Genome biologyGenomicsPeer-reviewed journal article
Annotation of snoRNA abundance across human tissues reveals complex snoRNA-host gene relationships
Étienne Fafard-CoutureiD, Danny BergeroniD, Sonia Couture, Sherif Abou ElelaiD, Michelle S. ScottiD
Abstract
BackgroundSmall nucleolar RNAs (snoRNAs) are mid-size non-coding RNAs required for ribosomal RNA modification, implying a ubiquitous tissue distribution linked to ribosome synthesis. However, increasing numbers of studies identify extra-ribosomal roles of snoRNAs in modulating gene expression, suggesting more complex snoRNA abundance patterns. Therefore, there is a great need for mapping the snoRNome in different human tissues as the blueprint for snoRNA functions. ResultsWe used a low structure bias RNA-Seq approach to accurately quantify snoRNAs and compare them to the entire transcriptome in seven healthy human tissues (breast, ovary, prostate, testis, skeletal muscle, liver and brain). We identified 475 expressed snoRNAs categorized in two abundance classes that differ significantly in their function, conservation level and correlation with their host gene: 390 snoRNAs are uniformly expressed and 85 are enriched in the brain or reproductive tissues. Most tissue-enriched snoRNAs are embedded in lncRNAs and display strong correlation of abundance with them, whereas uniformly expressed snoRNAs are mostly embedded in protein-coding host genes and are mainly non- or anticorrelated with them. 59% of the non-correlated or anticorrelated protein-coding host gene/snoRNA pairs feature dual-initiation promoters, as opposed to only 16% of the correlated non-coding host gene/snoRNA pairs. ConclusionsOur results demonstrate that snoRNAs are not a single homogeneous group of housekeeping genes but include highly regulated tissue-enriched RNAs. Indeed, our work indicates that the architecture of snoRNA host genes varies to uncouple the host and snoRNA expressions in order to meet the different snoRNA abundance levels and functional needs of human tissues.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 73
- FWCI
- 3.14
- Field percentile
- top 6%
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