Indispensable functions of ABL and PDGF receptor kinases in epithelial adherence of attaching/effacing pathogens under physiological conditions
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) and Citrobacter rodentium are attaching-and-effacing (A/E) pathogens that cause intestinal inflammation and diarrhea. The bacteria adhere to the intestinal epithelium, destroy microvilli, and induce actin-filled membranous pedestals but do not invade the mucosa. Adherence leads to activation of several host cell kinases, including FYN, n-SRC, YES, ABL, and ARG, phosphorylation of the bacterial translocated intimin receptor, and actin polymerization and pedestal formation in cultured cells. However, marked functional redundancy appears to exist between kinases, and their physiological importance in A/E pathogen infections has remained unclear. To address this question, we employed a novel dynamic in vitro infection model that mimics transient and short-term interactions in the intestinal tract. Screening of a kinase inhibitor library and RNA interference experiments in vitro revealed that ABL and platelet-derived growth factor (PDGF) receptor (PDGFR) kinases, as well as p38 MAP kinase, have unique, indispensable roles in early attachment of EPEC to epithelial cells under dynamic infection conditions. Studies with mutant EPEC showed that the attachment functions of ABL and PDGFR were independent of the intimin receptor but required bacterial bundle-forming pili. Furthermore, inhibition of ABL and PDGFR with imatinib protected against infection of mice with modest loads of C. rodentium, whereas the kinases were dispensable for high inocula or late after infection. These results indicate that ABL and PDGFR have indispensable roles in early A/E pathogen attachment to intestinal epithelial cells and for in vivo infection with limiting inocula but are not required for late intimate bacterial attachment or high inoculum infections.
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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) | 0.000 | 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 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".