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Insights into snoRNA biogenesis and processing from PAR-CLIP of snoRNA core proteins and small RNA sequencing

ABSTRACT: Background: In recent years, a variety of small RNAs derived from other RNAs with well-known functions such as tRNAs and snoRNAs, have been identified. The functional relevance of these RNAs is largely unknown. To gain insight into the complexity of snoRNA processing and the functional relevance of snoRNA-derived small RNAs, we sequenced long and short RNAs, small RNAs that co-precipitate with the Argonaute 2 protein and RNA fragments obtained in photoreactive nucleotide-enhanced crosslinking and immunoprecipitation (PAR-CLIP) of core snoRNA-associated proteins. Results: Analysis of these data sets revealed that many loci in the human genome reproducibly give rise to C/D box-like snoRNAs, whose expression and evolutionary conservation are typically less pronounced relative to the snoRNAs that are currently catalogued. We further found that virtually all C/D box snoRNAs are specifically processed inside the regions of terminal complementarity, retaining in the mature form only 4-5 nucleotides upstream of the C box and 2-5 nucleotides downstream of the D box. Sequencing of the total and Argonaute 2-associated populations of small RNAs revealed that despite their cellular abundance, C/D box-derived small RNAs are not efficiently incorporated into the Ago2 protein. Conclusions: We conclude that the human genome encodes a large number of snoRNAs that are processed along the canonical pathway and expressed at relatively low levels. Generation of snoRNA-derived processing products with alternative, particularly miRNA-like, functions appears to be uncommon.

ORGANISM(S): Homo sapiens

PROVIDER: GSE43666 | GEO | 2013/05/08

SECONDARY ACCESSION(S): PRJNA186963

REPOSITORIES: GEO

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Insights into snoRNA biogenesis and processing from PAR-CLIP of snoRNA core proteins and small RNA sequencing

Project description:Background: In recent years, a variety of small RNAs derived from other RNAs with well-known functions such as tRNAs and snoRNAs, have been identified. The functional relevance of these RNAs is largely unknown. To gain insight into the complexity of snoRNA processing and the functional relevance of snoRNA-derived small RNAs, we sequenced long and short RNAs, small RNAs that co-precipitate with the Argonaute 2 protein and RNA fragments obtained in photoreactive nucleotide-enhanced crosslinking and immunoprecipitation (PAR-CLIP) of core snoRNA-associated proteins. Results: Analysis of these data sets revealed that many loci in the human genome reproducibly give rise to C/D box-like snoRNAs, whose expression and evolutionary conservation are typically less pronounced relative to the snoRNAs that are currently catalogued. We further found that virtually all C/D box snoRNAs are specifically processed inside the regions of terminal complementarity, retaining in the mature form only 4-5 nucleotides upstream of the C box and 2-5 nucleotides downstream of the D box. Sequencing of the total and Argonaute 2-associated populations of small RNAs revealed that despite their cellular abundance, C/D box-derived small RNAs are not efficiently incorporated into the Ago2 protein. Conclusions: We conclude that the human genome encodes a large number of snoRNAs that are processed along the canonical pathway and expressed at relatively low levels. Generation of snoRNA-derived processing products with alternative, particularly miRNA-like, functions appears to be uncommon. PAR-CLIP profiling for snoRNP core proteins NOP56, NOP58, Fibrillarin, and Dyskerin in HEK293 cells. Small RNA profiling using RNA-seq in HEK293 and HeLa cells, small RNA profiling using IP-seq of Ago2 associated small RNAs.

2013-05-08 | E-GEOD-43666 | biostudies-arrayexpress

Next generation sequencing identifies discrete classes of box C/D snoRNAs featuring different ends and RNA binding protein dependency

Project description:The paired-end next-generation sequencing of all small RNAs of less than 200 nucleotides in length from four different human cell lines (SKOV3ip1, MCF-7, BJ-Tielf, INOF) allowed us to determine the exact sequence(s) and variations of human box C/D snoRNAs (small nucleolar RNAs), revealing processing patterns of this class of molecules. Two distinct groups of box C/D snoRNAs were identified based on the position of their ends with respect to their characteristic boxes and the terminal base pairing potential. Short box C/D snoRNAs start sharply 4 or 5 nucleotides upstream of their box C and end 2 or 3 nucleotides downstream of their box D. In contrast, long box C/D snoRNAs start 5 or 6 nucleotides upstream of their box C and end 4 or 5 nucleotides downstream of their box D, increasing the likelihood of formation of a k-turn between their boxes C and D. Sequencing of SKOV3ip1 cells following the depletions of NOP58, a core box C/D snoRNA-binding protein and of RBFOX2, a splicing factor, shows that the short box C/D snoRNA forms are significantly more affected by the depletion of RBFOX2 while the long snoRNA forms, which display more canonical box C/D snoRNA features, are significantly more affected by the depletion of NOP58. Together the data suggest that box C/D snoRNAs are divided into at least two groups of RNA with distinct maturation and functional preferences. Small RNAs (<200 nucleotides) were isolated from different human cell lines that were either untreated or depleted of NOP58 or RBFOX2 using specific siRNAs. The resulting libraries were multiplexed and paired-end sequenced using Illumina HiSeq.

