<i>TWIST1</i> controls cellular senescence and energy metabolism in mesenchymal stem cells
Bibliographic record
Abstract
Abstract Mesenchymal stem cells (MSC) are promising cells for regenerative medicine therapies, because they can differentiate towards multiple cell lineages. However, heterogeneity in differentiation capacity is one of the main drawbacks that limit their use clinically. Differences in the occurrence of cellular senescence and in the expression of the senescence associated secretory phenotype (SASP) in MSC populations contribute to their heterogeneity. Here, we show the involvement of TWIST1 expression in the regulation of MSC senescence, demonstrating that silencing of TWIST1 in MSCs increased the occurrence of senescence. These senescent MSCs had a SASP that was different from irradiation-induced senescent MSCs. In addition, metabolic evaluation performed by the Seahorse XF apparatus showed that both TWIST1 silencing-induced and irradiation-induced senescent MSCs had a higher oxygen consumption compared to control MSCs, while TWIST1 silencing-induced senescent MSCs had a low extracellular acidification rate compared to the irradiation-induced senescent MSCs. Overall, our data indicate how TWIST1 regulation influences senescence in human MSCs and that TWIST1 silencing-induced senescence is characterized by a specific expression of the SASP and the metabolic state.
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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 | 0.001 |
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".