snoSeeker: an advanced computational package for screening of guide and orphan snoRNA genes in the human genome

Small nucleolar RNAs (snoRNAs) represent an abundant group of non-coding RNAs in eukaryotes. They can be divided into guide and orphan snoRNAs according to the presence or absence of antisense sequence to rRNAs or snRNAs. Current snoRNA-searching programs, which are essentially based on sequence complementarity to rRNAs or snRNAs, exist only for the screening of guide snoRNAs. In this study, we have developed an advanced computational package, snoSeeker, which includes CDseeker and ACAseeker programs, for the highly efficient and specific screening of both guide and orphan snoRNA genes in mammalian genomes. By using these programs, we have systematically scanned four human–mammal whole-genome alignment (WGA) sequences and identified 54 novel candidates including 26 orphan candidates as well as 266 known snoRNA genes. Eighteen novel snoRNAs were further experimentally confirmed with four snoRNAs exhibiting a tissue-specific or restricted expression pattern. The results of this study provide the most comprehensive listing of two families of snoRNA genes in the human genome till date.     

Methodological obstacles in knocking down small noncoding RNAs

In the recent past, several thousand noncoding RNA (ncRNA) genes have been predicted within eukaryal genomes. However, for their functional analysis only a few high-throughput methods are currently available to knock down selected ncRNA species, such as microRNAs, which are targeted by antisense probes, termed antagomirs. We thus compared the efficiencies of four knockdown strategies, previously mainly employed for the analysis of protein-coding genes, to study the function of ncRNAs, in particular, small nucleolar RNAs (snoRNAs). Thereby, the class of snoRNAs represents one of the most abundant ncRNA species. The majority of snoRNAs has been shown to mediate nucleotide modifications by targeting ribosomal RNAs (rRNAs) through complementary antisense elements. However, some snoRNAs, termed “orphan snoRNAs,” lack telltale complementarities to rRNAs and thus their function remains elusive. We therefore applied RNA interference (RNAi), locked nucleic acid (LNA), or peptide nucleic acid antisense approaches, as well as a ribozyme-based strategy to knock down a snoRNA. As a proof of principle, we targeted the canonical U81 snoRNA, which has been shown to mediate modification of nucleotide A₃₉₁ within eukaryal 28S rRNA. Our results demonstrate that while RNAi is an unsuitable tool for snoRNA knockdown, a ribozyme-based strategy, as well as an LNA-antisense oligonucleotide approach, resulted in a decrease of U81 snoRNA expression levels up to 60%. However, no concomitant decrease in enzymatic activity of U81 snoRNA was observed, indicating that improvement of more efficient knockdown techniques for ncRNAs will be required in the future.     

Mining small RNA sequencing data: a new approach to identify small nucleolar RNAs in Arabidopsis

Small nucleolar RNAs (snoRNAs) are noncoding RNAs that direct 2′-O-methylation or pseudouridylation on ribosomal RNAs or spliceosomal small nuclear RNAs. These modifications are needed to modulate the activity of ribosomes and spliceosomes. A comprehensive repertoire of snoRNAs is needed to expand the knowledge of these modifications. The sequences corresponding to snoRNAs in 18–26-nt small RNA sequencing data have been rarely explored and remain as a hidden treasure for snoRNA annotation. Here, we showed the enrichment of small RNAs at Arabidopsis snoRNA termini and developed a computational approach to identify snoRNAs on the basis of this characteristic. The approach successfully uncovered the full-length sequences of 144 known Arabidopsis snoRNA genes, including some snoRNAs with improved 5′- or 3′-end annotation. In addition, we identified 27 and 17 candidates for novel box C/D and box H/ACA snoRNAs, respectively. Northern blot analysis and sequencing data from parallel analysis of RNA ends confirmed the expression and the termini of the newly predicted snoRNAs. Our study especially expanded on the current knowledge of box H/ACA snoRNAs and snoRNA species targeting snRNAs. In this study, we demonstrated that the use of small RNA sequencing data can increase the complexity and the accuracy of snoRNA annotation.     

