A phylogeny-based sampling strategy and power calculator informs genome-wide associations study design for microbial pathogens

Interred mechanisms of resistance and host immune evasion revealed through network-connectivity analysis of M. tuberculosis complex graph pangenome

Mycobacterium tuberculosis complex successfully adapts to environmental pressures through mechanisms of rapid adaptation which remain poorly understood despite knowledge gained through decades of research. In this study, we used 110 reference-quality, complete de novo assembled, long-read sequenced clinical genomes to study patterns of structural adaptation through a graph-based pangenome analysis, elucidating rarely studied mechanisms that enable enhanced clinical phenotypes offering a novel perspective to the species' adaptation. Across isolates, we identified a pangenome of 4,325 genes (3,767 core and 558 accessory), revealing 290 novel genes, and a substantially more complete account of difficult-to-sequence esx/pe/pgrs/ppe genes. Seventy-four percent of core genes were deemed non-essential in vitro, 38% of which support the pathogen's survival in vivo, suggesting a need to broaden current perspectives on essentiality. Through information-theoretic analysis, we reveal the ppe genes that contribute most to the species' diversity-several with known consequences for antigenic variation and immune evasion. Construction of a graph pangenome revealed topological variations that implicate genes known to modulate host immunity (Rv0071-73, Rv2817c, cas2), defense against phages/viruses (cas2, csm6, and Rv2817c-2821c), and others associated with host tissue colonization. Here, the prominent trehalose transport pathway stands out for its involvement in caseous granuloma catabolism and the development of post-primary disease. We show paralogous duplications of genes implicated in bedaquiline (mmpL5 in all L1 isolates) and ethambutol (embC-A) resistance, with a paralogous duplication of its regulator (embR) in 96 isolates. We provide hypotheses for novel mechanisms of immune evasion and antibiotic resistance through gene dosing that can escape detection by molecular diagnostics.IMPORTANCEM. tuberculosis complex (MTBC) has killed over a billion people in the past 200 years alone and continues to kill nearly 1.5 million annually. The pathogen has a versatile ability to diversify under immune and drug pressure and survive, even becoming antibiotic persistent or resistant in the face of harsh chemotherapy. For proper diagnosis and design of an appropriate treatment regimen, a full understanding of this diversification and its clinical consequences is desperately needed. A mechanism of diversification that is rarely studied systematically is MTBC's ability to structurally change its genome. In this article, we have de novo assembled 110 clinical genomes (the largest de novo assembled set to date) and performed a pangenomic analysis. Our pangenome provides structural variation-based hypotheses for novel mechanisms of immune evasion and antibiotic resistance through gene dosing that can compromise molecular diagnostics and lead to further emergence of antibiotic resistance.

Population genomics of Streptococcus mitis in UK and Ireland bloodstream infection and infective endocarditis cases

Streptococcus mitis is a leading cause of infective endocarditis (IE). However, our understanding of the genomic epidemiology and pathogenicity of IE-associated S. mitis is hampered by low IE incidence. Here we use whole genome sequencing of 129 S. mitis bloodstream infection (BSI) isolates collected between 2001-2016 from clinically diagnosed IE cases in the UK to investigate genetic diversity, antimicrobial resistance, and pathogenicity. We show high genetic diversity of IE-associated S. mitis with virtually all isolates belonging to distinct lineages indicating no predominance of specific lineages. Additionally, we find a highly variable distribution of known pneumococcal virulence genes among the isolates, some of which are overrepresented in disease when compared to carriage strains. Our findings suggest that S. mitis in patients with clinically diagnosed IE is not primarily caused by specific hypervirulent or antimicrobial resistant lineages, highlighting the accidental pathogenic nature of S. mitis in patients with clinically diagnosed IE.

