Abstract 203: Identification of Immunoregulatory microRNAs in Cardiac Tissue of Septic Mice Treated with Mesenchymal Stem Cells
Bibliographic record
Abstract
Despite diminution of myocardial dysfunction, organ injury, and animal mortality in polymicrobial models of sepsis upon systemic administration of bone marrow-derived mesenchymal stem cell (MSC), the underlying mechanism is immensely understudied. Here we hypothesize that MSC administration regulates the differential expression of host-derived miRNAs, which subsequently determine the transcriptional response profile in the heart, one of the crucial target organs in sepsis. By conducting cecum ligation and puncture (CLP) on mice, the polymicrobial model of sepsis was created. The miRNA expression between sham, CLP and MSC-treated groups of hearts were profiled and evaluated. Bioinformatics analysis identified a total of five miRNAs as significantly changed in MSC- vs. placebo-treated septic hearts (false discovery rate <0.05). To demonstrate the biological relevance of our in silico results, we determined differential expression of target mRNAs for all five miRNAs using mRNA transcriptional data, utilizing the Illumina microarray expression array. Putative mRNA-miRNA interactions were elucidated. Expression levels of the five miRNAs and their 318 putative targets were identified as significantly regulated following MSC administration in septic hearts. Functional enrichment pointed out roles in suppression of inflammation and apoptosis and upregulation of cardiac-specific structural proteins. Hub-gene analysis identified a central role for miR-187 and its target genes Itpkc, Lrrc59, Tbl1xr1 known to play fundamental roles in cardiac inflammation and cardiomyocyte apoptosis. Quantitative real-time PCR validated differential expression of these in silico targets in vivo, as well as markers of apoptosis, in murine septic hearts treated with placebo or MSCs. MSC administration results in the upregulation of host-derived miRNAs involved in protecting cardiomyocytes from sepsis-induced inflammation and apoptosis.
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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 | 0.000 |
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".