A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination

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.

Multi-omics analyses of cancer-linked clinical salmonellae reveal bacterial-induced host metabolic shift and mTOR-dependent cell transformation

Salmonellae are associated epidemiologically and experimentally with colon cancer. To understand how Salmonella induces cell transformation, we performed multi-omics and phenotypic analyses of Salmonella clinical strains isolated from patients later diagnosed with colon cancer (case strains) and control strains from patients without cancer. We show that high transformation efficiency is a frequent intrinsic feature of clinical (case and control) salmonellae, yet case strains showed higher transformation efficiency than control strains. Transformation efficiency correlates with gene expression, nutrient utilization, and intracellular virulence, but not with genetic features, suggesting a phenotypic convergence of Salmonella strains resulting in cell transformation. We show that both bacterial entry and intracellular replication are required for host cell transformation and are associated with hyperactivation of the mTOR pathway. Strikingly, transiently inactivating mTOR through chemical inhibition reverses the transformation phenotype instigated by Salmonella infection. This suggests that targeting the mTOR pathway could prevent the development of Salmonella-induced tumors.

Exploring genetic determinants of antimicrobial resistance in Brucella melitensis strains of human and animal origin from India

Introduction

Antimicrobial resistance (AMR) in Brucella melitensis, the causative agent of brucellosis, is of growing concern, particularly in low and middle-income countries. This study aimed to explore the genetic basis of AMR in B. melitensis strains from India.

Methods

Twenty-four isolates from humans and animals were subjected to antimicrobial susceptibility testing and whole-genome sequencing.

Results

Resistance to doxycycline (20.80%), ciprofloxacin (16.67%), cotrimoxazole (4.17%), and rifampicin (16.67%) was observed. Genome analysis revealed efflux-related genes like mprF, bepG, bepF, bepC, bepE, and bepD across all isolates, however, classical AMR genes were not detected. Mutations in key AMR-associated genes such as rpoB, gyrA, and folP were identified, intriguingly present in both resistant and susceptible isolates, suggesting a complex genotype-phenotype relationship in AMR among Brucella spp. Additionally, mutations in efflux genes were noted in resistant and some susceptible isolates, indicating their potential role in resistance mechanisms. However, mutations in AMR-associated genes did not consistently align with phenotypic resistance, suggesting a multifactorial basis for resistance.

Discussion

The study underscores the complexity of AMR in B. melitensis and advocates for a holistic multi-omics approach to fully understand resistance mechanisms. These findings offer valuable insights into genetic markers associated with AMR, guiding future research and treatment strategies.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

The rv2820c K114N mutation is related with capreomycin tolerance

As one of the factors affecting the treatment outcomes, drug tolerance in mycobacteriosis has not been paid due attention. Genome-wide association studies on 607 Mycobacterium tuberculosis clinical isolates with phenotypic drug susceptibility test data revealed that a K114N mutation on the rv2820c gene was highly enriched in capreomycin-resistant isolates (32/213, 15.02%). However, the mutation was also observed in capreomycin-sensitive isolates (10/394, 2.53%). In most cases (31/42, 73.81%), the rv2820c K114N mutation occurred in isolates with the known capreomycin resistance conferring mutation rrs A1401G. In contrast, the general frequency of the rv2820c K114N mutation was low in 7061 genomes downloaded from the National Center for Biotechnology Information database. To determine the impact of this mutation on the antimycobacterial activity of capreomycin, the intact rv2820c gene and the rv2820c K114N mutant were over-expressed in Mycobacterium smegmatis (Ms), and the results of susceptibility tests showed that the rv2820c K114N mutation did not affect the minimum inhibition concentration (MIC) of capreomycin. Subsequently, the data of time-kill assays showed that, it took only 2 h of capreomycin treatment (40 μg/ml, 5 × MIC) to kill 99.9% bacterial cells of Ms MC2155 pMV261::rv2820cH37Rv, while it took 6 h to achieve that for Ms MC2155 pMV261::rv2820cK114N. Taken together, these data suggested that the rv2820c K114N mutation is related with capreomycin tolerance, which merits further investigation.

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.

