Coupling erythropoietin secretion to mesenchymal stromal cells enhances their regenerative properties
Bibliographic record
Abstract
AIMS: Mesenchymal stromal cells (MSCs) possess intrinsic features that identify them as useful for treating ischaemic syndromes. Poor in vivo survival/engraftment of MSCs, however, limits their overall effectiveness. In this work, we tested whether genetically engineering MSCs to secrete erythropoietin (Epo) could represent a better therapeutic platform than MSCs in their native form. METHODS AND RESULTS: MSCs from C57Bl/6 mice were retrovirally transduced with either an empty vector or one that causes the production of Epo and were then analysed for the alterations in angiogenic and survival potential. Using a mouse model of myocardial infarction (MI), the regenerative potential of null MSCs and Epo-overexpressing MSCs (Epo+MSCs) was assessed using serial echocardiogram and invasive haemodynamic measurements. Infarct size, capillary density and neutrophil influx were assessed using histologic techniques. Using in vitro assays coupled with an in vivo Matrigel plug assay, we demonstrate that engineering MSCs to express Epo does not alter their immunophenotype or plasticity. However, relative to mock-modified MSCs [wild-type (WT)-MSCs], Epo+MSCs are more resilient to apoptotic stimuli and initiate a more robust host-derived angiogenic response. We also identify and characterize the autocrine loop established on MSCs by having them secrete Epo. Furthermore, in a murine model of MI, animals receiving intracardiac injections of Epo+MSCs exhibited significantly enhanced cardiac function compared with WT-MSCs and saline-injected control animals post-MI, owing to the increased myocardial capillary density and the reduced neutrophilia. CONCLUSION: Epo overexpression enhances the cellular regenerative properties of MSCs by both autocrine and paracrine pathways.
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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".