Human plasma contains > 40,000 different coding and non-coding RNAs that are potential biomarkers for human diseases. Here, we used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) combined with peak calling to simultaneously profile all RNA biotypes in apheresis-prepared human plasma pooled from healthy individuals. Extending previous TGIRT-seq analysis, we found that human plasma contains largely fragmented mRNAs from > 19,000 protein-coding genes, abundant full-length, mature tRNAs and other structured small non-coding RNAs, and less abundant tRNA fragments and mature and pre-miRNAs. Many of the mRNA fragments identified by peak calling correspond to annotated protein-binding sites and/or have stable predicted secondary structures that could afford protection from plasma nucleases. Peak calling also identified novel repeat RNAs, miRNA-sized RNAs, and putatively structured intron RNAs of potential biological, evolutionary, and biomarker significance, including a family of full-length excised intron RNAs, subsets of which correspond to mirtron pre-miRNAs or agotrons. Extracellular RNAs in human plasma have been avidly pursued as potential biomarkers for cancer and other human diseases . In healAs a substitute for retroviral RTs, we have been developing RNA-seq methods using thermostable group II intron reverse transcriptases (TGIRTs) . In addiThe comprehensive TGIRT-seq method used in this work to analyze human plasma RNAs employs TGIRT-template-switching for 3'-RNA-seq adapter addition followed by a single-stranded DNA ligation for 5' RNA-seq adapter addition . In a vaThe ability of TGIRT-seq to simultaneously profile mRNAs and non-coding RNAs from small amounts of starting material without size selection is advantageous for the analysis of extracellular RNAs, which are present in low concentrations in human plasma or EVs secreted by cultured human cells. In a previous study, TGIRT-seq showed that human plasma from a healthy male individual contained predominantly full-length tRNAs and other structured small ncRNAs, which could not be seen in RNA-seq studies using retroviral RTs, together with RNA fragments derived from large numbers of protein-coding genes and lncRNAs, with higher proportions of intron and antisense RNAs than in cellular RNA datasets . SimilarSince these earlier studies, TGIRT-seq methods have undergone improvements including: (i) the use of modified RNA-seq adapters that substantially decrease adapter-dimer formation; (ii) computational and biochemical methods for remediating residual end biases, enabling profiling of miRNAs with accuracy equivalent to the least biased current methods; and (iii) modified reaction conditions that increase the efficiency of RNA-seq adapter addition by TGIRT template switching, enabling the more efficient capture of target RNAs .Here, we used an updated TGIRT-seq method incorporating these improvements together with RNA-seq adapters that add a unique molecular identifier (UMI) to deconvolute duplicate reads to comprehensively profile RNAs present in commercial human plasma from healthy individuals prepared by apheresis, a method used to obtain large volumes of plasma for clinical purposes, including convalescent plasma from recovered COVID-19 patients . AdditioThe TGIRT-seq datasets in this study were obtained from commercial human plasma pooled from multiple healthy individuals and prepared by apheresis with EDTA as the anticoagulant . For each dataset, nucleic acids were extracted from 4 mL of plasma by using a QIAamp ccfDNA/RNA kit (Qiagen) and treated with NaOH to obtain RNA-free plasma DNA (n = 4) or with DNase I (n = 12), exonuclease I  or Exo I + DNase I (n = 2) to obtain plasma RNA with minimal residual DNA . Each preparation gave ~10 ng of nucleic acid, which yielded ~8 ng DNA or ~2 ng RNA, as judged by Bioanalyzer analysis . Althougr = 0.67–1.00  or trimmed mononucleosomes, and the other at 50–100 nt corresponding to nicked DNA strands that are protected by transcription factors or other bound proteins , in agreThe plasma RNA samples after treatment with DNase I (n = 12), Exo I ± DNase I (n = 3), or DNase I + T4 polynucleotide kinase to remove 3' phosphates (n = 3) gave read pairs mapping to 26,000–43,000 different genes . For thee.g., 5' half Gly-CCC, 2.9-fold increase; 5'-tRF Cys-GCA, 2-fold increase; see 1A modification site in the TψC loop, which is a strong stop for retroviral RTs and can be mistaken for the 5' end of 3'-tRFs  for Val-CAC, the most abundant isodecoder species) followed by Y RNAs , and 7SL RNA . Kraken2 assigns reads to bacterial, viral, and archaeal genomes based on matches to unique k-mer sequences in a database of non-redundant bacterial, viral, and archaeal sequences compiled from RefSeq. We thus identified 2.83 million RNA reads assigned to bacteria, with the most abundant phyla being Proteobacteria, Actinobacteria, and Firmicutes and the most abundant genera being Pseudomonas, Salmonella, Acinetobacter, Halomonas, and Bacillus, consistent with other studies .Our previous TGIRT-seq analysis of plasma from a healthy male individual, which was prepared from freshly drawn blood by sedimentation into a Ficoll-cushion to minimize cell breakage, revealed that the mRNAs in plasma are largely fragmented, consistent with other studies of plasma RNAs using different RNA-seq methods with or without size selection . In the e.g., To investigate the relationship between the mRNAs detected in plasma by TGIRT-seq and the polyadenylated mRNAs detected by other methods e.g., , we compr = 0.61), but with some, mostly lower abundance mRNAs undetected either by TGIRT-seq or SMART-Seq, and with SMART-Seq unable to detect non-polyadenylated histone mRNAs, which are relatively abundant in plasma (r = 0.58) or after additional chemical fragmentation followed by 3'-phosphate removal  and S100 calcium-binding proteins S100A8 and S100A9), which are involved in regulating immune responses . This reflects that many of the additional protein-coding gene transcripts detected after 3'-phosphate removal had relatively low read counts and that the abundance of only a small proportion of protein-coding gene transcripts increased significantly after this treatment . Because 3’ phosphates inhibit RNA-seq adapter addition by TGIRT template switching , neither the 5’- to 3’-coverage nor the abundance of most protein-coding transcripts detected in plasma by TGIRT-seq was strongly affected , and a requirement that the peak was detected in at least 5 of the 15 samples to avoid batch effects yielded 1,036 peaks that were enriched in the DNase-treated plasma RNA over the base line control. After further filtering to remove read alignments with MAPQ < 30  or ≥ 5 mismatches from the mapped locus, we were left with 950 high confidence peaks ranging in size from 59 to 1,207 nt with ≥ 5 high quality read alignments at the peak maximum . The peri.e., a mRNA, lncRNA, or pseudogene RNA). A smaller proportion  mapped to long RNAs but did not contain an ENCODE eCLIP-annotated RBP-binding site, and the remainder  were genomic repeats, including simple sequence repeats and transposable element RNAs. The distributions of lengths and read depths for different categories of peaks are shown in . The remaining 17 intron RNA peaks mapped within longer introns and corresponded to annotated binding sites for a largely different set of 13 RBPs , with a single occurrence of a spliceosomal protein EFTUD2 binding site among the intron fragments being the only RBP common to both sets of peaks , pseudogenes transcripts (10 binding sites), or annotated antisense RNAs (two binding sites), 46 mapped to introns, and surprisingly, 29 corresponded to short, putatively structured, full-length excised intron RNAs . These fof peaks . The chaPeak calling against the human genome reference sequence might miss RBP-binding sites that are close to or overlap exon junctions, as such reads were treated by MACS2 as long reads that span the intervening intron. To address this possibility, we mapped the reads in the combined DNase-treated datasets (n = 15) to a human transcriptome reference sequence (Ensemble human cDNA references) and obtained ~0.26 million deduplicated read pairs that were used as input for MACS2 without a control dataset . Using tAs expected for different populations of mRNAs in plasma and cells, scatter plots showed that the relative abundances of the different RBP-binding sites in plasma RNAs differed markedly from those in the ENCODE eCLIP cellular RNA datasets  analysis from the ENCODE website for 150 different RBPs; Two hundred and thirteen peaks that mapped to exons or pseudogenes in the human genome reference sequence did not correspond to ENCODE eCLIP-annotated RBP-binding sites or genomic repeats, and an additional 33 such peaks were identified by mapping to the human transcriptome . These pFTL) exon; a selenocysteine insertion sequence 1, which directs the translation of UGA as SelCys; and two potential pre-miRNA stem-loop structures, with the closest match for both being mouse miR-692, which has no annotated human homolog n and (TTC)n), and four were transposable element RNAs   . IGV alie.g., (CATTC)n and (TTAGGG)n) were called by MACS2 at some loci with roughly equal numbers of reads on both strands, possibly reflecting mismapping of DNA or RNA reads. To address this issue, we used an Empirical Bayes method  were not called as peaks but were identified as containing annotated miRNAs sequences. As discussed further below, seven of these 50 intron peaks corresponded to the same intron found in the All of the full-length excised intron RNAs detected in plasma corresponded to short introns with lengths 73–210 nt . All werDOCK6 intron 25 , corresponded to annotated agotrons, intron RNAs that were identified as binding to Argonaute-2 protein (AGO2) in HITS-CLIP-seq datasets and shown to repress mRNA translation in reporter assays , to our knowledge the first time such intron-derived pre-miRNAs have been sequenced, and none corresponded to the annotated mature miRNA.Ten of the excised intron RNAs that we detected in plasma corresponded to annotated mirtrons, miRNAs that are processed by DICER from debranched structured intron RNAs , which are located on a distant duplicated segment of the same chromosome (chromosome 16). Notably, the