Superabundant microRNAs are transcribed from human rDNA spacer promoters insulated by CTCF
Henikoff, S.; Henikoff, J. G.
Show abstract
MicroRNAs are [~]22-nucleotide RNAs processed from primary transcripts and exported from the nucleus to repress gene expression by base-pairing to mRNAs. We find that the highest levels of RNA Polymerase II at human microRNA genes are within the ribosomal gene repeat arrays (rDNAs). Alignment of public nascent transcript data to the hs1 human genome assembly reveals a 50-nucleotide transcript for both miR-1275 and miR-6724, which exits from the nucleus with exceptional rapidity. We show that the miR-1275/miR-6724 transcription unit is closely flanked by CCCTC-binding factor (CTCF) within a <400-bp span of the rDNA spacer promoter. MiR-1275/miR-6724 and microRNA precursors expressed from the 5 External Transcribed Spacer (5ETS) are exported independently of known RNA processing activities and are detected in exosomes and as circulating cancer biomarkers. We propose that rDNA spacer promoter and 5ETS microRNA genes have evolved for general regulatory functions in recipient cells.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Biochemical and structural insights into a 5' to 3' RNA ligase reveal a potential role in tRNA ligation 97%
- Transcription shapes 3D chromatin organization by interacting with loop extrusion 97%
- Obligate movements of an active site-linked surface domain control RNA polymerase elongation and pausing via a Phe-pocket anchor 96%
Similar papers in this journal
- A Nascent Peptide Code for Translational Control of mRNA Stability in Human Cells 96%
- β-actin dependent chromatin remodeling mediates compartment level changes in 3D genome architecture 96%
- Transposable elements strongly contribute to cell-specific and species-specific looping diversity in mammalian genomes. 96%
Similar papers in this journal
"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.