Prophage-encoded immune evasion factors are critical for Staphylococcus aureus host infection, switching, and adaptation

Staphylococcus aureus is a multi-host pathogen that causes infections in animals and humans globally. The specific genetic loci-and the extent to which they drive cross-species switching, transmissibility, and adaptation-are not well understood. Here, we conducted a population genomic study of 437 S. aureus isolates to identify bacterial genetic variation that determines infection of human and animal hosts through a genome-wide association study (GWAS) using linear mixed models. We found genetic variants tagging φSa3 prophage-encoded immune evasion genes associated with human hosts, which contributed ~99.9% of the overall heritability (~88%), highlighting their key role in S. aureus human infection. Furthermore, GWAS of pairs of phylogenetically matched human and animal isolates confirmed and uncovered additional loci not implicated in GWAS of unmatched isolates. Our findings reveal the loci that are critical for S. aureus host transmissibility, infection, switching, and adaptation and how their spread alters the specificity of host-adapted clones.

Mutations in the gdpP gene are a clinically relevant mechanism for β-lactam resistance in meticillin-resistant Staphylococcus aureus lacking mec determinants

In Staphylococcus aureus, resistance to β-lactamase stable β-lactam antibiotics is mediated by the penicillinbinding protein 2a, encoded by mecA or by its homologues mecB or mecC. However, a substantial number of meticillin-resistant isolates lack known mec genes and, thus, are called meticillin resistant lacking mec (MRLM). This study aims to identify the genetic mechanisms underlying the MRLM phenotype. A total of 141 MRLM isolates and 142 meticillin-susceptible controls were included in this study. Oxacillin and cefoxitin minimum inhibitory concentrations were determined by broth microdilution and the presence of mec genes was excluded by PCR. Comparative genomics and a genome-wide association study (GWAS) approach were applied to identify genetic polymorphisms associated with the MRLM phenotype. The potential impact of such mutations on the expression of PBP4, as well as on cell morphology and biofilm formation, was investigated. GWAS revealed that mutations in gdpP were significantly associated with the MRLM phenotype. GdpP is a phosphodiesterase enzyme involved in the degradation of the second messenger cyclic-di-AMP in S. aureus. A total of 131 MRLM isolates carried truncations, insertions or deletions as well as amino acid substitutions, mainly located in the functional DHH-domain of GdpP. We experimentally verified the contribution of these gdpP mutations to the MRLM phenotype by heterologous complementation experiments. The mutations in gdpP had no effect on transcription levels of pbp4; however, cell sizes of MRLM strains were reduced. The impact on biofilm formation was highly strain dependent. We report mutations in gdpP as a clinically relevant mechanism for β-lactam resistance in MRLM isolates. This observation is of particular clinical relevance, since MRLM are easily misclassified as MSSA (meticillin-susceptible S. aureus), which may lead to unnoticed spread of β-lactam-resistant isolates and subsequent treatment failure.

Bacterial evolution during human infection: Adapt and live or adapt and die

Microbes are constantly evolving. Laboratory studies of bacterial evolution increase our understanding of evolutionary dynamics, identify adaptive changes, and answer important questions that impact human health. During bacterial infections in humans, however, the evolutionary parameters acting on infecting populations are likely to be much more complex than those that can be tested in the laboratory. Nonetheless, human infections can be thought of as naturally occurring in vivo bacterial evolution experiments, which can teach us about antibiotic resistance, pathogenesis, and transmission. Here, we review recent advances in the study of within-host bacterial evolution during human infection and discuss practical considerations for conducting such studies. We focus on 2 possible outcomes for de novo adaptive mutations, which we have termed "adapt-and-live" and "adapt-and-die." In the adapt-and-live scenario, a mutation is long lived, enabling its transmission on to other individuals, or the establishment of chronic infection. In the adapt-and-die scenario, a mutation is rapidly extinguished, either because it carries a substantial fitness cost, it arises within tissues that block transmission to new hosts, it is outcompeted by more fit clones, or the infection resolves. Adapt-and-die mutations can provide rich information about selection pressures in vivo, yet they can easily elude detection because they are short lived, may be more difficult to sample, or could be maladaptive in the long term. Understanding how bacteria adapt under each of these scenarios can reveal new insights about the basic biology of pathogenic microbes and could aid in the design of new translational approaches to combat bacterial infections.