introns in the parent and all six pseudogenes differed by only a single nucleotide at a known SNP position and were more conserved than the flanking PKD1 exons (6–8 mismatches in the 211 bp 5’ exon and 1–3 mismatches in the 127 bp 3’ exon), possibly reflecting that this intron has a critical sequence-dependent function. Nevertheless, only one of the full-length excised introns, PKD1 intron 45 , was conserved in sequence across vertebrates and only six were conserved in sequence across primates , and an another 15 such peaks were found among those mapping to long RNAs but lacking an annotated RBP-binding site. Paralleling findings for mRNA fragments in plasma, most of these intron peaks  could be folded by RNAfold into a secondary structure with ΔG ≤ −14.6 kcal/mol  mRNA). All of these retrotransposed mRNA sequences had a short poly(A) tail (9–22 nt) and were flanked by short direct repeat target site duplications  mapping to internal segments of intron RNAs were part of retrotransposed mRNAs sequences  that had integrated into seven different long introns  tail flanked by two different Alu elements with a third Alu element inserted within the retrotransposed mRNA sequence , four to other highly represented genes , and one to a pseudogene  and/or were mirrored by a partially overlapping antisense DNA peak , but nevertheless sufficient to identify mRNA fragments originating from > 19,000 different protein-coding genes, with relatively uniform coverage across mRNA exons for highly represented genes . The numThe mRNA fragments present in plasma are likely a mixture of transient RNA degradation intermediates and more persistent RNA fragments that are protected from plasma nucleases by bound proteins, RNA structure, encapsulation in EVs, or some combination of these factors. Both the present and previous TGIRT-seq analyses indicated that many of the mRNA present in plasma have 3' OHs, which are required for efficient 3' RNA-seq adapter addition by TGIRT-template-switching, particularly under the high-salt reaction conditions used in the previous study . As mostTo identify those mRNA fragments in plasma that might be associated with bound proteins, we used a peak-calling algorithm and intersected the called RNA peaks with ENCODE eCLIP annotated RBP-binding site sequences. We thus identified numerous discrete mRNA and lncRNA fragments that are annotated binding sites for ~100 different RBPs, most of which were previously identified as part of the plasma proteome. Notably, we found plasma RNAs are particularly enriched in binding sites for stress granule proteins  corresponded to Repbase-annotated short tandem repeat and transposable element RNAs . TGIRT eTGIRT-seq peak calling also identified novel intron RNAs, including a family of short, full-length excised intron RNAs that had not been seen previously using other RNA-seq methods. These full-length excised introns ranged in length from 73 to 130 nt, are likely linear RNAs, had RNAfold-predicted secondary structures with ΔGs ≤ −18.7 kcal/mole, and in many cases, corresponded to annotated binding sites of spliceosomal proteins and/or AGO1-4. Twenty three of the 44 full-length excised intron RNAs that we detected in plasma corresponded either to annotated or potential mirtrons, intron pre-miRNAs that are processed by DICER to produce mature miRNAs  and/or tPKD1 intron 29  were conserved in sequence across primates  of the structured full-length excised introns that we detected in plasma did not correspond to identified agotrons or mirtrons. These introns could be mirtrons or agotrons that have yet to be annotated, or they could have some other function that remains to be determined. One of the most abundant full-length excised intron RNAs that we detected in plasma,  ID#333; , was higIn addition to full-length excised introns, peak calling identified a variety of intron RNA fragments, with more than half (16 of 31) corresponding to annotated RBP-binding sites and most having stable predicted RNA secondary structures or other structural features  that may afford protection from plasma nucleases. The RBPs potentially associated with these intron RNA fragments were a largely different set than those associated with the full-length excised intron RNAs and in some cases were related to a specific RNA splicing function or structural feature of the intron. Thus, an intron RNA peak containing complementary segments of a long-inverted repeat corresponded to an annotated binding for the dsRNA-binding protein ILF3, and an intron RNA peak containing tandem AGAA repeats corresponded to annotated binding site for the alternative splicing factor TRA2A, which recognizes this motif . Some ofTMSBX4, RPS29, and FTL), we detected antisense reads that could be cDNAs generated by an endogenous RT, possibly precursors of continuing retrotransposition events in human cells  was purchased from Innovative Research. The plasma was prepared by apheresis into K2-EDTA tubes and was certified by the provider as testing negative for HBV, HCV, HIV-1, HIV-2, and syphilis using FDA-approved methods.Nucleic acids were isolated from 4 mL of plasma by using a QIAamp ccfDNA/RNA kit (Qiagen 55184) following the manufacturer’s protocol, with the nucleic acids eluted in a final volume of 20 µL. To obtain plasma DNA, RNA was degraded by adding 1 µL of 5 N NaOH and incubating at 95°C for 3 min. The solution was then neutralized by adding 1 µL of 5 N HCl, and the products were cleaned up with an Oligo Clean and Concentrator kit (Zymo Research), with the DNA eluted in a final volume 10 µL.To obtain plasma RNA, DNA in the initial 20 µL nucleic acids solution was digested by adding 16 µL DNase I (1 U/µL) plus 4 µL 10X DNase I buffer , incubating for 15 min at room temperature, and cleaning up with a Zymo RNA Clean and Concentrator kit, with the RNA eluted in a volume of 10 µL. For one sample (ExoI_1), the DNase I-treated plasma RNA (~2 ng in 10 µL of eluant) was denatured by heating to 95°C for 3 min and immediately placed on ice, then digested with exonuclease I by adding 1 µL of enzyme (20 U/µL) plus 1.5 µL 10X Reaction Buffer (Lucigen) and incubating 1 hr at 37°C. The products were then cleaned up with a Zymo RNA Clean and Concentrator kit eluted in a final volume of 10 µL. In two other samples, the initial 20 µL nucleic acids sample was split into 10 µL aliquots and digested with Exonuclease I as above. One aliquot was cleaned up with a Zymo RNA Clean and Concentrator kit (ExoI_3), and other was treated with DNase I as above prior to clean up ExoI_2; .To remove 3’ phosphates and 2’, 3’ cyclic monophosphates, 2.5 µL of T4 polynucleotide kinase (5 U/µL) plus 2.5 µL 10X Reaction Buffer (Lucigen) were added to the initial 20 µL nucleic acids solution, and the samples were incubated for 30 min at 37°C, followed by incubation for 5 min at 70°C to denature the enzyme and clean up with a Zymo RNA Clean and Concentrator eluted in a volume of 20 µL. The samples were then incubated with DNase I prior to final cleaned up as above.Fragmentation of plasma RNA was done by using an NEBNext Magnesium RNA Fragmentation Module (New England Biolabs) following the manufacturer’s protocol. 2 µL of 10X Fragmentation Buffer was added to 20 µL of nucleic acids and incubated for 2 min at 94°C. The fragmented RNA was then cleaned up with a Zymo RNA Clean and Concentrator kit, treated with T4 polynucleotide kinase to remove 3’ phosphates and 2’, 3’ cyclic phosphates, cleaned up again with an RNA Clean and Concentrator kit, and treated with DNase I prior to final clean up as above.2, 20 mM Tris-HCl, pH 7.5, and 5 mM dithiothreitol. The synthetic RNA template/DNA primer substrate consisted of a 35-nt RNA oligonucleotide that contains an Illumina read 2 (R2) sequence with a 3'-blocking group  annealed to a complementary 36-nt DNA primer that leaves a single nucleotide 3'-DNA overhang , which directs TGIRT-template switching by base pairing to the 3' nucleotide of a target RNA. Reactions were initiated by adding 1 mM dNTPs , incubated for 15 min at 60°C, and terminated by adding 0.25 M NaOH and incubating at 95°C for 3 min, followed by neutralization with 0.25 M HCl, with the resulting cDNAs cleaned up by using a MinElute Reaction Cleanup Kit (Qiagen). The second adapter, a 5′-adenylated/3′-blocked  DNA containing the reverse complement of an Illumina read one sequence (R1R) and the 6-nt UMI at its 5' end was then ligated to the 3’ end of the cDNAs by using Thermostable 5′ AppDNA/RNA Ligase (New England Biolabs), as described  was processed for sequencing by using a SMART-Seq v4 Ultra Low Input RNA kit (Takara Bio) according to the manufacturer’s protocol with 12 cycles of PCR followed by two rounds of clean up with 1.3X Agencourt AMPure XP beads (Beckman Coulter). The resulting double-stranded DNAs were fragmented with a Covaris sonicator  using a 10% duty cycle, intensity at 5, and 200 cycles per burst, and Illumina-sequencer-compatible libraries were constructed by using an NEBNext Ultra II DNA Library Prep Kit (New England Biolabs).e.g., long homopolymers), and TGIRT-seq byproducts  were removed with Atropos (AAGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTTG --anywhere=GCTCTTCCGATCTT -b GCACACGTCTGAACTCCAGTCAC -b GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTT -a A{100} -a T{100} -G GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTT -B AAGATCGGAAGAGC -B GTGACTGGAGTTCAGACGTGTGC -b AAGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTTG -A T{100} -A A{100} --adapter AAGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTTG -A GATCGTCGGACTGTAGAACTCTGAACGTGTAGA --interleaved-input - --interleaved-output - --quiet --report-file /dev/stderr -f fastq.FASTQ files were processed by using the TGIRT-map pipeline  with mod Atropos  using ophttps://www.ncbi.nlm.nih.gov/tools/vecscreen/uvcurrent/#Replist) by BOWTIE2 (http://gtrnadb.ucsc.edu), 7SK RNA, 7SL RNA, Y-RNAs, vault RNAs  and high confidence miRNAs (http://www.mirbase.org) (Pass 3). The remaining unaligned reads were then aligned against the human genome reference sequence (hg19) sequentially with HISAT2 .Deduplication of mapped reads was done by UMI, CIGAR string, and genome coordinates . To accohttps://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm) with a mean fragment length of 160 nt using parameters: -ss NS50 -sam -p -l 75 f 5 m 160 s 10.A paired-end 75-nt simulated NextSeq library was generated by ART   and analyzed by Kraken2 (Unmapped reads from datasets for DNase-treated plasma RNA (n = 15) or NaOH-treated plasma DNA (n = 4) were combined with reads that mapped to the  Kraken2  using th Kraken2 . Mapping Kraken2  showed lhttp://gtrnadb.ucsc.edu], 7SK RNA, 7SL RNA, Y-RNAs, vault RNAs , high confidence and non-high confidence miRNAs [http://www.mirbase.org], snoRNAs, snRNAs, and miscellaneous RNAs ). The alignment coordinates were extracted as a BED file and deduplicated as described above for sequence data processing. BED files from libraries with the same treatments were combined, and reads mapping to human genome blacklist regions , rRNAs and Mt RNAs (Pass 2), and annotated sncRNAs , Ensembl genes (https://useast.ensembl.org/index.html), Repbase repeat regions (https://www.encodeproject.org/eclip/), and piRNA annotations from piRNAdb (https://www.pirnadb.org). An overlap score defined as the product of The annotation of peaks was done by BEDTools  intersec regions , RBP-binhttp://rfam.xfam.org). RNA secondary structures were predicted and MFEs calculated by RNAfold with default parameters using the ViennaRNA package . For long RNA, antisense, and unannotated peaks, we performed an RNA homology search against the covariance model from Rfam  using In package .e.g., (CATTC)n and (TTAGGG)n) were called by MACS2 on both strands, but in different ratios in the datasets for DNase-treated plasma RNA and NaOH-treated plasma DNA. To identify repeat sequences with a significantly higher counts in one orientation over the other and eliminate ambiguous read mappings, we aggregated the strand-specific fragment counts for all unique simple repeat sequences and their reverse-complements n and (GGGTA)n) in the combined DNase-treated plasma RNA (n = 15) and NaOH-treated plasma DNA (n = 4) datasets. To compensate for repeat sequences with low read counts, which could give misleading strand-specificity, we employed an Empirical Bayes method by using repeat sequences with high total counts (> 50 deduplicated reads) regardless of strand from the combined plasma DNA datasets to construct a prior beta distribution of strand specificities for all repeat sequences (We noticed that peaks for some simple repeat sequences (equences . This fiequences .https://github.com/wckdouglas/cfNA. (https://github.com/elifesciences-publications/cfNA).All scripts used for data processing are deposited in GitHub: The TGIRT-seq datasets in this manuscript are listed in the Supplementary File and have been deposited in the National Center for Biotechnology Information Sequence Read Archive (accession number: PRJNA640428). In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:In this paper the authors use a thermostable reverse transcriptase encoded by a so-called group II intron to sequence RNAs present in blood. This reverse transcriptase differs from retroviral reverse transcriptases in that it can read through structured regions of RNA. Results presented indicate that the population of RNAs in blood is more complex than previously thought.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Timothy Nilsen as Reviewing Editor and James Manley as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Mariano A Garcia-Blanco (Reviewer #1).Thank you for submitting your article "Identification of protein-protected mRNA fragments and structured intron RNAs in human plasma by TGIRT-seq peak calling" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife. Nevertheless, each raised significant concerns that need to be addressed via revision. Specifically, the reviewers felt that the bioinformatic analysis needs to be strengthened. They also requested that you provide some practical application of the work. Please address all of the points raised by the reviewers as thoroughly as possible.The reviewers agreed that the work was, in principle, suitable for Reviewer #1:The Lambowitz group has developed thermostable group II intron reverse transcriptases (TGIRTs) that strand switch and also have trans-lesion activity to provide a much wider view of RNA species analyzed by massively parallel RNA sequencing. In this manuscript they use several improvements to their methodology to identify RNA biotypes in human plasma pooled from several healthy individuals. Additionally, they implicate binding by proteins (RBPs) and nuclease-resistant structures to explain a fraction of the RNAs observed in plasma. Generally, I find the study fascinating and argue that the collection of plasma RNAs described is an important tool for those interested in extracellular RNAs. I think the possibility that RNPs are protecting RNA fragments in circulation is exciting and fits with elegant studies of insects and plants where RNAs are protected by this mechanism and are transmitted between species.I have one major comment for the authors to consider. In my view the use of pooled plasma samples prevented the important opportunity to provide a glimpse on human variation in plasma RNA biotypes. This significantly limits the use of this information to begin addressing RNA biotypes as biomarkers. While I realize that data from multiple individuals represents a significant undertaking and may be beyond the scope of this manuscript, I urge the authors to do two things: (1) downplay the significance of the current study on the development of biomarkers in the current manuscript . (2) Carry out an analysis in multiple individuals – including racially diverse individuals – very important information will come of this – similar to C. Burge's important study in Nature 2008 where it was clear that there is important individual variation in alternative splicing decisions – very likely genetically determined. This second suggestion could be added here or constitute a future manuscript.Reviewer #2:Yao et al. used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) to study apheresis plasma samples. The first interesting discovery is that they had identified a number of mRNA reads with putative binding sites of RNA-binding proteins. A second interesting discovery from this work is the detection of full-length excised intron RNAs.I have the following comments.1) One doubt that I have is how representative is apheresis plasma when compared with plasma that one obtains through routine centrifugation of blood. The authors have reported the comparison of apheresis plasma versus a single male plasma in a previous publication. I think that to address this important question, a much-increased number of samples would be necessary.2) For the important conclusion of the presence of binding sites of RNA-binding proteins in a proportion of apheresis plasma mRNA molecules, the authors need to explore whether there is any systemic difference in terms of mapping quality  between RBP binding sites and non-RBP binding sites, so that any artifacts of peaks caused by the alignment issues occurring in RNA-seq analysis could be revealed and solved subsequently. Furthermore, it would be prudent to perform immunoprecipitation experiments to confirm this conclusion in at least a proportion of the mRNA.3) In Figure 2D, one can observe that there are clearly more RNA reads in TGIRT-seq located in the 1st exon of ACTB, compared with SMART-seq. Is there any explanation? Will this signal be called as a peak  in the peak calling analysis (MACS2)? Is ACTB supposed to be bound by a certain RBP?4) For Figure 2A, it would be informative for the comparison of RNA yield and RNA size profile among different protocols if the author also added the results of TGIRT-seq.5) As shown in Figure 4C (the track of RBP binding sites), it seems quite pervasive in some gene regions. How many RBP binding sites from public eCLIP-seq results are used for overlapping peaks present in TGIRT-seq of plasma RNA? What percentage of plasma RNA reads are fallen within RBP binding sites? Are those peaks present in TGRIT-seq significantly enriched in RBPs binding regions?6) Since there is a considerable portion of TGIRT-seq reads related to simple repeat, one possible reason is likely the high abundance of endogenous repeat-related RNA species in plasma. Nonetheless, have authors studied whether the ligation steps in TGIRT-seq have any biases (e.g. GC content) when analyzing human reference RNAs and spike ins (Introduction paragraph three)?7) As described in Figure 2 legend, there are 0.25 million deduplicated reads for TGIRT-seq reads assigned to protein-coding genes transcripts which are far less than 2.18 million reads for SMART-seq. The authors need to discuss whether the current protocol of TGIRT-seq would cause potential dropouts in mRNA analysis, compared with SMART-seq?8) While scientific thought-provoking, the practical implication of the current work is still unclear. The authors have suggested that their work might have applications for biomarker development. Is it possible to provide one experimental example in the manuscript?Reviewer #3:In this work, Yao and colleagues described transcriptome profiling of human plasma from healthy individuals by TGIRT-seq. TGIRT is a thermostable group II intron reverse transcriptase that offers improved fidelity, processivity and strand-displacement activity, as compared to standard retroviral RT, so that it can read through highly structured regions. Similar analysis was performed previously , but this study incorporated several improvements in library preparation including optimization of template switching condition and modified adapters to reduce primer dimer and introduce UMI. In their analysis, the authors detected a variety of structural RNA biotypes, as well as reads from protein-coding mRNAs, although the latter is in low abundance. Compared to SMART-Seq, TGIRT-seq also achieved more uniform read coverage across gene bodies. One novel aspect of this study is the peak analysis of TGIRT-seq reads, which revealed ~900 peaks over background. The authors found that these peaks frequently overlap with RBP binding sites, while others tend to have stable predicted secondary structures, which explains why these regions are protected from degradation in plasma. Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts. On the other hand, the manuscript, in the current form, is relatively descriptive, and can be improved with a clearer message of specific knowledge that can be extracted from the data.Specific points:1) Several aspects of bioinformatics analysis can be clarified in more detail. For example, it is unclear how sequencing errors in UMI affect their de-duplication procedure. This is important for their peak analysis, so it should be explained clearly. Also, it is not described how exon junction reads (when mapped to the genome) are handled in peak calling, although the authors did perform complementary analysis by mapping reads to the reference transcriptome.2) Overall, the authors provided convincing data that TGIRT-seq has advantages in detecting a wide range of RNA biotypes, especially structured RNAs, compared to other protocols, but these data are more confirmatory, rather than completely new findings .3) The peak analysis is more novel. The authors observed that 50% of peaks in long RNAs overlap with eCLIP peaks. However, there is no statistical analysis to show whether this overlap is significant or simply due to the pervasive distribution of eCLIP peaks. In fact, it was reported by the original authors that eCLIP peaks cover 20% of the transcriptome. Similarly, the authors found that a high proportion of remaining peaks can fold into stable secondary structures, but this claim is not backed up by statistics either.4) Ranking of RBPs depends on the total number of RBP binding sites detected by eCLIP, which is determined by CLIP library complexity and sequencing depth. This issue should be at least discussed.5) Enrichment of RBP binding sites and structured RNA in TGIRT-seq data is certainly consistent with one's expectation. However, the paper can be greatly improved if the authors can make a clearer case what is new that can be learned, as compared to eCLIP data or other related techniques that purify and sequence RNA fragments crosslinked to proteins. What is the additional, independent evidence to show the predicted secondary structures are real?6) The authors should probably discuss how alignment errors can potentially affect detection of repetitive regions.7) Many figures are IGV screenshots, which can be difficult to follow. Some of them can probably be summarized to deliver the message better. Reviewer #1:[…]I have one major comment for the authors to consider. In my view the use of pooled plasma samples prevented the important opportunity to provide a glimpse on human variation in plasma RNA biotypes. This significantly limits the use of this information to begin addressing RNA biotypes as biomarkers. While I realize that data from multiple individuals represents a significant undertaking and may be beyond the scope of this manuscript, I urge the authors to do two things: (1) downplay the significance of the current study on the development of biomarkers in the current manuscript . (2) Carry out an analysis in multiple individuals – including racially diverse individuals – very important information will come of this – similar to C. Burge's important study in Nature 2008 where it was clear that there is important individual variation in alternative splicing decisions – very likely genetically determined. This second suggestion could be added here or constitute a future manuscript.eLife.The identification of biomarkers in human plasma is an important application of this study, as was noted by reviewer 3: "Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts." The present manuscript lays the foundation for such applications, which we have been carrying out in parallel. In one such study in collaboration with Dr. Naoto Ueno (MD Anderson), we used TGIRT-seq to identify combinations of mRNA and non-coding RNA biomarkers in FFPE-tumor slices, PBMCs and plasma from inflammatory breast cancer patients compared to non-IBC breast cancer patients and healthy controls , and in another, we explored the potential of using full-length excised intron (FLEXI) RNAs as biomarkers. In the latter study, we identified > 8,000 FLEXI RNAs in different human cell lines and tissues and found that they are expressed in a cell-type specific manner, including hundreds of differences between matched tumor and healthy tissues from breast cancer patients and cell lines. A manuscript describing these findings has been posted on bioRxiv . This new manuscript follows directly from the last sentence of the present manuscript and fully references the bioRxiv preprint currently under review for Reviewer #2:[…]1) One doubt that I have is how representative is apheresis plasma when compared with plasma that one obtains through routine centrifugation of blood. The authors have reported the comparison of apheresis plasma versus a single male plasma in a previous publication. I think that to address this important question, a much-increased number of samples would be necessary.Detailed comparison of plasma prepared by apheresis to that prepared by centrifugation would require a separate large-scale study, preferably by multiple laboratories using different methods to prepare plasma. However, our impression both from our findings and from the literature  is that apheresis-prepared plasma has very low levels of cellular contamination  compared to plasma prepared by centrifugation, even with protocols designed to minimize contamination from intact or broken cell .We do have additional information about the degree of variation in protein-coding gene transcripts detected by TGIRT-seq in plasma samples prepared by centrifugation from five healthy females controls in our collaborative study with Dr. Naoto Ueno , and we have added it to the manuscript citing a manuscript in preparation with permission from Dr. Ueno as follows:“The identities and relative abundances of different protein-coding gene transcripts in the apheresis-prepared plasma were broadly similar to those in the previous TGIRT analysis of plasma prepared by Ficoll-cushion sedimentation of blood from a healthy male individual   and between high quality plasma samples similarly prepared from five healthy females in a collaborative study with Dr. Naoto Ueno, M.D. Anderson .” See 2) For the important conclusion of the presence of binding sites of RNA-binding proteins in a proportion of apheresis plasma mRNA molecules, the authors need to explore whether there is any systemic difference in terms of mapping quality  between RBP binding sites and non-RBP binding sites, so that any artifacts of peaks caused by the alignment issues occurring in RNA-seq analysis could be revealed and solved subsequently. Furthermore, it would be prudent to perform immunoprecipitation experiments to confirm this conclusion in at least a proportion of the mRNA.We have added a figure panel comparing MAPQ scores for reads from peaks containing RBP-binding sites to those of other long RNA reads  and have added further details about the methods used to obtain peaks with high quality reads, including the following:“After further filtering to remove read alignments with MAPQ < 30  or ≥ 5 mismatches from the mapped locus, we were left with 950 high confidence peaks ranging in size from 59 to 1,207 nt with ≥ 5 high quality read alignments at the peak maximum (Supplementary file 1).”3) In Figure 2D, one can observe that there are clearly more RNA reads in TGIRT-seq located in the 1st exon of ACTB, compared with SMART-seq. Is there any explanation? Will this signal be called as a peak  in the peak calling analysis (MACS2)? Is ACTB supposed to be bound by a certain RBP?The higher coverage of the ACTB 5'-exon in the TGIRT-seq datasets reflects in part the more uniform 5' to 3' coverage of mRNA sequences by TGIRT-seq compared to SMART-seq, which is biased for 3'-mRNA sequences that have poly(A) tails . The signal in the first exon of ACTB was in fact called as a peak by MACS2 , which overlapped an annotated binding site for SERBP1 (see Supplementary file 1).4) For Figure 2A, it would be informative for the comparison of RNA yield and RNA size profile among different protocols if the author also added the results of TGIRT-seq.Figure 3D  shows a bioanalyzer trace of PCR amplified cDNAs obtained by SMART-Seq. These cDNAs correspond to 3' mRNA sequences that have poly(A) tails and are not comparable to the bioanalyzer profiles of plasma RNA  or read span distributions in the TGIRT-seq datasets , which are dominated by sncRNAs. The normalized 5' to 3' gene body coverage plots for protein-coding gene transcripts show that TGIRT-seq captures mRNA fragments that collectively span the entire mRNA sequence, whereas SMART-Seq is biased for 3' sequences linked to poly(A) . We also note that the normalized gene body coverage plots and mRNAs detected by TGIRT-seq remain similar, even if the plasma RNA is chemically fragmented prior to TGIRT-seq library construction .5) As shown in Figure 4C (the track of RBP binding sites), it seems quite pervasive in some gene regions. How many RBP binding sites from public eCLIP-seq results are used for overlapping peaks present in TGIRT-seq of plasma RNA? What percentage of plasma RNA reads are fallen within RBP binding sites? Are those peaks present in TGRIT-seq significantly enriched in RBPs binding regions?We noted that 109 RBP-binding sites were searched in the initial analysis, and we have now added further analyses for 150 RBPs currently available in ENCODE eCLIP datasets with irreproducible discovery rate (IDR) analysis . We have also added a tab to the Supplementary file 1 identifying the 109 and 150 RBPs whose binding sites were searched. The requested statistical analysis has been added in Figure 4—figure supplement 2C. The analysis shows that enrichment of RBP-binding site sequences in the 467 identified RBP-binding site peaks was statistically significant (p < 0.001) .6) Since there is a considerable portion of TGIRT-seq reads related to simple repeat, one possible reason is likely the high abundance of endogenous repeat-related RNA species in plasma. Nonetheless, have authors studied whether the ligation steps in TGIRT-seq have any biases (e.g. GC content) when analyzing human reference RNAs and spike ins (Introduction paragraph three)?We have added a note to the manuscript indicating that although repeat RNAs constitute a high proportion of the called peaks, they do not constitute