Presence of phage-plasmids in multiple serovars of Salmonella enterica

Evidence is accumulating in the literature that the horizontal spread of antimicrobial resistance (AMR) genes mediated by bacteriophages and bacteriophage-like plasmid (phage-plasmid) elements is much more common than previously envisioned. For instance, we recently identified and characterized a circular P1-like phage-plasmid harbouring a bla CTX-M-15 gene conferring extended-spectrum beta-lactamase (ESBL) resistance in Salmonella enterica serovar Typhi. As the prevalence and epidemiological relevance of such mechanisms has never been systematically assessed in Enterobacterales, in this study we carried out a follow-up retrospective analysis of UK Salmonella isolates previously sequenced as part of routine surveillance protocols between 2016 and 2021. Using a high-throughput bioinformatics pipeline we screened 47 784 isolates for the presence of the P1 lytic replication gene repL, identifying 226 positive isolates from 25 serovars and demonstrating that phage-plasmid elements are more frequent than previously thought. The affinity for phage-plasmids appears highly serovar-dependent, with several serovars being more likely hosts than others; most of the positive isolates (170/226) belonged to S. Typhimurium ST34 and ST19. The phage-plasmids ranged between 85.8 and 98.2 kb in size, with an average length of 92.1 kb; detailed analysis indicated a high amount of diversity in gene content and genomic architecture. In total, 132 phage-plasmids had the p0111 plasmid replication type, and 94 the IncY type; phylogenetic analysis indicated that both horizontal and vertical gene transmission mechanisms are likely to be involved in phage-plasmid propagation. Finally, phage-plasmids were present in isolates that were resistant and non-resistant to antimicrobials. In addition to providing a first comprehensive view of the presence of phage-plasmids in Salmonella, our work highlights the need for a better surveillance and understanding of phage-plasmids as AMR carriers, especially through their characterization with long-read sequencing.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova

The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.

bacLIFE: a user-friendly computational workflow for genome analysis and prediction of lifestyle-associated genes in bacteria

Bacteria have an extensive adaptive ability to live in close association with eukaryotic hosts, exhibiting detrimental, neutral or beneficial effects on host growth and health. However, the genes involved in niche adaptation are mostly unknown and their functions poorly characterized. Here, we present bacLIFE ( https://github.com/Carrion-lab/bacLIFE ) a streamlined computational workflow for genome annotation, large-scale comparative genomics, and prediction of lifestyle-associated genes (LAGs). As a proof of concept, we analyzed 16,846 genomes from the Burkholderia/Paraburkholderia and Pseudomonas genera, which led to the identification of hundreds of genes potentially associated with a plant pathogenic lifestyle. Site-directed mutagenesis of 14 of these predicted LAGs of unknown function, followed by plant bioassays, showed that 6 predicted LAGs are indeed involved in the phytopathogenic lifestyle of Burkholderia plantarii and Pseudomonas syringae pv. phaseolicola. These 6 LAGs encompassed a glycosyltransferase, extracellular binding proteins, homoserine dehydrogenases and hypothetical proteins. Collectively, our results highlight bacLIFE as an effective computational tool for prediction of LAGs and the generation of hypotheses for a better understanding of bacteria-host interactions.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.

Phylogenetic Survival Analysis

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.

Pseudomonas syringae coffee blight is associated with the horizontal transfer of plasmid-encoded type III effectors

The emergence of new pathogens is an ongoing threat to human health and agriculture. While zoonotic spillovers received considerable attention, the emergence of crop diseases is less well studied. Here, we identify genomic factors associated with the emergence of Pseudomonas syringae bacterial blight of coffee. Fifty-three P. syringae strains from diseased Brazilian coffee plants were sequenced. Comparative and evolutionary analyses were used to identify loci associated with coffee blight. Growth and symptomology assays were performed to validate the findings. Coffee isolates clustered in three lineages, including primary phylogroups PG3 and PG4, and secondary phylogroup PG11. Genome-wide association study of the primary PG strains identified 37 loci, including five effectors, most of which were encoded on a plasmid unique to the PG3 and PG4 coffee strains. Evolutionary analyses support the emergence of coffee blight in PG4 when the coffee-associated plasmid and associated effectors derived from a divergent plasmid carried by strains associated with other hosts. This plasmid was only recently transferred into PG3. Natural diversity and CRISPR-Cas9 plasmid curing were used to show that strains with the coffee-associated plasmid grow to higher densities and cause more severe disease symptoms in coffee. This work identifies possible evolutionary mechanisms underlying the emergence of a new lineage of coffee pathogens.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

BACKGROUND

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.

RESULTS

In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.

