Suppression of NF-κB Activation by Entamoeba histolytica in Intestinal Epithelial Cells Is Mediated by Heat Shock Protein 27
Bibliographic record
Abstract
Little is known about the pathogenesis of Entamoeba histolytica and how epithelial cells respond to the parasite. Herein, we characterized the interactions between E. histolytica and colonic epithelial cells and the role macrophages play in modulating epithelial cell responses. The human colonic epithelial cell lines Caco-2 and T84 were grown either as monoculture or co-cultured in transwell plates with differentiated human THP-1 macrophages for 24 h before stimulation with soluble amebic proteins (SAP). In naive epithelial cells, prolonged stimulation with SAP reduced the levels of heat shock protein (Hsp) 27 and 72. However in THP-1 conditioned intestinal epithelial cells SAP enhanced Hsp27 and Hsp72, which was dependent on the activation of ERK MAP kinase. Hsp synthesis induced by SAP conferred protection against oxidative and apoptotic injuries. Treatment with SAP inhibited NF-kappaB activation induced by interleukin-1beta; specifically, the NF-kappaB-DNA binding, nuclear translocation of p65 subunit, and phosphorylation of IkappaB-alpha were reduced. Gene silencing by small interfering RNA confirmed the role of Hsp27 in suppressing NF-kappaB activation at IkappaB kinase (IKK) level. By co-immunoprecipitation studies, we found that Hsp27 interacts with IKK-alpha and IKK-beta, and this association was increased in SAP-treated conditioned epithelial cells. Overexpression of wild type Hsp27 amplified the effects of SAP, whereas a phosphorylation-deficient mutant of Hsp27 abrogated SAP-induced NF-kappaB inhibition. In conditioned epithelial cells, Hsp27 was phosphorylated at serine 15 after prolonged exposure to SAP. This mechanism may explain the absence of colonic inflammation seen in the majority of individuals infected with E. histolytica.
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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 | 0.001 |
| 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) | 0.002 | 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".