Bone Marrow Mesenchymal Stem Cells' Secretome Exerts Neuroprotective Effects in a Parkinson's Disease Rat Model
Bibliographic record
Abstract
Parkinson’s disease (PD) is characterized by a selective loss of dopamine (DA) neurons in the human midbrain causing motor dysfunctions. The exact mechanism behind dopaminergic cell death is still not completely understood and, so far, no cure or neuroprotective treatment for PD is available. Recent studies have brought attention to the wide array of bioactive molecules produced by mesenchymal stem cells (MSCs), generally referred to as the secretome. Herein, we evaluated whether human MSCs-bone marrow derived (hBMSCs) secretome would be beneficial in a PD pre-clinical model, when compared directly with cell transplantation of hBMSCs alone. We used a 6-hydroxydpomanie (6-OHDA) rat PD model, and motor behavior was evaluated at different time points after treatments (1, 4 and 7 weeks). The impact of the treatments in the recovery of DA neurons was estimated by determining TH-positive neuronal densities in the substantia nigra and fibers in the striatum, respectively, at the end of the behavioral characterization. Furthermore, we determined the effect of the hBMSCs secretome on the neuronal survival of human neural progenitors in vitro, and characterized the secretome through proteomic-based approaches. This work demonstrates that the injection of hBMSCs secretome potentiated the histological recovery of DA neurons, when compared to transplantation of hBMSCs themselves, a fact that probably explains the improved behavioral performance of secretome-injected animals in the staircase test. Moreover, we observed that hBMSCs secretome induces higher levels of in vitro neuronal differentiation. Finally, the proteomic analysis revealed that hBMSCs secrete a variety of important exosome-related molecules such as those related with the ubiquitin-proteasome and histone systems. Overall, this work provided important insights on the potential use of hBMSCs secretome as a therapeutic tool for PD, and further confirms the importance of the secreted molecules rather than the transplantation of hBMSCs for the observed positive effects. These could be likely through normalization of defective processes in PD, namely proteostasis or altered gene transcription, which lately can lead to neuroprotective effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".