Interleukin-16 Inhibits Immunoglobulin E Production by B Lymphocytes
Bibliographic record
Abstract
BACKGROUND: The increased production of IgE is a hallmark of atopic disorders. CD4+ T cells regulate the production of Immunoglobulin (Ig) E by B cells. Interleukin (IL) 16, a CD4+ specific cytokine, is highly expressed at sites of allergic inflammation. Our aim was to determine the effect of IL-16 on IgE production in atopic subjects. METHODS: Freshly isolated peripheral blood mononuclear cells (PBMC) from atopic subjects were stimulated with recombinant IL (rIL) 4 and anti-CD40 antibody to promote IgE production in the presence or absence of rIL-16 added at different time intervals prior to stimulation. The levels of IgE in cell culture supernatants collected at day 14 were measured by ELISA. The effect of IL-16 on the expression of the Cepsilon transcript was evaluated by reverse-transcription polymerase chain reaction. To evaluate whether the modulatory effect of IL-16 on IgE production was mediated by interferon-gamma (IFN-gamma), anti-CD40/IL-4-stimulated PBMC were cultured in the presence of rIL-16 and neutralizing concentrations of anti-IFN-gamma antibody. RESULTS: PBMC stimulated with rIL-4 (400 U/ml) and anti-CD40 monoclonal antibody (0.5 microg/ml) produced significant amounts of IgE (range: 1.3-46.0 ng/ml). The addition of rIL-16 twenty-four hours before stimulation significantly reduced the levels of IgE released by anti-CD40/IL-4-stimulated PBMC (0.5-29.6 ng/ml, p < 0.05). IL-16 reduced the expression of the Cepsilon transcript in stimulated PBMC. IL-16 induced the expression of IFN-gamma mRNA. However, the use of anti-IFN-gamma antibody did not alter the effect of IL-16 on IgE production. Rescue doses of IL-13 did not restore the production of IgE by PBMC treated with IL-16. IL-16 did not alter IgE production in CD14-depleted cell preparations suggesting that the IL-16-mediated effects on IgE production may be related to CD14+ cells. CONCLUSION: These data show that IL-16 inhibits IgE production and therefore may play an important regulatory role in atopic disorders.
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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) | 0.003 | 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".