Identification of glycosylated nucleosides in small synthetic glyco‐RNAs
Bibliographic record
Abstract
Recently, the post-transcriptional modification of RNA with N-glycans was reported, changing the paradigm that RNAs are not commonly N-glycosylated. Moreover, glycan modifications of RNA are investigated for therapeutic targeting purposes. But the glyco-RNA field is in its infancy with many challenges to overcome. One question is how to accurately characterize glycosylated RNA constructs. Thus, we generated glycosylated forms of Y5 RNA mimics, a short non-coding RNA. The simple glycans lactose and sialyllactose were attached to the RNA backbone using azide-alkyne cycloadditions. Using nuclease digestion followed by LC-MS, we confirmed the presence of the glycosylated nucleosides, and characterized the chemical linkage. Next, we probed if glycosylation would affect the cellular response to Y5 RNA. We treated human foreskin fibroblasts in culture with the generated compounds. Key transcripts in the innate immune response were quantified by RT-qPCR. We found that under our experimental conditions, exposure of cells to the Y5 RNA did not trigger an interferon response, and glycosylation of this RNA did not have an impact. Thus, we have identified a successful approach to chemically characterize synthetic glyco-RNAs, which will be critical for further studies to elucidate how the presence of complex glycans on RNA affects the cellular response.
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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".