Intestinal Epithelial Cells Modulate Antigen-Presenting Cell Responses to Bacterial DNA
Bibliographic record
Abstract
Intestinal epithelial cells and antigen-presenting cells orchestrate mucosal innate immunity. This study investigated the role of bacterial DNA in modulating epithelial and bone marrow-derived antigen-presenting cells (BM-APCs) and subsequent T-lymphocyte responses. Murine MODE-K epithelial cells and BM-APCs were treated with DNA from either Bifidobacterium breve or Salmonella enterica serovar Dublin directly and under coculture conditions with CD4(+) T cells. Apical stimulation of MODE-K cells with S. Dublin DNA enhanced secretion of cytokines from underlying BM-APCs and induced interleukin-17 (IL-17) and gamma interferon (IFN-γ) secretion from CD4(+) T cells. Bacterial DNA isolated from either strain induced maturation and increased cytokine secretion from BM-APCs. Conditioned medium from S. Dublin-treated MODE-K cells elicited an increase in cytokine secretion similar to that seen for S. Dublin DNA. Treatment of conditioned medium from MODE-K cells with RNase and protease prevented the S. Dublin-induced increased cytokine secretion. Oral feeding of mice with B. breve DNA resulted in enhanced levels of colonic IL-10 and transforming growth factor β (TGFβ) compared with what was seen for mice treated with S. Dublin DNA. In contrast, feeding mice with S. Dublin DNA increased levels of colonic IL-17 and IL-12p70. T cells from S. Dublin DNA-treated mice secreted high levels of IL-12 and IFN-γ compared to controls and B. breve DNA-treated mice. These results demonstrate that intestinal epithelial cells are able to modulate subsequent antigen-presenting and T-cell responses to bacterial DNA with pathogenic but not commensal bacterial DNA inducing effector CD4(+) T lymphocytes.
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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".