Mesenchymal Stem/Stromal Cells Increase Cardiac miR-187-3p Expression in a Polymicrobial Animal Model of Sepsis
Bibliographic record
Abstract
ABSTRACT: Sepsis-induced myocardial dysfunction (MD) is an important pathophysiological feature of multiorgan failure caused by a dysregulated host response to infection. Patients with MD continue to be managed in intensive care units with limited understanding of the molecular mechanisms controlling disease pathogenesis. Emerging evidences support the use of mesenchymal stem/stromal cell (MSC) therapy for treating critically ill septic patients. Combining this with the known role that microRNAs (miRNAs) play in reversing sepsis-induced myocardial-dysfunction, this study sought to investigate how MSC administration alters miRNA expression in the heart. Mice were randomized to experimental polymicrobial sepsis induced by cecal ligation and puncture (CLP) or sham surgery, treated with either MSCs (2.5 × 105) or placebo (saline). Twenty-eight hours post-intervention, RNA was collected from whole hearts for transcriptomic and microRNA profiling. The top microRNAs differentially regulated in hearts by CLP and MSC administration were used to generate a putative mRNA-miRNA interaction network. Key genes, termed hub genes, within the network were then identified and further validated in vivo. Network analysis and RT-qPCR revealed that septic hearts treated with MSCs resulted in upregulation of five miRNAs, including miR-187, and decrease in three top hit putative hub genes (Itpkc, Lrrc59, and Tbl1xr1). Functionally, MSC administration decreased inflammatory and apoptotic pathways, while increasing cardiac-specific structural and functional, gene expression. Taken together, our data suggest that MSC administration regulates host-derived miRNAs production to protect cardiomyocytes from sepsis-induced MD.
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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 | 0.001 | 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) | 0.002 | 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".