Mast Cell-Activated Bone Marrow Mesenchymal Stromal Cells Regulate Proliferation and Lineage Commitment of CD34+ Progenitor Cells
Bibliographic record
Abstract
BACKGROUND: Shortly after allergen exposure, the number of bone marrow (BM) and circulating CD34(+) progenitors increases. We aim to analyze the possible mechanism whereby the allergic reaction stimulates BM to release these effector cells in increased numbers. We hypothesize that mast cells (MCs) may play a predominant role in this process. OBJECTIVE: To examine the effect of IgE-activated MCs on BM mesenchymal stromal cells which regulate proliferation and differentiation of CD34(+) progenitors. METHODS: Primary MCs were derived from CD34(+) precursors and activated with IgE/anti-IgE. BM mesenchymal stromal cells were co-cultured with CD34(+) progenitor cells and stimulated with IL-1/TNF or IgE/anti-IgE-activated MCs in Transwell system. RESULTS: BM mesenchymal stromal cells produce low level of thymic stromal lymphopoietin (TSLP) under steady state conditions, which is markedly increased by stimulation with proinflammatory cytokines IL-1 and TNF or IgE-activated MCs. The latter also triggers bone marrow-derived mesenchymal stromal cells production of G-CSF, and GM-CSF while inhibiting SDF-1. MC-activated mesenchymal stromal cells stimulate CD34(+) cells to proliferate and to regulate their expression of early allergy-associated genes. CONCLUSION AND CLINICAL RELEVANCE: This in vitro study indicates that IgE-activated MCs trigger BM mesenchymal stromal cells to release TSLP and hematopoietic growth factors and to regulate the proliferation and lineage commitment of CD34(+) precursor cells. The data predict that the effective inhibition of MCs should impair mobilization and accumulation of allergic effector cells and thereby reduce the severity of allergic diseases.
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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 | 0.000 |
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".