Label‐Free Quantitative Proteomics Distinguishes General and Site‐Specific Host Responses to <i>Pseudomonas aeruginosa</i> Infection at the Ocular Surface
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".