CONCLUSIONS

Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Genome-Wide Association Studies (GWAS) Approaches for the Detection of Genetic Variants Associated with Antibiotic Resistance: A Systematic Review

Antibiotic resistance is a significant threat to public health worldwide. Genome-wide association studies (GWAS) have emerged as a powerful tool to identify genetic variants associated with this antibiotic resistance. By analyzing large datasets of bacterial genomes, GWAS can provide valuable insights into the resistance mechanisms and facilitate the discovery of new drug targets. The present study aimed to undertake a systematic review of different GWAS approaches used for detecting genetic variants associated with antibiotic resistance. We comprehensively searched the PubMed and Scopus databases to identify relevant studies published from 2013 to February 2023. A total of 40 studies met our inclusion criteria. These studies explored a wide range of bacterial species, antibiotics, and study designs. Notably, most of the studies were centered around human pathogens such as Mycobacterium tuberculosis, Escherichia coli, Neisseria gonorrhoeae, and Staphylococcus aureus. The review seeks to explore the several GWAS approaches utilized to investigate the genetic mechanisms associated with antibiotic resistance. Furthermore, it examines the contributions of GWAS approaches in identifying resistance-associated genetic variants through binary and continuous phenotypes. Overall, GWAS holds great potential to enhance our understanding of bacterial resistance and improve strategies to combat infectious diseases.

Gut microbial structural variation associates with immune checkpoint inhibitor response

The gut microbiota may have an effect on the therapeutic resistance and toxicity of immune checkpoint inhibitors (ICIs). However, the associations between the highly variable genomes of gut bacteria and the effectiveness of ICIs remain unclear, despite the fact that merely a few gene mutations between similar bacterial strains may cause significant phenotypic variations. Here, using datasets from the gut microbiome of 996 patients from seven clinical trials, we systematically identify microbial genomic structural variants (SVs) using SGV-Finder. The associations between SVs and response, progression-free survival, overall survival, and immune-related adverse events are systematically explored by metagenome-wide association analysis and replicated in different cohorts. Associated SVs are located in multiple species, including Akkermansia muciniphila, Dorea formicigenerans, and Bacteroides caccae. We find genes that encode enzymes that participate in glucose metabolism be harbored in these associated regions. This work uncovers a nascent layer of gut microbiome heterogeneity that is correlated with hosts' prognosis following ICI treatment and represents an advance in our knowledge of the intricate relationships between microbiota and tumor immunotherapy.

Phylogenetic identification of influenza virus candidates for seasonal vaccines

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

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

BACKGROUND

While 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.

RESULTS

The 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.

CONCLUSIONS

These 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.

Chance Favors the Prepared Genomes: Horizontal Transfer Shapes the Emergence of Antibiotic Resistance Mutations in Core Genes

Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Improving photosynthetic efficiency toward food security: Strategies, advances, and perspectives

Photosynthesis in crops and natural vegetation allows light energy to be converted into chemical energy and thus forms the foundation for almost all terrestrial trophic networks on Earth. The efficiency of photosynthetic energy conversion plays a crucial role in determining the portion of incident solar radiation that can be used to generate plant biomass throughout a growth season. Consequently, alongside the factors such as resource availability, crop management, crop selection, maintenance costs, and intrinsic yield potential, photosynthetic energy use efficiency significantly influences crop yield. Photosynthetic efficiency is relevant to sustainability and food security because it affects water use efficiency, nutrient use efficiency, and land use efficiency. This review focuses specifically on the potential for improvements in photosynthetic efficiency to drive a sustainable increase in crop yields. We discuss bypassing photorespiration, enhancing light use efficiency, harnessing natural variation in photosynthetic parameters for breeding purposes, and adopting new-to-nature approaches that show promise for achieving unprecedented gains in photosynthetic efficiency.

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.

happi: a hierarchical approach to pangenomics inference

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.

