MicroRNA Expression Profiling of Bone Marrow–Derived Proangiogenic Cells (PACs) in a Mouse Model of Hindlimb Ischemia: Modulation by Classical Cardiovascular Risk Factors
Bibliographic record
Abstract
Background. Classical cardiovascular risk factors (CRF) are associated with impaired angiogenic activities of bone marrow-derived proangiogenic cells (PACs) related to peripheral artery diseases (PAD) and ischemia-induced neovascularization. microRNAs (miRs) are key regulators of gene expression, and they are involved in the modulation of PAC function and PAC paracrine activity. However, the effects of CRF on the modulation of miR expression in PACs are unknown. Aims and Methods. We used a model of hindlimb ischemia and next generation sequencing (NGS) to perform a complete profiling of miRs in PACs isolated from the bone marrow of mice subjected to three models of CRF: aging, smoking (SMK) and hypercholesterolemia (HC). Results. Around 570 miRs were detected in PACs in the different CRF models. When excluding miRs with a very low expression level (˂ 100 RPM), 40-61 miRs were found to be significantly modulated by aging, SMK or HC. In each CRF condition, we identified downregulated pro-angiogenic miRs and upregulated anti-angiogenic miRs that could contribute to explain PAC dysfunction. Interestingly, several miRs were similarly downregulated (e.g. miR-542-3p, miR-29) or upregulated (e.g. miR-501, miR-92a) in all CRF conditions. In silico approaches including KEGG and cluster dendogram analyses identified predictive effects of these miRs on pathways having key roles in the modulation of angiogenesis and PAC function, including VEGF signaling, extracellular matrix (ECM) remodeling, PI3K/AKT/MAPK signaling, TGFb pathway, p53 and cell cycle progression. Conclusion. This study describes for the first time the effects of CRF on the modulation of miR profile in PACs related to PAD and ischemia-induced neovascularization. We found that several angiogenesis-modulating miRs (angiomiRs) are similarly altered in different CRF conditions. Our findings constitute a solid framework for the identification of miRs that could be targeted in PACs in order to improve their angiogenic function, and for the future development of novel therapies to improve neovascularization and reduce tissue damage in patients with severe PAD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".