Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe.

Horizontal gene transfer (HGT) plays a critical role in the evolution and diversification of many microbial species. The resulting dynamics of gene gain and loss can have important implications for the development of antibiotic resistance and the design of vaccine and drug interventions. Methods for the analysis of gene presence/absence patterns typically do not account for errors introduced in the automated annotation and clustering of gene sequences. In particular, methods adapted from ecological studies, including the pangenome gene accumulation curve, can be misleading as they may reflect the underlying diversity in the temporal sampling of genomes rather than a difference in the dynamics of HGT. Here, we introduce Panstripe, a method based on generalized linear regression that is robust to population structure, sampling bias, and errors in the predicted presence/absence of genes. We show using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analyzing several diverse bacterial genome data sets representing major human pathogens.

Multidrug-resistant E. coli encoding high genetic diversity in carbohydrate metabolism genes displace commensal E. coli from the intestinal tract.

Extra-intestinal pathogenic Escherichia coli (ExPEC) can cause a variety of infections outside of the intestine and are a major causative agent of urinary tract infections. Treatment of these infections is increasingly frustrated by antimicrobial resistance (AMR) diminishing the number of effective therapies available to clinicians. Incidence of multidrug resistance (MDR) is not uniform across the phylogenetic spectrum of E. coli. Instead, AMR is concentrated in select lineages, such as ST131, which are MDR pandemic clones that have spread AMR globally. Using a gnotobiotic mouse model, we demonstrate that an MDR E. coli ST131 is capable of out-competing and displacing non-MDR E. coli from the gut in vivo. This is achieved in the absence of antibiotic treatment mediating a selective advantage. In mice colonised with non-MDR E. coli strains, challenge with MDR E. coli either by oral gavage or co-housing with MDR E. coli colonised mice results in displacement and dominant intestinal colonisation by MDR E. coli ST131. To investigate the genetic basis of this superior gut colonisation ability by MDR E. coli, we assayed the metabolic capabilities of our strains using a Biolog phenotypic microarray revealing altered carbon metabolism. Functional pangenomic analysis of 19,571 E. coli genomes revealed that carriage of AMR genes is associated with increased diversity in carbohydrate metabolism genes. The data presented here demonstrate that independent of antibiotic selective pressures, MDR E. coli display a competitive advantage to colonise the mammalian gut and points to a vital role of metabolism in the evolution and success of MDR lineages of E. coli via carriage and spread.

The effect of recombination on the evolution of a population of Neisseria meningitidis.

Neisseria meningitidis (the meningococcus) is a major human pathogen with a history of high invasive disease burden, particularly in sub-Saharan Africa. Our current understanding of the evolution of meningococcal genomes is limited by the rarity of large-scale genomic population studies and lack of in-depth investigation of the genomic events associated with routine pathogen transmission. Here, we fill this knowledge gap by a detailed analysis of 2839 meningococcal genomes obtained through a carriage study of over 50,000 samples collected systematically in Burkina Faso, West Africa, before, during, and after the serogroup A vaccine rollout, 2009-2012. Our findings indicate that the meningococcal genome is highly dynamic, with highly recombinant loci and frequent gene sharing across deeply separated lineages in a structured population. Furthermore, our findings illustrate how population structure can correlate with genome flexibility, as some lineages in Burkina Faso are orders of magnitude more recombinant than others. We also examine the effect of selection on the population, in particular how it is correlated with recombination. We find that recombination principally acts to prevent the accumulation of deleterious mutations, although we do also find an example of recombination acting to speed the adaptation of a gene. In general, we show the importance of recombination in the evolution of a geographically expansive population with deep population structure in a short timescale. This has important consequences for our ability to both foresee the outcomes of vaccination programs and, using surveillance data, predict when lineages of the meningococcus are likely to become a public health concern.

Quantifying bacterial evolution in the wild: A birthday problem for Campylobacter lineages.

