MicroRNA‐301a Alters Dicer Expression in Primary Human Atrial Cells and Bone Marrow‐Derived Mesenchymal Progenitor Cells: Implications for Cardiac Fibrosis
Bibliographic record
Abstract
It is known that multiple cell types can contribute to cardiac fibrosis including both atrial fibroblasts (AFs) and bone marrow‐derived progenitor cells (MPCs). We have previously shown that MPCs display a myofibroblast phenotype in vitro that is linked to altered microRNA(miR)‐301 expression, a miR affiliated with maintaining proliferation in many cell types. We have also shown that miR‐301a influences a dichotomous phenotype in MPCs isolated from patients undergoing open heart surgery. The objective of this experiment was to further understand how this phenotype change may be influenced. We performed a microarray analysis investigating potential targets of miR‐301a. From this screen, Dicer was identified as a potential target of mir‐301a. Dicer is responsible for activating miRs in the cell, therefore altering protein expression and ultimately influencing phenotype. As both MPCs and AFs display a dichotomous phenotype where each cell type displays a phenotype that pathologically contributes to fibrosis, we transfected both MPCs and AFs with miR‐301a. AFs were also isolated from patients undergoing open heart surgery. We performed qRT‐PCR analysis and found that the mRNA of Dicer was significantly reduced in transfected cells, and observed decreases in levels of both mRNA and protein of collagen I and non‐muscle myosin IIA (NMMIIA). These proteins are present in myofibroblasts, the cell type predominantly responsible for causing cardiac fibrosis. Our results provide insight into a possible cellular mechanism that governs the pro‐fibrotic phenotypes of AFs and MPCs, which could be partially caused by altered Dicer expression with its attendant effect on miRNA processing in the cell.
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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 | 0.001 |
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".