A joint product of our design is our ability to measure in-group bias in lying behavior in our population of subjects (comparing behavior of subjects in the same and different business schools). The experiment provides clear evidence of a lack of such bias.In a competitive business environment, dishonesty can pay. Self-interested executives and managers can have incentive to shade the truth for personal gain. In response, the business community has considered how to commit these executives and managers to a higher ethical standard. The MBA Oath and the Dutch Bankers Oath are examples of such a commitment device. The question we test herein is whether the oath can be used as an effective form of ethics management for future executives/managers—who for our experiment we recruited from a leading French business school—by actually improving their honesty. Using a classic Sender-Receiver strategic game experiment, we reinforce professional identity by pre-selecting the group to which Receivers belong. This allows us to determine whether taking the oath deters lying among future managers. Our results suggest “yes and no.” We observe that these future executives/managers who took a solemn honesty oath as a Sender were  Started in 2001 by a coalition of 2, 000 MBA students from the Harvard Business School, the MBA Oath initiative now covers graduates, advisors and alumni signers from over 500 MBA programs around the world.Recent scandals in the business community have raised serious concerns about whether the competitive culture fosters dishonest behavior for the sake of personal profit  relative to Selfish lies (33%). The magnitude of Selfish lying is similar to that of students in other non-business fields, around 33%. Second, lying is of the same magnitude whether future managers are matched with a peer from the same school or not; we do not observe significant in-group/out-group effects in lying behavior. Third, the oath significantly reduces lying for the Selfish lie case (lying declined by 70%); but the oath had no significant effect on reducing Pareto lies (lies dropped by 14%). This lack of behavioral response, however, does not mean subjects are insensitive to the oath when telling mutually beneficial lies. Using “happiness” as a proxy for subject's internal response, we find that the oath makes lying psychologically more costly—making lying under oath more problematic than without—although not to an extent that is sufficient to change behavior when lying is payoff maximizing for both sides.The experiment closely follows the extension by Jacquemet et al.  of the sThe outcome of the roll of die was .” Our game replicates the ‘loaded environment' condition of Jacquemet et al. . In the “selfish lie” condition, the payoffs implemented by option Y are : if the sender chooses to lie and the receiver follows the sender's message, the lie imposes a loss of 5 on the receiver, while the sender gains 1. We summarize the type of lie accordingly based on the variation in payoff induced by a lie as T. In the Pareto lie, by contrast, both the sender and the receiver benefit from the lie: the payoffs implemented by option Y are , and the lie is accordingly denoted T. We facilitate inter-study comparison by purposefully selecting the values of the payoff parameters in each treatment to closely follow those used in Erat and Gneezy , the receiver is also a future manager coming from the same business school. In the out-group treatment (Out), the receiver is a student from another school or university, and specialized in a discipline other than Management. These affiliations are made salient on the decision screen for subjects in both roles: once the role has been announced on the screen, a message appears informing participants that “The player A (B) with whom you will interact studies (Out: does not study) at [Name of the business school].”We manipulate group identity within pairs thanks to the school in which participants currently study. The lab is located close to a leading business school, whose students represent a large share (47% as of march 2021) of the lab's subject pool. The master in management offered by this business school lies in the world top-10 according to Quacquarelli Symonds, 2020No-oath condition while in this office, subjects randomly draw a sheet of paper from an envelope presented to them by the experimenter. The paper indicates the name of the seat they are assigned to in the lab. They are then invited to enter the lab using a side door located between the lab and the office, and the monitor invites the next subject to enter the office.Before entering the laboratory, each subject is first invited (one by one) to enter an adjacent office. The other subjects could neither hear nor see what happened in the office, as the door was always closed before the start of the procedure. In the Oath condition. In contrast, the Senders are exposed to the truth-telling oath procedure designed by Jacquemet et al. . The monitor makes clear to subjects that they are free to sign the form, and that neither participation to the experiment nor experimental earnings are conditional on their