RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history. Issue 5 (7th March 2022)

• Record Type: Journal Article
• Authors: Baar, Till; Dümcke, Sebastian; Gressel, Saskia; Schwalb, Björn; Dilthey, Alexander; Cramer, Patrick; Tresch, Achim
• Editors: Steinmetz, L M
• Is Part Of: G3. Volume 12:Issue 5(2022)
• Publication Date: 2022-03-07
• Subjects: Alu elements -- Alu transcription -- RNA labeling -- retrotransposons; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkac054
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Alu elements are one of the most successful groups of RNA retrotransposons and make up 11% of the human genome with over 1 million individual loci. They are linked to genetic defects, increases in sequence diversity, and influence transcriptional activity. Still, their RNA metabolism is poorly understood yet. It is even unclear whether Alu elements are mostly transcribed by RNA Polymerase II or III. We have conducted a transcription shutoff experiment by α-amanitin and metabolic RNA labeling by 4-thiouridine combined with RNA fragmentation (TT-seq) and RNA-seq to shed further light on the origin and life cycle of Alu transcripts. We find that Alu RNAs are more stable than previously thought and seem to originate in part from RNA Polymerase II activity, as previous reports suggest. Their expression however seems to be independent of the transcriptional activity of adjacent genes. Furthermore, we have developed a novel statistical test for detecting the expression of quantitative trait loci in Alu elements that relies on the de Bruijn graph representation of all Alu sequences. It controls for both statistical significance and biological relevance using a tuned k -mer representation, discovering influential sequence features missed by regular motif search. In addition, we discover several point mutations using a generalized linear model, and motifs of interest, which also match transcription factor-binding motifs.