ST-61 (13%) predominant in C. jejuni and ST-829 (62%) and ST-1068 (38%) predominant in C. coli. Interestingly, isolates with similar genomic and gene contents were detected within and between SSCF over time, suggesting that Campylobacter could be transmitted between farms and may persist in a given SSCF over time. Virulence-associated genes (n = 35) involved in the uptake and utilization of potassium and organic compounds (succinate, gluconate, oxoglutarate, and malate) were detected only in the C. jejuni isolates, while 45 genes associated with increased resistance to environmental stresses (capsule production, cell envelope integrity, and iron uptake) were detected only in the C. coli isolates. Campylobacter coli isolates were also sub-divided into two distinct clusters based on the presence of unique prophages (n = 21) or IncQ conjugative plasmid/type-IV secretion system genes (n = 15). Campylobacter coli isolates harbored genes associated with resistance to streptomycin (aadE-Cc; 54%) and quinolone (gyrA-T86I; 77%), while C. jejuni had resistance genes for kanamycin (aph3'-IIIa; 20%). Both species harbored resistance genes associated with β-lactam (especially, blaOXA-193; up to 100%) and tetracycline (tetO; up to 59%).

Discussion/Conclusion

Our study demonstrated that Campylobacter genome plasticity associated with conjugative transfer might provide resistance to certain antimicrobials and viral infections via the acquisition of protein-encoding genes involved in mechanisms such as ribosomal protection and capsule modification.

Genomic insights into zoonotic transmission and antimicrobial resistance in Campylobacter jejuni from farm to fork: a one health perspective

BACKGROUND

Campylobacteriosis represents a global public health threat with various socio-economic impacts. Among different Campylobacter species, Campylobacter jejuni (C. jejuni) is considered to be the foremost Campylobacter species responsible for most of gastrointestinal-related infections. Although these species are reported to primarily inhabit birds, its high genetic and phenotypic diversity allowed their adaptation to other animal reservoirs and to the environment that may impact on human infection.

MAIN BODY

A stringent and consistent surveillance program based on high resolution subtyping is crucial. Recently, different epidemiological investigations have implemented high-throughput sequencing technologies and analytical pipelines for higher resolution subtyping, accurate source attribution, and detection of antimicrobial resistance determinants among these species. In this review, we aim to present a comprehensive overview on the epidemiology, clinical presentation, antibiotic resistance, and transmission dynamics of Campylobacter, with specific focus on C. jejuni. This review also summarizes recent attempts of applying whole-genome sequencing (WGS) coupled with bioinformatic algorithms to identify and provide deeper insights into evolutionary and epidemiological dynamics of C. jejuni precisely along the farm-to-fork continuum.

CONCLUSION

WGS is a valuable addition to traditional surveillance methods for Campylobacter. It enables accurate typing of this pathogen and allows tracking of its transmission sources. It is also advantageous for in silico characterization of antibiotic resistance and virulence determinants, and hence implementation of control measures for containment of infection.

Functional analysis of intergenic regulatory regions of genes encoding surface adhesins in Staphylococcus aureus isolates from periprosthetic joint infections

Staphylococcus aureus is a leading cause of prosthetic joint infections (PJI). Surface adhesins play an important role in the primary attachment to plasma proteins that coat the surface of prosthetic devices after implantation. Previous efforts to identify a genetic component of the bacterium that confers an enhanced capacity to cause PJI have focused on gene content, kmers, or single-nucleotide polymorphisms (SNPs) in coding sequences. Here, using a collection of S. aureus strains isolated from PJI and wounds, we investigated whether genetic variations in the regulatory region of genes encoding surface adhesins lead to differences in their expression levels and modulate the capacity of S. aureus to colonize implanted prosthetic devices. The data revealed that S. aureus isolates from the same clonal complex (CC) contain a specific pattern of SNPs in the regulatory region of genes encoding surface adhesins. As a consequence, each clonal lineage shows a specific profile of surface proteins expression. Co-infection experiments with representative isolates of the most prevalent CCs demonstrated that some lineages have a higher capacity to colonize implanted catheters in a murine infection model, which correlated with a greater ability to form a biofilm on coated surfaces with plasma proteins. Together, results indicate that differences in the expression level of surface adhesins may modulate the propensity of S. aureus strains to cause PJI. Given the high conservation of surface proteins among staphylococci, our work lays the framework for investigating how diversification at intergenic regulatory regions affects the capacity of S. aureus to colonize the surface of medical implants.

