Proteomics

Dataset Information

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The core proteome of biofilm-grown clinical Pseudomonas aeruginosa isolates


ABSTRACT: Comparative genomics has greatly facilitated the identification of shared as well as unique features among individual cells or tissues, and thus offers the potential to find disease markers. While proteomics is recognized for its potential to generate quantitative maps of protein expression, comparative proteomics in bacteria has been largely restricted to the comparison of single cell lines or mutant strains. In this study, we used a data independent acquisition (DIA) technique, which enables global protein quantification of large sample cohorts, to record the proteome profiles of overall 27 whole genome sequenced and transcriptionally profiled clinical isolates of the opportunistic pathogen Pseudomonas aeruginosa. Analysis of the proteome profiles across the 27 clinical isolates grown under planktonic and biofilm growth conditions led to the identification of a core biofilm-associated protein profile. Furthermore, we found that protein-to-mRNA ratios between different P. aeruginosa strains are well correlated, indicating conserved patterns of post-transcriptional regulation. Uncovering core regulatory pathways, which drive biofilm formation and associated antibiotic tolerance in bacterial pathogens, promise to give clues to interactions between bacterial species and their environment and could provide useful targets for new clinical interventions to combat biofilm-associated infections.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Pseudomonas Aeruginosa (strain Ucbpp-pa14) Bacteria

TISSUE(S): Cell Culture

DISEASE(S): Bacterial Infectious Disease

SUBMITTER: Jelena Erdmann  

LAB HEAD: Christof Lenz

PROVIDER: PXD015073 | Pride | 2019-10-30

REPOSITORIES: Pride

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Publications

The Core Proteome of Biofilm-Grown Clinical <i>Pseudomonas aeruginosa</i> Isolates.

Erdmann Jelena J   Thöming Janne G JG   Pohl Sarah S   Pich Andreas A   Lenz Christof C   Häussler Susanne S  

Cells 20190923 10


Comparative genomics has greatly facilitated the identification of shared as well as unique features among individual cells or tissues, and thus offers the potential to find disease markers. While proteomics is recognized for its potential to generate quantitative maps of protein expression, comparative proteomics in bacteria has been largely restricted to the comparison of single cell lines or mutant strains. In this study, we used a data independent acquisition (DIA) technique, which enables g  ...[more]

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