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Transcriptome Sequence of Antibiotic-Treated Pseudomonas aeruginosa.

ABSTRACT: Pseudomonas aeruginosa is known to tolerate antibiotic therapy during infection. This prevents clearance of infection and negatively impacts patient outcomes. Here, we report the transcriptome sequence of antibiotic-treated and untreated P. aeruginosa cultures and the differential gene expression observed when treated cells are compared to untreated cells.

SUBMITTER: Esani S

PROVIDER: S-EPMC6430322 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Transcriptome Sequence of Antibiotic-Treated Pseudomonas aeruginosa.

Esani Saika S Chen Tsute T Leung Kai P KP Van Laar Tricia A TA

Microbiology resource announcements 20190321 12

<i>Pseudomonas aeruginosa</i> is known to tolerate antibiotic therapy during infection. This prevents clearance of infection and negatively impacts patient outcomes. Here, we report the transcriptome sequence of antibiotic-treated and untreated <i>P. aeruginosa</i> cultures and the differential gene expression observed when treated cells are compared to untreated cells. ...[more]

PMID: 30938705

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Transcriptome Sequence of Antibiotic-Treated Pseudomonas aeruginosa

Project description:Pseudomonas aeruginosa is known to tolerate antibiotic therapy during infection. This prevents clearance of infection and negatively impacts patient outcomes. Here, we report the transcriptome sequence of antibiotic-treated and untreated P. aeruginosa cultures and the differential gene expression observed when treated cells are compared to untreated cells.

2018-10-31 | GSE120602 | GEO

Transcriptome Sequence of Antibiotic-Treated Pseudomonas aeruginosa

Project description:Transcriptome Sequence of Antibiotic-Treated Pseudomonas aeruginosa

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Human tear fluid modulates the Pseudomonas aeruginosa transcriptome to alter antibiotic susceptibility.

Project description:PurposePreviously, we showed that tear fluid protects corneal epithelial cells against Pseudomonas aeruginosa without suppressing bacterial viability. Here, we studied how tear fluid affects bacterial gene expression.MethodsRNA-sequencing was used to study the P. aeruginosa transcriptome after tear fluid exposure (5 h, 37 oC). Outcomes were further investigated by biochemical and physiological perturbations to tear fluid and tear-like fluid (TLF) and assessment of bacterial viability following tear/TLF pretreatment and antibiotic exposure.ResultsTear fluid deregulated ~180 P. aeruginosa genes ≥8 fold versus PBS including downregulating lasI, rhlI, qscR (quorum sensing/virulence), oprH, phoP, phoQ (antimicrobial resistance) and arnBCADTEF (polymyxin B resistance). Upregulated genes included algF (biofilm formation) and hemO (iron acquisition). qPCR confirmed tear down-regulation of oprH, phoP and phoQ. Tear fluid pre-treatment increased P. aeruginosa resistance to meropenem ~5-fold (4 μg/ml), but enhanced polymyxin B susceptibility ~180-fold (1 μg/ml), the latter activity reduced by dilution in PBS. Media containing a subset of tear components (TLF) also sensitized bacteria to polymyxin B, but only ~22.5-fold, correlating with TLF/tear fluid Ca2+ and Mg2+ concentrations. Accordingly, phoQ mutants were not sensitized by TLF or tear fluid. Superior activity of tear fluid versus TLF against wild-type P. aeruginosa was heat resistant but proteinase K sensitive.ConclusionP. aeruginosa responds to human tear fluid by upregulating genes associated with bacterial survival and adaptation. Meanwhile, tear fluid down-regulates multiple virulence-associated genes. Tears also utilize divalent cations and heat resistant/proteinase K sensitive component(s) to enhance P. aeruginosa sensitivity to polymyxin B.

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Draft Genome Sequence of the Multiple Antibiotic Resistant Pseudomonas aeruginosa PAO1-UB Subline.

Project description:We report the draft genome sequence and antibiotic susceptibility of Pseudomonas aeruginosa strain PAO1-UB, a subline of the common reference strain PAO1. This strain was sequenced in order to provide information on the genome dynamics of PAO1 sublines and their genes conferring resistance to multiple antibiotics.