Genome-wide association study of Klebsiella pneumoniae identifies variations linked to carbapenems resistance

Klebsiella pneumoniae (KP) is one of the microorganisms that can acquire carbapenem-resistance (CR), and few antimicrobial therapy options exist for infections caused by Carbapenem-Resistant KP (CRKP). In recent years, with the increase of carbapenem resistance rates, treating CRKP has become a serious public health threat in clinical practice. We have collected 2,035 clinical KP isolates from a tertiary hospital in China. Whole genome sequencing data coupled with their binary antimicrobial susceptibility testing data were obtained to conduct the genome-wide association study using a bayesian-based method, including single nucleotide polymorphisms (SNPs) and genes. We identified 28 and 37 potential maker genes associated with imipenem and meropenem resistance, respectively. Among which 19 of them were selected in both drugs by genome-wide association study (GWAS), 11 genes among them were simultaneously validated in independent datasets. These genes were likely related to biofilm formation, efflux pump, and DNA repairing. Moreover, we identified 13 significant CR related SNPs in imipenem or meropenem, with one SNP located in the non-coding region and validated in the independent datasets. Our study indicates complex mechanisms of carbapenems resistance and further investigation of CRKP-related factors are warranted to better understand their contributions to carbapenems resistance. These identified biomarkers may provide targets for future drug interventions or treatments.

Prokaryotic Pangenomes Act as Evolving Ecosystems

Understanding adaptation to the local environment is a central tenet and a major focus of evolutionary biology. But this is only part of the adaptionist story. In addition to the external environment, one of the main drivers of genome composition is genetic background. In this perspective, I argue that there is a growing body of evidence that intra-genomic selective pressures play a significant part in the composition of prokaryotic genomes and play a significant role in the origin, maintenance and structuring of prokaryotic pangenomes.

A scalable analytical approach from bacterial genomes to epidemiology

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Genome-Wide Association Study of Listeria monocytogenes Isolates Causing Three Different Clinical Outcomes

Heterogeneity in virulence potential of L. monocytogenes subgroups have been associated with genetic elements that could provide advantages in certain environments to invade, multiply, and survive within a host. The presence of gene mutations has been found to be related to attenuated phenotypes, while the presence of groups of genes, such as pathogenicity islands (PI), has been associated with hypervirulent or stress-resistant clones. We evaluated 232 whole genome sequences from invasive listeriosis cases in human and ruminants from the US and Europe to identify genomic elements associated with strains causing three clinical outcomes: central nervous system (CNS) infections, maternal-neonatal (MN) infections, and systemic infections (SI). Phylogenetic relationships and virulence-associated genes were evaluated, and a gene-based and single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) were conducted in order to identify loci associated with the different clinical outcomes. The orthologous results indicated that genes of phage phiX174, transfer RNAs, and type I restriction-modification (RM) system genes along with SNPs in loci involved in environmental adaptation such as rpoB and a phosphotransferase system (PTS) were associated with one or more clinical outcomes. Detection of phenotype-specific candidate loci represents an approach that could narrow the group of genetic elements to be evaluated in future studies.

Predictive modeling of Pseudomonas syringae virulence on bean using gradient boosted decision trees

Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are assigned pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then used gradient boosting machine learning to predict each strain’s virulence on bean based on whole genome k-mers, type III secreted effector k-mers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains as reflected in their lower level of host specificity. It also acts as a proof-of-principle to demonstrate the power of machine learning for predicting host specific adaptation.

Pseudomonas syringae is a genetically diverse Gammaproteobacterial species complex responsible for numerous agronomically important crop diseases. Strains in the P. syringae species complex are frequently categorized into pathovars depending on pathogenic characteristics such as host of isolation and disease symptoms. Common bean pathogens from P. syringae are known to cause two major diseases: (1) pathovar phaseolicola strains from phylogroup 3 cause halo blight disease, characterized by large necrotic lesions surrounded by a chlorotic zone or halo of yellow tissue; and (2) pathovar syringae strains from phylogroup 2 causes bacterial spot disease, characterized by brown leaf spots. While halo blight can cause serious crop losses, bacterial spot disease is generally of minor agronomic concern. Recently, statistical genetic and machine learning approaches have been applied to genomic data to identify genes underlying traits of interest or predict the outcome of host-microbe interactions. Here, we apply machine learning to P. syringae genomic data to predict virulence on bean. We first characterized the virulence of P. syringae isolates on common bean using a seed infection assay and then applied machine learning to the genomic data from the same strains to generate a predictive model for virulence on bean. We found that machine learning models built with k-mers from either full genome data or virulence factors could predict bean virulence with high accuracy. We also confirmed prior work showing that phylogroup 3 halo blight pathogens display a stronger degree of phylogenetic clustering and host specificity compared to phylogroup 2 brown spot pathogens. This works serves as a proof-of-principle for the power of machine learning for predicting host specificity and may find utility in agricultural diagnostic microbiology.