Plant U13 orthologues and orphan snoRNAs identified by RNomics of RNA from Arabidopsis nucleoli

Small nucleolar RNAs (snoRNAs) and small Cajal body-specific RNAs (scaRNAs) are non-coding RNAs whose main function in eukaryotes is to guide the modification of nucleotides in ribosomal and spliceosomal small nuclear RNAs, respectively. Full-length sequences of Arabidopsis snoRNAs and scaRNAs have been obtained from cDNA libraries of capped and uncapped small RNAs using RNA from isolated nucleoli from Arabidopsis cell cultures. We have identified 31 novel snoRNA genes (9 box C/D and 22 box H/ACA) and 15 new variants of previously described snoRNAs. Three related capped snoRNAs with a distinct gene organization and structure were identified as orthologues of animal U13snoRNAs. In addition, eight of the novel genes had no complementarity to rRNAs or snRNAs and are therefore putative orphan snoRNAs potentially reflecting wider functions for these RNAs. The nucleolar localization of a number of the snoRNAs and the localization to nuclear bodies of two putative scaRNAs was confirmed by in situ hybridization. The majority of the novel snoRNA genes were found in new gene clusters or as part of previously described clusters. These results expand the repertoire of Arabidopsis snoRNAs to 188 snoRNA genes with 294 gene variants.     

RNomics: an experimental approach that identifies 201 candidates for novel, small, non-messenger RNAs in mouse

In mouse brain cDNA libraries generated from small RNA molecules we have identified a total of 201 different expressed RNA sequences potentially encoding novel small non-messenger RNA species (snmRNAs). Based on sequence and structural motifs, 113 of these RNAs can be assigned to the C/D box or H/ACA box subclass of small nucleolar RNAs (snoRNAs), known as guide RNAs for rRNA. While 30 RNAs represent mouse homologues of previously identified human C/D or H/ACA snoRNAs, 83 correspond to entirely novel snoRNAS: Among these, for the first time, we identified four C/D box snoRNAs and four H/ACA box snoRNAs predicted to direct modifications within U2, U4 or U6 small nuclear RNAs (snRNAs). Furthermore, 25 snoRNAs from either class lacked antisense elements for rRNAs or snRNAS: Therefore, additional snoRNA targets have to be considered. Surprisingly, six C/D box snoRNAs and one H/ACA box snoRNA were expressed exclusively in brain. Of the 88 RNAs not belonging to either snoRNA subclass, at least 26 are probably derived from truncated heterogeneous nuclear RNAs (hnRNAs) or mRNAS: Short interspersed repetitive elements (SINEs) are located on five RNA sequences and may represent rare examples of transcribed SINES: The remaining RNA species could not as yet be assigned either to any snmRNA class or to a part of a larger hnRNA/mRNA. It is likely that at least some of the latter will represent novel, unclassified snmRNAS:     

Direct cloning of double-stranded RNAs from RNase protection analysis reveals processing patterns of C/D box snoRNAs and provides evidence for widespread antisense transcript expression

We describe a new method that allows cloning of double-stranded RNAs (dsRNAs) that are generated in RNase protection experiments. We demonstrate that the mouse C/D box snoRNA MBII-85 (SNORD116) is processed into at least five shorter RNAs using processing sites near known functional elements of C/D box snoRNAs. Surprisingly, the majority of cloned RNAs from RNase protection experiments were derived from endogenous cellular RNA, indicating widespread antisense expression. The cloned dsRNAs could be mapped to genome areas that show RNA expression on both DNA strands and partially overlapped with experimentally determined argonaute-binding sites. The data suggest a conserved processing pattern for some C/D box snoRNAs and abundant expression of longer, non-coding RNAs in the cell that can potentially form dsRNAs.     

Identification of novel ribonucleo-protein complexes from the brain-specific snoRNA MBII-52

Small nucleolar RNAs (snoRNAs) guide nucleotide modifications within ribosomal RNAs or spliceosomal RNAs by base-pairing to complementary regions within their RNA targets. The brain-specific snoRNA MBII-52 lacks such a complementarity to rRNAs or snRNAs, but instead has been reported to target the serotonin receptor 2C pre-mRNA, thereby regulating pre-mRNA editing and/or alternative splicing. To understand how the MBII-52 snoRNA might be involved in these regulatory processes, we isolated the MBII-52 snoRNP from total mouse brain by an antisense RNA affinity purification approach. Surprisingly, by mass spectrometry we identified 17 novel candidates for MBII-52 snoRNA binding proteins, which previously had not been reported to be associated with canonical snoRNAs. Among these, Nucleolin and ELAVL1 proteins were confirmed to independently and directly interact with the MBII-52 snoRNA by coimmunoprecipitation. Our findings suggest that the MBII-52 snoRNA assembles into novel RNA-protein complexes, distinct from canonical snoRNPs.     