Measuring molecular evolution in bacteria typically requires estimation of the rate at which nucleotide changes accumulate in strains sampled at different times that share a common ancestor. This approach has been useful for dating ecological and evolutionary events that coincide with the emergence of important lineages, such as outbreak strains and obligate human pathogens. However, in multi-host (niche) transmission scenarios, where the pathogen is essentially an opportunistic environmental organism, sampling is often sporadic and rarely reflects the overall population, particularly when concentrated on clinical isolates. This means that approaches that assume recent common ancestry are not applicable. Here we present a new approach to estimate the molecular clock rate in Campylobacter that draws on the popular probability conundrum known as the 'birthday problem'. Using large genomic datasets and comparative genomic approaches, we use isolate pairs that share recent common ancestry to estimate the rate of nucleotide change for the population. Identifying synonymous and non-synonymous nucleotide changes, both within and outside of recombined regions of the genome, we quantify clock-like diversification to estimate synonymous rates of nucleotide change for the common pathogenic bacteria Campylobacter coli (2.4 x 10-6 s/s/y) and Campylobacter jejuni (3.4 x 10-6 s/s/y). Finally, using estimated total rates of nucleotide change, we infer the number of effective lineages within the sample time frame-analogous to a shared birthday-and assess the rate of turnover of lineages in our sample set over short evolutionary timescales. This provides a generalizable approach to calibrating rates in populations of environmental bacteria and shows that multiple lineages are maintained, implying that large-scale clonal sweeps may take hundreds of years or more in these species.

Co-evolutionary Signals Identify Burkholderia pseudomallei Survival Strategies in a Hostile Environment.

The soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis and a significant cause of human morbidity and mortality in many tropical and subtropical countries. The species notoriously survives harsh environmental conditions but the genetic architecture for these adaptations remains unclear. Here we employed a powerful combination of genome-wide epistasis and co-selection studies (2,011 genomes), condition-wide transcriptome analyses (82 diverse conditions), and a gene knockout assay to uncover signals of "co-selection"-that is a combination of genetic markers that have been repeatedly selected together through B. pseudomallei evolution. These enabled us to identify 13,061 mutation pairs under co-selection in distinct genes and noncoding RNA. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions, highlighting the conditions as one of the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed a BPSL1661 role under nutrient deprivation, and explored the functional basis of co-selection gene network surrounding BPSL1661 in facilitating the bacterial survival under nutrient depletion. Our findings suggest that nutrient-limited conditions have been the common selection pressure acting on this species, and allelic variation of BPSL1661 may have promoted B. pseudomallei survival during harsh environmental conditions by facilitating bacterial adherence to different surfaces, cells, or living hosts.

Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae.

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

Identification of multiplicatively acting modulatory mutational signatures in cancer.

BACKGROUND: A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. RESULTS: We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model-multiplicatively acting modulatory processes-and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. CONCLUSION: We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis.

Detection of vancomycin-resistant Enterococcus faecium hospital-adapted lineages in municipal wastewater treatment plants indicates widespread distribution and release into the environment.

Vancomycin-resistant Enterococcus faecium (VREfm) is a leading cause of healthcare-associated infection. Reservoirs of VREfm are largely assumed to be nosocomial although there is a paucity of data on alternative sources. Here, we describe an integrated epidemiological and genomic analysis of E. faecium associated with bloodstream infection and isolated from wastewater. Treated and untreated wastewater from 20 municipal treatment plants in the East of England, United Kingdom was obtained and cultured to isolate E. faecium, ampicillin-resistant E. faecium (AREfm), and VREfm. VREfm was isolated from all 20 treatment plants and was released into the environment by 17/20 plants, the exceptions using terminal ultraviolet light disinfection. Median log10 counts of AREfm and VREfm in untreated wastewater from 10 plants in direct receipt of hospital sewage were significantly higher than 10 plants that were not. We sequenced and compared the genomes of 423 isolates from wastewater with 187 isolates associated with bloodstream infection at five hospitals in the East of England. Among 481 E. faecium isolates belonging to the hospital-adapted clade, we observed genetic intermixing between wastewater and bloodstream infection, with highly related isolates shared between a major teaching hospital in the East of England and 9/20 plants. We detected 28 antibiotic resistance genes in the hospital-adapted clade, of which 23 were represented in bloodstream, hospital sewage, and municipal wastewater isolates. We conclude that our findings are consistent with widespread distribution of hospital-adapted VREfm beyond acute healthcare settings with extensive release of VREfm into the environment in the East of England.