A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremia

Outcomes of severe bacterial infections are determined by the interplay between host, pathogen, and treatments. While human genomics has provided insights into host factors impacting Staphylococcus aureus infections, comparatively little is known about S. aureus genotypes and disease severity. Building on the hypothesis that bacterial pathoadaptation is a key outcome driver, we developed a genome-wide association study (GWAS) framework to identify adaptive mutations associated with treatment failure and mortality in S. aureus bacteremia (1,358 episodes). Our research highlights the potential of vancomycin-selected mutations and vancomycin minimum inhibitory concentration (MIC) as key explanatory variables to predict infection severity. The contribution of bacterial variation was much lower for clinical outcomes (heritability <5%); however, GWASs allowed us to identify additional, MIC-independent candidate pathogenesis loci. Using supervised machine learning, we were able to quantify the predictive potential of these adaptive signatures. Our statistical genomics framework provides a powerful means to capture adaptive mutations impacting severe bacterial infections.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

A multidrug-resistant Salmonella enterica Typhimurium DT104 complex lineage circulating among humans and cattle in the USA lost the ability to produce pertussis-like toxin ArtAB

Salmonella enterica subsp. enterica serotype Typhimurium definitive type 104 (DT104) can infect both humans and animals and is often multidrug-resistant (MDR). Previous studies have indicated that, unlike most S . Typhimurium, the overwhelming majority of DT104 strains produce pertussis-like toxin ArtAB via prophage-encoded genes artAB . However, DT104 that lack artAB have been described on occasion. Here, we identify an MDR DT104 complex lineage circulating among humans and cattle in the USA, which lacks artAB (i.e. the ‘U.S. artAB -negative major clade’; n =42 genomes). Unlike most other bovine- and human-associated DT104 complex strains from the USA (n =230 total genomes), which harbour artAB on prophage Gifsy-1 (n =177), members of the U.S. artAB -negative major clade lack Gifsy-1, as well as anti-inflammatory effector gogB . The U.S. artAB -negative major clade encompasses human- and cattle-associated strains isolated from ≥11 USA states over a 20-year period. The clade was predicted to have lost artAB , Gifsy-1 and gogB circa 1985–1987 (95 % highest posterior density interval 1979.0–1992.1). When compared to DT104 genomes from other regions of the world (n =752 total genomes), several additional, sporadic artAB , Gifsy-1 and/or gogB loss events among clades encompassing five or fewer genomes were observed. Using phenotypic assays that simulate conditions encountered during human and/or bovine digestion, members of the U.S. artAB -negative major clade did not differ from closely related Gifsy-1/artAB /gogB -harbouring U.S. DT104 complex strains (ANOVA raw P >0.05); thus, future research is needed to elucidate the roles that artAB , gogB and Gifsy-1 play in DT104 virulence in humans and animals.

The genetic network underlying the evolution of pathogenicity in avian Escherichia coli

Introduction

Colibacillosis is a worldwide prevalent disease in poultry production linked to Escherichia coli strains that belong to the avian pathogenic E. coli (APEC) pathotype. While many virulence factors have been linked to APEC isolates, no single gene or set of genes has been found to be exclusively associated with the pathotype. Moreover, a comprehensive description of the biological processes linked to APEC pathogenicity is currently lacking.

Methods

In this study, we compiled a dataset of 2015 high-quality avian E. coli genomes from pathogenic and commensal isolates, based on publications from 2000 to 2021. We then conducted a genome-wide association study (GWAS) and integrated candidate gene identification with available protein-protein interaction data to decipher the genetic network underlying the biological processes connected to APEC pathogenicity.

Results

Our GWAS identified variations in gene content for 13 genes and SNPs in 3 different genes associated with APEC isolates, suggesting both gene-level and SNP-level variations contribute to APEC pathogenicity. Integrating protein-protein interaction data, we found that 15 of these genes clustered in the same genetic network, suggesting the pathogenicity of APEC might be due to the interplay of different regulated pathways. We also found novel candidate genes including an uncharacterized multi-pass membrane protein (yciC) and the outer membrane porin (ompD) as linked to APEC isolates.

Discussion

Our findings suggest that convergent pathways related to nutrient uptake from host cells and defense from host immune system play a major role in APEC pathogenicity. In addition, the dataset curated in this study represents a comprehensive historical genomic collection of avian E. coli isolates and constitutes a valuable resource for their comparative genomics investigations.

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.

