Mesenchymal stromal/stem cells modulate response to experimental sepsis-induced lung injury via regulation of miR-27a-5p in recipient mice
Bibliographic record
Abstract
INTRODUCTION: Mesenchymal stromal cell (MSC) therapy mitigates lung injury and improves survival in murine models of sepsis. Precise mechanisms of therapeutic benefit remain poorly understood. OBJECTIVES: To identify host-derived regulatory elements that may contribute to the therapeutic effects of MSCs, we profiled the microRNAome (miRNAome) and transcriptome of lungs from mice randomised to experimental polymicrobial sepsis-induced lung injury treated with either placebo or MSCs. METHODS AND RESULTS: A total of 11 997 genes and 357 microRNAs (miRNAs) expressed in lungs were used to generate a statistical estimate of association between miRNAs and their putative mRNA targets; 1395 miRNA:mRNA significant association pairs were found to be differentially expressed (false discovery rate ≤0.05). MSC administration resulted in the downregulation of miR-27a-5p and upregulation of its putative target gene VAV3 (adjusted p=1.272E-161) in septic lungs. In human pulmonary microvascular endothelial cells, miR-27a-5p expression levels were increased while VAV3 was decreased following lipopolysaccharide (LPS) or tumour necrosis factor (TNF) stimulation. Transfection of miR-27a-5p mimic or inhibitor resulted in increased or decreased VAV3 message, respectively. Luciferase reporter assay demonstrated specific binding of miR-27a-5p to the 3'UTR of VAV3. miR27a-5p inhibition mitigated TNF-induced (1) delayed wound closure, increased (2) adhesion and (3) transendothelial migration but did not alter permeability. In vivo, cell infiltration was attenuated by intratracheal coinstillation of the miR-27a-5p inhibitor, but this did not protect against endotoxin-induced oedema formation. CONCLUSIONS: Our data support involvement of miR-27a-5p and VAV3 in cellular adhesion and infiltration during acute lung injury and a potential role for miR-27a-based therapeutics for acute respiratory distress syndrome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".