Estrogen-Induced Gene Expression in Bone Marrow c-kit+ Stem Cells and Stromal Cells: Identification of Specific Biological Processes Involved in the Functional Organization of the Stem Cell Niche
Bibliographic record
Abstract
The recent interest in the role of bone marrow (BM)-derived endothelial progenitor cells (EPCs) and the benefits of estrogen on cardiovascular health brought us to evaluate if estrogen could affect cardiac repair more broadly by regulating biological processes involved in the functional organization of the BM stem cell (SC) niche. To assess such possibility, we evaluated gene expression profiles of BM c-kit+ SCs and CD44+ stromal cells (StroCs) after exposure to a physiological concentration of 17beta-estradiol (17betaE). Data analysis showed that 17betaE altered the expression (>1.5 fold) of 509 and 682 gene probes in c-kit+ SCs and CD44+ StroCs, respectively. Among them, 199 genes in c-kit+ SCs and 283 in CD44+ StroCs were associated to biological process categories of the Gene Ontology classification. Within processes highly regulated by 17betaE, we identified key factors involved in adhesion, migration, proteolysis, and signaling by which 17betaE influences physiological regulation of the functional organization of the SC niche. Together, our results demonstrate that estrogen benefits on cardiovascular health could involve other BM-derived cells than EPCs and that this capacity of estrogen to influence the physiology of the BM SC niche deserves to be investigated clinically.
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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) | 0.001 | 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".