decision. Whatever their choice, subjects must give the form back to the experimenter, are thanked and invited to draw their seat according to the procedures implemented in the No-oath condition. To avoid communication between subjects prior to the experiment, one monitor stayed in the laboratory during the entire process and helps them find their seat in the room. Subjects receive no information about whether (i) other subjects were exposed to the oath procedure, or (ii) whether anyone else decided to sign the oath or notThis no-oath procedure is also applied to receivers in the t et al. . Once thex ante heterogeneity, this task is implemented at the start of the experiment, before the sender-receiver game. Senders are not exposed to this task; which allows us to compare their behavior to other experiments using the same sender-receiver game. At the end of the experiment, subjects are asked to fill in several questionnaires aimed at further measuring individual heterogeneity: their level of happiness ; their self-reported honesty ; the perceived honesty of other subjects ; and two measures of cognitive abilities: a 10 items version of the Raven  version of the dice under the cup task introduced by Fischbacher and Föllmi-Heusi . Subjecthe Raven 's progrehe Raven 's Creatihe Raven : subjectOath conditions.To implement the group identity treatment variables, subjects were invited separately to the same sessions depending on whether they registered in our subject pool database . The average individual payoff is 12.02 euros for an average 1 h participation. We ran 13 sessions  of the experiment at the GATE-Lab between September 2019 and February 2020. Oathsession agreed to sign the oath. This ensures the behavioral effect of the oath cannot be attributed to self-selection into compliance with the oath request.Once in the laboratory, participants are informed about the instructions of each step of the experiment on their screen . This difference prevails whether senders participate to the No-oath  or to the Oath condition .In our design, the manipulation of the group identity of the receiver is instrumental and aims to reinforce the self-identity of senders as future managers. We check the internal validity of the consequences of this group assignment by comparing the answers to the two IOS questions among the senders  75.0% of receivers decide to follow the message they receive. This proportion is slightly higher among receivers in the Out conditions, who follow the message 70.1% of the time Oath and 74.6% of receivers in No-oath decide to follow the message .An important confounding effect in sender-receiver games is the possibility that lying arises as an attempt to counteract the willingness of receivers not to follow the message received  whereas the report that pays the most is over-represented . The two almost perfectly balance, as the middle report is in line with the theoretical expectation for both groups . Importantly, lying behavior is overall similar between future managers and other subjects . Under the assumption that receivers' behavior is representative of their group, we conclude that the behavior of senders in our experiment cannot be attributed to group-specific attitudes toward lying.In No-oath conditions. We observe the induced differences in perceived identity do not translate into different lying behavior in the sender-receiver game. Irrespective of the type of lie, 56.0% of messages sent by future managers in No-oath are dishonest. We observe a small difference in the proportion of dishonest messages between the In and the Out conditions. When future managers send messages to future managers, 51.5% are dishonest as compared to 60.3%. p = 0.731, bootstrap χ2 test) and the two marginal distributions of selfish and Pareto lies  are statistically no different across the two No-oath conditions.Last, our experiment incidentally provides evidence on in-group-bias in lying behavior by focusing on the In and Out) Oath and the two No-oath conditions. Overall, offering future managers the possibility to sign a truth-telling oath decreases lying by 26.8%. Irrespective of the type of lie, the overall proportion of dishonest messages decreases from 56% in No-oath to 41% in Oath . Oath is more than three times higher . This increase in the share of fully honest messages is compensated by a decrease in the share of each one of the three possible patterns of lie. As a results of these sharp differences, the joint distribution is significantly different between Oath and No-oath . The marginal distributions, displayed on the right-hand side of the figure, indicate that the oath is much more powerful on selfish lies, that happen at the expense of the receiver, than on Pareto lies, that are mutually beneficial to the sender and the receiver. The share of selfish lies is more than twice lower among subjects under oath . The slight decrease in Pareto lies is not significant , and such behavior remains widespread even under oath.We first look at the overall effect of the oath on lying behavior in the sender-receiver game by pooling the two  as a reference, so that the coefficients