Protein Kinase C Mediates Enterohemorrhagic Escherichia coli O157:H7-Induced Attaching and Effacing Lesions
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli serotype O157:H7 causes outbreaks of diarrhea, hemorrhagic colitis, and the hemolytic-uremic syndrome. E. coli O157:H7 intimately attaches to epithelial cells, effaces microvilli, and recruits F-actin into pedestals to form attaching and effacing lesions. Lipid rafts serve as signal transduction platforms that mediate microbe-host interactions. The aims of this study were to determine if protein kinase C (PKC) is recruited to lipid rafts in response to E. coli O157:H7 infection and what role it plays in attaching and effacing lesion formation. HEp-2 and intestine 407 tissue culture epithelial cells were challenged with E. coli O157:H7, and cell protein extracts were then separated by buoyant density ultracentrifugation to isolate lipid rafts. Immunoblotting for PKC was performed, and localization in lipid rafts was confirmed with an anti-caveolin-1 antibody. Isoform-specific PKC small interfering RNA (siRNA) was used to determine the role of PKC in E. coli O157:H7-induced attaching and effacing lesions. In contrast to uninfected cells, PKC was recruited to lipid rafts in response to E. coli O157:H7. Metabolically active bacteria and cells with intact lipid rafts were necessary for the recruitment of PKC. PKC recruitment was independent of the intimin gene, type III secretion system, and the production of Shiga toxins. Inhibition studies, using myristoylated PKCζ pseudosubstrate, revealed that atypical PKC isoforms were activated in response to the pathogen. Pretreating cells with isoform-specific PKC siRNA showed that PKCζ plays a role in E. coli O157:H7-induced attaching and effacing lesions. We concluded that lipid rafts mediate atypical PKC signal transduction responses to E. coli O157:H7. These findings contribute further to the understanding of the complex array of microbe-eukaryotic cell interactions that occur in response to infection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".