Identification of non-coding genetic variants in samples from hypoxemic respiratory disease patients that affect the transcriptional response to hypoxia
Bibliographic record
Abstract
A wide range of diseases course with an unbalance between the consumption of oxygen by tissues and its supply. This situation triggers a transcriptional response, mediated by the hypoxia inducible factors (HIFs), that aims to restore oxygen homeostasis. Little is known about the inter-individual variation in this response and its role in the progression of disease. Herein, we sought to identify common genetic variants mapping to hypoxia response elements (HREs) and characterize their effect on transcription. To this end, we constructed a list of genome-wide HIF-binding regions from publicly available experimental datasets and studied the genetic variability in these regions by targeted re-sequencing of genomic samples from 96 chronic obstructive pulmonary disease and 144 obstructive sleep apnea patients. This study identified 14 frequent variants disrupting potential HREs. The analysis of the genomic regions containing these variants by means of reporter assays revealed that variants rs1009329, rs6593210 and rs150921338 impaired the transcriptional response to hypoxia. Finally, using genome editing we confirmed the functional role of rs6593210 in the transcriptional regulation of EGFR. In summary, we found that inter-individual variability in non-coding regions affect the response to hypoxia and could potentially impact on the progression of pulmonary diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".