Reciprocal adaptation of rice and Xanthomonas oryzae pv. oryzae: cross-species 2D GWAS reveals the underlying genetics

A 1D/2D genome-wide association study strategy was adopted to investigate the genetic systems underlying the reciprocal adaptation of rice (Oryza sativa) and its bacterial pathogen, Xanthomonas oryzae pv. oryzae (Xoo) using the whole-genome sequencing and large-scale phenotyping data of 701 rice accessions and 23 diverse Xoo strains. Forty-seven Xoo virulence-related genes and 318 rice quantitative resistance genes (QR-genes) mainly located in 41 genomic regions, and genome-wide interactions between the detected virulence-related genes and QR genes were identified, including well-known resistance genes/virulence genes plus many previously uncharacterized ones. The relationship between rice and Xoo was characterized by strong differentiation among Xoo races corresponding to the subspecific differentiation of rice, by strong shifts toward increased resistance/virulence of rice/Xoo populations and by rich genetic diversity at the detected rice QR-genes and Xoo virulence genes, and by genome-wide interactions between many rice QR-genes and Xoo virulence genes in a multiple-to-multiple manner, presumably resulting either from direct protein-protein interactions or from genetic epistasis. The observed complex genetic interaction system between rice and Xoo likely exists in other crop-pathogen systems that would maintain high levels of diversity at their QR-loci/virulence-loci, resulting in dynamic coevolutionary consequences during their reciprocal adaptation.

Sample size calculation for phylogenetic case linkage

Sample size calculations are an essential component of the design and evaluation of scientific studies. However, there is a lack of clear guidance for determining the sample size needed for phylogenetic studies, which are becoming an essential part of studying pathogen transmission. We introduce a statistical framework for determining the number of true infector-infectee transmission pairs identified by a phylogenetic study, given the size and population coverage of that study. We then show how characteristics of the criteria used to determine linkage and aspects of the study design can influence our ability to correctly identify transmission links, in sometimes counterintuitive ways. We test the overall approach using outbreak simulations and provide guidance for calculating the sensitivity and specificity of the linkage criteria, the key inputs to our approach. The framework is freely available as the R package phylosamp, and is broadly applicable to designing and evaluating a wide array of pathogen phylogenetic studies.

Sequencing the genetic material of viral and bacterial pathogens has become an important part of tracking and combating human infectious diseases. Specifically, comparing the pathogen DNA or RNA sequences collected from infected individuals can allow researchers and public health experts to determine who infected whom, or detect when a pathogen entered a specific country or geographic area. However, it is often impossible to collect samples from every single infected person, and these missing sequences can pose problems for this type of analysis, especially if there is some bias behind which samples were selected for sequencing. We have developed a mathematical framework that allows users to determine the probability their conclusions about pathogen transmission are correct given the number and proportion of samples from a pathogen outbreak they have sequenced. This framework is freely available, easy to use, and broadly generalizable to any pathogen, and we hope that it can be used to inform the design and sampling strategies behind future sequencing-based studies.

Genome-Wide Association Studies for the Detection of Genetic Variants Associated With Daptomycin and Ceftaroline Resistance in Staphylococcus aureus

Background

As next generation sequencing (NGS) technologies have experienced a rapid development over the last decade, the investigation of the bacterial genetic architecture reveals a high potential to dissect causal loci of antibiotic resistance phenotypes. Although genome-wide association studies (GWAS) have been successfully applied for investigating the basis of resistance traits, complex resistance phenotypes have been omitted so far. For S. aureus this especially refers to antibiotics of last resort like daptomycin and ceftaroline. Therefore, we aimed to perform GWAS for the identification of genetic variants associated with DAP and CPT resistance in clinical S. aureus isolates.

Materials/methods

To conduct microbial GWAS, we selected cases and controls according to their clonal background, date of isolation, and geographical origin. Association testing was performed with PLINK and SEER analysis. By using in silico analysis, we also searched for rare genetic variants in candidate loci that have previously been described to be involved in the development of corresponding resistance phenotypes.