2014-07-01 | E-GEOD-55946 | biostudies-arrayexpress

Next generation sequencing identifies discrete classes of box C/D snoRNAs featuring different ends and RNA binding protein dependency

2014-07-01 | GSE55946 | GEO

SnoRNA-facilitated protein secretion revealed by transcriptome-wide snoRNA target identification

Project description:Small nucleolar RNAs (snoRNA) are non-coding RNAs known for guiding RNA modifications including 2ʹ-O-methylation (Nm) and pseudouridine (Ψ). While snoRNAs may also interact with other RNAs such as mRNA, the full repertoire of RNAs targeted by snoRNA remains elusive due to the lack of effective technologies that identify snoRNA targets transcriptome-wide. Here we develop a chemical crosslinking-based approach that comprehensively detects cellular RNA targets of snoRNAs, yielding thousands of previously unrecognized snoRNA-mRNA interactions in human cells and mouse brain tissues. Many interactions occur outside of snoRNA-guided rRNA modification sites, hinting at non-canonical functions beyond RNA modification. We find that one of these snoRNAs, SNORA73, targets mRNAs that encode secretory proteins and membrane proteins. SNORA73 also interacts with 7SL RNA, part of the signal recognition particle (SRP) required for protein secretion. The mRNA-SNORA73-7SL RNA interactions enhance the association of the SNORA73-target mRNAs with SRP, thereby facilitating secretion of the encoded proteins.

2024-11-21 | GSE261702 | GEO

Annotation of snoRNA abundance across human tissues reveals complex snoRNA-host gene relationships

Project description:Background: Small 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. Results: We 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 identify 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, compared to only 16% of the correlated non-coding host gene/snoRNA pairs. Conclusions: Our 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.

2021-05-13 | GSE157846 | GEO

Regulated snoRNA expression during differentiation corresponds with a change in ribosomal RNA pseudouridylation and altered pre-ribosomal RNA processing [Nano77]

Project description:H/ACA small nucleolar RNAs (snoRNAs) guide pseudouridylation as part of a small nucleolar ribonucleoprotein complex (snoRNP). Disruption of H/ACA snoRNA expression in stem cells impairs pluripotency, yet it remains unclear how H/ACA snoRNAs contribute to differentiation. To determine if H/ACA snoRNA expression is dynamic during differentiation, we comprehensively profiled H/ACA snoRNA expression in multiple murine cell types and during differentiation in three cellular models, including mouse embryonic stem cells and mouse myoblasts. We determined that H/ACA snoRNA expression is cell-type specific, and we identified a subset of snoRNAs that are specifically regulated during differentiation. Additionally, we demonstrated that a decrease in Snora27 expression upon differentiation corresponds to a decrease in pseudouridylation of its target site within the E-site transfer RNA (tRNA) binding region of the 28S ribosomal RNA (rRNA) in the large ribosomal subunit. Many of the snoRNAs regulated during differentiation have target nucleotides in the 28S rRNA, and we found that pre-rRNA processing of large subunit precursors is altered during differentiation. Together, these data suggest a model in which H/ACA snoRNAs are specifically regulated during differentiation to potentially alter pseudouridylation and fine tune ribosome function.

2020-07-09 | GSE140622 | GEO

Regulated snoRNA expression during differentiation corresponds with a change in ribosomal RNA pseudouridylation and altered pre-ribosomal RNA processing [Nano104]

2020-07-09 | GSE140618 | GEO

Regulated snoRNA expression during differentiation corresponds with a change in ribosomal RNA pseudouridylation and altered pre-ribosomal RNA processing [RNA-seq and ncRNA-seq]

2020-07-09 | GSE139812 | GEO

Genome-wide prediction and analysis of yeast RNase III-dependent snoRNA processing signals

Project description:In Saccharomyces cerevisiae, the maturation of both pre-rRNA and pre-small nucleolar RNAs (pre-snoRNAs) involves common factors, thereby providing a potential mechanism for the coregulation of snoRNA and rRNA synthesis. In this study, we examined the global impact of the double-stranded-RNA-specific RNase Rnt1p, which is required for pre-rRNA processing, on the maturation of all known snoRNAs. In silico searches for Rnt1p cleavage signals, and genome-wide analysis of the Rnt1p-dependent expression profile, identified seven new Rnt1p substrates. Interestingly, two of the newly identified Rnt1p-dependent snoRNAs, snR39 and snR59, are located in the introns of the ribosomal protein genes RPL7A and RPL7B. In vitro and in vivo experiments indicated that snR39 is normally processed from the lariat of RPL7A, suggesting that the expressions of RPL7A and snR39 are linked. In contrast, snR59 is produced by a direct cleavage of the RPL7B pre-mRNA, indicating that a single pre-mRNA transcript cannot be spliced to produce a mature RPL7B mRNA and processed by Rnt1p to produce a mature snR59 simultaneously. The results presented here reveal a new role of yeast RNase III in the processing of intron-encoded snoRNAs that permits independent regulation of the host mRNA and its associated snoRNA. RNA from wild type, rnt1d and rnt1ts grown at 26 C and 37 C on rich media

2015-01-21 | E-GEOD-65111 | biostudies-arrayexpress

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets

Project description:Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2’-O-ribose methylation through base pairing with targeted sequences. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA-RNA interaction datasets detect many snoRNAs binding messenger RNA. However, these studies provide a narrow portrait of snoRNA targets forming under specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA machine learning interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, and position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. The interactions of snoRNAs are not randomly distributed but often accumulate in functionally related transcripts sharing common regulatory elements suggesting coordinated regulatory function. The wide scope of snoGloBe makes it an excellent tool for discovering viral RNA targets, which is evident from its capacity to identify snoRNAs targeting SARS-CoV-2 RNA, known to be heavily methylated. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.

2022-05-18 | GSE184173 | GEO

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