The Retrospective on Atypical Brucella Species Leads to Novel Definitions

The genus Brucella currently comprises twelve species of facultative intracellular bacteria with variable zoonotic potential. Six of them have been considered as classical, causing brucellosis in terrestrial mammalian hosts, with two species originated from marine mammals. In the past fifteen years, field research as well as improved pathogen detection and typing have allowed the identification of four new species, namely Brucella microti, Brucella inopinata, Brucella papionis, Brucella vulpis, and of numerous strains, isolated from a wide range of hosts, including for the first time cold-blooded animals. While their genome sequences are still highly similar to those of classical strains, some of them are characterized by atypical phenotypes such as higher growth rate, increased resistance to acid stress, motility, and lethality in the murine infection model. In our review, we provide an overview of state-of-the-art knowledge about these novel Brucella sp., with emphasis on their phylogenetic positions in the genus, their metabolic characteristics, acid stress resistance mechanisms, and their behavior in well-established in cellulo and in vivo infection models. Comparison of phylogenetic classification and phenotypical properties between classical and novel Brucella species and strains finally lead us to propose a more adapted terminology, distinguishing between core and non-core, and typical versus atypical brucellae, respectively.

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 Study Reveals Host Factors Affecting Conjugation in Escherichia coli

The emergence and dissemination of antibiotic resistance threaten the treatment of common bacterial infections. Resistance genes are often encoded on conjugative elements, which can be horizontally transferred to diverse bacteria. In order to delay conjugative transfer of resistance genes, more information is needed on the genetic determinants promoting conjugation. Here, we focus on which bacterial host factors in the donor assist transfer of conjugative plasmids. We introduced the broad-host-range plasmid pKJK10 into a diverse collection of 113 Escherichia coli strains and measured by flow cytometry how effectively each strain transfers its plasmid to a fixed E. coli recipient. Differences in conjugation efficiency of up to 2.7 and 3.8 orders of magnitude were observed after mating for 24 h and 48 h, respectively. These differences were linked to the underlying donor strain genetic variants in genome-wide association studies, thereby identifying candidate genes involved in conjugation. We confirmed the role of fliF, fliK, kefB and ucpA in the donor ability of conjugative elements by validating defects in the conjugation efficiency of the corresponding lab strain single-gene deletion mutants. Based on the known cellular functions of these genes, we suggest that the motility and the energy supply, the intracellular pH or salinity of the donor affect the efficiency of plasmid transfer. Overall, this work advances the search for targets for the development of conjugation inhibitors, which can be administered alongside antibiotics to more effectively treat bacterial infections.

RecPD: A Recombination-aware measure of phylogenetic diversity

A critical step in studying biological features (e.g., genetic variants, gene families, metabolic capabilities, or taxa) is assessing their diversity and distribution among a sample of individuals. Accurate assessments of these patterns are essential for linking features to traits or outcomes of interest and understanding their functional impact. Consequently, it is of crucial importance that the measures employed for quantifying feature diversity can perform robustly under any evolutionary scenario. However, the standard measures used for quantifying and comparing the distribution of features, such as prevalence, phylogenetic diversity, and related approaches, either do not take into consideration evolutionary history, or assume strictly vertical patterns of inheritance. Consequently, these approaches cannot accurately assess diversity for features that have undergone recombination or horizontal transfer. To address this issue, we have devised RecPD, a novel recombination-aware phylogenetic-diversity statistic for measuring the distribution and diversity of features under all evolutionary scenarios. RecPD utilizes ancestral-state reconstruction to map the presence / absence of features onto ancestral nodes in a species tree, and then identifies potential recombination events in the evolutionary history of the feature. We also derive several related measures from RecPD that can be used to assess and quantify evolutionary dynamics and correlation of feature evolutionary histories. We used simulation studies to show that RecPD reliably reconstructs feature evolutionary histories under diverse recombination and loss scenarios. We then applied RecPD in two diverse real-world scenarios including a preliminary study type III effector protein families secreted by the plant pathogenic bacterium Pseudomonas syringae and growth phenotypes of the Pseudomonas genus and demonstrate that prevalence is an inadequate measure that obscures the potential impact of recombination. We believe RecPD will have broad utility for revealing and quantifying complex evolutionary processes for features at any biological level.

Phylogenetic diversity is an important concept utilized in evolutionary ecology which has extensive applications in population genetics to help us understand how evolutionary processes have distributed genetic variation among individuals of a species, and how this impacts phenotypic diversification over time. However, existing approaches for studying phylogenetic diversity largely assume that the genetic features follow vertical inheritance, which is frequently violated in the case of microbial genomes due to horizontal transfer. To address this shortcoming, we present RecPD, a recombination-aware phylogenetic diversity measure, which incorporates ancestral state reconstruction to quantify the phylogenetic diversity of genetic features mapped onto a species phylogeny. Through simulation experiments we show that RecPD robustly reconstructs the evolutionary histories of features evolving under various scenarios of recombination and loss. When applied to a real-world example of type III secreted effector protein families from the plant pathogenic bacterium Pseudomonas syringae, RecPD reveals that horizontal transfer has played an important role in shaping the phylogenetic distributions of a substantial proportion of families across the P. syringae species complex. Furthermore, we demonstrate that the traditional measures of feature prevalence are unsuitable as a measure for comparing feature diversity. We also provide a R package implementation of RecPD for public use: https://github.com/cedatorma/recpd.

