Mass Spectrometry-Based Proteomics Identification of Enteropathogenic <i>Escherichia coli</i> Pedestal Constituents
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) co-opt host signaling pathways and recruit numerous host proteins to motile morphological structures, called pedestals, at sites of bacterial attachment. These pedestals are hallmarks of EPEC-based disease, and the identification and characterization of the functions of pedestal proteins continue to steadily increase. To identify additional constituents in an unbiased manner, we developed a strategy where EPEC pedestals were elongated artificially, severed, and then concentrated prior to their analysis by mass spectrometry (MS)-based proteomics. We identified >90 unique mammalian proteins over multiple experimental trials from our preparations. Seventeen predicted molecules were significantly higher in abundance (p < 0.05) when compared to both the negative controls and sample means. Validation of two identified proteins (cyclophilin A [nonactin-associated] and transgelin [actin-associated]) by immunolocalization was used to confirm our analysis, and both showed enrichment at EPEC pedestals. The EPEC pedestal concentration technique developed here together with the identification of novel pedestal proteins not only provides a resource for the further characterization of molecular components within these structures but also demonstrates that EPEC pedestals can be used as a model system for the identification of novel functions of proteins not normally thought to be at actin-based structures.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".