Results

GWAS revealed MprF P314L and L826F to be significantly associated with DAP resistance. These mutations were found to be homogenously distributed among clonal lineages suggesting convergent evolution. Additionally, rare and yet undescribed single nucleotide polymorphisms could be identified within mprF and putative candidate genes. Finally, we could show that each DAP resistant isolate exhibited at least one amino acid substitution within the open reading frame of mprF. Due to the presence of strong population stratification, no genetic variants could be associated with CPT resistance. However, the investigation of the staphylococcal cassette chromosome mec (SCCmec) revealed various mecA SNPs to be putatively linked with CPT resistance. Additionally, some CPT resistant isolates revealed no mecA mutations, supporting the hypothesis that further and still unknown resistance determinants are crucial for the development of CPT resistance in S. aureus.

Conclusion

We hereby confirmed the potential of GWAS to identify genetic variants that are associated with antibiotic resistance traits in S. aureus. However, precautions need to be taken to prevent the detection of spurious associations. In addition, the implementation of different approaches is still essential to detect multiple forms of variations and mutations that occur with a low frequency.

In-host population dynamics of Mycobacterium tuberculosis complex during active disease

Tuberculosis (TB) is a leading cause of death globally. Understanding the population dynamics of TB’s causative agent Mycobacterium tuberculosis complex (Mtbc) in-host is vital for understanding the efficacy of antibiotic treatment. We use longitudinally collected clinical Mtbc isolates that underwent Whole-Genome Sequencing from the sputa of 200 patients to investigate Mtbc diversity during the course of active TB disease after excluding 107 cases suspected of reinfection, mixed infection or contamination. Of the 178/200 patients with persistent clonal infection >2 months, 27 developed new resistance mutations between sampling with 20/27 occurring in patients with pre-existing resistance. Low abundance resistance variants at a purity of ≥19% in the first isolate predict fixation in the subsequent sample. We identify significant in-host variation in 27 genes, including antibiotic resistance genes, metabolic genes and genes known to modulate host innate immunity and confirm several to be under positive selection by assessing phylogenetic convergence across a genetically diverse sample of 20,352 isolates.

Exact mapping of Illumina blind spots in the Mycobacterium tuberculosis genome reveals platform-wide and workflow-specific biases

Whole-genome sequencing (WGS) is fundamental to Mycobacterium tuberculosis basic research and many clinical applications. Coverage across Illumina-sequenced M. tuberculosis genomes is known to vary with sequence context, but this bias is poorly characterized. Here, through a novel application of phylogenomics that distinguishes genuine coverage bias from deletions, we discern Illumina ‘blind spots’ in the M. tuberculosis reference genome for seven sequencing workflows. We find blind spots to be widespread, affecting 529 genes, and provide their exact coordinates, enabling salvage of unaffected regions. Fifty-seven pe/ppe genes (the primary families assumed to exhibit Illumina bias) lack blind spots entirely, while the remaining pe/ppe genes account for 55.1 % of blind spots. Surprisingly, we find coverage bias persists in homopolymers as short as 6 bp, shorter tracts than previously reported. While G+C-rich regions challenge all Illumina sequencing workflows, a modified Nextera library preparation that amplifies DNA with a high-fidelity polymerase markedly attenuates coverage bias in G+C-rich and homopolymeric sequences, expanding the ‘Illumina-sequenceable’ genome. Through these findings, and by defining workflow-specific exclusion criteria, we spotlight effective strategies for handling bias in M. tuberculosis Illumina WGS. This empirical analysis framework may be used to systematically evaluate coverage bias in other species using existing sequencing data.