Fast and flexible bacterial genomic epidemiology with PopPUNK.

The routine use of genomics for disease surveillance provides the opportunity for high-resolution bacterial epidemiology. Current whole-genome clustering and multilocus typing approaches do not fully exploit core and accessory genomic variation, and they cannot both automatically identify, and subsequently expand, clusters of significantly similar isolates in large data sets spanning entire species. Here, we describe PopPUNK (Population Partitioning Using Nucleotide K -mers), a software implementing scalable and expandable annotation- and alignment-free methods for population analysis and clustering. Variable-length k-mer comparisons are used to distinguish isolates' divergence in shared sequence and gene content, which we demonstrate to be accurate over multiple orders of magnitude using data from both simulations and genomic collections representing 10 taxonomically widespread species. Connections between closely related isolates of the same strain are robustly identified, despite interspecies variation in the pairwise distance distributions that reflects species' diverse evolutionary patterns. PopPUNK can process 103-104 genomes in a single batch, with minimal memory use and runtimes up to 200-fold faster than existing model-based methods. Clusters of strains remain consistent as new batches of genomes are added, which is achieved without needing to reanalyze all genomes de novo. This facilitates real-time surveillance with consistent cluster naming between studies and allows for outbreak detection using hundreds of genomes in minutes. Interactive visualization and online publication is streamlined through the automatic output of results to multiple platforms. PopPUNK has been designed as a flexible platform that addresses important issues with currently used whole-genome clustering and typing methods, and has potential uses across bacterial genetics and public health research.

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting.

BACKGROUND: Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. RESULTS: In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability", by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model. Boosting heritability uses in particular a multiple sample splitting strategy which leads in general to a stable and accurate estimate. We use both simulated data and real antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the attractive features of our inference strategy. CONCLUSIONS: Boosting is shown to offer a reliable and practically useful tool for inference about heritability.

The binding mechanism of the virulence factor Streptococcus suis adhesin P subtype to globotetraosylceramide is associated with systemic disease.

Streptococcus suis is part of the pig commensal microbiome but strains can also be pathogenic, causing pneumonia and meningitis in pigs as well as zoonotic meningitis. According to genomic analysis, S. suis is divided into asymptomatic carriage, respiratory and systemic strains with distinct genomic signatures. Because the strategies to target pathogenic S. suis are limited, new therapeutic approaches are needed. The virulence factor S. suis adhesin P (SadP) recognizes the galabiose Galα1-4Gal-oligosaccharide. Based on its oligosaccharide fine specificity, SadP can be divided into subtypes PN and PO We show here that subtype PN is distributed in the systemic strains causing meningitis, whereas type PO is found in asymptomatic carriage and respiratory strains. Both types of SadP are shown to predominantly bind to pig lung globotriaosylceramide (Gb3). However, SadP adhesin from systemic subtype PN strains also binds to globotetraosylceramide (Gb4). Mutagenesis studies of the galabiose-binding domain of type PN SadP adhesin showed that the amino acid asparagine 285, which is replaced by an aspartate residue in type PO SadP, was required for binding to Gb4 and, strikingly, was also required for interaction with the glycomimetic inhibitor phenylurea-galabiose. Molecular dynamics simulations provided insight into the role of Asn-285 for Gb4 and phenylurea-galabiose binding, suggesting additional hydrogen bonding to terminal GalNAc of Gb4 and the urea group. Thus, the Asn-285-mediated molecular mechanism of type PN SadP binding to Gb4 could be used to selectively target S. suis in systemic disease without interfering with commensal strains, opening up new avenues for interventional strategies against this pathogen.

Fine-Scale Haplotype Structure Reveals Strong Signatures of Positive Selection in a Recombining Bacterial Pathogen.