for each of the 3 remaining outcomes can be interpreted in a natural way—a negative sign indicates a decrease in the corresponding lying behavior. We introduce treatment variables and their interactions, as well as subject's age, gender, closeness to other subjects, cognitive abilities (as measured by Raven and CRT scores) and participation to previous experiments.The estimated parameters, presented in To sum-up, our experiment provides clear evidence that a truth-telling oath disciplines lying behavior among future managers, but only if lying is detrimental to others. When lying rather serves both the sender's and the receiver's interest, by contrast, we observe very little to no behavioral response to the oath. The obvious question raised by those results is whether lying is perceived as dishonest when it is mutually beneficial, and if yes why we do not observe the same drastic decrease as when lying is selfish.To answer this question, we use the self-reported level of happiness collected at the end of the survey as a proxy of the internal response of subjects to the oath  and the Pareto lie  situations. Under oath, by contrast, lying comes with a sharp shift in happiness as compared to truth-telling for both kinds of lies: the median level of happiness is lower among liars as compared to truth-tellers in the context of both selfish lies  and Pareto lies . Median happiness reaches its lowest level, equal to 3.5, among subjects who engage in both a selfish and a Pareto lie Focusing on Despite a disappearance of occupational oaths at the end of the 20th Century Prodi, , the ideOur baseline framework (no honesty oath) leads to several useful findings. First, in contrast with what has been observed among criminals . The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Most of the sequences in the human genome do not code for proteins but generate thousands of non-coding RNAs (ncRNAs) with regulatory functions. High-throughput sequencing technologies and bioinformatic tools significantly expanded our knowledge about ncRNAs, highlighting their key role in gene regulatory networks, through their capacity to interact with coding and non-coding RNAs, DNAs and proteins. NcRNAs comprise diverse RNA species, including amongst others PIWI-interacting RNAs (piRNAs), involved in transposon silencing, and small nucleolar RNAs (snoRNAs), which participate in the modification of other RNAs such as ribosomal RNAs and transfer RNAs. Recently, a novel class of small ncRNAs generated from the cleavage of tRNAs or pre-tRNAs, called tRNA-derived small RNAs (tRFs) has been identified. tRFs have been suggested to regulate protein translation, RNA silencing and cell survival. While for other ncRNAs an implication in several pathologies is now well established, the potential involvement of piRNAs, snoRNAs and tRFs in human diseases, including diabetes, is only beginning to emerge. In this review, we summarize fundamental aspects of piRNAs, snoRNAs and tRFs biology. We discuss their biogenesis while emphasizing on novel sequencing technologies that allow ncRNA discovery and annotation. Moreover, we give an overview of genomic approaches to decrypt their mechanisms of action and to study their functional relevance. The review will provide a comprehensive landscape of the regulatory roles of these three types of ncRNAs in metabolic disorders by reporting their differential expression in endocrine pancreatic tissue as well as their contribution to diabetes incidence and diabetes-underlying conditions such as inflammation. Based on these discoveries we discuss the potential use of piRNAs, snoRNAs and tRFs as promising therapeutic targets in metabolic disorders. The publication in 2001 of the entire Human Genome Sequence . RNAs cahttps://www.idf.org/). Having diabetes, strongly impacts the life quality and expectancy of patients due to increased risk of cardiac, vascular, renal and neuropathic complications. Diabetes is characterized by chronic hyperglycemia resulting from the body’s inability to meet its insulin requirements. In type 1 diabetes (T1D), which accounts for approximately 10% of diabetes cases, an autoimmune response directed against pancreatic β-cells leads to a drastic loss of insulin-secreting cells and a near complete lack of the hormone -interacting RNAs (piRNAs) are 21-35 nucleotide-long RNAs that interact with the Piwi subfamily of Argonaute proteins. piRNAs are typically phosphorylated at 5’ end, and 2-O-methylated at the 3’ end. The piRNA pathway is highly enriched in foetal germ cells where it is required for repressing transposon activity, thereby maintaining genome integrity and ensuring faithful gametogenesis . A seconUnlike other Argonaute-associated small ncRNAs, such as miRNAs and short interfering RNAs (siRNAs), piRNAs originate from single-stranded RNA precursors called piRNA clusters and do not require the endoribonuclease Dicer for maturation. In most metazoans, piRNA loci are unidirectionally transcribed . These pde novo piRNA annotation. The initial algorithm built to predict novel piRNAs was based on k-mer motifs (k-mer motifs and 5’ uridine position) and genomic