Proteomic Analysis of the Intestinal Epithelial Cell Response to Enteropathogenic Escherichia coli
Bibliographic record
Abstract
We present the first large scale proteomic analysis of a human cellular response to a pathogen. Enteropathogenic Escherichia coli (EPEC) is an enteric human pathogen responsible for much childhood morbidity and mortality worldwide. EPEC uses a type III secretion system (TTSS) to inject bacterial proteins into the cytosol of intestinal epithelial cells, resulting in diarrhea. We analyzed the host response to TTSS-delivered EPEC effector proteins by infecting polarized intestinal epithelial monolayers with either wild-type or TTSS-deficient EPEC. Host proteins were isolated and subjected to quantitative profiling using isotope-coded affinity tagging (ICAT) combined with electrospray ionization tandem mass spectrometry. We identified over 2000 unique proteins from infected Caco-2 monolayers, of which approximately 13% are expressed differentially in the presence of TTSS-delivered EPEC effector proteins. We validated these data in silico and through immunoblotting and immunofluorescence microscopy. The identified changes extend cytoskeletal observations made in less relevant cell types and generate testable hypotheses with regard to host proteins potentially involved in EPEC-induced diarrhea. These data provide a framework for future biochemical analyses of host-pathogen interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".