Parallel host shifts in a bacterial plant pathogen suggest independent genetic solutions

While there are documented host shifts in many bacterial plant pathogens, the genetic foundation of host shifts is largely unknown. Xylella fastidiosa is a bacterial pathogen found in over 600 host plant species. Two parallel host shifts occurred-in Brazil and Italy-in which X. fastidiosa adapted to infect olive trees, whereas related strains infected coffee. Using 10 novel whole-genome sequences from an olive-infecting population in Brazil, we investigated whether these olive-infecting strains diverged from closely related coffee-infecting strains. Several single-nucleotide polymorphisms, many derived from recombination events, and gene gain and loss events separated olive-infecting strains from coffee-infecting strains in this clade. The olive-specific variation suggests that this event was a host jump with genetic isolation between coffee- and olive-infecting X. fastidiosa populations. Next, we investigated the hypothesis of genetic convergence in the host shift from coffee to olive in both populations (Brazil and Italy). Each clade had multiple mutations and gene gain and loss events unique to olive, yet no overlap between clades. Using a genome-wide association study technique, we did not find any plausible candidates for convergence. Overall, this work suggests that the two populations adapted to infect olive trees through independent genetic solutions.

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.

On-person adaptive evolution of Staphylococcus aureus during treatment for atopic dermatitis

The opportunistic pathogen Staphylococcus aureus frequently colonizes the inflamed skin of people with atopic dermatitis (AD) and worsens disease severity by promoting skin damage. Here, we show, by longitudinally tracking 23 children treated for AD, that S. aureus adapts via de novo mutations during colonization. Each patient's S. aureus population is dominated by a single lineage, with infrequent invasion by distant lineages. Mutations emerge within each lineage at rates similar to those of S. aureus in other contexts. Some variants spread across the body within months, with signatures of adaptive evolution. Most strikingly, mutations in capsule synthesis gene capD underwent parallel evolution in one patient and across-body sweeps in two patients. We confirm that capD negativity is more common in AD than in other contexts, via reanalysis of S. aureus genomes from 276 people. Together, these findings highlight the importance of the mutation level when dissecting the role of microbes in complex disease.

Genome-wide association studies in plant pathosystems: success or failure?

Harnessing natural genetic variation is an established alternative to artificial genetic variation for investigating the molecular dialog between partners in plant pathosystems. Herein, we review the successes of genome-wide association studies (GWAS) in both plants and pathogens. While GWAS in plants confirmed that the genetic architecture of disease resistance is polygenic, dynamic during the infection kinetics, and dependent on the environment, GWAS shortened the time of identification of quantitative trait loci (QTLs) and revealed both complex epistatic networks and a genetic architecture dependent upon the geographical scale. A similar picture emerges from the few GWAS in pathogens. In addition, the ever-increasing number of functionally validated QTLs has revealed new molecular plant defense mechanisms and pathogenicity determinants. Finally, we propose recommendations to better decode the disease triangle.

Migration Rates on Swim Plates Vary between Escherichia coli Soil Isolates: Differences Are Associated with Variants in Metabolic Genes

This study investigates migration phenotypes of 265 Escherichia coli soil isolates from the Buffalo River basin in Minnesota, USA. Migration rates on semisolid tryptone swim plates ranged from nonmotile to 190% of the migration rate of a highly motile E. coli K-12 strain. The nonmotile isolate, LGE0550, had mutations in flagellar and chemotaxis genes, including two IS3 elements in the flagellin-encoding gene fliC. A genome-wide association study (GWAS), associating the migration rates with genetic variants in specific genes, yielded two metabolic variants (rygD-serA and metR-metE) with previous implications in chemotaxis. As a novel way of confirming GWAS results, we used minimal medium swim plates to confirm the associations. Other variants in metabolic genes and genes that are associated with biofilm were positively or negatively associated with migration rates. A determination of growth phenotypes on Biolog EcoPlates yielded differential growth for the 10 tested isolates on d-malic acid, putrescine, and d-xylose, all of which are important in the soil environment. IMPORTANCE E. coli is a Gram-negative, facultative anaerobic bacterium whose life cycle includes extra host environments in addition to human, animal, and plant hosts. The bacterium has the genomic capability of being motile. In this context, the significance of this study is severalfold: (i) the great diversity of migration phenotypes that we observed within our isolate collection supports previous (G. NandaKafle, A. A. Christie, S. Vilain, and V. S. Brözel, Front Microbiol 9:762, 2018, https://doi.org/10.3389/fmicb.2018.00762; Y. Somorin, F. Abram, F. Brennan, and C. O'Byrne, Appl Environ Microbiol 82:4628-4640, 2016, https://doi.org/10.1128/AEM.01175-16) ideas of soil promoting phenotypic heterogeneity, (ii) such heterogeneity may facilitate bacterial growth in the many different soil niches, and (iii) such heterogeneity may enable the bacteria to interact with human, animal, and plant hosts.

