Investigating the miRNA regulatory landscape of OGT and OGA via the 3’UTR and 5’UTR regions utilizing the miRFluR high‐throughput platform
Bibliographic record
Abstract
The dynamic post‐translational modification of serine or threonine residues by O‐linked N‐acetyl‐beta‐D‐glucosamine (O‐GlcNAc) contributes to diverse cellular processes including epigenetic modifications, transcription, metabolism, and cell signaling that play significant roles in development and normal physiology. O‐GlcNAcylation is catalyzed by O‐GlcNAc transferase and the modification is removed by O‐GlcNAcase (OGA). These genes are highly regulated at multiple levels, but little is known about their regulation by microRNAs (miRs). miRs are small non‐coding RNAs that fine‐tune protein expression through binding to messenger RNA (mRNA). In this work, we built a comprehensive dataset of OGT and OGA regulation via both their 3’UTR and 5’UTRs. Downregulation was almost exclusively mediated through binding to the 3’UTR. We observed independent regulation of OGT and OGA by the majority of regulatory miRs, which did not overlap. However, we did see significant co‐regulation of OGT and OGA by a subset of miRs. This is in keeping with the known transcriptional regulation of these genes. In summary, this work provides a better understanding of OGT and OGA regulation through miRNA binding via both the 3’UTR and 5’UTR regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".