information to achieve 86% and 89% prediction accuracy in human and fruit fly data, respectively , closed-loop, non-polyadenylated RNA molecules, well-conserved, mainly located in the nucleolus and detected in all eukaryotic organisms , 25. Thevia RNA polymerase II or III) or polycistronic (via RNA polymerase II) regions  are still being used to validate genomic analyses  are 73-90 nucleotide long RNAs that help decode mRNA sequences into proteins. Their role as amino acid carriers during protein translation is well documented. tRNAs are the most abundant cellular RNAs, comprising approximately 12% of total RNA in most cells , 48. Dueradation . In addiBoth pre-tRNAs and mature tRNAs can be fragmented in concert with tRNA expression, or in response to a stress stimulus –56. WhilLike other ncRNAs, detection of tRFs has been substantially improved by the advancement of high-throughput small RNA sequencing. The fragments can be annotated by aligning the sequencing data to mature and pre-tRNA sequences using publicly available genomic tRNA databases, such a GtRNAdb . This peConcurrent with the growing high-throughput data, several tRF databases and bioinformatic tools have been developed. Databases such as MINTmap and tRF2Cancer extract and catalogue raw and normalized tRF levels from published human small RNA-seq data under physiological and pathological conditions , 63. tRFvia the aforementioned “ping-pong” process. Guided by the piRNAs, PIWI proteins cleave the targeted mRNAs 10 nucleotides upstream of the 5’ end of the piRNA as a guide, leading to the formation of a new piRNA in the opposite direction from the 5’ end. Thus, piRNAs belonging to the same pair generated by the ping-pong ring share 10 nucleotides at their 5’ end   . Thus, t elegans , 75. Witvia alternative splicing or translation control  and small nuclear RNAs (snRNAs), in order to ensure their maturation in the nucleus, protect them from nucleolytic degradation, thus indirectly affecting the function of the ribosome and of the spliceosome . The vas control . Along t control . The cap control . A signincluding Figure 3ncluding . So far,ncluding –84.The function of a non-negligible fraction of the so-called orphan snoRNAs remains unknown due to the lack of complementarity of their ASE with any RNA sequence or to a structure that differs from conventional snoRNAs (shorter or longer or with boxes without consensus motifs or with both C/D and H/ACA boxes) that potentially indicate a different mechanism of action , 85, 86.A significant degree of uncertainty remains also about a brand-new class of small RNA molecules that seem to derive from mature snoRNAs . Deep seThe knowledge acquired so far about the function of snoRNAs has emerged thanks to advanced techniques which we will discuss in the following paragraph. The first approaches used to elucidate the role of snoRNAs were oriented towards the detection and localization of canonical modifications (2’-O-ribose methylation and pseudouridylation) on target RNAs. One strategy consists in performing a reverse transcription (RT) at high concentration of dNTPs  followed by high-resolution electrophoresis allowing the identification of the modified sites. Another alternative based on a similar principle, consists in using an unbiased high-throughput 2′-O-ribose methylation profiling technique called RimSeq . In this2+-fragmented cross-linked RNA segments on streptavidin beads before sequencing  , 96, 97,quencing . Finallyquencing .Overall, these techniques, which consist in capturing RNA: RNA interactions upstream of sequencing, have been proven effective in the identification of predicted snoRNA targets but also in the discovery of new non-canonical factors targeted by snoRNAs , 106.in vivo and in vitro, using photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) of AGO1-4, co-immunoprecipitation (co-IP) of tRFs, and co-IP of various members of the AGO family  . In this in mice .Computational predictions of tRF targets have also been implemented in tRF research. These tools generally entail adaptations of miRNA target prediction algorithms. tRFtarget predicts mRNA targets of tRFs by combining the results of two prediction tools, RNAhybrid and IntaRNA, which rely on maximum complementary length and free energy between the small RNA and mRNA transcript . While tvia sperm affects metabolic organs and to demonstrate a causal relationship.piRNAs but also miRNAs and tRFs stand out as key mediators in the transgenerational inheritance of paternal epigenetic marks . The transgenerational transmission of a metabolic phenotype has been studied using a rat model in which male progenitors (F0) were fed a high-fat diet (HFD) for 12 weeks in adulthood . Newbornpiwi null mutant shows a reduction in fat stores with reduced triacylglycerides and glycogen content  , 137. Sevia extracellular vesicles results in an inflammatory context in mouse models and human subjects  and to accomplish relevant regulatory activities . Early-p systems –155. SomWe are only beginning to appreciate the role of piRNAs, snoRNAs and tRFs in pancreatic β-cells and in metabolic tissues and their possible involvement