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

Whole-genome sequencing, strain composition, and predicted antimicrobial resistance of Streptococcus pneumoniae causing invasive disease in England in 2017-20: a prospective national surveillance study

BACKGROUND

Surveillance of the invasive disease burden caused by Streptococcus pneumoniae in England is performed by the UK Health Security Agency (UKHSA). In 2017, UKHSA switched from phenotypic methods to whole-genome sequencing (WGS) approaches for pneumococcal surveillance. Here, we present the first results of national WGS surveillance, up to the start of the COVID-19 pandemic, with the aim of describing the population genomics of this important pathogen.

METHODS

We examined prospective national surveillance data from England, using bacterial isolates from cases of invasive pneumococcal disease (IPD) submitted to the national reference laboratory at UKHSA. A bioinformatic pipeline was developed to quality control WGS data and routinely report species and serotype. We assembled isolate data, assigned global pneumococcal sequencing clusters (GPSCs), and predicted antimicrobial resistance (AMR) profiles for isolates that passed further quality control. We collected additional data on patient outcomes and characteristics using enhanced surveillance questionnaires completed by patients' general practitioners. We used logistic regression analysis to assess the effects of various genomic and patient characteristics on the outcomes of IPD.

FINDINGS

In England, between July 1, 2017, and Feb 29, 2020, there were 15 400 cases of IPD. From these cases, 13 749 (89·3%) isolates were sequenced, passed quality control, and were included in analyses. Serotype diversity was high during the study period, with 2751 (20%) isolates serotyped as 13-valent pneumococcal conjugate vaccine (PCV13) types, whereas serotype 8 was the most prevalent serotype (n=3074 [22·4%]) overall. There were 157 GPSCs within the collection, with GSPC3 the most common, encompassing 98·7% (3033 of 3074) of serotype 8 isolates. Most isolates (n=10 198 [74·2%]) did not contain AMR-associated genes. Resistance to co-trimoxazole was the most frequently predicted resistance (n=2331 [17%]), followed by resistance to tetracycline (n=1199 [8·7%]) and β-lactams (n=1149 [8·4%]). Logistic regression analysis found the presence of AMR-associated genes significantly increased the odds of patient death (odds ratio 1·18, 95% CI 1·01-1·38). Some GPSCs were also associated with a significant increase in the odds of patient death, such as GPSC12 (1·88, 1·48-2·38). Isolates from 2018 were associated with a significant increase in the odds of patient death (1·12, 1·00-1·25), whereas younger patient age was significantly associated with a reduction in the odds of patient death compared with being aged 85 years or older.

INTERPRETATION

WGS-based surveillance has allowed us to interrogate country-wide population dynamics driving changes in pneumococcal serotype frequency. Here, we observe a stable but diverse population before the COVID-19 pandemic restrictions were enforced in England, with low rates of AMR. These findings will provide the baseline for pandemic and post-pandemic data, to collectively inform implementation and development of the vaccination programme within the country.

FUNDING

None.

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.

A genome-wide investigation of Mycoplasma hominis genes associated with gynecological infections or infertility

Background and aim

Mycoplasma hominis is a human pathogenic bacterium that causes a wide range of genital infections and reproductive issues. Previously, based on an extended multilocus sequence typing scheme, we provided evidence for the segregation of M. hominis clinical strains into two distinct pathotypes: gynecological infections or infertility. Here, based on whole genome sequencing (WGS) data, we sought to provide a more refined picture of the phylogenetic relationship between these two M. hominis pathotypes, with the aim to delineate the underlying genetic determinants.

Methods

We carried out WGS of 62 Tunisian M. hominis clinical strains collected over a 17-year period. The majority of these clinical strains are associated with infertility (n = 53) and the remaining nine isolates are from gynecological infections cases. An alignment-free distance-based procedure (Jolytree) was used to infer phylogenetic relationships among M. hominis isolates, while the phylogenetic method treeWAS was used to determine the statistical association between pathotypes of interest and genotypes at all loci.

Results

The total pangenome of M. hominis strains was found to contain 1,590 genes including 966 core genes and 592 accessory genes, representing 60 and 37% of the total genome, respectively. Collectively, phylogenetic analyses based on WGS confirmed the distinction between the two M. hominis pathotypes. Strikingly, genome wide association analyses identified 4 virulence genes associated with gynecological infections, mainly involved in nucleotide salvage pathways and tolerance to oxidative stress, while five genes have been associated with infertility cases, two of which are implicated in biofilm formation.

