Transcriptional Mechanisms for Induction of 5-HT1AReceptor mRNA and Protein in Activated B and T Lymphocytes
Bibliographic record
Abstract
Serotonin (5-HT) up-regulates B and T lymphocyte proliferation by activating mitogen-induced cell surface 5-HT(1A) receptors. The mechanism of 5-HT(1A) receptor induction by B and T cell mitogens at the mRNA and protein levels in mouse splenocytes was addressed. Quantitation by RNase protection assay showed maximal increases of 3.4-, 3.0-, 3.8-, and 4.9-fold in relative 5-HT(1A) mRNA levels after 48 h of stimulation of splenocytes with lipopolysaccharide, phytohemagglutinin, concanavalin A, or phorbol 12-myristate 13-acetate plus ionomycin, respectively, as compared with unstimulated cells. Mitogens did not alter 5-HT(1A) mRNA stability (t(12) = 26 h), but induction of 5-HT(1A) mRNA was blocked by the transcriptional inhibitor actinomycin D (10 microgram/ml) and by inhibition of nuclear factor-kappaB signaling. Additionally, mitogenic stimulation of transcription was paralleled by increased cell surface 5-HT(1A) receptor immunoreactivity in splenocytes. Thus, mitogen-induced 5-HT(1A) receptor expression appears to involve transcriptional regulation by the nuclear factor-kappaB signaling cascade. Increased expression of the 5-HT(1A) receptor in activated B and T lymphocytes may enhance the immune response and provide therapeutic target for tissue inflammation and immune stimulation.
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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) | 0.002 | 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".