in different forms of diabetes. A better understanding of the mode of action and of the contribution of these small ncRNAs to the etiology of diabetes will help identifying potential targets for the development of new pharmaceutical principles to prevent or treat this metabolic disorder. The design of drugs to modulate the level or the activity of piRNAs, snoRNAs or tRFs will certainly take advantage of the knowledge gathered from the growing interest for siRNA- or miRNA-based therapeutic approaches, a field of intense investigation. Thus, there is hope that if relevant piRNAs, snoRNAs or tRFs targets will be identified, the development of therapeutic approaches to modulate the activities of these small ncRNAs will be greatly accelerated.CJ, MB, and RR searched the literature and wrote the article. All authors contributed to the article and approved the submitted version.The authors are supported by a grant from the Swiss National Science Foundation (310030_188447).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Oxidation of RNA has implications on its structure and function, thus making this an area with great potential for discovery, and of relevance due to the important biological roles involving this biopolymer. Developments in recent years have shown that reactive oxygen species can damage RNA in different cellular compartments, of different types (coding and noncoding), and of various sizes. The present collection of articles represents a compilation of work that highlights the importance of this topic, and the need to further investigate the relevance of oxidative stress and oxidative damage for RNA biology. Understanding how oxidatively damaged RNA is handled and turned-over intracellularly, as well as its biological outcomes, will have an impact on disease prevention/treatment, in addition to the potential discovery of new RNA-based mechanisms. The collection is comprised of reviews, hypotheses, and original research articles. The topics span from the involvement of oxidized RNA in disease progression/development, to mechanistic aspects that involve oxidative stress and oxidatively generated lesions within RNA, to the use of techniques aimed at detecting/characterizing processes involving oxidized RNA. Importantly, it also highlights the interdisciplinary nature of this topic and the potential for collaborations that span medicine, biology, biochemistry, biophysics and chemistry, to mention a few.In brief, the reviews provide a detailed account on the relationship between RNA oxidation and various diseases; the consequences of oxidatively generated modifications in RNA on protein synthesis and cell signaling; and the impact of oxidative stress in bacteria, specifically RNA machines involved in transcription and translation. Furthermore, a literature supported hypothesis describes the potential processing and decay of oxidized RNA by enzymes with specific roles, e.g., in RNA binding, editing, and degradation; and the role of stress granules in this context. Lastly, the original research articles display the use/development of tools that allow the study of oxidized RNA and related processes, which employ analytical, chemical, and molecular-biology tools to this end.Fasnacht and Polacek provide a detailed review, and state of research, on oxidative stress in bacteria, while making emphasis that similar/parallel mechanisms are present in other domains of life. The review introduces the reader to various reactive oxygen species (ROS), as well as their sources, while making mention of the potential modifications of various biomolecules/biopolymers, i.e., nucleic acids, amino acids, and cofactors. Then it goes on to discuss how bacteria deal with stress, via activation of different stress response regulons, before explicitly describing on the impact of oxidative stress on the central dogma. Beginning with oxidation of DNA, and its role on transcription, where the production of sRNAs, in response to oxidative stress has important implications. The authors then go on to summarize the process of translation by analyzing work carried out on mRNA (elongation/stability), tRNA, as well as the ribosome. In these works, 8-oxo-7,8-dihydroguanine (8-oxoG) is mentioned as the lesion of interest, while RNA modifications such as m6A or inosine may play a role in allowing/triggering the degradation of damaged mRNAs. Mistranslation events are also described, along with potential outcomes and handling. Regarding rRNA, the presence of 5-hydroxycytosine (5-OH-C) is mentioned along with the ability of bacteria to use oxidation events in the ribosome functions; where oxidation at select-sites as a function of divalent metal location was elaborated. The authors end with a note on protein oxidation, and the need for further research in this area overall.Li et al. provide an overview regarding the relationship of oxidized RNA and disease. In this regard, the role of enzymes such as PNPase, YB-1, and PCB 1/2 is discussed, followed by a description of work that relates RNA oxidation with neurodegenerative, cardiovascular, and metabolic diseases, amongst others. Most of the work refers to 