Conclusion

In sum, this study further established the categorization of M. hominis into two pathotypes, and led to the identification of the associated genetic loci, thus holding out promising prospects for a better understanding of the differential interaction of M. hominis with its host.

Genome wide analyses reveal the role of mutator phenotypes in Mycobacterium tuberculosis drug resistance emergence

Antimicrobial combination therapy is widely used to combat Mycobacterium tuberculosis (Mtb), yet resistance rates continue to rise. Mutator strains, with defects in DNA repair genes, drive resistance in other bacterial infections, but their role in Mtb remains unclear. Here, we study the contribution of single nucleotide polymorphisms (SNPs) in DNA Repair, Replication, and Recombination (3 R) genes to Mtb resistance. Through large-scale bioinformatics analysis of 53,589 whole-genomes, we identified 18 novel SNPs in lineages 2 and 4 linked to genotypic drug resistance in 3 R genes, covering 12.5% of clinical isolates with available genome sequences. Notably, a number of the detected SNPs were positively selected during Mtb evolution. Experimental tests showed that mutM, fpgg2, xthA, and nucS mutants had increased the mutation frequency compared to the wild type. Our findings highlight the role of 3 R gene mutations in resistance, emphasizing the need for surveillance to improve early detection and control strategies.

Methicillin-sensitive Staphylococcus aureus lineages contribute towards poor patient outcomes in orthopaedic device-related infections

Staphylococci are the most common cause of orthopaedic device-related infections (ODRIs), with Staphylococcus aureus responsible for a third or more of cases. This prospective clinical and laboratory study investigated the association of genomic and phenotypic variation with treatment outcomes in ODRI isolates. Eighty-six invasive S. aureus isolates were collected from patients with ODRI, and clinical outcome was assessed after a follow-up examination of 24 months. Each patient was then considered to have been 'cured' or 'not cured' based on predefined clinical criteria. Whole-genome sequencing and molecular characterization identified isolates belonging to globally circulating community- and hospital-acquired lineages. Most isolates were phenotypically susceptible to methicillin and lacked the staphylococcal cassette chromosome mec cassette [methicillin-susceptible S. aureus (MSSA); 94%] but contained several virulence genes, including toxins and biofilm genes. Whilst recognizing the role of the host immune response, we identified genetic variance, which could be associated with the infection severity or clinical outcome. Whilst this and several other studies reinforce the role antibiotic resistance [e.g. methicillin-resistant S. aureus (MRSA) infection] has on treatment failure, it is important not to overlook MSSA that can cause equally destructive infections and lead to poor patient outcomes.

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation

A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to inclusion of allochthonous strains or metadata errors, the stated sources of strains in public databases are often incorrect, and strains may not be adapted to the habitat from which they were isolated. We describe a new tool, aurora, that identifies autochthonous strains and the genes associated with habitats while acknowledging the potential role of the habitat adaptation trait in shaping phylogeny.

Adaptive laboratory evolution of Micrococcus luteus and identification of genes associated with radioresistance through genome-wide association study

Micrococcus luteus (V017) is a Gram-positive bacterium that was isolated from a sterilization area exposed to 60Co radiation. In this study, we performed an adaptive laboratory evolution experiment with M. luteus, exposing it to 24 continuous cycles of gamma irradiation at four different doses (1.5 kGy, 3.5 kGy, 5.5 kGy, and 7.5 kGy). This led to the creation of four evolved populations with different levels of radioresistance, which were positively correlated with the radiation dose applied. The survival rate of the evolved population that underwent adaptive treatment at the highest dose (7.5 kGy) was 0.69% after exposure to 5.5 kGy, which is about five orders of magnitude higher than that of the original strain V017. Furthermore, 76 evolved strains were selected from these populations, and their genomes were re-sequenced, uncovering a total of 3072 mutations. A genome-wide association study identified 56 single nucleotide polymorphisms (SNPs) significantly associated with radioresistance, linked to 62 candidate genes. Ultimately, 9 genes were selected for functional validation. Inactivating 6 of these genes, including H0H31_RS03855 (SMC family ATPase, SbcC), H0H31_RS04250 (ribonuclease HII), H0H31_RS04570 (endonuclease VIII), H0H31_RS07595 (bifunctional 3'-5' exonuclease/DNA polymerase I), H0H31_RS00170 (serine/threonine phosphatase PPP), and H0H31_RS05860 (CBS-domain-containing protein), significantly increased sensitivity to gamma radiation, underscoring their importance in radioresistance.

