Back

RNMT is recruited to RNA by interaction with RNA G-quadraplexes

Hepburn, L. A.; Granneman, S.; Cowling, V. H.; Clara Silva, J.

2025-11-16 biochemistry
10.1101/2025.11.16.688685 bioRxiv
Show abstract

The 7-methylguanosine (m7G) cap protects RNA pol II transcripts from exonucleases and allows interaction with cap binding proteins which direct processing and translation. Duringm7G cap formation in mammals, nascent transcripts receive a guanosine cap which is methylated by RNA guanine-7 methyltransferase (RNMT). Unlike other capping enzymes which interact directly with RNA pol II, RNMT is recruited to the guanosine cap by interactions with RNA and the cap itself. RNMT is regulated during cell differentiation with significant impact on which RNAs are expressed and translated. The gene-specificity of RNMT was unexplained since RNMT in complex with activating subunit, RAM, binds to RNA without sequence preference. Through the development of improved RNA-protein detection, CLIP-ART, we report that RNMT interacts directly with RNA G4 quadraplexes (rG4). RNMT predominantly interacts with rG4s in the 5 UTR (untranslated regions) of mRNAs, indicating a mechanism for anchoring RNMT adjacent to the guanosine cap substrate. RNMT interacts with rG4s in transcripts which encode proteins involved in growth and proliferation. The RNMT-rG4 interaction is specific to the RNMT monomer, rather than RNMT-RAM, indicating a mechanism by which differential regulation of RNMT and RAM, can lead to cap methylation of specific RNAs involved in growth control. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/688685v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@f546a1org.highwire.dtl.DTLVardef@f000d4org.highwire.dtl.DTLVardef@c665f5org.highwire.dtl.DTLVardef@14fcfd0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

Matching journals

The top 2 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.