Dendritic Cell Differentiation Induced by a Self-Peptide Derived from Apolipoprotein E
Bibliographic record
Abstract
Dendritic cells (DCs) are professional APCs and potent stimulators of naive T cells. Since DCs have the ability to immunize or tolerize T cells they are unique candidates for use in immunotherapy. Our laboratory has discovered that a naturally processed self-peptide from apolipoprotein E, Ep1.B, induces DC-like morphology and surface marker expression in a murine monocytic cell line (PU5-1.8), human monocytic cell line (U937), murine splenocytes, and human peripheral blood monocytes. Microscopy and flow cytometric analysis revealed that Ep1.B-treated cells display decreased adherence to plastic and increased aggregation, dendritic processes, and expression of DC surface markers, including DEC-205, CD11c, B7.1, and B7.2. These effects were observed in both PU5-1.8 cells and splenocytes from various mouse strains including BALB/c, C57BL/6, NOD/Lt, and C3H/HeJ. Coadministration of Ep1.B with OVA antigenic peptide functions in dampening specific immune response to OVA. Ep1.B down-regulates proliferation of T cells and IFN-gamma production and stimulates IL-10 secretion in immunized mice. Ep1.B-induced differentiation resulted in the activation of PI3K and MAPK signaling pathways, including ERK1/2, p38, and JNK. We also found that NF-kappaB, a transcription factor essential for DC differentiation, is critical in mediating the effects of Ep1.B. Ep1.B-induced differentiation is independent of MyD88-dependent pathway of TLR signaling. Cumulatively, these findings suggest that Ep1.B acts by initiating a signal transduction cascade in monocytes leading to their differentiation into DCs.
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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 | 0.000 |
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".