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.

High-throughput phenotype-to-genotype testing of meningococcal carriage and disease isolates detects genetic determinants of disease-relevant phenotypic traits

Genome-wide association studies (GWAS) with binary or single phenotype data have successfully identified disease-associated genotypes and determinants of antimicrobial resistance. We describe a novel phenotype-to-genotype approach for a major bacterial pathogen that involves simultaneously testing for associations among multiple disease-related phenotypes and linkages between phenotypic variation and genetic determinants. High-throughput assays quantified variation among 163 Neisseria meningitidis serogroup W ST-11 clonal complex isolates for 11 phenotypic traits. A comparison of carriage and two disease subgroups detected significant differences between groups for eight phenotypic traits. Candidate genotypic testing indicated that indels in csw, a capsular biosynthesis gene, were associated with reduced survival in antibody-depleted heat-inactivated serum. GWAS testing detected 341 significant genetic variants (3 single-nucleotide polymorphisms and 338 unitigs) across all traits except serum bactericidal antibody-depleted assays. Growth traits were associated with variants of capsular biosynthesis genes, carbonic anhydrase, and an iron-uptake system while adhesion-linked variation was in pilC2, marR, and mutS. Multiple phase variation states or combinatorial phasotypes were associated with significant differences in multiple phenotypes. Controlling for group effects through regression and recursive random forest approaches detected group-independent effects for nalP with biofilm formation and fetA with a growth trait. Through random forest testing, nine phenotypes were weakly predictive of MenW:cc11 sub-lineage, original or 2013, for disease isolates while three characteristics separated carriage and disease isolates with >80% accuracy. This study demonstrates the power of combining high-throughput phenotypic testing of pathogenically relevant isolate collections with genomics for identifying genetic determinants of specific disease-relevant phenotypes and the pathobiology of microbial pathogens.IMPORTANCENext-generation sequencing technologies have led to the creation of extensive microbial genome sequence databases for several bacterial pathogens. Mining of these databases is now imperative for unlocking the maximum benefits of these resources. We describe a high-throughput methodology for detecting associations between phenotypic variation in multiple disease-relevant traits and a range of genetic determinants for Neisseria meningitidis, a major causative agent of meningitis and septicemia. Phenotypic variation in 11 disease-related traits was determined for 163 isolates of the hypervirulent ST-11 lineage and linked to specific single-nucleotide polymorphisms, short sequence variants, and phase variation states. Application of machine learning algorithms to our data outputs identified combinatorial phenotypic traits and genetic variants predictive of a disease association. This approach overcomes the limitations of generic meta-data, such as disease versus carriage, and provides an avenue to explore the multi-faceted nature of bacterial disease, carriage, and transmissibility traits.

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.

Employing genome-wide association studies to investigate acid adaptation mechanisms in Listeria monocytogenes

Listeria monocytogenes is a critical foodborne pathogen known to develop adaptation traits in mildly acidic food processing environments. This study analyzed the genomic data of 49 strains derived from clinical and food sources, utilizing genome-wide association studies (GWAS) to explore the correlation between the genotypic and phenotypic traits of L. monocytogenes, thereby identifying the genetic determinants of its acid adaptation capability. The findings revealed no significant association between the collected acid adaptation genes and the bacterial growth parameters. The GWAS results indicated that numerous single nucleotide polymorphism (SNP) sites were significantly correlated with the growth parameters of L. monocytogenes in a pH = 5.0 acidic environment, whereas the associations diminished as the pH approached neutrality at pH = 6.7. Analysis and annotation of synonymous mutation loci revealed that non-synonymous mutations primarily impact function. The phenotypes pH = 5.0, ΔpH (5.0-5.5), SNPλ, and SNPμmax show the strongest associations with non-synonymous mutation loci. The genes lmo0017, lmo1173, lmo0794, and lmo2783 are significant non-synonymous mutation loci influencing acid adaptation. These genes play critical roles in intracellular pH regulation, cell wall synthesis and environmental response control, directly or indirectly affecting bacterial acid tolerance. Future research could leverage existing data combined with machine learning and causal inference methods to further dissect the genotype-phenotype causal relationships, identifying causative genetic factors that govern the acid adaptation in L. monocytogenes, providing insights for risk assessment and management strategies in food safety.

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.