Comparative Genome Analysis of Enterococcus cecorum Reveals Intercontinental Spread of a Lineage of Clinical Poultry Isolates

Enterococcus cecorum is an emerging pathogen responsible for osteomyelitis, spondylitis, and femoral head necrosis causing animal suffering and mortality and requiring antimicrobial use in poultry. Paradoxically, E. cecorum is a common inhabitant of the intestinal microbiota of adult chickens. Despite evidence suggesting the existence of clones with pathogenic potential, the genetic and phenotypic relatedness of disease-associated isolates remains little investigated. Here, we sequenced and analyzed the genomes and characterized the phenotypes of more than 100 isolates, the majority of which were collected over the last 10 years from 16 French broiler farms. Comparative genomics, genome-wide association studies, and the measured susceptibility to serum, biofilm-forming capacity, and adhesion to chicken type II collagen were used to identify features associated with clinical isolates. We found that none of the tested phenotypes could discriminate the origin of the isolates or the phylogenetic group. Instead, we found that most clinical isolates are grouped phylogenetically, and our analyses selected six genes that discriminate 94% of isolates associated with disease from those that are not. Analysis of the resistome and the mobilome revealed that multidrug-resistant clones of E. cecorum cluster into a few clades and that integrative conjugative elements and genomic islands are the main carriers of antimicrobial resistance. This comprehensive genomic analysis shows that disease-associated clones of E. cecorum belong mainly to one phylogenetic clade. IMPORTANCE Enterococcus cecorum is an important pathogen of poultry worldwide. It causes a number of locomotor disorders and septicemia, particularly in fast-growing broilers. Animal suffering, antimicrobial use, and associated economic losses require a better understanding of disease-associated E. cecorum isolates. To address this need, we performed whole-genome sequencing and analysis of a large collection of isolates responsible for outbreaks in France. By providing the first data set on the genetic diversity and resistome of E. cecorum strains circulating in France, we pinpoint an epidemic lineage that is probably also circulating elsewhere that should be targeted preferentially by preventive strategies in order to reduce the burden of E. cecorum-related diseases.

Recent transposable element bursts are associated with the proximity to genes in a fungal plant pathogen

The activity of transposable elements (TEs) contributes significantly to pathogen genome evolution. TEs often destabilize genome integrity but may also confer adaptive variation in pathogenicity or resistance traits. De-repression of epigenetically silenced TEs often initiates bursts of transposition activity that may be counteracted by purifying selection and genome defenses. However, how these forces interact to determine the expansion routes of TEs within a pathogen species remains largely unknown. Here, we analyzed a set of 19 telomere-to-telomere genomes of the fungal wheat pathogen Zymoseptoria tritici. Phylogenetic reconstruction and ancestral state estimates of individual TE families revealed that TEs have undergone distinct activation and repression periods resulting in highly uneven copy numbers between genomes of the same species. Most TEs are clustered in gene poor niches, indicating strong purifying selection against insertions near coding sequences, or as a consequence of insertion site preferences. TE families with high copy numbers have low sequence divergence and strong signatures of defense mechanisms (i.e., RIP). In contrast, small non-autonomous TEs (i.e., MITEs) are less impacted by defense mechanisms and are often located in close proximity to genes. Individual TE families have experienced multiple distinct burst events that generated many nearly identical copies. We found that a Copia element burst was initiated from recent copies inserted substantially closer to genes compared to older copies. Overall, TE bursts tended to initiate from copies in GC-rich niches that escaped inactivation by genomic defenses. Our work shows how specific genomic environments features provide triggers for TE proliferation in pathogen genomes.

Phylogeography and transmission of Mycobacterium tuberculosis spanning prisons and surrounding communities in Paraguay

Recent rises in incident tuberculosis (TB) cases in Paraguay and the increasing concentration of TB within prisons highlight the urgency of targeting strategies to interrupt transmission and prevent new infections. However, whether specific cities or carceral institutions play a disproportionate role in transmission remains unknown. We conducted prospective genomic surveillance, sequencing 471 Mycobacterium tuberculosis complex genomes, from inside and outside prisons in Paraguay's two largest urban areas, Asunción and Ciudad del Este, from 2016 to 2021. We found genomic evidence of frequent recent transmission within prisons and transmission linkages spanning prisons and surrounding populations. We identified a signal of frequent M. tuberculosis spread between urban areas and marked recent population size expansion of the three largest genomic transmission clusters. Together, our findings highlight the urgency of strengthening TB control programs to reduce transmission risk within prisons in Paraguay, where incidence was 70 times that outside prisons in 2021.