a similarly high proportion of the total RNA reads . The TGIRT-seq analysis of human reference RNAs and spike-ins showed that TGIRT-seq recapitulates the relative abundance of human transcripts and spike-in comparably to non-strand-specific TruSeq-v2 and better than strand-specific TruSeq-v3 . Additionally, we used miRNA reference sets for detailed analysis of TGIRT-seq biases, including developing a computer algorithm for bias correction based on a random forest regression model that provides insight into different factors that contribute to these biases . Overall GC content does not make a significant contribution to TGIRT-seq biases . Instead, biases in TGIRT-seq are largely confined to the first three nucleotides at the 5'-end (due to bias of the thermostable 5' App DNA ligase used for 5' RNA-seq adapter addition) and the 3' nucleotide (due to TGIRT-template switching). These end biases are not expected to significantly impact the quantitation of repeat RNAs.7) As described in Figure 2 legend, there are 0.25 million deduplicated reads for TGIRT-seq reads assigned to protein-coding genes transcripts which are far less than 2.18 million reads for SMART-seq. The authors need to discuss whether the current protocol of TGIRT-seq would cause potential dropouts in mRNA analysis, compared with SMART-seq?We have added the following to the manuscript:“The larger number of mRNA reads compared to TGIRT-seq (0.28 million) largely reflects that SMART-seq selectively profiles polyadenylated mRNAs, while TGIRT-seq profiles mRNAs together with other more abundant RNA biotypes. In addition, ultra-low input SMART-Seq is not strand-specific, resulting in redundant sense and antisense strand reads .”The manuscript contains the following statement regarding potential dropouts:r = 0.61), but with some, mostly lower abundance mRNAs undetected either by TGIRT-seq or SMART-Seq, and with SMART-Seq unable to detect non-polyadenylated histone mRNAs, which are relatively abundant in plasma .”“A scatter plot comparing the relative abundance of transcripts originating from different genes showed that most of the polyadenylated mRNAs detected in DNase I-treated plasma RNA by ultra-low input SMART-Seq were also detected by TGIRT-seq at similar TPM values when normalized for protein-coding gene reads (8) While scientific thought-provoking, the practical implication of the current work is still unclear. The authors have suggested that their work might have applications for biomarker development. Is it possible to provide one experimental example in the manuscript?We addressed the relevance of the manuscript to biomarker identification and noted parallel studies that supports this application in the response to reviewer 1, comment 1. We have also modified the final paragraph of the Discussion:“The ability of TGIRT-seq to simultaneously profile a wide variety of RNA biotypes in human plasma, including structured RNAs that are intractable to retroviral RTs, may be advantageous for identifying optimal combinations of coding and non-coding RNA biomarkers that could then be incorporated in target RNA panels for diagnosis and routine monitoring of disease progression and response to treatment. […] Finally, we note that in addition to their biological and evolutionary interest, short full-length excised intron RNAs and intron RNA fragments, such as those identified here, may be uniquely well suited to serve as stable RNA biomarkers, whose expression is linked to that of numerous protein-coding genes."Reviewer #3:[…]1) Several aspects of bioinformatics analysis can be clarified in more detail. For example, it is unclear how sequencing errors in UMI affect their de-duplication procedure. This is important for their peak analysis, so it should be explained clearly.We have added details of the procedure used for de-duplication to the following paragraph in Materials and methods:“Deduplication of mapped reads was done by UMI, CIGAR string, and genome coordinates . […] The counts for each read were readjusted to overcome potential UMI saturation for highly-expressed genes by implementing the algorithm described in , using sequencing tools (https://github.com/wckdouglas/sequencing_tools).”Also, it is not described how exon junction reads (when mapped to the genome) are handled in peak calling, although the authors did perform complementary analysis by mapping reads to the reference transcriptome.We have added this to first sentence of the paragraph describing peak calling against the transcriptome reference, which now reads as follows:"Peak calling against the human genome reference sequence might miss RBP-binding sites that are close to or overlap exon junctions, as such reads were treated by MACS2 as long reads that span the intervening intron."2) Overall, the authors provided convincing data that TGIRT-seq has advantages in detecting a wide range of RNA biotypes, especially structured RNAs, compared to other protocols, but these data are more confirmatory, rather than completely new findings .We modified the first paragraph of the Discussion to explicitly describe what is added by the present manuscript compared to Qin et al., 2016. Additionally, further analysis in response to the reviewers' comments resulted in the interesting finding that stress granule proteins comprised a high proportion of the RBPs whose binding sites were enriched in plasma RNAs (to our knowledge a completely new finding), consistent with a previously suggested link between RNP granules, EV packaging, and RNA export . Also highlighted in the Discussion paragraph five.3) The peak analysis is more novel. The authors observed that 50% of peaks in long RNAs overlap with eCLIP peaks. However, there is no statistical analysis to show whether this overlap is significant or simply due to the pervasive distribution of eCLIP peaks. In fact, it was reported by the original authors that eCLIP peaks cover 20% of the transcriptome.We have added statistical analysis, which shows that the enrichment of RBP-binding sites in the 467 called peaks is statistically significant at p < 0.001 , as well as scatter plots identifying proteins whose binding sites were more highly represented in plasma than cellular RNAs or vice versa .Similarly, the authors found that a high proportion of remaining peaks can fold into stable secondary structures, but this claim is not backed up by statistics either.First, near the beginning of the paragraph describing these findings, we added the following to provide a guide as to what can and can't be concluded by RNAfold:"To evaluate whether these peaks contained RNAs that could potentially fold into stable secondary structures, we used RNAfold, a tool that is widely used for this purpose with the understanding that the predicted structures remain to be validated and could differ under physiological conditions or due to interactions with proteins."Second, at the end of the same paragraph, we have added the requested statistics:"Subject to the caveats above regarding conclusions drawn from RNAfold, simulations using peaks randomly generated from long RNA gene sequences indicated that enrichment of RNAs with more stable secondary structures (lower MFEs) in the called RNA peaks was statistically significant ."4) Ranking of RBPs depends on the total number of RBP binding sites detected by eCLIP, which is determined by CLIP library complexity and sequencing depth. This issue should be at least discussed.We have added scatter plots in Figure 6 and Figure 6—figure supplement 1, which show that the relative abundance of different RBP-binding sites detected in plasma differs markedly from that for cellular RNAs in the eCLIP datasets  analysis from the ENCODE web site). As mentioned in comments above, this analysis identified a number of RBP-binding sites that were substantially enriched in plasma RNAs compared to cellular RNAs or vice versa and led to what we think is the important new finding that plasma RNAs are enriched binding sites for a number of stress granule proteins . We thank the reviewers for this and related comments that led to this additional analysis.5) Enrichment of RBP binding sites and structured RNA in TGIRT-seq data is certainly consistent with one's expectation. However, the paper can be greatly improved if the authors can make a clearer case what is new that can be learned, as compared to eCLIP data or other related techniques that purify and sequence RNA fragments crosslinked to proteins. What is the additional, independent evidence to show the predicted secondary structures are real?Compared to CLIP and related methods, peak calling enables more facile identification of candidate RBPs and putatively structured RNAs for further analysis and may be particularly useful for the vanishingly small amounts of RNA present in plasma and other bodily fluids. New findings resulting from peak calling in the present manuscript include that plasma RNAs are enriched in binding sites for stress granule proteins (see above) and the discovery of a variety of novel RNAs, including the full-length excised intron RNAs first identified here and subsequently studied in cellular RNAs in Yao et al. (https://doi.org/10.1101/2020.09.07.285114). We also note that peak calling enables the identification of protein-protected and structured mRNA regions that are relatively stable in plasma and may be more reliably detected in targeted liquid biopsy assays than are more labile mRNA regions.6) The authors should probably discuss how alignment errors can potentially affect detection of repetitive regions.In the Empirical Bayes method that we used for the analysis of repeats, repeat sequences were quantified by aggregate counts irrespective of the genomic locus to which they mapped , which should not be affected by alignment errors.7) Many figures are IGV screenshots, which can be difficult to follow. Some of them can probably be summarized to deliver the message better.Some IGV-based figures are crucial for showing key features of the RNAs that are called as peaks . However, in the process of reformatting, we have switched in and added non-IGV main text figures including Figure 2 , Figure 3 (TGIRT-seq versus SMART-Seq), Figure 4 (repeats), and Figure 6 (new figure comparing relative abundance of RBP-binding sites in plasma versus cells).