OUP accepted manuscript

MOTIVATION

Genome-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.

RESULTS

Here, 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 IMPLEMENTATION

We 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.

Genome-wide association study of gastric cancer- and duodenal ulcer-derived Helicobacter pylori strains reveals discriminatory genetic variations and novel oncoprotein candidates

Genome-wide association studies (GWASs) can reveal genetic variations associated with a phenotype in the absence of any hypothesis of candidate genes. The problem of false-positive sites linked with the responsible site might be bypassed in bacteria with a high homologous recombination rate, such as Helicobacter pylori, which causes gastric cancer. We conducted a small-sample GWAS (125 gastric cancer cases and 115 controls) followed by prediction of gastric cancer and control (duodenal ulcer) H. pylori strains. We identified 11 single nucleotide polymorphisms (eight amino acid changes) and three DNA motifs that, combined, allowed effective disease discrimination. They were often informative of the underlying molecular mechanisms, such as electric charge alteration at the ligand-binding pocket, alteration in subunit interaction, and mode-switching of DNA methylation. We also identified three novel virulence factors/oncoprotein candidates. These results provide both defined targets for further informatic and experimental analyses to gain insights into gastric cancer pathogenesis and a basis for identifying a set of biomarkers for distinguishing these H. pylori-related diseases.

A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA

Shotgun metagenomic sequencing is a powerful approach to study microbiomes in an unbiased manner and of increasing relevance for identifying novel enzymatic functions. However, the potential of metagenomics to relate from microbiome composition to function has thus far been underutilized. Here, we introduce the Metagenomics Genome-Phenome Association (MetaGPA) study framework, which allows linking genetic information in metagenomes with a dedicated functional phenotype. We applied MetaGPA to identify enzymes associated with cytosine modifications in environmental samples. From the 2365 genes that met our significance criteria, we confirm known pathways for cytosine modifications and proposed novel cytosine-modifying mechanisms. Specifically, we characterized and identified a novel nucleic acid-modifying enzyme, 5-hydroxymethylcytosine carbamoyltransferase, that catalyzes the formation of a previously unknown cytosine modification, 5-carbamoyloxymethylcytosine, in DNA and RNA. Our work introduces MetaGPA as a novel and versatile tool for advancing functional metagenomics.

Many industrial processes, such as starch processing and oil refinement, use chemicals that cause harm to the environment. These can often be switched to more sustainable biological processes that are powered by proteins called enzymes.

Enzymes are micro-factories that speed up biochemical reactions in most living things. Communities of microorganisms (also known as microbiomes) are an amazing but often untapped resource for discovering enzymes that can be harnessed for industrial purposes. To gain a better picture of the microbes present within a population, researchers often extract and sequence the genetic material of all microorganisms in an environmental sample, also known as the metagenome. While current methods for analyzing the metagenome are good at identifying new species, they often provide limited information about the microorganism’s functional role within the community. This makes it difficult to find new enzymes that may be useful for industry.

Here, Yang, Lin et al. have developed a new technique called Metagenomics Genome-Phenome Association, or MetaGPA for short. The method works in a similar way to genome-wide association studies (GWAS) which are used to identify genes involved in human disease. However, instead of disease associated genes in humans, MetaGPA finds microbial genes that are associated with a biological process useful for biotechnology.

Like GWAS, the new approach created by Yang, Lin et al. compares two groups: the first contains microorganisms that carry out a specific process, and the second contains all organisms in the microbiome. The metagenome of each group is extracted and a computational pipeline is then applied to identify genes, including those coding for enzymes, that are found more often in the group performing the desired task.

To test the technique, Yang, Lin et al. used MetGPA to find new enzymes involved in DNA modification. Microbiome samples were collected from coastal water and sewage, and the computational pipeline was applied to discover genes that are associated with this process. Further analysis revealed that one of the identified genes codes for an enzyme that introduces a previously unknown change to DNA.

MetaGPA could be applied to other processes and microbiomes, and, if successful, may help researchers to identify more diverse enzymes than is currently available. This could scale up the discovery of new enzymes that can be used to power industrial reactions.

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.

Machine learning to predict the source of campylobacteriosis using whole genome data

Campylobacteriosis is among the world's most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential.