O-Glycosylated RNA Identification and Prediction by Solid-Phase Chemoenzymatic TnORNA Method and PONglyRNA Tool
Bibliographic record
Abstract
Recent studies have shown that the cell surface undergoes post-transcriptional modification by N-linked glycosylation. However, the question of whether RNA can be glycosylated by O-glycans remains to be explored. The presence of O-glycosylation in cells and tissues is indirectly revealed by the presence of O-glycans on RNAs following treatment with O-glycoproteases. To identify RNA O-glycosylation, we have developed a chemoenzymatic method for capturing and enriching O-glycosylated RNA (O-glycoRNA) using covalent immobilization on a solid support. GalNAcEXO selectively releases Tn-containing O-glycosylated RNAs (TnORNA). Following the confirmation of small RNA integrity after galactosidase oxidation and GalNAcEXO digestion, we employed this method to compare the expression of O-glycoRNAs and N-glycoRNAs in pancreatic cancer cell lines and tissues. We found that glycosylated miR-103a-3p, miR-122–5p, and miR-4492 regulate pancreatic cancer cell growth and proliferation through the PI3K-Akt pathway. In vitro assays and PDAC tissue analysis confirmed the potential regulatory roles of Tn- O -glycosylated miRNAs in pancreatic tumor growth and metastasis. Furthermore, a significant number (131) of miRNAs carrying both N- and Tn- O -glycosylation were identified, indicating the potential co-occurrence of N-linked and O-linked glycosylation on small RNAs. We have also developed PONglyRNA, an online bioinformatic tool for the prediction of glycosylated RNAs, which showed competitive performance in validation tests and potentials in improving glycosylation site prediction ( http://ponglyrna.797000.xyz:8880/ ). In conclusion, this study establishes robust experimental and computational tools for identifying O-linked glycoRNAs. Additionally, it uncovers the novel role of glycosylation in PDAC development and progression through altered glycosylation of oncogenic miRNAs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".