Phylogenetic Analysis of Bacterial Pathogen Genomes

The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes.

Genome-Wide Identification of Variants Associated with Antifungal Drug Resistance

Genomic approaches are widely applied to study the genetic basis of antifungal drug resistance in clinical isolates and experimental studies. Whole-genome sequencing of clinical isolates can comprehensively identify mutations associated with drug resistance and their frequency across fungal populations. In addition, genome comparison of serially collected isolates, such as from patient samples or in vitro drug selection experiments, will identify a small number of changes that can be evaluated for association with drug resistance. Here, we provide a detailed protocol for the computational analysis of genome sequences to identify variants associated with drug resistance.

Comparative Genomics of Streptococcus oralis Identifies Large Scale Homologous Recombination and a Genetic Variant Associated with Infection

The viridans group streptococci (VGS) are a large consortium of commensal streptococci that colonize the human body. Many species within this group are opportunistic pathogens causing bacteremia and infective endocarditis (IE), yet little is known about why some strains cause invasive disease. Identification of virulence determinants is complicated by the difficulty of distinguishing between the closely related species of this group. Here, we analyzed genomic data from VGS that were isolated from blood cultures in patients with invasive infections and oral swabs of healthy volunteers and then determined the best-performing methods for species identification. Using whole-genome sequence data, we characterized the population structure of a diverse sample of Streptococcus oralis isolates and found evidence of frequent recombination. We used multiple genome-wide association study tools to identify candidate determinants of invasiveness. These tools gave consistent results, leading to the discovery of a single synonymous single nucleotide polymorphism (SNP) that was significantly associated with invasiveness. This SNP was within a previously undescribed gene that was conserved across the majority of VGS species. Using the growth in the presence of human serum and a simulated infective endocarditis vegetation model, we were unable to identify a phenotype for the enriched allele in laboratory assays, suggesting a phenotype may be specific to natural infection. These data highlighted the power of analyzing natural populations for gaining insight into pathogenicity, particularly for organisms with complex population structures like the VGS. IMPORTANCE The viridians group streptococci (VGS) are a large collection of closely related commensal streptococci, with many being opportunistic pathogens causing invasive diseases, such as bacteremia and infective endocarditis. Little is known about virulence determinants in these species, and there is a distinct lack of genomic information available for the VGS. In this study, we collected VGS isolates from invasive infections and healthy volunteers and performed whole-genome sequencing for a suite of downstream analyses. We focused on a diverse sample of Streptococcus oralis genomes and identified high rates of recombination in the population as well as a single genome variant highly enriched in invasive isolates. The variant lies within a previously uncharacterized gene, nrdM, which shared homology with the anaerobic ribonucleoside triphosphate reductase, nrdD, and was highly conserved among VGS. This work increased our knowledge of VGS genomics and indicated that differences in virulence potential among S. oralis isolates were, at least in part, genetically determined.

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.

Comparative genomics study of Staphylococcus aureus isolated from cattle and humans reveals virulence patterns exclusively associated with bovine clinical mastitis strains

Staphylococcus aureus causes nosocomial and intramammary infections in humans and cattle, respectively. A large number of virulence factors are thought to play important roles in the pathogenesis of this bacterium. Currently, genome-wide and data-analysis studies are being used to better understand its epidemiology. In this study, we conducted a genome wide comparison and phylogenomic analyses of S. aureus to find specific virulence patterns associated with clinical and subclinical mastitis strains in cattle and compare them with those of human origin. The presence/absence of key virulence factors such as adhesin, biofilm, antimicrobial resistance, and toxin genes, as well as the phylogeny and sequence type of the isolates were evaluated. A total of 248 genomes (27 clinical mastitis, 43 subclinical mastitis, 21 milk, 53 skin-related abscesses, 49 skin infections, and 55 pus from cellulitis) isolated from 32 countries were evaluated. We found that the cflA, fnbA, ebpS, spa, sdrC, coa, emp, vWF, atl, sasH, sasA, and sasF adhesion genes, as well as the aur, hglA, hglB, and hglC toxin genes were highly associated in clinical mastitis strains. The strains had diverse genetic origins (72 protein A and 48 sequence types with ST97, ST8 and ST152 being frequent in isolates from clinical mastitis, abscess, and skin infection, respectively). Further, our phylogenomic analyses suggested that zoonotic and/or zooanthroponotic transmission may have occurred. These findings contribute to a better understanding of S. aureus epidemiology and the relationships between adhesion mechanisms, biofilm formation, antimicrobial resistance, and toxins and could aid in the development of improved vaccines and strain genotyping methods.