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Proteome of Pseudomonas aeruginosa treated by non-antibiotic pharmaceutical

Project description:Antimicrobial resistance (AMR) has been an increasingly serious threat to global public health. The contribution of non-antibiotic pharmaceuticals to the development of antibiotic resistance has been overlooked.Proteomic analysis was used to explore the underlying mechanism of AMR caused by non-antibiotics.

2024-01-26 | PXD048908 |

Coexistence with Pseudomonas aeruginosa alters Staphylococcus aureus transcriptome, antibiotic resistance and internalization into epithelial cells.

Project description:Cystic fibrosis (CF) is the most common life-threatening genetic disease among Caucasians. CF patients suffer from chronic lung infections due to the presence of thick mucus, caused by cftr gene dysfunction. The two most commonly found bacteria in the mucus of CF patients are Staphylococcus aureus and Pseudomonas aeruginosa. It is well known that early-infecting P. aeruginosa strains produce anti-staphylococcal compounds and inhibit S. aureus growth. More recently, it has been shown that late-infecting P. aeruginosa strains develop commensal-like/coexistence interaction with S. aureus. The aim of this study was to decipher the impact of P. aeruginosa strains on S. aureus. RNA sequencing analysis showed 77 genes were specifically dysregulated in the context of competition and 140 genes in the context of coexistence in the presence of P. aeruginosa. In coexistence, genes encoding virulence factors and proteins involved in carbohydrates, lipids, nucleotides and amino acids metabolism were downregulated. On the contrary, several transporter family encoding genes were upregulated. In particular, several antibiotic pumps belonging to the Nor family were upregulated: tet38, norA and norC, leading to an increase in antibiotic resistance of S. aureus when exposed to tetracycline and ciprofloxacin and an enhanced internalization rate within epithelial pulmonary cells. This study shows that coexistence with P. aeruginosa affects the S. aureus transcriptome and virulence.

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Comparative transcriptome analyses of Pseudomonas aeruginosa.

Project description:One of the hallmarks of bacterial survival is their ability to adapt rapidly to changing environmental conditions. Niche adaptation is a response to the signals received that are relayed, often to regulators that modulate gene expression. In the post-genomic era, DNA microarrays are used to study the dynamics of gene expression on a global scale. Numerous studies have used Pseudomonas aeruginosa--a Gram-negative environmental and opportunistic human pathogenic bacterium--as the model organism in whole-genome transcriptome analysis. This paper reviews the transcriptome studies that have led to immense advances in our understanding of the biology of this intractable human pathogen. Comparative analysis of 23 P. aeruginosa transcriptome studies has led to the identification of a unique set of genes that are signal specific and a core set that is differentially regulated. The 303 genes in the core set are involved in bacterial homeostasis, making them attractive therapeutic targets.

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Pseudomonas aeruginosa transcriptome during human infection.

Project description:Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium's primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.

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Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.

Project description:Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.

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Fitness Landscape of Antibiotic Tolerance in Pseudomonas aeruginosa Biofilms

Project description:Bacteria in biofilms have higher antibiotic tolerance than their planktonic counterparts. A major outstanding question is the degree to which the biofilm-specific cellular state and its constituent genetic determinants contribute to this hyper-tolerant phenotype. Here, using genome-wide functional profiling of a complex, heterogeneous mutant population of Pseudomonas aeruginosa MPAO1, we identified large sets of mutations that contribute to antibiotic tolerance predominantly in the biofilm or planktonic setting only. Our mixed population-based experimental design recapitulated the complexity of natural biofilms and, unlike previous studies, revealed clinically observed behaviors including the emergence of quorum sensing-deficient mutants. Our study revealed a substantial contribution of the cellular state to the antibiotic tolerance of biofilms, providing a rational foundation for the development of novel therapeutics against P. aeruginosa biofilm-associated infections. This dataset compares the expression of SAH108, a strain with enhanced antibiotic tolerance in the biofilm state, to expression in wild-type strains.

2011-11-21 | GSE26142 | GEO

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