Analyzing the miRNA regulatory landscape of OGT identifies evolutionarily conserved upregulation.
Bibliographic record
Abstract
O-GlcNAc transferase (OGT) is the key enzyme involved in post-translationally modifying cytoplasmic and nuclear proteins with O-GlcNAc. Maintenance of cellular O-GlcNAcylation levels is critical to cell health and requires precise transcriptional and post-transcriptional control. Herein we examine the miRNA regulation of OGT by the human miRNAome using our high-throughput miRFluR assay. We found >200 miRNA regulators of OGT, including 17 down- and 15 upregulatory miRNAs previously identified in CLIP datasets. We validated the impact of select miRNA on OGT and O-GlcNAc levels using both miRNA mimics and inhibitors that reduce endogenous miRNA levels. We focused our studies on two miRNA families, the downregulatory let-7 family and the upregulatory miR-148/152 family. For the let-7 family, we found that only let-7a-3p and let-7g-3p strongly downregulated OGT. Downregulation required two seed-dependent binding sites. Evolutionary analysis found that the more recent of the two sites emerged in placental mammals. A similar conservation pattern was observed for the site of regulation by the miR-148/152 family, which was previously identified in CLIP datasets. All three miRNA in this family upregulated OGT. Phylogenetic analysis revealed that this upregulatory site has been conserved for the past 98.7 million years. The emergence of these regulatory sites correlates with that of disease states that both OGT and the miRNA are known to impact. Overall, our results provide important insights into OGT, miRNA regulation and conservation through evolution.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".