Regulation of Human Growth Hormone Receptor Expression by MicroRNAs
Bibliographic record
Abstract
Human GH binds to its receptor (GHR) on target cells and activates multiple intracellular pathways, leading to changes in gene expression, differentiation, and metabolism. GHR deficiency is associated with growth and metabolic disorders whereas increased GHR expression has been reported in certain cancers, suggesting that the GHR gene requires tight controls. Several regulatory mechanisms have been found within its 5'-untranslated region (UTR) promoter and coding regions. However, the 3'-UTR has not been previously examined. MicroRNAs (miRNAs) are small (19-22 nucleotides) noncoding RNAs that downregulate gene expression mainly through targeting the 3'-UTR of mRNAs and enhancing their degradation or inhibiting translation. In the present study, we investigated whether miRNAs regulate GHR expression. To define putative miRNA binding sites in the GHR 3'-UTR, we used multiple in silico prediction tools, analyzed conservation across species and the presence of parallel sites in GH/IGF axis-related genes, and searched for reports linking miRNAs to GHR-related physiological or pathophysiological activities. To test prioritized sites, we cotransfected a wild-type GHR 3'-UTR luciferase reporter vector as well as miRNA binding site mutants into HEK293 cells with miRNA mimics. Furthermore, we tested whether the miRNAs altered endogenous GHR mRNA and protein levels in HEK293 cells and in 2 cancer cell lines (MCF7 and LNCaP). Our experiments have identified miRNA (miR)-129-5p, miR-142-3p, miR-202, and miR-16 as potent inhibitors of human GHR expression in normal (HEK293) and cancer (MCF7 and LNCaP) cells. This study paves the way for the development of miRNA inhibitors as therapeutic agents in GH/GHR-related pathophysiologies, including cancer.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".