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.

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.

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.

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.

Importance of flagella in acute and chronic Pseudomonas aeruginosa infections.

Pseudomonas aeruginosa is an environmental microorganism and a causative agent of diverse acute and chronic, biofilm-associated infections. Advancing research-based knowledge on its adaptation to conditions within the human host is bound to reveal novel strategies and targets for therapeutic intervention. Here, we investigated the traits that P. aeruginosa PA14 as well as a virulence attenuated ΔlasR mutant need to survive in selected murine infection models. Experimentally, the genetic programs that the bacteria use to adapt to biofilm-associated versus acute infections were dissected by passaging transposon mutant libraries through mouse lungs (acute) or mouse tumours (biofilm-infection). Adaptive metabolic changes of P. aeruginosa were generally required during both infection processes. Counter-selection against flagella expression was observed during acute lung infections. Obviously, avoidance of flagella-mediated activation of host immunity is advantageous for the wildtype bacteria. For the ΔlasR mutant, loss of flagella did not confer a selective advantage. Apparently, other pathogenesis mechanisms are active in this virulence attenuated strain. In contrast, the infective process of P. aeruginosa in the chronic biofilm model apparently required expression of flagellin. Together, our findings imply that the host immune reactions against the infectious agent are very decisive for acuteness and duration of the infectious disease. They direct disease outcome.

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.

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa.

Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and ß-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution.

Deep transcriptome profiling of clinical Klebsiella pneumoniae isolates reveals strain and sequence type-specific adaptation.

Health-care-associated infections by multi-drug-resistant bacteria constitute one of the greatest challenges to modern medicine. Bacterial pathogens devise various mechanisms to withstand the activity of a wide range of antimicrobial compounds, among which the acquisition of carbapenemases is one of the most concerning. In Klebsiella pneumoniae, the dissemination of the K. pneumoniae carbapenemase is tightly connected to the global spread of certain clonal lineages. Although antibiotic resistance is a key driver for the global distribution of epidemic high-risk clones, there seem to be other adaptive traits that may explain their success. Here, we exploited the power of deep transcriptome profiling (RNA-seq) to shed light on the transcriptomic landscape of 37 clinical K. pneumoniae isolates of diverse phylogenetic origins. We identified a large set of 3346 genes which was expressed in all isolates. While the core-transcriptome profiles varied substantially between groups of different sequence types, they were more homogenous among isolates of the same sequence type. We furthermore linked the detailed information on differentially expressed genes with the clinically relevant phenotypes of biofilm formation and bacterial virulence. This allowed for the identification of a diminished expression of biofilm-specific genes within the low biofilm producing ST258 isolates as a sequence type-specific trait.

Pseudomonas aeruginosa ceftolozane-tazobactam resistance development requires multiple mutations leading to overexpression and structural modification of AmpC.

We compared the dynamics and mechanisms of resistance development to ceftazidime, meropenem, ciprofloxacin, and ceftolozane-tazobactam in wild-type (PAO1) and mutator (PAOMS, ΔmutS) P. aeruginosa. The strains were incubated for 24 h with 0.5 to 64× MICs of each antibiotic in triplicate experiments. The tubes from the highest antibiotic concentration showing growth were reinoculated in fresh medium containing concentrations up to 64× MIC for 7 consecutive days. The susceptibility profiles and resistance mechanisms were assessed in two isolated colonies from each step, antibiotic, and strain. Ceftolozane-tazobactam-resistant mutants were further characterized by whole-genome analysis through RNA sequencing (RNA-seq). The development of high-level resistance was fastest for ceftazidime, followed by meropenem and ciprofloxacin. None of the mutants selected with these antibiotics showed cross-resistance to ceftolozane-tazobactam. On the other hand, ceftolozane-tazobactam resistance development was much slower, and high-level resistance was observed for the mutator strain only. PAO1 derivatives that were moderately resistant (MICs, 4 to 8 µg/ml) to ceftolozane-tazobactam showed only 2 to 4 mutations, which determined global pleiotropic effects associated with a severe fitness cost. High-level-resistant (MICs, 32 to 128 µg/ml) PAOMS derivatives showed 45 to 53 mutations. Major changes in the global gene expression profiles were detected in all mutants, but only PAOMS mutants showed ampC overexpression, which was caused by dacB or ampR mutations. Moreover, all PAOMS mutants contained 1 to 4 mutations in the conserved residues of AmpC (F147L, Q157R, G183D, E247K, or V356I). Complementation studies revealed that these mutations greatly increased ceftolozane-tazobactam and ceftazidime MICs but reduced those of piperacillin-tazobactam and imipenem, compared to those in wild-type ampC. Therefore, the development of high-level resistance to ceftolozane-tazobactam appears to occur efficiently only in a P. aeruginosa mutator background, in which multiple mutations lead to overexpression and structural modifications of AmpC.

Quantitative contributions of target alteration and decreased drug accumulation to Pseudomonas aeruginosa fluoroquinolone resistance.

Quinolone antibiotics constitute a clinically successful and widely used class of broad-spectrum antibiotics; however, the emergence and spread of resistance increasingly limits the use of fluoroquinolones in the treatment and management of microbial disease. In this study, we evaluated the quantitative contributions of quinolone target alteration and efflux pump expression to fluoroquinolone resistance in Pseudomonas aeruginosa. We generated isogenic mutations in hot spots of the quinolone resistance-determining regions (QRDRs) of gyrA, gyrB, and parC and inactivated the efflux regulator genes so as to overexpress the corresponding multidrug resistance (MDR) efflux pumps. We then introduced the respective mutations into the reference strain PA14 singly and in various combinations. Whereas the combined inactivation of two efflux regulator-encoding genes did not lead to resistance levels higher than those obtained by inactivation of only one efflux regulator-encoding gene, the combination of mutations leading to increased efflux and target alteration clearly exhibited an additive effect. This combination of target alteration and overexpression of efflux pumps was commonly observed in clinical P. aeruginosa isolates; however, these two mechanisms were frequently found not to be sufficient to explain the level of fluoroquinolone resistance. Our results suggest that there are additional mechanisms, independent of the expression of the MexAB-OprM, MexCD-OprJ, MexEF-OprN, and/or MexXY-OprM efflux pump, that increase ciprofloxacin resistance in isolates with mutations in the QRDRs.

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.

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.

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.

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.