Major Host Factors Involved in Epithelial Cell Invasion of Campylobacter jejuni: Role of Fibronectin, Integrin Beta1, FAK, Tiam-1, and DOCK180 in Activating Rho GTPase Rac1
Bibliographic record
Abstract
Host cell entry by the food-borne pathogen Campylobacter jejuni has been reported as one of the primary reasons of tissue damage in infected humans, however, molecular invasion mechanisms and cellular factors involved in this process are widely unclear. Here we used knockout cell lines derived from fibronectin(-/-), integrin beta1(-/-), and focal adhesion kinase (FAK)(-/-) deficient mice and corresponding wild-type (WT) controls, to study C. jejuni-induced signaling cascades involved in the bacterial invasion process. Using high resolution scanning electron microscopy, GTPase pull-downs, G-LISA, and gentamicin protection assays we found that each of these host cell factors is indeed required for activation of the small Rho GTPase member Rac1 and maximal host cell invasion of this pathogen. Interestingly, membrane ruffling, tight engulfment of bacteria and invasion were only seen during infection of WT control cells, but not in fibronectin(-/-), integrin beta1(-/-), and FAK(-/-) knockout cell lines. We also demonstrate that C. jejuni activates FAK autophosphorylation activity at Y-397 and phosphorylation of Y-925, which is required for stimulating two downstream guanine exchange factors, DOCK180 and Tiam-1, which are upstream of Rac1. Small interfering (si) RNA studies further show that DOCK180 and Tiam-1 act cooperatively to trigger Rac1 activation and C. jejuni invasion. Moreover, mutagenesis data indicate that the bacterial fibronectin-binding protein CadF and the intact flagellum are involved in Rho GTPase activation and host cell invasion. Collectively, our results suggest that C. jejuni infection of host epithelial target cells hijacks a major fibronectin → integrin beta1 → FAK → DOCK180/Tiam-1 signaling cascade, which has a crucial role for Rac1 GTPase activity and bacterial entry into host target cells.
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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) | 0.001 | 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".