It has been demonstrated experimentally that the coronavirus can enter the central nervous system through olfactory nerves and can even reach medulla. Neurological manifestations are observed more frequently in patients with coronavirus disease.The aim of the review is to seek evidence for infection of the nervous system by the human coronavirus and study the neurological manifestations of the coronavirus and its treatment. A search was done in PubMed, Google Scholar, CrossRef, and Scopus. There is evidence for the coronavirus infection of the nervous system from experimental studies, autopsy reports, and clinical studies. The virus can damage the nervous system either by direct viral damage to the neural cells or by immunopathology. Cerebral edema, neuronal degeneration, encephalitis, meningoencephalitis, acute disseminated encephalomyelitis, Guillain–Barré Syndrome, Bickerstaff’s brainstem encephalitis, Miller Fisher syndrome, polyneuritis, toxic encephalopathy, and stroke can occur. The coronavirus has been demonstrated in the cerebrospinal fluid by polymerase chain reaction technique in infected patients. The abnormalities of the coagulation system increase the risk of cerebrovascular disease. Chloroquine analogs, lopinavir/ritonavir combination, remdesivir, dexamethasone, and immunoglobulin have been shown to be useful for the treatment.There is substantial evidence for infection of the nervous system by the different strains of the human coronavirus. The coronavirus enters the nervous system either by the blood or from the olfactory nerves. The neurological diseases correlate with the severity of the coronavirus disease. The treatment is mainly supportive. The reports of patients with encephalitis, encephalomyelitis, and brainstem encephalitis show slow recovery. But a stroke has a high mortality. The coronavirus outbreaks have caused the severe acute respiratory syndrome (SARS) in 2002–2003 and Middle East respiratory syndrome (MERS) in 2012 . Acute dThe aim of the review is to seek evidence for infection of the nervous system by the human coronavirus and study the neurological manifestations of the coronavirus and its treatment. The aim includes the study of pathogenesis of the neurological diseases, investigations for diagnosis, treatment, and the prognosis.A search was done in PubMed, Google Scholar, CrossRef, and Scopus for articles on neurological manifestations of corionavirus. The keywords used were the Coronavirus, Coronavirus disease-19(COVID-19), Viral Encephalitis, Encephalomyelitis, Stroke, and Antiviral Agents.All available articles on the subject were included. Articles containing experimental studies, protocols, clinical studies, case reports, series, comments, and reviews were studied.The articles were downloaded from the web. A qualitative analysis was done. The evidence was assessed from the experimental studies. The clinical data were tabulated. The pathogenetic mechanisms were sequenced. The treatment guidelines and outcomes were analyzed.The outcomes were assessed in terms of severity of neurological disease, recovery time, mortality, and response to treatments such as pharmacotherapy, ventilation, and supportive measures.A total of 51 articles are studied. Only those studies with promising positive results had been taken from the literature about treatment. The types of articles were one protocol, six reviews, four pathogenetic studies, seven experimental reports, sixteen clinical case and seires reports, ten pharmacological studies, and seven articles on immunotherapy. A descriptive analysis was done under the sections: pathogenesis, clinical profile, investigations, treatment, and prognosis. The points centered were evidence of infection of the nervous system by the coronavirus, routes of entry of the virus into the nervous system, neurological manifestations, laboratory and radiological evaluation, and evolving treatments.The viral strains studied in experimental studies are 229E and OC43 . The demThe human coronavirus infection of neuronal cell lines leads to an increased production of intracellular infectious viral particles, a stronger induction of the unfolded protein response (UPR), and an increased neuronal cell death by apoptosis . AutopsyThe pathogenesis is depicted in Fig. The protocol of the World Health Organization ( WHO) mentions headache, seizures, alterarion of consciousness, and other neurological signs in the clinical profile of coronavirus infection . ClinicaPresentation like ADEM with numbness and weakness of the limbs, dysmetria was seen in a 15-year-old boy . She hadInflammation of the brain by the virus leads to encephalitis with high fever, headache, vomiting, deterioration of consciousness, and seizures . MorigucSigns such as ophthalmoplegia and limb weakness as in Bickerstaff’s brainstem encephalitis (BBE) or Guillain-Barré syndrome (GBS) also have been seen . In a reToscano and colleagues reported five cases of COVID-19-associated Guillain–Barré syndrome . Four ofIn the report by Mao and colleagues, out of two hundred fourteen patients hospitalized with COVID-19, seventy-eight (36.4%) had neurological manifestations . Both isIn observational series study of fifty-eight patients admitted with acute respiratory distress syndrome (ARDS) due to COVID-19, forty-nine had neurological signs . The stuThe clinical manifestations and the neurological diseases reported are summarized in Tables One of the diagnostic tests is the detection of viral RNA in a clinical sample by reverse transcriptase-polymerase chain reaction (RT-PCR) . The speIncreased level of D-dimer in the serum indicates a high chance of ischemic stroke and computerized tomography (CT) or MRI scan should be done in case of symptoms , 30. In MRI can show contrast enhancement of lesions in the meninges, brain, spinal cord, and nerve roots in patients with encephalitis, myelitis, meningitis, and Guillain–Barré Syndrome . PerfusiSince there is no definitive treatment for coronavirus infection, management is mainly symptomatic and supportive care . Oxygen Although there is no evidence for pharmacotherapy for the coronavirus from any randomized controlled trial (RCT), around 300 clinical trials are ongoing . ChloroqIn vitro studies demonstrated that the antiviral drugs such as lopinavir and ribavirin can inhibit the cytopathic effect of the coronavirus . In a stGS-5734, a nucleoside analog synthesized at Gilead Sciences, inhibited replication of coronavirus in human airway epithelial cell culture and reduced lung viral load in a mouse model . It is aThe effect of steroids in COVID is controversial. The literature does not support the routine use of corticosteroids in COVID-19 . So C anCurrently, there is no effective vaccine against the virus. But the spike (S) glycoprotein is used in most of the experiments as the major inducer of neutralizing antibodies, to develop a vaccine . RNA vacThe main factors predisposing to severe illness are old age and associated comorbidities . The repThe pandemic is still continuing and evolving. So there are no conclusive analytic studies. The ongoing clinical studies are expected to give good results. Future research can be planned based on the current knowledge.There is substantial evidence for infection of the nervous system by the different strains of the human coronavirus. There is evidence from experimental studies, autopsy report, clinical studies, CSF studies, and MRI findings. Neurological manifestations occur in more than one third of patients with COVID. The coronavirus may enter the nervous system either by hematogenous dissemination or from the olfactory nerves to the cortex. It can cause neurological disease due to direct viral infection or by immunopathology. Cerebral edema, neuronal degeneration, encephalitis, meningo-encephalitis, acute disseminated encephalomyelitis, Guillain–Barré Syndrome, Bickerstaff’s brainstem encephalitis, Miller Fisher syndrome, polyneuritis, toxic encephalopathy, and stroke can occur. The neurological diseases correlate with the severity of COVID. Treatment is mainly supportive. Chloroquin analogs, lopinavir/ritonavir combination, remdesivir, and immunoglobulin have been shown to be useful for the treatment. There is a recent report of efficacy of dexamethasone. Slow recovery is seen in a neurological coronavirus disease. But stroke carries a high mortality.


Hepatitis C virus (HCV) infects liver cells and often causes chronic infection, also leading to liver cirrhosis and cancer. In the cytoplasm, the viral structural and non-structural (NS) proteins are directly translated from the plus strand HCV RNA genome. The viral proteins NS3 to NS5B proteins constitute the replication complex that is required for RNA genome replication via a minus strand antigenome. The most C-terminal protein in the genome is the NS5B replicase, which needs to initiate antigenome RNA synthesis at the very 3′-end of the plus strand. Using ribosome profiling of cells replicating full-length infectious HCV genomes, we uncovered that ribosomes accumulate at the HCV stop codon and about 30 nucleotides upstream of it. This pausing is due to the presence of conserved rare, inefficient Wobble codons upstream of the termination site. Synonymous substitution of these inefficient codons to efficient codons has negative consequences for viral RNA replication but not for viral protein synthesis. This pausing may allow the enzymatically active replicase core to find its genuine RNA template in  Flaviviridae, genus Hepacivirus. The virus comes as a mixed lipo-viro particle and mostly infects human liver hepatocytes by binding to a variety of receptors [Hepatitis C virus (HCV) is an enveloped RNA virus classified in the family eceptors ,2. In maeceptors ,4,5,6. Teceptors ,11,12,13eceptors ,13. Moreeceptors ,15,16, ieceptors ,18. Chroeceptors ,15. Treaeceptors ,20,21. AThe HCV genome is a positive-sense RNA  of about 9600 nucleotides (nts) in length . After ecis elements involved in the regulation of virus genome replication largely reside in the untranslated regions (UTRs) at the genome ends. In the 5′UTR, these cis elements include the 5′-terminal stem-loops (SL) I and II and the region in between that is used for binding of the liver-specific microRNA (miR) 122. In contrast to the usually negative action of microRNAs on normal cellular mRNAs [The viral translation is regulated cap-independently by the IRES ,24, and ar mRNAs ,33, miR-ar mRNAs ,34,35,36ar mRNAs ,38,39. Tar mRNAs ,31. cis-regulatory RNA elements in the NS5B coding region is the cis replication element (CRE)  that is required for replication [In addition, a variety of regulatory elements is found, in particular, in the NS5B coding region at the end of the coding region ,42,43,44lication . Togethelication ,47,48,49lication , and it lication . The 3′Ulication ,52,53, alication . Moreovelication ,39,55. Tlication ,43—this cis. In light of these facts, we aimed at analyzing the possible role of translation events at the end of the NS5B protein-coding region and their possible role in regulating the initiation of RNA genome replication. We found that two ribosomes are pausing at the translation termination site and at the next ribosome occupied position directly