The Pseudomonas aeruginosa Transcriptional Landscape Is Shaped by Environmental Heterogeneity and Genetic Variation

Dötsch, Andreas; Schniederjans, Monika; Khaledi, Ariane; Hornischer, Klaus; Schulz, Sebastian; Bielecka, Agata; Eckweiler, Denitsa; Pohl, Sarah; Häußler, Susanne

doi:10.1128/mbio.00749-15

Cited by 76 publications

(94 citation statements)

References 47 publications

(70 reference statements)

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“…We determined the antimicrobial resistance profiles of 135 clinical P. aeruginosa isolates that had been sampled from different regions across Europe and from various infection sites. For all of these isolates, transcriptional profiles were recorded previously, and the phylogenetic relatedness was determined by using a k-mer approach (30). Figure 1 depicts a neighbor-joining tree based on the sequence similarity of 148 commonly expressed genes and includes five reference strains (PA14, PAO1, PA7, PACS2, and LESB58).…”

Section: Resultsmentioning

confidence: 99%

“…Whole-transcriptome sequencing of the clinical isolates was previously performed by our group by the use of a custommade protocol with barcoded RNA libraries to enable pooled sequencing of several samples (30,31). The clinical isolates had been grown under standard conditions (LB broth; 37°C) and harvested in RNAprotect (Qiagen) at an optical density at 600 nm (OD 600 ) of 2 before the transcriptome sequencing (RNA-seq) was performed (31).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, transcrip-tome-wide association studies of 135 phylogenetically broadly distributed P. aeruginosa isolates that had been RNA sequenced previously by our group (30,31) were performed, and resistance to the three clinically most important classes of antipseudomonal antibiotics-fluoroquinolones, ␤-lactams, and aminoglycosides-was systematically explored. Indeed, transcriptional profiles of resistant isolates expressed distinct and resistance-specific sequence variation and gene expression patterns.…”

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confidence: 99%

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Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Khaledi

Schniederjans

Pohl

et al. 2016

Antimicrob Agents Chemother

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Khaledi

Schniederjans

Pohl

et al. 2016

Antimicrob Agents Chemother

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“…A recent study has suggested that a mixture of strains influenced traits such as drug response in ways that were difficult to predict from the study of single strains (9). Clinical isolates of P. aeruginosa, even from the same respiratory sample, can have striking differences in phenotypes, including colony morphology, quorum-sensing regulation, and motility (9)(10)(11)(12)(13), and recent data highlight that there is gene expression diversity even among phenotypically similar strains (9). Given the significant limitations of the aforementioned laboratory approaches, novel methods for directly interrogating population-level microbial gene expression in the CF lung are needed to provide accurate information about the growth and virulence of P. aeruginosa in the CF airway.…”

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confidence: 99%

Use of a Multiplex Transcript Method for Analysis of Pseudomonas aeruginosa Gene Expression Profiles in the Cystic Fibrosis Lung

et al. 2016

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The discovery of therapies that modulate Pseudomonas aeruginosa virulence or that can eradicate chronic P. aeruginosa lung infections associated with cystic fibrosis (CF) will be advanced by an improved understanding of P. aeruginosa behavior in vivo. We demonstrate the use of multiplexed Nanostring technology to monitor relative abundances of P. aeruginosa transcripts across clinical isolates, in serial samples, and for the purposes of comparing microbial physiology in vitro and in vivo. The expression of 75 transcripts encoded by genes implicated in CF lung disease was measured in a variety of P. aeruginosa strains as well as RNA serial sputum samples from four P. aeruginosa-colonized subjects with CF collected over 6 months. We present data on reproducibility, the results from different methods of normalization, and demonstrate high concordance between transcript relative abundance data obtained by Nanostring or transcriptome sequencing (RNA-Seq) analysis. Furthermore, we address considerations regarding sequence variation between strains during probe design. Analysis of P. aeruginosa grown in vitro identified transcripts that correlated with the different phenotypes commonly observed in CF clinical isolates. P. aeruginosa transcript profiles in RNA from CF sputum indicated alginate production in vivo, and transcripts involved in quorum-sensing regulation were less abundant in sputum than strains grown in the laboratory. P. aeruginosa gene expression patterns from sputum clustered closely together relative to patterns for laboratory-grown cultures; in contrast, laboratory-grown P. aeruginosa showed much greater transcriptional variation with only loose clustering of strains with different phenotypes. The clustering within and between subjects was surprising in light of differences in inhaled antibiotic and respiratory symptoms, suggesting that the pathways represented by these 75 transcripts are stable in chronic CF P. aeruginosa lung infections. Several factors complicate the comparison of in vitro studies on Pseudomonas aeruginosa with infections in the cystic fibrosis (CF) lung. First, many different P. aeruginosa strains cause CF lung infections; thus, reliance on commonly used laboratory strains might limit our understanding of P. aeruginosa in CF (1-3). Second, laboratory or cell culture studies rarely incorporate coinfecting species and lack components of the lung environment, such as immune response factors, that shape P. aeruginosa phenotypes (4, 5). We know little about how these environmental stimuli influence P. aeruginosa (6-8). To complicate matters further, a single CF sputum sample contains mixtures of P. aeruginosa genotypes and phenotypes. A recent study has suggested that a mixture of strains influenced traits such as drug response in ways that were difficult to predict from the study of single strains (9). Clinical isolates of P. aeruginosa, even from the same respiratory sample, can have striking differences in phenotypes, including colony morphology, quorum-sensing regulation, and motili...

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“…(Stover et al, 2000). Although, there are clusters of closely related strains whose sequence signatures are conserved in the gene pool (Kiewitz and Tümmler, 2000), the P. aeruginosa genome exhibits great plasticity (Klockgether et al, 2011;Dötsch, 2015;Shen et al, 2006;Liu et al, 2012). The highest cell concentrations in both strains isolated in this study were observed, over the 20 days incubation period, when diesel fuel was used as the sole source of carbon rather than SAE 90 gear oil.…”

Section: Discussionmentioning

confidence: 69%

Isolation and characterization of petroleum product emulsifying Pseudomonas strains from a generating set fuel tank

Lebonguy¹,

Goma-Tchimbakala²,

Miambi³

et al. 2017

Afr. J. Microbiol. Res.

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This study characterized microbial strains isolated from diesel fuel samples collected from the tank of a generating set at the Institute of Research for Development in Pointe-Noire (Congo). Two bacterial isolates (G2 and G3) were distinguished by their color on agar plates and were characterized by their API 20E biochemical profiles and by 16S rRNA gene sequencing. The phenotypic properties of these isolates were consistent with their assignment to the genus Pseudomonas. Comparative 16S rRNA gene sequence analysis demonstrated that the G2 and G3 isolates were close relatives of P. aeruginosa strain GIM 32 and P. aeruginosa strain NV2, respectively, with 97% sequence identity. These two P. aeruginosa strains were able to grow in a mineral salt medium supplemented with 2% diesel fuel or SAE 90 gear oil as the only source of carbon. P. aeruginosa G3 showed faster growth and was able to emulsify diesel fuel (53%), gasoline (90%) and hexane (95%) more strongly than P. aeruginosa G2.

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The Pseudomonas aeruginosa Transcriptional Landscape Is Shaped by Environmental Heterogeneity and Genetic Variation

Cited by 76 publications

References 47 publications

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Use of a Multiplex Transcript Method for Analysis of Pseudomonas aeruginosa Gene Expression Profiles in the Cystic Fibrosis Lung

Isolation and characterization of petroleum product emulsifying Pseudomonas strains from a generating set fuel tank

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