Profiling the Regulatory Landscape of Sialylation through miRNA Targeting of CMP- Sialic Acid Synthetase
Bibliographic record
Abstract
Abstract Cell surface sialic acid is an important glycan modification that contributes to both normal and pathological physiology. The enzyme cytidine monophosphate N-acetylneuraminic acid synthetase (CMAS) biosynthesizes the activated sugar donor cytidine monophosphate (CMP) sialic acid, which is required for all sialylation. CMAS levels impact sialylation with corresponding biological effects. The mechanisms that regulate CMAS are relatively uncharacterized. Herein, we use a high throughput genetically encoded fluorescence assay (miRFluR) to comprehensively profile the posttranscriptional regulation of CMAS by miRNA. These small non-coding RNAs have been found to impact glycosylation. Mapping the interactions of the human miRNAome with the 3’-untranslated region of CMAS, we identified miRNA whose impact on CMAS expression was either downregulatory or upregulatory. This follows previous work from our laboratory and others showing that miRNA regulation is bidirectional. Validation of the high-throughput results confirmed our findings. We also identified the direct binding sites for 2 upregulatory and 2 downregulatory miRNAs. Functional enrichment analysis for miRNAs upregulating CMAS revealed associations with pancreatic cancer, where sialic acid metabolism and the α-2,6-sialyltransferase ST6GAL1 have been found to be important. We found that miRNA associated with the enriched signature enhanced pancreatic cell-surface α-2,6-sialylation via CMAS expression in the absence of effects on ST6GAL1. We also find overlap between the miRNA regulation of CMAS and that of previously analyzed sialyltransferases. Overall, our work points to the importance of miRNA in regulating sialylation levels in disease and add further evidence to the bidirectional nature of miRNA regulation.
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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 | 0.001 | 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".