upstream. This pausing is caused by the use of rare, inefficient codons, and it serves to enhance replication. We hypothesized that this ribosome pausing provides enough time for the NS5B replicase emerging from the ribosome to find its genuine template, the 3′-end of the viral genome, in In order to investigate possible ribosome pausing in the NS5B region, we analyzed the ribosome occupancy profile of the HCV plus-strand genome A. Full-lInterestingly, we do not see a prominent 80S ribosome peak at the HCV start codon (position 341). This is consistent with our previous finding that the attachment of the small ribosomal 40S subunit to the HCV IRES is slow, while the complete 80S ribosome formed after 60S subunit joining leaves the initiation site without substantial delay . In addiOver the entire genome, we see many peaks, with three prominent peaks (by peak height) near HCV positions 1980 and 3100 and at the polyprotein stop codon. The strongest peak located at the HCV coding sequence stop codon around position 9440 B particuConsidering possible mechanistic reasons for this ribosome pausing event at the end of the NS5B coding region, we first checked for RNA secondary structures, which could be considered to slow down ribosome movement by acting as a “roadblock”, like in ribosomal frameshifting ,64,65,66Secondly, the ds probability profile does not show a peak at the end of the coding region, which is remarkably higher than other regions, and that could be suitable for explaining the strong ribosome occupancy peak at the end of the coding region. Nevertheless, at the position of the CRE element , are strongly conserved to be rare codons among HCV isolates D. The coIn order to analyze the possible function of the above rare codons in HCV translation and replication, we designed a replicon system that contains the HCV genome ends required for replication as well as the NS3-NS5B replication protein cassette A,B. TranAfter transfection of these replicon RNAs into Huh-7.5 hepatoma cells, we analyzed HCV protein expression by measuring the HiBiT tag after 4, 8, 24, 48, and 72 h. A “GND” NS5B polymerase deficient  repliconIn contrast, when we inserted the HiBiT tag upstream of the NS3 gene segment D, HiBiT In order to analyze the function of the conserved rare codons near the NS5B C-terminus, we mutated the rare leucine and valine codons at positions 9392, 9398, 9407, and 9413  is very similar at 4 h and still at 24 h after transfection D. This iHowever, at 72 and even more at 96 h after transfection, the overall HiBiT expression from the replicons with the original rare codons is significantly higher than HiBiT expression from the mutant carrying efficient (common) codons D. TherebTaken together, the mutations of the low-efficiency rare codons near the NS5B stop codon to highly efficient common codons make HCV RNA replication (but no translation) less efficient. The HCV NS5B polymerase is translated as the last gene of the viral genome. Other proteins of the replication complex, e.g., NS4B, which is important for the recruitment of membranes as a housing for the replication complexes, and NS5A, which dimerizes ,84 and aHere, we showed that rare, inefficient codons (mainly coding for leucine) are conserved at the end of the NS5B coding sequence, which serve to pause two ribosomes at the translation termination site and just upstream of it. We identified this ribosome pausing by ribosome profiling, a method that can be used to identify open reading frames, translation starts and stops, and ribosome occupancy on single mRNAs by the evaluation of translation starts and stops ,86. TherMutation of the rare codons to efficient codons has significantly impaired expression of the HiBiT tag included in the viral replication protein expression cassette D. If thicis, while the protein is still held in place by being stuck with its C-terminus in the exit tunnel of the paused ribosome. Our analysis of NS5B codon sequences  shows thThis action is supported by the intrinsic structure of the NS5B polymerase. The NS5B protein contains a stretch of about 55 amino acids at the C-terminus that is not part of the folded core of the polymerase but extrudes from one side of the enzyme core . The 20 cis to its genuine HCV RNA plus-strand 3′-end template present in close vicinity, in order to avoid the abortion of viral replication, as well as aberrant RNA synthesis initiation on non-cognate cellular RNA templates. NS5B can initiate RNA synthesis by a primer-independent mechanism . It can cis is also met by the characteristics of the genuine template. The NS5B coding region includes the CRE, which binds the NS5B polymerase protein at least as strong as the 3′X region [The requirement for NS5B to find its RNA template in X region ,47,48,49X region , the RNAX region . AlthougX region , RNA synX region . In contrast to the fragile initiation event, once loaded onto its template, elongation of RNA synthesis by NS5B polymerase is highly processive. The corresponding complexes of NS5B with dsRNA are stable for more than 300 s , which iOur results in this study show that the translation of NS5B is slowed down in the C-terminal portion of NS5B. The ribosome placed on the termination site obviously takes time to complete termination since we see a strong ribosome peak at the termination site. This ribosome has one NS5B protein molecule still stuck in its ribosomal exit tunnel. Since the ribosomal exit tunnel shields about 30–40 amino acids from proteolytic degradation , we mustBesides, the ribosome in the upstream peak may carry an already properly folded NS5B polymerase core. The valine codon in the active site of the upstream ribosome is 33 nucleotides upstream of the stop codon, i.e., 11 amino acids. Together with the 30 or 40 amino acids protected in the exit tunnel, this means that about 41 to 51 AAs are not allowed to fold together with the upstream portion of NS5B. In turn, at least the upstream protein up to amino acid 540 actually may fold. In the NS5B structure, the amino acids around position 540 are located in a stretch of amino acids that attach to the surface of the NS5B core  but may cis, i.e., the 3′-end of the HCV RNA in SL 1. The vicinity of the 3′X region with the paused ribosome is supported by the “kissing loop” interaction of the CRE/5BSL3.2 with the SL 2 of the 3′X region [From the above data and our own findings, we can hypothesize what the function of the ribosomal pausing at rare codons upstream of the HCV stop codon may be . The 3′-X region ,55 and bX region ,38,39. We can only speculate if the 3′-terminal SL 1 or the CRE is bound first by emerging NS5B protein B; howeveFinally, the NS5B molecule loaded to the HCV genome 3′-end has enough time to complete initiation and proceeds with RNA synthesis elongation D. In conTaken together, we showed that ribosomal pausing at rare codons near the end of the HCV NS5B replicase coding region is involved in efficient RNA replication, directly linking viral genome translation to the initiation of RNA synthesis. The JFH1/J6 chimeric full-length HCV genome (Jc1) was obtained from Ralf Bartenschlager . The 5′UThe sequence coding for the HiBiT tag (amino acids VSGWRLFLLIS), together with sequences coding for upstream (GGGSGG) and downstream (GGSGGG) glycine-serine linkers, was cloned into the HCV 5′UTR-core replicon construct between the codons for arginine (R) 543 and leucine (L) 544 in the sequence TPLPEAR-LLDLSS (NS5B AAs 537–549). This insertion point is located about 20 AAs upstream of the hydrophobic membrane anchor of the NS5B polymerase . The sec2 and 10 mM DTT, 25 ng/µL of linearized plasmid DNA, and 1 U/µL of T7 RNA polymerase. After 3 h at 37 °C, another 0.5 U/µL of T7 RNA polymerase was added, and the transcription reaction incubated for another 2 h. Transcription reactions were frozen at −20 °C for at least 30 min to inactivate the T7 RNA polymerase, and template DNA was digested by 2 U RNase-free DNase I per 1 µg of DNA for 15 min at 37 °C. RNA was extracted with phenol/chloroform, and ethanol precipitated. Finally, RNA transcripts were dissolved in equal amounts of RNase-free water. Replicon RNAs to be transfected were generated by in vitro-transcription from plasmid DNA templates linearized with EcoRI . In vitr5 cells/well with 0.5 mL 10 % DMEM. Transfection with Lipofectamine 2000 was performed at about 70% cell confluency. First, 0.75 µL of Lipofectamine reagent per well (3 µL Lipofectamine per µg RNA) and 125 µL serum/antibiotic-free DMEM were premixed, vortexed for 5 s, and incubated for 5 min. The 0.25 µg (0.1 pmol) of RNA solubilized in water was then added, and the mixture vortexed for 3 s. Mixtures were incubated at room temperature for 15 min, gently inverted 10 times, and carefully applied to cells dropwise. Cells were further incubated with complete DMEM for the times indicated.RNAs were transfected into Huh-7.5 cells using Lipofectamine 2000 . For HiBiT expression experiments, about 24 h before transfection, Huh-7.5 cells were seeded in 24 well plates to 0.5×10WST-1 tests were started 45 min prior to a given time point, treating only 4 to 5 wells at a time to minimize cell stress. The medium was removed from wells, washed with 200 µL colorless DMEM , and WST-1 reagent solution (protected from light) was diluted 1:50 in 200 µL of colorless DMEM and added to the cells. The colorless or slightly red WST-1 tetrazolium salt substrate is converted to a red/yellowish formazan product by mitochondrial respiratory chain activity and serves as an indirect measure for total live cell mass in a well. After 30 min incubation, 100 µL of the supernatant was transferred to a 96 well plate, and WST-1 was measured in a Tecan microplate reader as absorption difference at 450 nm, using 650 nm as a reference. HiBiT expression was corrected for WST-1 measurements. Expression of the HiBiT tag was used as a measure not only for translation but indirectly also for genome replication since the amount of HiBiT expression represents the abundance of translation template RNAs and thus can be regarded as an indirect measure for RNA abundance. Detection of the HiBiT tag is very sensitive and very specific with low background ,79. Therp < 0.05; **: p < 0.01; ***: p < 0.001), with a p-value < 0.05 considered significant. Data are represented as means +/− standard deviations . “n” indicates the number of independent experiments. Statistical significance was calculated by a two-tailed Student´s t-test  per 15 cm dish. The cell lysate was briefly cleared by centrifugation and then divided into two parts—one part used for ribosome profiling, and the other part used for transcriptome analysis. For ribosome profiling [Culture of Huh-7.5 hepatocarcinoma cells , in vitrrofiling ,101,102,rofiling . Gradienrofiling . RNA wasrofiling . Three rrofiling . Reads whttp://varna.lri.fr) [RNAs’ structures were predicted with RNAfold on the Vienna RNAfold WebServer . For the.lri.fr) . Base paRNA double-strand (ds) probability (1–single strand probability) was calculated using RNAfold for 6 nts in the middle of windows with sizes of 150, 200, 250, and 300 nts moved in steps of 5 nts, and means over 25 nts were plotted over the nt number in the middle of the window.


