Therapeutic Effects of Mesenchymal Stromal Cells Require Mitochondrial Transfer and Quality Control
Bibliographic record
Abstract
Due to their beneficial effects in an array of diseases, Mesenchymal Stromal Cells (MSCs) have been the focus of intense preclinical research and clinical implementation for decades. MSCs have multilineage differentiation capacity, support hematopoiesis, secrete pro-regenerative factors and exert immunoregulatory functions promoting homeostasis and the resolution of injury/inflammation. The main effects of MSCs include modulation of immune cells (macrophages, neutrophils, and lymphocytes), secretion of antimicrobial peptides, and transfer of mitochondria (Mt) to injured cells. These actions can be enhanced by priming (i.e., licensing) MSCs prior to exposure to deleterious microenvironments. Preclinical evidence suggests that MSCs can exert therapeutic effects in a variety of pathological states, including cardiac, respiratory, hepatic, renal, and neurological diseases. One of the key emerging beneficial actions of MSCs is the improvement of mitochondrial functions in the injured tissues by enhancing mitochondrial quality control (MQC). Recent advances in the understanding of cellular MQC, including mitochondrial biogenesis, mitophagy, fission, and fusion, helped uncover how MSCs enhance these processes. Specifically, MSCs have been suggested to regulate peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC1α)-dependent biogenesis, Parkin-dependent mitophagy, and Mitofusins (Mfn1/2) or Dynamin Related Protein-1 (Drp1)-mediated fission/fusion. In addition, previous studies also verified mitochondrial transfer from MSCs through tunneling nanotubes and via microvesicular transport. Combined, these effects improve mitochondrial functions, thereby contributing to the resolution of injury and inflammation. Thus, uncovering how MSCs affect MQC opens new therapeutic avenues for organ injury, and the transplantation of MSC-derived mitochondria to injured tissues might represent an attractive new therapeutic approach.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".