The emergence and diversification of a zoonotic pathogen from within the microbiota of intensively farmed pigs

The expansion and intensification of livestock production is predicted to promote the emergence of pathogens. As pathogens sometimes jump between species, this can affect the health of humans as well as livestock. Here, we investigate how livestock microbiota can act as a source of these emerging pathogens through analysis of Streptococcus suis, a ubiquitous component of the respiratory microbiota of pigs that is also a major cause of disease on pig farms and an important zoonotic pathogen. Combining molecular dating, phylogeography, and comparative genomic analyses of a large collection of isolates, we find that several pathogenic lineages of S. suis emerged in the 19th and 20th centuries, during an early period of growth in pig farming. These lineages have since spread between countries and continents, mirroring trade in live pigs. They are distinguished by the presence of three genomic islands with putative roles in metabolism and cell adhesion, and an ongoing reduction in genome size, which may reflect their recent shift to a more pathogenic ecology. Reconstructions of the evolutionary histories of these islands reveal constraints on pathogen emergence that could inform control strategies, with pathogenic lineages consistently emerging from one subpopulation of S. suis and acquiring genes through horizontal transfer from other pathogenic lineages. These results shed light on the capacity of the microbiota to rapidly evolve to exploit changes in their host population and suggest that the impact of changes in farming on the pathogenicity and zoonotic potential of S. suis is yet to be fully realized.

PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies

Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing collections of bacterial genomes. First, a sub-sampling approach was undertaken to reduce the allele frequency and effect size of a known and detectable genotype-phenotype relationship by modifying phenotype labels. Second, a phenotype-simulation approach was conducted to simulate phenotypes from existing genetic variants. We implemented both approaches into a computational pipeline (PowerBacGWAS) that supports power calculations for burden testing, pan-genome and variant GWAS; and applied it to collections of Enterococcus faecium, Klebsiella pneumoniae and Mycobacterium tuberculosis. We used this pipeline to determine sample sizes required to detect causal variants of different minor allele frequencies (MAF), effect sizes and phenotype heritability, and studied the effect of homoplasy and population diversity on the power to detect causal variants. Our pipeline and user documentation are made available and can be applied to other bacterial populations. PowerBacGWAS can be used to determine sample sizes required to find statistically significant associations, or the associations detectable with a given sample size. We recommend to perform power calculations using existing genomes of the bacterial species and population of study.

Mutation rate dynamics reflect ecological change in an emerging zoonotic pathogen

Mutation rates vary both within and between bacterial species, and understanding what drives this variation is essential for understanding the evolutionary dynamics of bacterial populations. In this study, we investigate two factors that are predicted to influence the mutation rate: ecology and genome size. We conducted mutation accumulation experiments on eight strains of the emerging zoonotic pathogen Streptococcus suis. Natural variation within this species allows us to compare tonsil carriage and invasive disease isolates, from both more and less pathogenic populations, with a wide range of genome sizes. We find that invasive disease isolates have repeatedly evolved mutation rates that are higher than those of closely related carriage isolates, regardless of variation in genome size. Independent of this variation in overall rate, we also observe a stronger bias towards G/C to A/T mutations in isolates from more pathogenic populations, whose genomes tend to be smaller and more AT-rich. Our results suggest that ecology is a stronger correlate of mutation rate than genome size over these timescales, and that transitions to invasive disease are consistently accompanied by rapid increases in mutation rate. These results shed light on the impact that ecology can have on the adaptive potential of bacterial pathogens.

Identifying virulence determinants of multidrug-resistant Klebsiella pneumoniae in Galleria mellonella

Infections caused by Klebsiella pneumoniae are a major public health threat. Extensively drug-resistant and even pan-resistant strains have been reported. Understanding K. pneumoniae pathogenesis is hampered by the fact that murine models of infection offer limited resolution for non-hypervirulent strains which cause the majority of infections. The insect Galleria mellonella larva is a widely used alternative model organism for bacterial pathogens. We have performed genome-scale fitness profiling of a multidrug-resistant K. pneumoniae ST258 strain during infection of G. mellonella, to determine if this model is suitable for large-scale virulence factor discovery in this pathogen. Our results demonstrated a dominant role for surface polysaccharides in infection, with contributions from siderophores, cell envelope proteins, purine biosynthesis genes and additional genes of unknown function. Comparison with a hypervirulent strain, ATCC 43816, revealed substantial overlap in important infection-related genes, as well as additional putative virulence factors specific to ST258, reflecting strain-dependent fitness effects. Our analysis also identified a role for the metalloregulatory protein NfeR (YqjI) in virulence. Overall, this study offers new insight into the infection fitness landscape of K. pneumoniae, and provides a framework for using the highly flexible and easily scalable G. mellonella infection model to dissect molecular virulence mechanisms of bacterial pathogens.