A novel snoRNA can direct site-specific 2'-O-ribose methylation of snRNAs in Oryza sativa

Small nucleolar RNAs (snoRNAs) are a kind of noncoding RNAs, and the vast majority of snoRNAs are involved in site-specific modifications of rRNAs. A novel box C/D snoRNA called snoR124 was found inOryza sativa, and it can direct 2'-O-ribose methylation of spliceosomal small nuclear RNAs (snRNAs). The snoRNA has two antisense elements, and the results of primer extensions at different dNTP concentrations provide evidence that snoR124 guide 2'-O-methylations of the C76 residue in the U4 snRNA and the T91 residue in the U5 snRNA. In addition, this snoRNA is located in a snoRNA gene cluster with another 7 snoRNAs which are identified to direct ribose methylations in rRNAs. This is consistent with the opinion that the snoRNA gene organization in plant is mainly gene cluster. The snoR124 is the first example of a snoRNA that directs modifications of RNAs other than rRNAs in plant; it will avail to get more insights into the function of snoRNAs in plant.     

Cellular localization of long non-coding RNAs affects silencing by RNAi more than by antisense oligonucleotides

Thousands of long non-coding RNAs (lncRNAs) have been identified in mammalian cells. Some have important functions and their dysregulation can contribute to a variety of disease states. However, most lncRNAs have not been functionally characterized. Complicating their study, lncRNAs have widely varying subcellular distributions: some reside predominantly in the nucleus, the cytoplasm or in both compartments. One method to query function is to suppress expression and examine the resulting phenotype. Methods to suppress expression of mRNAs include antisense oligonucleotides (ASOs) and RNA interference (RNAi). Antisense and RNAi-based gene-knockdown methods vary in efficacy between different cellular compartments. It is not known if this affects their ability to suppress lncRNAs. To address whether localization of the lncRNA influences susceptibility to degradation by either ASOs or RNAi, nuclear lncRNAs (MALAT1 and NEAT1), cytoplasmic lncRNAs (DANCR and OIP5-AS1) and dual-localized lncRNAs (TUG1, CasC7 and HOTAIR) were compared for knockdown efficiency. We found that nuclear lncRNAs were more effectively suppressed using ASOs, cytoplasmic lncRNAs were more effectively suppressed using RNAi and dual-localized lncRNAs were suppressed using both methods. A mixed-modality approach combining ASOs and RNAi reagents improved knockdown efficacy, particularly for those lncRNAs that localize to both nuclear and cytoplasmic compartments.     

Metal resistance in yeast mediated by the expression of a maize 20S proteasome alpha subunit

A novel 72 nt small nucleolar RNA (snoRNA) called U87 was found in rat liver cells. This RNA possesses the features of C/D box snoRNA family: boxes C, D', C', D, and 11 nt antisense element complementary to 28S ribosomal RNA (rRNA). The vast majority of C/D box snoRNAs direct site-specific 2'-O-ribose methylation of rRNAs. U87 RNA is suggested to be involved in 2'-O-methylation of a G(3468) residue in 28S rRNA. U87 RNA was detected in different mammalian species with slight length variability. Rat and mouse U87 RNA gene was characterized. Unlike the majority of C/D box snoRNAs U87 RNA lacks the terminal stem required for snoRNA processing. However, U87 gene is flanked by 7 bp inverted repeats potentially able to form a terminal stem in U87 RNA precursor.     

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

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. 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, but are limited by specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, 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. SnoGloBe is also an excellent tool for discovering viral RNA targets, as shown by its capacity to identify snoRNAs targeting the heavily methylated SARS-CoV-2 RNA. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.