Methylation of riboses at 2'-OH group is one of the most common RNA modifications found in number of cellular RNAs from almost any species which belong to all three life domains. This modification was extensively studied for decades in rRNAs and tRNAs, but recent data revealed the presence of 2'-O-methyl groups also in low abundant RNAs, like mRNAs.Ribose methylation is formed in RNA by two alternative enzymatic mechanisms: either by stand-alone protein enzymes or by complex assembly of proteins associated with snoRNA guides (sno(s)RNPs). In that case one catalytic subunit acts at various RNA sites, the specificity is provided by base pairing of the sno(s)RNA guide with the target RNA. In this review we compile available information on 2'-OH ribose methylation in different RNAs, enzymatic machineries involved in their biosynthesis and dynamics, as well as on the physiological functions of these modified residues.

Developing methods for accurate detection of RNA modifications remains a major challenge in epitranscriptomics. Next-generation sequencing-based mapping approaches have recently emerged but, often, they are not quantitative and lack specificity. Pseudouridine (ψ), produced by uridine isomerization, is one of the most abundant RNA modification. ψ mapping classically involves derivatization with soluble carbodiimide (CMCT), which is prone to variation making this approach only semi-quantitative. Here, we developed ‘HydraPsiSeq’, a novel quantitative ψ mapping technique relying on specific protection from hydrazine/aniline cleavage. HydraPsiSeq is quantitative because the obtained signal directly reflects pseudouridine level. Furthermore, normalization to natural unmodified RNA and/or to synthetic in vitro transcripts allows absolute measurements of modification levels. HydraPsiSeq requires minute amounts of RNA (as low as 10–50 ng), making it compatible with high-throughput profiling of diverse biological and clinical samples. Exploring the potential of HydraPsiSeq, we profiled human rRNAs, revealing strong variations in pseudouridylation levels at ∼20–25 positions out of total 104 sites. We also observed the dynamics of rRNA pseudouridylation throughout chondrogenic differentiation of human bone marrow stem cells. In conclusion, HydraPsiSeq is a robust approach for the systematic mapping and accurate quantification of pseudouridines in RNAs with applications in disease, aging, development, differentiation and/or stress response.

Analysis of RNA modifications by traditional physico-chemical approaches is labor intensive, requires substantial amounts of input material and only allows site-by-site measurements. The recent development of qualitative and quantitative approaches based on next-generation sequencing (NGS) opens new perspectives for the analysis of various cellular RNA species. The Illumina sequencing-based RiboMethSeq protocol was initially developed and successfully applied for mapping of ribosomal RNA (rRNA) 2′-O-methylations. This method also gives excellent results in the quantitative analysis of rRNA modifications in different species and under varying growth conditions. However, until now, RiboMethSeq was only employed for rRNA, and the whole sequencing and analysis pipeline was only adapted to this long and rather conserved RNA species. A deep understanding of RNA modification functions requires large and global analysis datasets for other important RNA species, namely for transfer RNAs (tRNAs), which are well known to contain a great variety of functionally-important modified residues. Here, we evaluated the application of the RiboMethSeq protocol for the analysis of tRNA 2′-O-methylation in Escherichia coli and in Saccharomyces cerevisiae. After a careful optimization of the bioinformatic pipeline, RiboMethSeq proved to be suitable for relative quantification of methylation rates for known modified positions in different tRNA species.

Bacterial RNA has emerged as an important activator of innate immune responses by stimulating Toll-like receptors TLR7 and TLR8 in humans. Guanosine 2 ′ ′ ′ ′ ′-O-methylation at position 18 (Gm18) in bacterial tRNA was shown to antagonize tRNAinduced TLR7/8 activation, suggesting a potential role of Gm18 as an immune escape mechanism. This modification also occurs in eukaryotic tRNA, yet a physiological immune function remained to be tested. We therefore set out to investigate the immune modulatory role of Gm18 in both prokaryotic and eukaryotic microorganisms, Escherichia coli and Saccharomyces cerevisiae, and in human cells. Using RiboMethSeq analysis we show that mutation of trmH in E. coli, trm3 in S. cereviase, and CRISPR/Cas9-induced knockout of TARBP1 in H. sapiens results in loss of Gm18 within tRNA. Lack of Gm18 across the kingdoms resulted in increased immunostimulation of peripheral blood mononuclear cells when activated by tRNA preparations. In E. coli, lack of 2 ′ ′ ′ ′ ′-O-methyltransferase trmH also enhanced immune stimulatory properties by whole cellular RNA. In contrast, lack of Gm18 in yeasts and human cells did not affect immunostimulation by whole RNA preparations. When using live E. coli bacteria, lack of trmH did not affect overall immune stimulation although we detected a defined TLR8/RNA-dependent gene expression signature upon E. coli infection. Together, these results demonstrate that Gm18 is a global immune inhibitory tRNA modification across the kingdoms and contributes to tRNA recognition by innate immune cells, but as an individual modification has insufficient potency to modulate recognition of the investigated microorganisms.

Methods for the detection of m6A by RNA-Seq technologies are increasingly sought after. We here present NOseq, a method to detect m6A residues in defined amplicons by virtue of their resistance to chemical deamination, effected by nitrous acid. Partial deamination in NOseq affects all exocyclic amino groups present in nucleobases and thus also changes sequence information. The method uses a mapping algorithm specifically adapted to the sequence degeneration caused by deamination events. Thus, m6A sites with partial modification levels of ∼50% were detected in defined amplicons, and this threshold can be lowered to ∼10% by combination with m6A immunoprecipitation. NOseq faithfully detected known m6A sites in human rRNA, and the long non-coding RNA MALAT1, and positively validated several m6A candidate sites, drawn from miCLIP data with an m6A antibody, in the transcriptome of Drosophila melanogaster. Conceptually related to bisulfite sequencing, NOseq presents a novel amplicon-based sequencing approach for the validation of m6A sites in defined sequences.

A major trend in the epitranscriptomics field over the last 5 years has been the highthroughput analysis of RNA modifications by a combination of specific chemical treatment (s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m 5 C), pseudouridine (y), 1-methyladenosine (m 1 A), and 2′-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in various RNA species such as rRNA, tRNA, and snRNA. Such applications require adaptation of the initially published protocol(s), both at the wet bench and in the bioinformatics analysis. In this manuscript, we describe the optimization of RiboMethSeq bioinformatics at the level of initial read treatment, alignment to the reference sequence, counting the 5′-and 3′ends, and calculation of the RiboMethSeq scores, allowing precise detection and quantification of the Nm-related signal. These improvements introduced in the original pipeline permit a more accurate detection of Nm candidates and a more precise quantification of Nm level variations. Applications of the improved RiboMethSeq treatment pipeline for different cellular RNA types are discussed.

Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads. Here we describe a relatively simple computational strategy for semi-automatic collapsing of highly redundant tRNA datasets into a non-redundant collection of reference tRNA sequences. The relevance of the approach was validated by analysis of experimentally obtained tRNA-sequencing datasets for different prokaryotic and eukaryotic model organisms. The data demonstrate that non-redundant tRNA reference sequences allow improving unambiguous mapping of deep sequencing data.

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