Mining zebrafish microbiota reveals key community-level resistance against fish pathogen infection

The long-known resistance to pathogens provided by host-associated microbiota fostered the notion that adding protective bacteria could prevent or attenuate infection. However, the identification of endogenous or exogenous bacteria conferring such protection is often hindered by the complexity of host microbial communities. Here, we used zebrafish and the fish pathogen Flavobacterium columnare as a model system to study the determinants of microbiota-associated colonization resistance. We compared infection susceptibility in germ-free, conventional and reconventionalized larvae and showed that a consortium of 10 culturable bacterial species are sufficient to protect zebrafish. Whereas survival to F. columnare infection does not rely on host innate immunity, we used antibiotic dysbiosis to alter zebrafish microbiota composition, leading to the identification of two different protection strategies. We first identified that the bacterium Chryseobacterium massiliae individually protects both larvae and adult zebrafish. We also showed that an assembly of 9 endogenous zebrafish species that do not otherwise protect individually confer a community-level resistance to infection. Our study therefore provides a rational approach to identify key endogenous protecting bacteria and promising candidates to engineer resilient microbial communities. It also shows how direct experimental analysis of colonization resistance in low-complexity in vivo models can reveal unsuspected ecological strategies at play in microbiota-based protection against pathogens.

BACTOME-a reference database to explore the sequence- and gene expression-variation landscape of Pseudomonas aeruginosa clinical isolates

Extensive use of next-generation sequencing (NGS) for pathogen profiling has the potential to transform our understanding of how genomic plasticity contributes to phenotypic versatility. However, the storage of large amounts of NGS data and visualization tools need to evolve to offer the scientific community fast and convenient access to these data. We introduce BACTOME as a database system that links aligned DNA- and RNA-sequencing reads of clinical Pseudomonas aeruginosa isolates with clinically relevant pathogen phenotypes. The database allows data extraction for any single isolate, gene or phenotype as well as data filtering and phenotypic grouping for specific research questions. With the integration of statistical tools we illustrate the usefulness of a relational database structure for the identification of phenotype-genotype correlations as an essential part of the discovery pipeline in genomic research. Furthermore, the database provides a compilation of DNA sequences and gene expression values of a plethora of clinical isolates to give a consensus DNA sequence and consensus gene expression signature. Deviations from the consensus thereby describe the genomic landscape and the transcriptional plasticity of the species P. aeruginosa. The database is available at https://bactome.helmholtz-hzi.de.

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.

Spatiotemporal control of FlgZ activity impacts Pseudomonas aeruginosa flagellar motility

The c-di-GMP-binding effector protein FlgZ has been demonstrated to control motility in the opportunistic pathogen Pseudomonas aeruginosa and it was suggested that c-di-GMP-bound FlgZ impedes motility via its interaction with the MotCD stator. To further understand how motility is downregulated in P. aeruginosa and to elucidate the general control mechanisms operating during bacterial growth, we examined the spatiotemporal activity of FlgZ. We re-annotated the P. aeruginosaflgZ open reading frame and demonstrated that FlgZ-mediated downregulation of motility is fine-tuned via three independent mechanisms. First, we found that flgZ gene is transcribed independently from flgMN in stationary growth phase to increase FlgZ protein levels in the cell. Second, FlgZ localizes to the cell pole upon c-di-GMP binding and third, we describe that FimV, a cell pole anchor protein, is involved in increasing the polar localized c-di-GMP bound FlgZ to inhibit both, swimming and swarming motility. Our results shed light on the complex dynamics and spatiotemporal control of c-di-GMP-dependent bacterial motility phenotypes and on how the polar anchor protein FimV, the motor brake FlgZ and the stator proteins function to repress flagella-driven swimming and swarming motility.

Elucidation of sigma factor-associated networks in Pseudomonas aeruginosa reveals a modular architecture with limited and function-specific crosstalk

Sigma factors are essential global regulators of transcription initiation in bacteria which confer promoter recognition specificity to the RNA polymerase core enzyme. They provide effective mechanisms for simultaneously regulating expression of large numbers of genes in response to challenging conditions, and their presence has been linked to bacterial virulence and pathogenicity. In this study, we constructed nine his-tagged sigma factor expressing and/or deletion mutant strains in the opportunistic pathogen Pseudomonas aeruginosa. To uncover the direct and indirect sigma factor regulons, we performed mRNA profiling, as well as chromatin immunoprecipitation coupled to high-throughput sequencing. We furthermore elucidated the de novo binding motif of each sigma factor, and validated the RNA- and ChIP-seq results by global motif searches in the proximity of transcriptional start sites (TSS). Our integrated approach revealed a highly modular network architecture which is composed of insulated functional sigma factor modules. Analysis of the interconnectivity of the various sigma factor networks uncovered a limited, but highly function-specific, crosstalk which orchestrates complex cellular processes. Our data indicate that the modular structure of sigma factor networks enables P. aeruginosa to function adequately in its environment and at the same time is exploited to build up higher-level functions by specific interconnections that are dominated by a participation of RpoN.

