Ovomucin Hydrolysates Reduce Bacterial Adhesion and Inflammation in Enterotoxigenic <i>Escherichia coli</i> (ETEC) K88-Challenged Intestinal Epithelial Cells
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) K88 is the most common cause of diarrhea in neonatal and postweaning pigs. After adhering to small intestinal epithelial cells via glycoprotein receptor recognition, the pathogen can produce enterotoxins, impair intestinal integrity, trigger watery diarrhea, and induce inflammation via nuclear factor κB (NF-κB) and mitogen-activated protein kinase phosphatase (MAPK) pathways. Inhibiting ETEC K88 adhesion to cell surfaces by interfering with the receptor-fimbriae recognition provides a promising strategy to prevent the initiation and progression of infection. Ovomucin is a highly glycosylated protein in chicken egg white with diverse bioactivities. Ovomucin hydrolysates prepared by the enzymes Protex 26L (OP) and pepsin/pancreatin (OPP) were previously revealed to prevent adhesion of ETEC K88 to IPEC-J2 cells. Herein, we investigated the protective effects of ovomucin hydrolysates on ETEC K88-induced barrier integrity damage and inflammation in IPEC-J2 and Caco-2 cells. Both hydrolysates inhibited ETEC K88 adhesion to cells and protected epithelial cell integrity by restoring transepithelial electronic resistance (TEER) values. Removing sialic acids in the hydrolysates reduced their antiadhesive capacities. Ovomucin hydrolysates suppressed ETEC-induced activation of NF-κB and MAPK signaling pathways in both cell lines. The ability of ETEC K88 in activating calcium/calmodulin-dependent protein kinase 2 (CaMK II), elevating intracellular Ca 2+ concentration, and inducing oxidative stress was attenuated by both hydrolysates. In conclusion, this study demonstrated the potential of ovomucin hydrolysates to prevent ETEC K88 adhesion and alleviate inflammation and oxidative stress in intestinal epithelial 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) | 0.000 | 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".