Ribonuclease (RNA) modifications can alter cellular function and lead to differential immune responses by acting as discriminators between RNAs from different phyla. RNA glycosylation has recently been observed at the cell surface, and its dysregulation in disease may change RNA functions. However, determining which RNA substrates can be glycosylated remains to be explored. Here, we develop a solid-phase chemoenzymatic method (SPCgRNA) for targeting glycosylated RNAs, by which glycosylated RNA substrates can be specifically recognized. We found the differential N-glycosylation of small RNAs in hTERT-HPNE and MIA PaCa-2 cancer cells using SPCgRNA. RNA-Seq showed that the changes in glyco-miRNAs prepared from SPCgRNA were consistent with those of traditional methods. The KEGG signaling pathway analysis revealed that differential miRNA glycosylation can affect tumor cell proliferation and survival. Further studies found that NGI-1 significantly inhibited the proliferation, migration, and circulation of MIA PaCa-2 and promoted cell apoptosis. In addition, β-1,4-galactosyltransferase 1 (B4GALT1) not only affected the expression level of glycosylated miRNAs hsa-miR-21-5p but also promoted cell apoptosis and inhibited the cell cycle possibly through the p53 signaling pathway, while B4GALT1 and p53 were also affected following the hsa-miR-21-5p increase. These results suggest that B4GALT1 may catalyze miRNAs glycosylation, which further promotes cancer cell progression.
Glycosylated RNAs (glycoRNAs), a recently discovered class of membrane-associated glyco-molecules, remain poorly understood in function and clinical value due to limited detection methods. Here, we show a dual recognition Förster resonance energy transfer (drFRET) strategy using nucleic acid probes to detect N-acetylneuraminic acid-modified RNAs, enabling sensitive, selective profiling of glycoRNAs on small extracellular vesicles (sEVs) from minimal biofluids (10 μl initial biofluid). Using drFRET, we identify 5 prevalent sEV glycoRNAs derived from 7 cancer cell lines. In a 100-patient cohort (6 cancer types and non-cancer controls), sEV glycoRNA profiles achieve 100% accuracy (95% confidence interval) in distinguishing cancers from non-cancer cases and 89% accuracy in classifying specific cancer types. Furthermore, drFRET reveal that sEV glycoRNAs specifically interact with Siglec proteins and P-selectin, which is critical for sEV cellular internalization. The drFRET strategy provides a versatile and sensitive platform for the imaging and functional analysis of sEV glycoRNAs, with promising implications for clinical applications.
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