Knockdown of SIRT6 Enables Human Bone Marrow Mesenchymal Stem Cell Senescence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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