Evidence for MicroRNA-Mediated Regulation of Steroidogenesis by Hypoxia
Bibliographic record
Abstract
Environmental hypoxia can occur in both natural and occupational environments. Over the recent years, the ability of hypoxia to cause endocrine disruption via perturbations in steroid synthesis (steroidogenesis) has become increasingly clear. To further understand the molecular mechanism underlying hypoxia-induced endocrine disruption, the steroid-producing human cell line H295R was used to identify microRNAs (miRNAs) affecting steroidogenic gene expression under hypoxia. Hypoxic treatment of H295R cells resulted in the downregulation of seven steroidogenic genes and one of these, CYP19A1 (aromatase), was shown to be regulated by the transcription factor hypoxia-inducible factor-1 (HIF-1). Using bioinformatic and luciferase reporter analyses, miR-98 was identified to be a CYP19A1-targeting miRNA from a subset of HIF-1-inducible miRNAs. Gain- and loss-of-function analysis suggested that under hypoxia, the increased expression of miR-98 led to the downregulation of CYP19A1 mRNA and protein expression and that it may have contributed to a reduction in estradiol (E2) production. Intriguingly, luciferase reporter assays using deletion constructs of a proximal 5′-flanking region of miR-98 did not reveal a hypoxia-responsive element (HRE)-containing promoter. Overall, this study provided evidence for the role of miRNAs in regulating steroidogenesis and novel insights into the molecular mechanisms of hypoxia-induced endocrine disruption.
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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 | 0.000 | 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) | 0.001 | 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".