miRNA Profile and Role in the Immunological and Inflammatory Microenvironment of the Stroma
Bibliographic record
Abstract
Background: Due to their self-renewal capacity, multi-lineage potential, and immunomodulatory properties, mesenchymal stromal cells (MSCs) are an attractive tool for different therapeutic strategies.Foreskin (FSK), considered as a biological waste material, has already been shown to be a valuable source of MSCs.Besides their typical fibroblast like morphology and International Society for cellular Therapy compliant phenotype, foreskin-MSCs (FSK-MSCs) are clonogenic, and highly proliferative cells with multi-lineage and strong immunomodulatory capacities.Of importance, FSK-MSCs properly adjust their fate following exposure to inflammatory signals.Being potent regulators of gene expression, miRNAs are www.videleaf.cominvolved in modulating nearly all cellular processes and in orchestrating the roles of different immune cells.In this study, we characterized the miRNome of FSK-MSCs by determining the expression profile of 380 different miRNAs in inflammation primed vs. control non-primed cells.Methods: TaqMan low density array (TLDA) was performed to identify dysregulated miRNAs after exposing FSK-MSCs to inflammatory signals.Quantitative real-time RT-PCR was carried out to validate the observations.DIANA-miRPath analysis web server was used to identify potential pathways that could be targeted by the dysregulated miRNAs.Results: Sixteen miRNAs were differentially expressed in inflammation-primed vs. non-primed FSK-MSCs.The expression level of miR-27a, -145, -149, -194, -199a, -221, -328, -345, -423-5p, -485-3p, -485-5p, -615-5p and -758 was downregulated whilst that of miR-155, -363 and -886-3p was upregulated.Target pathway prediction of those differentially expressed miRNAs identified different inflammation linked pathways.Conclusions: After determining their miRNome, we identified a striking effect of inflammatory signals on the miRNAs' expression levels in FSK-MSCs.Our results highlight a potential role of miRNAs in modulating the transcription programs of FSK-MSCs in response to inflammatory signals.Further, we propose that specific miRNAs could serve as interesting targets to manipulate some functions of FSK-MSCs, thus ameliorating their therapeutic potential.
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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".