Abstract 263: Thrombin-Mediated Human Aortic Endothelial Barrier Dysfunction Alters Microrna Pathways Involved in Cell Survival, Injury, and Cancer
Bibliographic record
Abstract
Introduction: Endothelial cells (EC) must maintain an effective physiologic barrier in a highly dynamic environment. Capable of regulating other cells within the vasculature (e.g. smooth muscle cells), ECs govern response to injury and inflammation. microRNAs (miR) are emerging as critical regulators of vascular disease, implicating EC miRs in homeostasis maintenance. Increased EC permeability has been documented in atherosusceptible regions of the aorta suggesting it plays a role in disease development, but whether this is a cause or consequence is unknown. Hypothesis: We hypothesized EC barrier dysfunction is a critical event that perpetuates chronic vascular disease via altered miR profiles. Methods: Human aortic endothelial cells (HAEC) cultured in transwells were exposed to thrombin (0.5, 1, 2U/ml) and permeability measured by fluorescence flux across monolayers at 90 minutes. Total HAEC RNA was isolated at various timepoints with individual miR transcripts counted using nanoString nCounter® (n=6 samples). miR that was altered >2-fold compared to controls were analyzed using nSolver ™ software and Ingenuity® Pathway Analysis (IPA). Results: Thrombin exposure increased EC permeability to 130±8.4% of untreated controls (2U/ml thrombin; p<0.05; n=16 transwells). Heatmaps from miR counts showed extensive miR profile changes in response to thrombin (n=6 samples (control, 90min, 4h, 24h)). Using IPA, miRs were clustered based on seed regions and matched with experimentally confirmed mRNA targets. At all timepoints, mRNA targets were shown to be most involved in cancer and injury disease pathways, with top cellular functions identified as cell movement, proliferation, growth, and survival. Conclusions: In response to a permeability insult, HAEC miR levels are significantly altered. Although our preliminary data need to be further substantiated, they highlight a possible mechanism whereby EC barrier dysfunction is a critical event that perpetuates chronic vascular disease. In this way, we might envision a scenario not unlike cancer (which has been compared to atherosclerosis), where an initial appropriate endothelial repair response becomes dysregulated in the context of ongoing injury (e.g. permeability) in atherosusceptible regions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".