Lactoferrin downregulates pro-inflammatory cytokines upexpressed in intestinal epithelial cells infected with invasive or noninvasive <i>Escherichia coli</i> strainsThis paper is one of a selection of papers published in this Special Issue, entitled 7th International Conference on Lactoferrin: Structure, Function, and Applications, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Intestinal epithelial cells are able to differentially interact with commensal or pathogenic microorganisms, triggering a physiological or destructive inflammation, respectively. To mimic commensal-enteroinvasive bacteria-host cell interaction, we infected Caco-2 cells with noninvasive Escherichia coli HB101 and with recombinant invasive E. coli HB101(pRI203). Using DNA microarray mRNA profiling and ELISA assays, we studied the expression of several cytokine and cytokine-related genes in infected Caco-2 cells in the absence or presence of bovine lactoferrin (bLf). Infection of Caco-2 cells with the noninvasive strain induced a slight increase in the expression of interleukin 8 (IL-8), whereas infection with invasive E. coli HB101(pRI203) induced a significant increase in the expression of IL-8 as well as other pro-inflammatory cytokines. The addition of bLf, in native- or holo-form, did not influence expression of cytokine genes by uninfected Caco-2 cells, but it decreased expression of IL-8 by cells infected with E.coli HB101. Moreover, except for IL-8, bLfs dramatically downregulated pro-inflammatory cytokines upexpressed by Caco-2 cells infected with the invasive strain. Although IL-8 was decreased by bLfs, it remained upregulated, suggesting that it could be a signal of persistence of intracellular bacteria. The bLf ability to reduce expression of some pro-inflammatory cytokines, which appears independent of its iron saturation, might represent an important natural mechanism in regulating epithelial cell responses to pathogenic bacteria and in limiting cell damage and the spread of infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".