Maternal- and Somatic-type snoRNA Expression and Processing in Zebrafish Development
Pagano, J. F. B.; Locati, M. D.; Ensik, W. A.; Olst, M. v.; van Leeuwen, S.; De Leeuw, W. C.; Nehrdich, U.; Spaink, H. P.; Rauwerda, H.; Jonker, M. J.; Dekker, R. J.; Breit, T. M.
Show abstract
Small nucleolar RNAs (snoRNAs) are non-coding RNAs that play an important role in the complex maturation process of ribosomal RNAs (rRNAs). SnoRNAs are categorized in classes, with each class member having several variants present in a genome. Similar to our finding of specific rRNA expression types in zebrafish embryogenesis, we discovered preferential maternal- and somatic-expression for snoRNAs. Most snoRNAs and their variants have higher expression levels in somatic tissues than in eggs, yet we identified three snoRNAs; U3, U8 and snoZ30 of which specific variants show maternal- or somatic-type expression. For U3 and U8 we also found small-derived snoRNAs that lack their 5 rRNA recognition part and are essentially Domain II hairpin structures (U-DII). These U-DII snoRNAs from variants showed similar preferential expression, in which maternal-type variants are prominently expressed in eggs and subsequently replaced by a somatic-type variants during embryogenesis. This differential expression is related to the organization in tandem repeats (maternal type) or solitary (somatic-type) genes of the involved U snoRNA loci. The collective data showed convincingly that the preferential expression of snoRNAs is achieved by transcription regulation, as well as through RNA processing. Finally, we observed small-RNAs derived from internal transcribed spacers (ITSs) of a U3 snoRNA loci that via complementarity binding, may be involved in the biosynthesis of U3-DII snoRNAs. Altogether, the here described maternal- and somatic-type snoRNAs are the latest addition to the developing story about the dual ribosome system in zebrafish development.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of RNA 3' ends and termination sites in Haloferax volcanii 94%
- Direct RNA-RNA interaction between Neat1 and RNA targets, as a mechanism for RNAs paraspeckle retention 94%
- Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation 93%
Similar papers in this journal
- SnoBIRD: A tool to identify C/D box snoRNAs and refine their annotation across all eukaryotes 94%
- Functional tagging of endogenous proteins and rapid selection of cell pools (Rapid generation of endogenously tagged piwi in ovarian somatic sheath cells.) 94%
- Landscape of semi-extractable RNAs across five human cell lines 94%
Similar papers in this journal
- Comprehensive annotation and characterization of planarian tRNA and tRNA-derived fragments (tRFs) 95%
- The importance of m6A topology in chicken embryo mRNA; a precise mapping of m6A at the conserved chicken β-actin zipcode 93%
- 5’ tRNA halves are highly expressed in the primate hippocampus and sequence-specifically regulate gene expression 93%
Similar papers in this journal
- The developmental transcriptome of Parhyale hawaiensis: microRNAs and mRNAs show different expression dynamics during the maternal-zygotic transition 95%
- FUS modulates the level of ribosomal RNA modifications by regulating a subset of snoRNA expression 94%
- Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data 94%
Similar papers in this journal
- Transcriptome analysis indicates dominant effects on ribosome and mitochondrial function of a premature termination codon mutation in the zebrafish gene psen2 93%
- Tunable self-cleaving ribozymes for modulating gene expression in eukaryotic systems 93%
- Identification of novel regulators of dendrite arborization using cell type-specific RNA metabolic labeling 92%
"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.