Benchmarking bacterial genome-wide association study methods using simulated genomes and phenotypes

Genome-wide association studies (GWASs) have the potential to reveal the genetics of microbial phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-wide linkage, making it challenging to separate true 'hits' (i.e. mutations that cause a phenotype) from non-causal linked mutations. GWAS methods attempt to correct for population structure in different ways, but their performance has not yet been systematically and comprehensively evaluated under a range of evolutionary scenarios. Here, we developed a bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of mutation, recombination and other evolutionary parameters, along with a subset of causal mutations underlying a phenotype of interest. We assessed the performance (recall and precision) of three widely used single-locus GWAS approaches (cluster-based, dimensionality-reduction and linear mixed models, implemented in plink, pyseer and gemma) and one relatively new multi-locus model implemented in pyseer, across a range of simulated sample sizes, recombination rates and causal mutation effect sizes. As expected, all methods performed better with larger sample sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to correct for population structure were considerably variable according to the choice of parameters. Notably, the multi-locus elastic net (lasso) approach was consistently amongst the highest-performing methods, and had the highest power in detecting causal variants with both low and high effect sizes. Most methods reached the level of good performance (recall >0.75) for identifying causal mutations of strong effect size [log odds ratio (OR) ≥2] with a sample size of 2000 genomes. However, only elastic nets reached the level of reasonable performance (recall=0.35) for detecting markers with weaker effects (log OR ~1) in smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-wide linkage, relative to single-locus models. However, all methods performed relatively poorly on highly clonal (low-recombining) genomes, suggesting room for improvement in method development. These findings show the potential for multi-locus models to improve bacterial GWAS performance. BacGWASim code and simulated data are publicly available to enable further comparisons and benchmarking of new methods.

Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges

Tuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.

Genomic correlates of extraintestinal infection are linked with changes in cell morphology in Campylobacter jejuni

Campylobacter jejuni is the most common cause of bacterial diarrheal disease in the world. Clinical outcomes of infection can range from asymptomatic infection to life-threatening extraintestinal infections. This variability in outcomes for infected patients has raised questions as to whether genetic differences between C. jejuni isolates contribute to their likelihood of causing severe disease. In this study, we compare the genomes of ten C. jejuni isolates that were implicated in extraintestinal infections with reference gastrointestinal isolates, in order to identify unusual patterns of sequence variation associated with infection outcome. We identified a collection of genes that display a higher burden of uncommon mutations in invasive isolates compared with gastrointestinal close relatives, including some that have been previously linked to virulence and invasiveness in C. jejuni. Among the top genes identified were mreB and pgp1, which are both involved in determining cell shape. Electron microscopy confirmed morphological differences in isolates carrying unusual sequence variants of these genes, indicating a possible relationship between extraintestinal infection and changes in cell morphology.

Large-scale genomic analysis shows association between homoplastic genetic variation in Mycobacterium tuberculosis genes and meningeal or pulmonary tuberculosis

Background

Meningitis is the most severe manifestation of tuberculosis. It is largely unknown why some people develop pulmonary TB (PTB) and others TB meningitis (TBM); we examined if the genetic background of infecting M. tuberculosis strains may be relevant.

Methods

We whole-genome sequenced M. tuberculosis strains isolated from 322 HIV-negative tuberculosis patients from Indonesia and compared isolates from patients with TBM (n = 106) and PTB (n = 216). Using a phylogeny-adjusted genome-wide association method to count homoplasy events we examined phenotype-related changes at specific loci or genes in parallel branches of the phylogenetic tree. Enrichment scores for the TB phenotype were calculated on single nucleotide polymorphism (SNP), gene, and pathway level. Genetic associations were validated in an independent set of isolates.

Results

Strains belonged to the East-Asian lineage (36.0%), Euro-American lineage (61.5%), and Indo-Oceanic lineage (2.5%). We found no association between lineage and phenotype (Chi-square = 4.556; p = 0.207). Large genomic differences were observed between isolates; the minimum pairwise genetic distance varied from 17 to 689 SNPs. Using the phylogenetic tree, based on 28,544 common variable positions, we selected 54 TBM and 54 PTB isolates in terminal branch sets with distinct phenotypes. Genetic variation in Rv0218, and absence of Rv3343c, and nanK were significantly associated with disease phenotype in these terminal branch sets, and confirmed in the validation set of 214 unpaired isolates.

