miRNA Regulation of α‐2,6‐ Sialylation: Comprehensive Analysis of ST6GAL1 & 2
Bibliographic record
Abstract
MicroRNAs (miRs) are endogenous non‐coding RNAs that modulate gene expression at either the transcriptional or translational level through interaction with untranslated regions of mRNA. Works from the Mahal Lab and others have shown miRs as major regulators of glycosylation. ST6GAL1 and ST6GAL2 decorate glycoproteins with α‐2,6‐sialic acid, an epitope with important roles in immunology and cancer biology. Herein, we analyze miRs that regulate ST6Gal1 and ST6Gal2 gene expression through interaction with 3′UTR regions of target mRNAs. Using miRFluR, a high throughput fluorescent sensor‐based method recently introduced by the Mahal Lab, we analyzed miR regulation of ST6Gal1 & 2 with miR mimics representing the currently known human miRome (~2700 miRs). Our analysis identified 89 miR regulators of ST6GAL1 and 102 regulators of ST6GAL2. Selected hits were validated by Western blot analysis and RT‐PCR. Finally, we took advantage of SNA staining assay using fluorescence microscopy to validate the sialylation outcome by ST6GAL1 after treatment with specific miR mimics. Our data reveals miRNA regulation of α‐2,6‐sialylation that is associated with pancreatic and other cancers.
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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 | 0.000 |
| 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) | 0.001 | 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".