Back

EphrinA5 regulates cell motility by modulating the targeting of DNMT1 to the Ncam1 promoter via lncRNA/DNA triplex formation

Yildiz, C. B.; Kundu, T.; Gehrmann, J.; Koesling, J.; Ravaei, A.; Jakovcevski, M.; Pensold, D.; Zimmermann, O.; Rossetti, G.; Gesteira Costa Filho, I.; Zimmer-Bensch, G.

2023-03-25 molecular biology
10.1101/2023.03.25.534129 bioRxiv
Show abstract

Cell-cell communication is mediated by membrane receptors and their cognate ligands, such as the Eph/ephrin system, and dictates physiological processes, including cell proliferation and migration. However, whether and how Eph/ephrin signaling culminates in transcriptional regulation is largely unknown. Epigenetic mechanisms are key for integrating external "signals", e.g., from neighboring cells, into the transcriptome. We have previously reported that ephrinA5 stimulation of immortalized cerebellar granule (CB) cells elicits transcriptional changes of lncRNAs and protein-coding genes. LncRNAs represent important adaptors for epigenetic writers through which they regulate gene expression. Here, we investigate the interaction of lncRNA with protein-coding genes by the combined power of in silico modeling of RNA/DNA interactions and respective wet lab approaches, in the context of ephrinA5-dependent regulation of cellular motility. We found that Snhg15, a cancer-related lncRNA, forms a triplex structure with the Ncam1 promoter and interacts with DNMT1. EphrinA5 stimulation leads to reduced Snhg15 expression, diminished Snhg15/DNMT1 interaction and decreased DNMT1 association with the Ncam1 promoter. These findings can explain the attenuated Ncam1 promoter methylation and elevated Ncam1 expression that in turn elicits decreased cell motility of CB cells. Hence, we propose that ephrinA5 influences gene transcription via lncRNA-targeted DNA methylation underlying the regulation of cellular motility.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.