Identifying genetic variation in bacteria that has been shaped by ecological differences remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show that derived alleles <300 bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles than synonymous alleles. This general pattern is driven by a small fraction of highly diverse genes, many of which exhibit evidence of interspecies horizontal gene transfer and an excess of intermediate frequency alleles. Extensive simulations show that two distinct forms of positive selection can create these patterns of genetic variation: directional selection on horizontally transferred alleles or balancing selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.

gplas: a comprehensive tool for plasmid analysis using short-read graphs.

SUMMARY: Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. AVAILABILITY AND IMPLEMENTATION: Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast hierarchical Bayesian analysis of population structure.

We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analyzing an alignment of over 110 000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximize the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and subclades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.

Genome-wide epistasis and co-selection study using mutual information.

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Nucleotide diversity of functionally different groups of immune response genes in Old World camels based on newly annotated and reference-guided assemblies.

BACKGROUND: Immune-response (IR) genes have an important role in the defense against highly variable pathogens, and therefore, diversity in these genomic regions is essential for species' survival and adaptation. Although current genome assemblies from Old World camelids are very useful for investigating genome-wide diversity, demography and population structure, they have inconsistencies and gaps that limit analyses at local genomic scales. Improved and more accurate genome assemblies and annotations are needed to study complex genomic regions like adaptive and innate IR genes. RESULTS: In this work, we improved the genome assemblies of the three Old World camel species - domestic dromedary and Bactrian camel, and the two-humped wild camel - via different computational methods. The newly annotated dromedary genome assembly CamDro3 served as reference to scaffold the NCBI RefSeq genomes of domestic Bactrian and wild camels. These upgraded assemblies were then used to assess nucleotide diversity of IR genes within and between species, and to compare the diversity found in immune genes and the rest of the genes in the genome. We detected differences in the nucleotide diversity among the three Old World camelid species and between IR gene groups, i.e., innate versus adaptive. Among the three species, domestic Bactrian camels showed the highest mean nucleotide diversity. Among the functionally different IR gene groups, the highest mean nucleotide diversity was observed in the major histocompatibility complex. CONCLUSIONS: The new camel genome assemblies were greatly improved in terms of contiguity and increased size with fewer scaffolds, which is of general value for the scientific community. This allowed us to perform in-depth studies on genetic diversity in immunity-related regions of the genome. Our results suggest that differences of diversity across classes of genes appear compatible with a combined role of population history and differential exposures to pathogens, and consequent different selective pressures.

Systematic longitudinal survey of invasive Escherichia coli in England demonstrates a stable population structure only transiently disturbed by the emergence of ST131.

Escherichia coli associated with urinary tract infections and bacteremia has been intensively investigated, including recent work focusing on the virulent, globally disseminated, multidrug-resistant lineage ST131. To contextualize ST131 within the broader E. coli population associated with disease, we used genomics to analyze a systematic 11-yr hospital-based survey of E. coli associated with bacteremia using isolates collected from across England by the British Society for Antimicrobial Chemotherapy and from the Cambridge University Hospitals NHS Foundation Trust. Population dynamics analysis of the most successful lineages identified the emergence of ST131 and ST69 and their establishment as two of the five most common lineages along with ST73, ST95, and ST12. The most frequently identified lineage was ST73. Compared to ST131, ST73 was susceptible to most antibiotics, indicating that multidrug resistance was not the dominant reason for prevalence of E. coli lineages in this population. Temporal phylogenetic analysis of the emergence of ST69 and ST131 identified differences in the dynamics of emergence and showed that expansion of ST131 in this population was not driven by sequential emergence of increasingly resistant subclades. We showed that over time, the E. coli population was only transiently disturbed by the introduction of new lineages before a new equilibrium was rapidly achieved. Together, these findings suggest that the frequency of E. coli lineages in invasive disease is driven by negative frequency-dependent selection occurring outside of the hospital, most probably in the commensal niche, and that drug resistance is not a primary determinant of success in this niche.

Strain Variability of Listeria monocytogenes under NaCl Stress Elucidated by a High-Throughput Microbial Growth Data Assembly and Analysis Protocol.

