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.

bWe compared the dynamics and mechanisms of resistance development to ceftazidime, meropenem, ciprofloxacin, and ceftolozane-tazobactam in wild-type (PAO1) and mutator (PAOMS, ⌬mutS) 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 g/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 g/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 piperacillintazobactam 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.

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.

No abstract

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.

e 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 ␤-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.

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.

The identification of commensal bacteria conferring resistance against pathogens is often hindered by the complexity of host microbial communities. We used germ-free, conventional and re-conventionalized zebrafish as a model to study the determinant of microbiota-associated colonization resistance against the fish pathogen Flavobacterium columnare. We showed that a consortium of 10 culturable endogenous bacterial species protects zebrafish from lethal intestinal damages caused by F. columnare infection using two different scenarios that do not rely on host innate immunity. Alteration of microbiota composition upon antibiotic dysbiosis first identified Chryseobacterium massilliae as a key bacterium protecting both larvae and adult zebrafish. We also showed that an assembly of 9 species that do not otherwise protect individually confer a community-level resistance to infection. Our study reveals the ecological strategies at play in microbiotabased protection against pathogens in a low-complexity in vivo model, opening perspectives for the rational engineering of resilient microbial communities.