For these 40 individuals, the genomic and epigenetic data were also available. This dataset offers a unique opportunity to understand how library construction influences differential gene expression, alternative splicing and molecular QTL (quantitative loci) analyses for human primary cells.Both poly(A) enrichment and ribosomal RNA depletion are commonly used for RNA sequencing. Either has its advantages and disadvantages that may lead to biases in the downstream analyses. To better access these effects, we carried out both ribosomal RNA-depleted and poly(A)-selected RNA-seq for CD4 Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12932711 RNA sequencing (RNA-seq) that utilises next-generation sequencing (NGS) is a powerful tool to understand transcriptional diversity and regulation at bulk and single-cell level. Using RNA-seq, we not only can perform differential gene expression analysis with better resolution, but also comprehensively study alternative splicing, RNA editing and allele-specific expression, and all of which can be extended to investigate molecular quantitative trait loci (QTL) when genotypes are available at a population level.2, whereas the other 5% is polyadenylated positive (poly(A)+) mRNA4. The two most commonly used selection methods, poly(A)-selected and rRNA-depleted (ribo-minus), selectively removes a distinct set of RNAs: poly(A) negative RNAs and rRNA, respectively. The poly(A)-selected protocol enriches poly(A) + transcripts including mRNAs and many non-coding RNAs6, and also reduces the amounts of pre-mRNAs. It has become a widely used RNA-seq protocol thanks to its low-noise rate. In contrast, rRNA-depleted removes cytoplasmic and mitochondrial rRNA and thus includes poly(A) + mRNA, as well as non-coding RNAs or protein-coding mRNAs that are not polyadenylated7. These two library construction protocols, sequencing different fractions of the transcriptome, may lead to complex technical bias. Indeed, it is reported that different RNA sample preparations may result in significant variations in the quantification of gene expression5. Furthermore, the influences introduced by these two protocols on splicing quantification, and molecular QTL analysis (such as expression QTL) are largely unknown. Currently, large population genetic studies, such as GTEx8, lymphoblastoid cell lines (LCL) from the 1000 genome and HapMap project9 mostly used poly(A)-selected protocol, therefore lacking the information of non-poly(A)-transcripts. In this study, we constructed both rRNA-depleted and poly(A)-selected RNA-seq libraries for CD4 +T cells from 40 donors in the Blueprint project10. This dataset informs understanding of how these two library construction methods affect downstream analyses, and helps develop bioinformatic tools to avoid or reduce artefacts that were introduced during experimental procedures.In a eukaryotic cell, 80% of the total RNAs are ribosomal RNA (rRNA)10. As described previously, blood was obtained from donors who were members of the NIHR Cambridge BioResource (http://www.cambridgebioresource.org.uk/) with informed consent (REC 12/EE/0040) at the NHS Blood and Transplant, Cambridge. The schematic for sample collection and processes were shown in Fig. These methods are expanded versions of descriptions in our previously published studies+ naive T cells were negatively selected using an EasySep Human Naive CD4+ T Cell Enrichment Kit (StemCell) according to the manufacturer’s instructions. The purity of each cell preparation was assessed by multicolor FACS using conjugated antibodies for CD4  and CD45RA  for naive CD4+ T cells. The purity of the cells was provided in Table Whole blood was diluted 1:1 in a buffer of Dulbecco’s Phosphate Buffered Saline  containing 13 mM sodium citrate tribasic dehydrate (Sigma) and 0.2% human serum albumin  and separated using an isotonic Percoll gradient of 1.078 g/ml (Fisher Scientific). Peripheral blood mononuclear cells (PBMCs) were collected and washed twice with buffer, diluted to 25 million cells/mL and separated into two layers, a monocyte rich layer and a lymphocyte rich layer, using a Percoll gradient of 1.066 g/ml. Cells from each layer were washed in sampleemented PBS (13 mM sodium citrate and 0.2% HSA) and the subsets were purified using an antibody/magnetic bead strategy. CD4Following purification, cells were lysed in TRIZOL reagent (Life Technologies) at a concentration of approximately 2.5 million cells/ml. RNA was extracted as per the manufacturer’s instructions, resuspended in ultra-pure water and quantified  prior to library preparation. The RIN (RNA integrity number) values were provided in Table http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and RSeQC package (v3.0.1)11 (http://rseqc.sourceforge.net/).The quality of the raw sequence data was checked using FastQC software (v0.11.8) (12 and HISAT2 (v2.1.0)13 with default parameters, using the Gencode version 19 annotation (https://www.gencodegenes.org/human/).The paired-end reads were aligned to the human reference genome (GRCh37) using STAR (v2.7.1a)11, the geneBody_coverage.py script was used for calculating the gene body coverage of the mapped reads; the read_distribution.py script was used to calculate how mapped reads were distributed over genome feature; the tin.py script was used to evaluate RNA integrity at the transcript level.In RSeQC package14, respectively. The parameter of “-s reverse” was set for the strand-specific rRNA-depleted samples, whereas “-s no” for the non-strand specific poly(A)-selected samples. The raw read counts were then normalized by their library size factors and were regularized-logarithm (rlog) transformed to stabilize the variance across the samples using DESeq2 (v1.28.1)15. Pearson’s correlation coefficients were calculated for each gene between the paired RNA-seq samples from the same individual. For unsupervised clustering analysis, we required that a gene has more than 10 reads in 20% of the samples. These quantification files were stored in public repository16.The gene expression and “ExonOnly” expression level which only includes exonic reads were quantified using the HTSeq (v0.11.2)17.The gene expression quantifications were corrected for batches using ComBat from the sva R package (v3.36.0)18. The multi-omics processed files, including the sample information, the quantifications of gene expression, isoform, exonOnly (before and after ComBat17), splicing junction from both protocols, the genotype from WGS, the quantification of DNA methylation and Chip-seq of two histone markers (H3K4me1 and H3K27ac) of the corresponding individuals, were uploaded in figshare16. The other multi-omics files in three major human immune cell types  from up to 197 individuals were published previously10, including the additional 132 rRNA-depleted RNA-seq for naive CD4+ T cells and genotypes from WGS at European Genome-phenome Archive (EGA) under accession numbers EGAD0000100267119 and EGAD0000100266320, respectively.The raw fastq files and aligned BAM files for the paired RNA-seq in naive CD4 + T cells from 40 individuals were deposited at Synapse11. TIN calculates a score ranging from 0 to 100 for each expressed transcript, and the medTIN  can be used to measure the RNA integrity at the sample level. All the poly(A)-selected samples have TIN scores above 77, with a mean = 78.58 and SD = 0.64 -selected and rRNA-depleted libraries was 38.8 M SD = 15.93) and 60.3 M (SD = 16.7), respectively. The quality of sequencing per base was assessed using FastQC, and the Q30 is over 81.3%. There is no significant difference in the distribution of average quality score per base between the poly(A)-selected and rRNA-depleted libraries Fig. . The rea5.93 and 12 and HISAT213 aligners, respectively. For these 40 paired samples, the average correlation coefficient is over 0.94, with a relatively higher correlation using STAR aligner . When only concerned exonic reads (ExonOnly) using STAR aligner, the correlation coefficients were further improved  -selected and rRNA-depleted RNA-seq data, we quantified gene expression based on both STAR34) Fig. , indicat34) Fig. , suggestNext, we compared the number and biotypes of the detected gene from both sequencing libraries after filtering the lowly expressed genes . While on average 78.21% of genes were shared, 12.5% and 9.3% of genes were specific in poly(A)-selected and rRNA-depleted library, respectively. We found that these percentages of shared and library-protocol-specific genes were with little variances (SD ranging from 0.55% to 0.67%) in the difference of sequencing depth Fig. , indicat17, the 40 paired samples from different library construction methods were all clustered together  of the data profiles in the three cell types (monocyte (n = 196), neutrophil (n = 197), CD4 + -selected n = 40)) of the Blueprint project revealed that samples from the same cell type clustered closely, regardless of library construction methods Fig. . Next, wher Fig. , indicatIn general, the rRNA-depleted protocol captures different RNA species and is more efficient in quantifying linear non-poly(A) transcripts and circular RNAs. Therefore when the researchers are interested in analysing them, rRNA-depleted protocol is the one to use. However, if the protein-coding genes are the primary research targets, the poly(A)-selected protocol can identify more genes using the same sequencing depth and yield much less intronic reads. Nevertheless, our results showed that the shared genes between two protocols are highly correlated after applying ExonOnly quantification and batch correction.10 have comprehensive information including WGS, DNA methylation and Chip-seq of two histone markers (H3K4me1 and H3K27ac). This enables further investigation of the effect of library construction on multi-omics integration analysis and population genetics such as molecular QTLs of expression, splicing and RNA editing.Using these 40 paired poly(A)-selected and rRNA-depleted RNA-seq data from naive CD4+ T cell, the effects of library construction on the quantification of gene expression, alternative splicing and RNA editing can be assessed. One can also use this dataset to test the quantification or batch correction methods which can minimize the biases caused by library protocols. Furthermore, the 40 individuals enrolled in Blueprint ProjectSample Information


Epitranscriptomics analyze the biochemical modifications borne by RNA and their downstream influence. From this point of view, epitranscriptomics represent a new layer for the control of genetic information and can affect a variety of molecular processes including the cell cycle and the differentiation. In physiological conditions, hematopoiesis is a tightly regulated process that produces differentiated blood cells starting from hematopoietic stem cells. Alteration of this process can occur at different levels in the pathway that leads from the genetic information to the phenotypic manifestation producing malignant hematopoiesis. This review focuses on the role of epitranscriptomic events that are known to be implicated in normal and malignant hematopoiesis, opening a new pathophysiological and therapeutic scenario. Moreover, an evolutionary vision of this mechanism will be provided. Recent technological advances have made it possible to gain evidence of a wide range of post-transcriptional RNA modifications that play pivotal roles in fundamental biological processes including the regulation of gene expression. The information implicated in this field of study is collectively known as epitranscriptomics.During maturation, RNA becomes the substrate of various enzymes that can introduce chemical modifications into ribonucleotide residues. Usually, chemical alterations may occur in the base, or at the 2′-hydroxyl of the ribose, or both, and sometimes multiple sequential changes may involve the same nucleoside .RNA modifications can vary significantly in terms of the type of modification, location, abundance, and distribution both between RNA molecules and between organisms and organelles. Environmental and growth conditions can also affect the pattern of change .https://iimcb.genesilico.pl/modomics/), ~170 different RNA modifications have been identified in coding and non-coding RNAs . Si. Si28].