Dataset Information

Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.

ABSTRACT: Mass spectrometry-based proteomics enables the unbiased and sensitive profiling of cellular proteomes and extracellular environments. Recent technological and bioinformatic advances permit identifying dual biological systems in a single experiment, supporting investigation of infection from both the host and pathogen perspectives. At the ocular surface, Pseudomonas aeruginosa is commonly associated with biofilm formation and inflammation of the ocular tissues, causing damage to the eye. The interaction between P. aeruginosa and the immune system at the site of infection describes limitations in clearance of infection and enhanced pathogenesis. Here, the extracellular environment (eye wash) of murine ocular surfaces infected with a clinical isolate of P. aeruginosa is profiled and neutrophil marker proteins are detected, indicating neutrophil recruitment to the site of infection. The first potential diagnostic markers of P. aeruginosa-associated keratitis are also identified. In addition, the deepest murine corneal proteome to date is defined and proteins, categories, and networks critical to the host response are detected. Moreover, the first identification of bacterial proteins attached to the ocular surface is reported. The findings are validated through in silico comparisons and enzymatic profiling. Overall, the work provides comprehensive profiling of the host-pathogen interface and uncovers differences between general and site-specific host responses to infection.

SUBMITTER: Yeung J

PROVIDER: S-EPMC7079286 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.

Yeung Jason J Gadjeva Mihaela M Geddes-McAlister Jennifer J

Proteomics 20200107 2

Mass spectrometry-based proteomics enables the unbiased and sensitive profiling of cellular proteomes and extracellular environments. Recent technological and bioinformatic advances permit identifying dual biological systems in a single experiment, supporting investigation of infection from both the host and pathogen perspectives. At the ocular surface, Pseudomonas aeruginosa is commonly associated with biofilm formation and inflammation of the ocular tissues, causing damage to the eye. The inte ...[more]

PMID: 31874121

Similar Datasets

Project description:Epidemic strains of Pseudomonas aeruginosa often have increased levels of antibiotic resistance, and these resistance phenotypes are difficult to predict. We used quantitative proteomics to compare the responses of laboratory strain PAO1 and two isolates of the Liverpool epidemic strain (LES) to four antibiotics and hydrogen peroxide. The majority of proteome changes were unique to either one isolate or treatment, but smaller groups of proteins were differentially abundant in more than one sample. Proteins in these shared adaptive responses represent promising avenues for further investigation to understand the contribution of these proteins to resistance and their potential as targets for novel treatments. We observed the largest overlap between the proteome profiles of PAO1 challenged with tobramycin and hydrogen peroxide, and our data support previous work suggesting a role for heat shock protein IbpA in response to tobramycin. Our proteome profiling uncovered extensive changes in LESB58 in response to carbenicillin, with more than 1,000 proteins significantly changed in abundance. This included unique changes in the abundance of proteins involved in cell wall synthesis and division. We used phase contrast microscopy to check for corresponding changes in cell morphology and show that LESB58 maintained shorter cell lengths under treatment with β-lactams. We propose that the ability to maintain the processes of cell wall synthesis and division through changes in protein abundances contributes to the high levels of β-lactam resistance in LESB58. IMPORTANCE Pseudomonas aeruginosa is an important pathogen often associated with hospital-acquired infections and chronic lung infections in people with cystic fibrosis. P. aeruginosa possesses a wide array of intrinsic and adaptive mechanisms of antibiotic resistance, and the regulation of these mechanisms is complex. Label-free quantitative proteomics is a powerful tool to compare susceptible and resistant strains of bacteria and their responses to antibiotic treatments. Here we compare the proteomes of three isolates of P. aeruginosa with different antibiotic resistance profiles in response to five challenge conditions. We uncover unique and shared proteome changes for the widely used laboratory strain PAO1 and two isolates of the Liverpool epidemic strain of P. aeruginosa, LESlike1 and LESB58. Our data set provides insight into antibiotic resistance in clinically relevant Pseudomonas isolates and highlights proteins, including those with uncharacterized functions, which can be further investigated for their role in adaptive responses to antibiotic treatments.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.

Publications

Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets