Knockdown of SIRT6 Enables Human Bone Marrow Mesenchymal Stem Cell Senescence
Bibliographic record
Abstract
Autologous bone marrow mesenchymal stem cell (BM-MSC) transplantation is a novel strategy for treating ischemic heart disease. However, limited benefits have been reported in aging patients. Rejuvenation of aged human BM-MSCs (hBM-MSCs) could be a means to improve the efficacy of stem cell transplantation in older patients. While it has been shown that sirtuin 6 (SIRT6) is an important antiaging factor in various cells, the role of SIRT6 in hBM-MSCs remains unknown. The hBM-MSCs from different ages were cultured for quantifying SIRT6 expression by mRNA and Western blotting. The cell proliferative and migration abilities were evaluated by BrdU staining, cell growth curves, and scratch assay. Senescence-associated β-galactosidase (SA-β-Gal) activity and aging-associated p16 (cyclin-dependent kinase inhibitor 2A) expression were also quantified. The knockdown of SIRT6 in hBM-MSCs was used to investigate its impact on aging. SIRT6 expression increased with age, while the proliferative and migration abilities of aged hBM-MSCs were decreased compared with young cells. Knockdown of SIRT6 impaired the proliferative, migration, and oxidative stress resistance potentials of BM-MSCs. SA-β-Gal activity and p16 expression were increased in aged cells compared with young ones and in siRNA SIRT6 knockdown cells compared with their controls. Aging results in compensatory overexpression of SIRT6 in hBM-MSCs. Downregulation of SIRT6 in these cells resulted in less cell proliferation and migration but increased SA-β-Gal activity and p16 expression. These results suggest that SIRT6 regulates the aging process in hBM-MSCs and could serve as a target for their rejuvenation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".