Genome-Wide Association Studies Identify an Association of Transferrin Binding Protein B Variation and Invasive Serogroup Y Meningococcal Disease in Older Adults

BACKGROUND

Neisseria meningitidis serogroup Y, especially ST-23 clonal complex (Y:cc23), represents a larger proportion of invasive meningococcal disease (IMD) in older adults compared to younger individuals. This study explored the meningococcal genetic variation underlying this association.

METHODS

Maximum-likelihood phylogenies and the pangenome were analyzed using whole-genome sequence (WGS) data from 200 Y:cc23 isolates in the Neisseria PubMLST database. Genome-wide association studies (GWAS) were performed on WGS data from 250 Y:cc23 isolates from individuals with IMD aged ≥65 years versus < 65 years.

RESULTS

Y:cc23 meningococcal variants did not cluster by age group or disease phenotype in phylogenetic analyses. Pangenome comparisons found no differences in presence or absence of genes in IMD isolates from the different age groups. GWAS identified differences in nucleotide polymorphisms within the transferrin-binding protein B (tbpB) gene in isolates from individuals ≥65 years of age. TbpB structure modelling suggests these may impact binding of human transferrin.

CONCLUSIONS

These data suggest differential iron scavenging capacity amongst Y:cc23 meningococci isolated from older compared to younger patients. Iron acquisition is essential for many bacterial pathogens including the meningococcus. These polymorphisms may facilitate colonization, thereby increasing the risk of disease in vulnerable older people with altered nasopharyngeal microbiomes and nutritional status.

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.

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.

Genomic elements located in the accessory repertoire drive the adaptation to biocides in Listeria monocytogenes strains from different ecological niches

In response to the massive use of biocides for controlling Listeria monocytogenes (hereafter Lm) contaminations along the food chain, strains showing biocide tolerance emerged. Here, accessory genomic elements were associated with biocide tolerance through pangenome-wide associations performed on 197 Lm strains from different lineages, ecological, geographical and temporal origins. Mobile elements, including prophage-related loci, the Tn6188_qacH transposon and pLMST6_emrC plasmid, were widespread across lineage I and II food strains and associated with tolerance to benzalkonium-chloride (BC), a quaternary ammonium compound (QAC) widely used in food processing. The pLMST6_emrC was also associated with tolerance to another QAC, the didecyldimethylammonium-chloride, displaying a pleiotropic effect. While no associations were detected for chemically reactive biocides (alcohols and chlorines), genes encoding for cell-surface proteins were associated with BC or polymeric biguanide tolerance. The latter was restricted to lineage I strains from animal and the environment. In conclusion, different genetic markers, with polygenic nature or not, appear to have driven the Lm adaptation to biocide, especially in food strains but also from animal and the environment. These markers could aid to monitor and predict the spread of biocide tolerant Lm genotypes across different ecological niches, finally reducing the risk of such strains in food industrial settings.

A phylogeny-aware GWAS framework to correct for heritable pathogen effects on infectious disease traits

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host-pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.

Combined comparative genomics and clinical modeling reveals plasmid-encoded genes are independently associated with Klebsiella infection

Members of the Klebsiella pneumoniae species complex frequently colonize the gut and colonization is associated with subsequent infection. To identify genes associated with progression from colonization to infection, we undertook a case-control comparative genomics study. Concordant cases (N = 85), where colonizing and invasive isolates were identical strain types, were matched to asymptomatically colonizing controls (N = 160). Thirty-seven genes are associated with infection, 27 of which remain significant following adjustment for patient variables and bacterial phylogeny. Infection-associated genes are not previously characterized virulence factors, but instead a diverse group of stress resistance, regulatory and antibiotic resistance genes, despite careful adjustment for antibiotic exposure. Many genes are plasmid borne, and for some, the relationship with infection is mediated by gut dominance. Five genes were validated in a geographically-independent cohort of colonized patients. This study identifies several genes reproducibly associated with progression to infection in patients colonized by diverse Klebsiella.