Conclusions

Using homoplasy counting we identified genetic variation in three separate genes to be associated with the TB phenotype, including one (Rv0218) which encodes a secreted protein that could play a role in host-pathogen interaction by altering pathogen recognition or acting as virulence effector.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4498-z) contains supplementary material, which is available to authorized users.

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

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.

Transmissible Mycobacterium tuberculosis Strains Share Genetic Markers and Immune Phenotypes

RATIONALE

Successful transmission of tuberculosis depends on the interplay of human behavior, host immune responses, and Mycobacterium tuberculosis virulence factors. Previous studies have been focused on identifying host risk factors associated with increased transmission, but the contribution of specific genetic variations in mycobacterial strains themselves are still unknown.

OBJECTIVES

To identify mycobacterial genetic markers associated with increased transmissibility and to examine whether these markers lead to altered in vitro immune responses.

METHODS

Using a comprehensive tuberculosis registry (n = 10,389) and strain collection in the Netherlands, we identified a set of 100 M. tuberculosis strains either least or most likely to be transmitted after controlling for host factors. We subjected these strains to whole-genome sequencing and evolutionary convergence analysis, and we repeated this analysis in an independent validation cohort. We then performed immunological experiments to measure in vitro cytokine production and neutrophil responses to a subset of the original strains with or without the identified mutations associated with increased transmissibility.

MEASUREMENTS AND MAIN RESULTS

We identified the loci espE, PE-PGRS56, Rv0197, Rv2813-2814c, and Rv2815-2816c as targets of convergent evolution among transmissible strains. We validated four of these regions in an independent set of strains, and we demonstrated that mutations in these targets affected in vitro monocyte and T-cell cytokine production, neutrophil reactive oxygen species release, and apoptosis.

CONCLUSIONS

In this study, we identified genetic markers in convergent evolution of M. tuberculosis toward enhanced transmissibility in vivo that are associated with altered immune responses in vitro.

The Promise of Whole Genome Pathogen Sequencing for the Molecular Epidemiology of Emerging Aquaculture Pathogens

Aquaculture is the fastest growing food-producing sector, and the sustainability of this industry is critical both for global food security and economic welfare. The management of infectious disease represents a key challenge. Here, we discuss the opportunities afforded by whole genome sequencing of bacterial and viral pathogens of aquaculture to mitigate disease emergence and spread. We outline, by way of comparison, how sequencing technology is transforming the molecular epidemiology of pathogens of public health importance, emphasizing the importance of community-oriented databases and analysis tools.

Identifying lineage effects when controlling for population structure improves power in bacterial association studies

Bacteria pose unique challenges for genome-wide association studies because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome(1,2). Although methods developed for human studies can correct for strain structure(3,4), this risks considerable loss-of-power because genetic differences between strains often contribute substantial phenotypic variability(5). Here, we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3,144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence, our method pinpoints locus-specific effects where possible and boosts power by detecting lineage-level differences when fine-mapping is intractable.

Microbial Speciation

What are species? How do they arise? These questions are not easy to answer and have been particularly controversial in microbiology. Yet, for those microbiologists studying environmental questions or dealing with clinical issues, the ability to name and recognize species, widely considered the fundamental units of ecology, can be practically useful. On a more fundamental level, the speciation problem, the focus here, is more mechanistic and conceptual. What is the origin of microbial species, and what evolutionary and ecological mechanisms keep them separate once they begin to diverge? To what extent are these mechanisms universal across diverse types of microbes, and more broadly across the entire the tree of life? Here, we propose that microbial speciation must be viewed in light of gene flow, which defines units of genetic similarity, and of natural selection, which defines units of phenotype and ecological function. We discuss to what extent ecological and genetic units overlap to form cohesive populations in the wild, based on recent evolutionary modeling and population genomics studies. These studies suggest a continuous "speciation spectrum," which microbial populations traverse in different ways depending on their balance of gene flow and natural selection.