Pseudomonas aeruginosa is well known for its high adaptability to a large range of environmental conditions, including those encountered within the human host. Transcription initiation represents a major regulatory target which drives versatility, and enables bacterial adaptation to challenging conditions and expression of virulence and pathogenicity. In bacteria, this process is largely orchestrated by sigma factors. Here, we performed an integrative approach, and by the combined use of three global profiling technologies uncovered the networks of 10 alternative sigma factors in the opportunistic pathogen P. aeruginosa. We demonstrate that these networks largely represent self-contained functional modules which exhibit a limited but highly specific crosstalk to build up higher-level functions. Our results do not only give extensive information on sigma factor binding sites throughout the P. aeruginosa genome, but also advance the understanding of sigma factor network architecture which provides bacteria with a framework to function adequately in their environment.

Radically different phylogeographies and patterns of genetic variation in two European brown frogs, genus Rana

We reconstruct range-wide phylogeographies of two widespread and largely co-occurring Western Palearctic frogs, Rana temporaria and R. dalmatina. Based on tissue or saliva samples of over 1000 individuals, we compare a variety of genetic marker systems, including mitochondrial DNA, single-copy protein-coding nuclear genes, microsatellite loci, and single nucleotide polymorphisms (SNPs) of transcriptomes of both species. The two focal species differ radically in their phylogeographic structure, with R. temporaria being strongly variable among and within populations, and R. dalmatina homogeneous across Europe with a single strongly differentiated population in southern Italy. These differences were observed across the various markers studied, including microsatellites and SNP density, but especially in protein-coding nuclear genes where R. dalmatina had extremely low heterozygosity values across its range, including potential refugial areas. On the contrary, R. temporaria had comparably high range-wide values, including many areas of probable postglacial colonization. A phylogeny of R. temporaria based on various concatenated mtDNA genes revealed that two haplotype clades endemic to Iberia form a paraphyletic group at the base of the cladogram, and all other haplotypes form a monophyletic group, in agreement with an Iberian origin of the species. Demographic analysis suggests that R. temporaria and R. dalmatina have genealogies of roughly the same time to coalescence (TMRCA ~3.5 mya for both species), but R. temporaria might have been characterized by larger ancestral and current effective population sizes than R. dalmatina. The high genetic variation in R. temporaria can therefore be explained by its early range expansion out of Iberia, with subsequent cycles of differentiation in cryptic glacial refugial areas followed by admixture, while the range expansion of R. dalmatina into central Europe is a probably more recent event.

The assessment of hemispheric lateralization in functional MRI--robustness and reproducibility

Various methods have been proposed to calculate a lateralization index (LI) on the basis of functional magnetic resonance imaging (fMRI) data. Most of them are either based on the extent of the activated brain region (i.e., the number of "active" voxels) or the magnitude of the fMRI signal change. The purpose of the present study was to investigate the characteristics of various variants of these approaches and to identify the one that yields the most robust and reproducible results. Robustness was assessed by evaluating the dependence on arbitrary external parameters, reproducibility was assessed by Pearson's correlation coefficient. LIs based on active voxels counts at one single fixed statistical threshold as well as LIs based on unthresholded signal intensity changes (i.e., based on all voxels in a region of interest) yielded neither robust nor reproducible laterality results. Instead, the lateralization of a cognitive function was best described by "thresholded" signal intensity changes where the activity measure was based on signal intensity changes in those voxels in a region of interest that exceeded a predefined activation level. However, not all other approaches should be discarded completely since they have their own specific application fields. First, LIs based on active voxel counts in the form of p-value-dependent lateralization plots (LI=LI(p)) can be used as a straightforward measure to describe hemispheric dominance. Second, LIs based on active voxel counts at variable thresholds (standardized by the total number of active voxels) are a good alternative for big regions of interest since LIs based on signal intensity changes are restricted to small ROIs.

How does the brain accommodate to increased task difficulty in word finding? A functional MRI study

In functional imaging of the brain, the difficulty of a task may be critical for the pattern of activation. Increased task difficulty could lead to increased activation in task-specific regions or to activation of additional, "compensatory" regions. A previous study with functional transcranial Doppler sonography (fTCD) showed no evidence that increased difficulty in word retrieval leads to a recruitment of areas homologous to language-related regions. The question remains how the brain accommodates increasing task difficulty. Because of limitations of fTCD method, we used functional magnetic resonance imaging (fMRI) in this study. We manipulated word retrieval difficulty in healthy subjects (n = 14) to determine whether the classical language-related brain regions are activated with increasing difficulty in word retrieval. fMRI demonstrated that with increased task difficulty (I) the lateralization of language-associated brain activation remained constant, (II) no additional activation of language-related regions of the dominant hemisphere, nor of homologous regions of the subdominant hemisphere, was evident, (III) additional activation was found in right posterior parietal cortex--typically associated with sustained attention and executive control. Thus, increased difficulty in word retrieval leads to coactivation of distinct brain areas, working together in a large cognitive network, rather than to increased activation of typically language-related areas.