Abstract 330: MicroRNA Dysregulation in Patients with Hereditary Hemorrhagic Telangiectasia
Bibliographic record
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is a rare, understudied, autosomal dominant disorder that is characterized by angiogenic abnormalities. Gene mutations in HHT involve key angiogenic factors. Clinical manifestations of HHT include benign telangiectasias and potentially life threatening arteriovenous malformations (AVMs) that can occur in the pulmonary and cerebral circulation. Currently, there is no effective treatment for HHT. PBMCs have been implicated in the pathogenesis of HHT and the goal of this study was to profile and characterize PBMC microRNAs, to determine whether they had a role in the observed angiogenic abnormalities. A total of 28 HHT patients with confirmed mutations in either ENG , ACVRL1 or SMAD4 , and 25 controls were recruited. PBMCs were isolated from 16 ml of peripheral blood and total RNA was obtained. PBMC expression profiling was conducted with a human miR array analysis. Dysregulated miRs identified from the analysis were then validated with RT-qPCR. Significant differences were determined using a two-tailed t- test. Of the 800 miRs screened, 167 dysregulated miRs were identified in PBMCs. Selected PBMC miRs (MiRs-28-5p, -30b-5p, -361-3p and -374a-5p) were validated with RT-qPCR. PBMC miR-361-3p ( p =0.025) known to target IGF1, was found to be significantly decreased compared to controls. IGF1 messenger RNA (mRNA) levels in PBMCS of HHT patients was significantly elevated (p=0.005) compared to controls. Our results show dysregulation of select miRs in PBMCs from HHT patients. We also show an increase in IGF1 mRNA, an important angiogenic factor, in PBMCs that may be related to the downregulation of miR-361-3p in HHT patients. This may represent an important pathogenic mechanism that is involved in the angiogenic dysfunction observed in HHT and may provide for a unique miR therapeutic target.
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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) | 0.003 | 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".