Clinical value of whole-genome sequencing of Mycobacterium tuberculosis

Whole-genome sequencing (WGS) is now common as a result of new technologies that can rapidly sequence a complete bacterial genome for US$500 or less. Many studies have addressed questions about tuberculosis with WGS, and knowing the sequence of the entire genome, rather than only a few fragments, has greatly increased the precision of molecular epidemiology and contact tracing. Additionally, topics such as the mutation rate, drug resistance, the target of new drugs, and the phylogeny and evolution of the Mycobacterium tuberculosis complex bacteria have been elucidated by WGS. Nonetheless, WGS has not explained differences in transmissibility between strains, or why some strains are more virulent than others or more prone to development of multidrug resistance. With advances in technology, WGS of clinical specimens could become routine in high-income countries; however, its relevance will probably depend on easy to use software to efficiently process the sequences produced and accessible genomic databases that can be mined in future studies.

The evolution of Campylobacter jejuni and Campylobacter coli

The global significance of Campylobacter jejuni and Campylobacter coli as gastrointestinal human pathogens has motivated numerous studies to characterize their population biology and evolution. These bacteria are a common component of the intestinal microbiota of numerous bird and mammal species and cause disease in humans, typically via consumption of contaminated meat products, especially poultry meat. Sequence-based molecular typing methods, such as multilocus sequence typing (MLST) and whole genome sequencing (WGS), have been instructive for understanding the epidemiology and evolution of these bacteria and how phenotypic variation relates to the high degree of genetic structuring in C. coli and C. jejuni populations. Here, we describe aspects of the relatively short history of coevolution between humans and pathogenic Campylobacter, by reviewing research investigating how mutation and lateral or horizontal gene transfer (LGT or HGT, respectively) interact to create the observed population structure. These genetic changes occur in a complex fitness landscape with divergent ecologies, including multiple host species, which can lead to rapid adaptation, for example, through frame-shift mutations that alter gene expression or the acquisition of novel genetic elements by HGT. Recombination is a particularly strong evolutionary force in Campylobacter, leading to the emergence of new lineages and even large-scale genome-wide interspecies introgression between C. jejuni and C. coli. The increasing availability of large genome datasets is enhancing understanding of Campylobacter evolution through the application of methods, such as genome-wide association studies, but MLST-derived clonal complex designations remain a useful method for describing population structure.

The advent of genome-wide association studies for bacteria

Significant advances in sequencing technologies and genome-wide association studies (GWAS) have revealed substantial insight into the genetic architecture of human phenotypes. In recent years, the application of this approach in bacteria has begun to reveal the genetic basis of bacterial host preference, antibiotic resistance, and virulence. Here, we consider relevant differences between bacterial and human genome dynamics, apply GWAS to a global sample of Mycobacterium tuberculosis genomes to highlight the impacts of linkage disequilibrium, population stratification, and natural selection, and finally compare the traditional GWAS against phyC, a contrasting method of mapping genotype to phenotype based upon evolutionary convergence. We discuss strengths and weaknesses of both methods, and make suggestions for factors to be considered in future bacterial GWAS.

Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Genome-wide association studies (GWASs) have become an increasingly important approach for eukaryotic geneticists, facilitating the identification of hundreds of genetic polymorphisms that are responsible for inherited diseases. Despite the relative simplicity of bacterial genomes, the application of GWASs to identify polymorphisms responsible for important bacterial phenotypes has only recently been made possible through advances in genome sequencing technologies. Bacterial GWASs are now about to come of age thanks to the availability of massive datasets, and because of the potential to bridge genomics and traditional genetic approaches that is provided by improving validation strategies. A small number of pioneering GWASs in bacteria have been published in the past 2 years, examining from 75 to more than 3,000 strains. The experimental designs have been diverse, taking advantage of different processes in bacteria for generating variation. Analysis of data from bacterial GWASs can, to some extent, be performed using software developed for eukaryotic systems, but there are important differences in genome evolution that must be considered. The greatest experimental advantage of bacterial GWASs is the potential to perform downstream validation of causality and dissection of mechanism. We review the recent advances and remaining challenges in this field and propose strategies to improve the validation of bacterial GWASs.