Listeria monocytogenes causes the severe foodborne illness listeriosis and survives in food-associated environments due to its high stress tolerance. A data assembly and analysis protocol for microbial growth experiments was compiled to elucidate the strain variability of L. monocytogenes stress tolerance. The protocol includes measurement of growth ability under stress (step 1), selection of a suitable method for growth parameter calculation (step 2), comparison of growth patterns between strains (step 3), and biological interpretation of the discovered differences (step 4). In step 1, L. monocytogenes strains (n = 388) of various serovars and origins grown on media with 9.0% NaCl were measured using a Bioscreen C microbiology reader. Technical variability of the growth measurements was assessed and eliminated. In step 2, the growth parameters determined by Gompertz, modified-Gompertz, logistic, and Richards models and model-free splines were compared, illustrating differences in the suitability of these methods to describe the experimental data. In step 3, hierarchical clustering was used to describe the NaCl tolerance of L. monocytogenes measured by strain-specific variation in growth ability; tolerant strains had higher growth rates and maximum optical densities and shorter lag phases than susceptible strains. The spline parameter area under the curve best classified "poor," "average," and "good" growers. In step 4, the tested L. monocytogenes lineage I strains (serovars 4b and 1/2b) proved to be significantly more tolerant toward 9.0% NaCl than lineage II strains (serovars 1/2a, 1/2c, and 3a). Our protocol provides systematic tools to gain comparable data for investigating strain-specific variation of bacterial growth under stress.IMPORTANCE The pathogen Listeria monocytogenes causes the foodborne disease listeriosis, which can be fatal in immunocompromised individuals. L. monocytogenes tolerates several environmental stressors and can persist in food-processing environments and grow in foodstuffs despite traditional control measures such as high salt content. Nonetheless, L. monocytogenes strains differ in their ability to withstand stressors. Elucidating the intraspecies strain variability of L. monocytogenes stress tolerance is crucial for the identification of particularly tolerant strains. To enhance reliable identification of variability in bacterial stress tolerance phenotypes, we compiled a large-scale protocol for the entire data assembly and analysis of microbial growth experiments, providing a systematic approach and checklist for experiments on strain-specific growth ability. Our study illustrated the diversity and strain-specific variation of L. monocytogenes stress tolerance with an unprecedented scope and discovered biologically relevant serovar- and lineage-dependent phenotypes of NaCl tolerance.

Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis.

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

The Contribution of Genetic Variation of Streptococcus pneumoniae to the Clinical Manifestation of Invasive Pneumococcal Disease.

Background: Different clinical manifestations of invasive pneumococcal disease (IPD) have thus far mainly been explained by patient characteristics. Here we studied the contribution of pneumococcal genetic variation to IPD phenotype. Methods: The index cohort consisted of 349 patients admitted to 2 Dutch hospitals between 2000-2011 with pneumococcal bacteremia. We performed genome-wide association studies to identify pneumococcal lineages, genes, and allelic variants associated with 23 clinical IPD phenotypes. The identified associations were validated in a nationwide (n = 482) and a post-pneumococcal vaccination cohort (n = 121). The contribution of confirmed pneumococcal genotypes to the clinical IPD phenotype, relative to known clinical predictors, was tested by regression analysis. Results: Among IPD patients, the presence of pneumococcal gene slaA was a nationwide confirmed independent predictor of meningitis (odds ratio [OR], 10.5; P = .001), as was sequence cluster 9 (serotype 7F: OR, 3.68; P = .057). A set of 4 pneumococcal genes co-located on a prophage was a confirmed independent predictor of 30-day mortality (OR, 3.4; P = .003). We could detect the pneumococcal variants of concern in these patients' blood samples. Conclusions: In this study, knowledge of pneumococcal genotypic variants improved the clinical risk assessment for detrimental manifestations of IPD. This provides us with novel opportunities to target, anticipate, or avert the pathogenic effects related to particular pneumococcal variants, and indicates that information on pneumococcal genotype is important for the diagnostic and treatment strategy in IPD. Ongoing surveillance is warranted to monitor the clinical value of information on pneumococcal variants in dynamic microbial and susceptible host populations.