Deep-sequencing of endothelial cells exposed to hypoxia reveals the complexity of known and novel microRNAs
Why this work is in the frame
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Bibliographic record
Abstract
In order to understand the role of microRNAs (miRNAs) in vascular physiopathology, we took advantage of deep-sequencing techniques to accurately and comprehensively profile the entire miRNA population expressed by endothelial cells exposed to hypoxia. SOLiD sequencing of small RNAs derived from human umbilical vein endothelial cells (HUVECs) exposed to 1% O₂ or normoxia for 24 h yielded more than 22 million reads per library. A customized bioinformatic pipeline identified more than 400 annotated microRNA/microRNA* species with a broad abundance range: miR-21 and miR-126 totaled almost 40% of all miRNAs. A complex repertoire of isomiRs was found, displaying also 5' variations, potentially affecting target recognition. High-stringency bioinformatic analysis identified microRNA candidates, whose predicted pre-miRNAs folded into a stable hairpin. Validation of a subset by qPCR identified 18 high-confidence novel miRNAs as detectable in independent HUVEC cultures and associated to the RISC complex. The expression of two novel miRNAs was significantly down-modulated by hypoxia, while miR-210 was significantly induced. Gene ontology analysis of their predicted targets revealed a significant association to hypoxia-inducible factor signaling, cardiovascular diseases, and cancer. Overexpression of the novel miRNAs in hypoxic endothelial cells affected cell growth and confirmed the biological relevance of their down-modulation. In conclusion, deep-sequencing accurately profiled known, variant, and novel microRNAs expressed by endothelial cells in normoxia and hypoxia.
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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 | 0.000 | 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) | 0.000 | 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 it