Serotonin augments smooth muscle differentiation of bone marrow stromal cells
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Bibliographic record
Abstract
Bone marrow stromal cells (BMSCs) contain a subset of multipotent stem cells. Here, we demonstrate that serotonin, a biogenic amine released by platelets and mast cells, can induce the smooth muscle differentiation of BMSCs. Brown Norway rat BMSCs stimulated with serotonin had increased expression of the smooth muscle markers smooth muscle myosin heavy chain (MHC) and α actin (α-SMA) by qPCR and Western blot, indicating smooth muscle differentiation. This was accompanied by a concomitant down-regulation of the microRNA miR-25-5p, which was found to negatively regulate smooth muscle differentiation. Serotonin upregulated serum response factor (SRF) and myocardin, transcription factors known to induce contractile protein expression in smooth muscle cells, while it down-regulated Elk1 and Kruppel-like factor 4 (KLF4), known to induce proliferation. Serotonin increased SRF binding to promoter regions of the MHC and α-SMA genes, assessed by chromatin immunoprecipitation assay. Induction of smooth muscle differentiation by serotonin was blocked by the knock-down of SRF and myocardin. Transforming growth factor (TGF)-β1 was constitutively expressed by BMSCs and serotonin triggered its release. Inhibition of miR-25-5p augmented TGF-β1 expression, however the differentiation of BMSCs was not mediated by TGF-β1. These findings demonstrate that serotonin promotes a smooth muscle-like phenotype in BMSCs by altering the balance of SRF, myocardin, Elk1 and KLF4 and miR-25-5p is involved in modulating this balance. Therefore, serotonin potentially contributes to the pathogenesis of diseases characterized by tissue remodeling with increased smooth muscle mass.
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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 | 0.001 | 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) | 0.000 | 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 it