8-oxoG as the biomarker for quantifying/detecting/studying oxidative damage on RNA. The authors point out the need to better understand this phenomenon. Delving into a more detailed look on mechanistic aspects, Tanaka and Chock discuss on the impact of oxidative modification on different types of RNA, namely tRNA, mRNA, mtRNA, miRNA, and rRNA. Furthermore, oxidation of RNA and ribonucleotides in signaling pathways is reviewed, e.g., on inflammatory responses, as apoptotic triggers, or in signaling. In this work, abasic sites as well as the nucleobase lesions 8-oxoG, 8-oxoA, 5-OHC, 5-OHU, and 4-pyrimidinone (arising from 2-thiouridine oxidation) are described. The authors make mention that regulatory mechanisms mediated by oxidized RNA remain to be established, and that potential outcomes arising from oxidized RNA could impact cell growth, tumorigenesis, and/or antiviral defense systems. Besides the aforementioned enzymes, cyt c, AUF1, MutT/MTH1, and APE1 (known for its functions on DNA repair) were among other mentioned enzymes with roles involving oxidized RNA. In regards to the intracellular handling of oxidized RNA, Alluri et al., take into consideration that nature has devised ways to eliminate, or repair, such damage/stress on RNA. The authors propose that machinery within stress granules, i.e., ADAR1, Tudor-SN, and STAU1, may play a role in handling the metabolism of oxidized RNA, from RNA binding and editing, to trafficking, to degradation. All in agreement with reports that show accumulation of oxidized RNAs in stress granules. The hypothesized mechanism is promising and should prove useful in providing new research directions in this area.On a human health context, Sapio et al. explored the biogenesis of pre-rRNA on mammalian cells, under oxidative stress. To this end, a reporter based on a redox-sensitive green fluorescent protein is described, along with the use of immunofluorescence to detect 8-oxoG in nucleolar RNA. Overall, it was found that oxidative stress leads to perturbations in pre-rRNA transcription and post-transcription processing, although the exact RNA components are yet to be established. Furthermore, the reported protocols and tools should prove useful in measuring the impact of oxidative stress on other types of RNA. On another study, Estevez et al. used mass spectrometry to study the RNA-protective role of proteins, methylation , and hypermodification  under oxidative stress. Besides 8-oxoG, the guanidinohydantoin, FapyG , 8-oxoA, and FapyA lesions were also observed and quantified. Interestingly, the product distribution differed as a function of degree of RNA-protein association and type or ROS generated. The role of amino acids such as tryptophan and lysine as protective entities, given their reduction potentials, is discussed. Lastly, in regards to the decay and processing of mRNA, Phillips et al. explored the processive nature of the exoribonuclease Xrn-1 towards oligonucleotides of RNA containing 8-oxoG. The authors found that this oxidative lesion poses a challenge to Xrn-1 function and results in stalling at these sites. To rationalize this observation, various chemical modifications were introduced (via solid-phase synthesis), specifically 8-bromoguanosine, m1G, and m6,6A, which aided in the final conclusions. The study highlights the potential implications arising from the presence of oxidatively generated lesions in RNA, and the need to further detail mechanisms involved in the handling/decay of oxidized RNA.In the tool development area, Overall, it is worth noting that, while 8-oxoG has been the quintessential oxidative lesion to study oxidation of RNA (and DNA), it is likely that other lesions or oxidatively induced modifications (some mentioned in the various reports herein) may also have important roles. Understanding their impact on RNA structure and function, in its many types, as well as their potential purposes and how they are handled, will be of importance. This is an area of high potential for discovery and we look forward to seeing its implications on various fields and the impact of this compilation on this, and other fields.


MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the  Ribosome profiling, or footprinting (a.k.a. Ribo-seq), has allowed for a detailed assessment of whole cellular transcriptomes . The RibThe term “non-coding RNA” had classically referred to a very large and diverse group of RNA molecules that number in the thousands . Within The evidence that lncRNAs can be translated was initially provided by LINC00116 that was found to code for a 56-amino acid functional microprotein found in mitochondria  on-line platform that allows for interactive analysis and visualization of Ribo-seq and shotgun RNA sequencing (RNA-seq) data aligned to transcriptomes , 2021. T1 provides data aligned to the transcriptomes of several organisms and a rich repertoire of functional visualizations for the analysis of ribosome profiling data. Here we focus on Homo sapiens and the function “Single transcript plot” to manually examine transcripts of interest. For further explanation on the other analyses available within Trips-Viz, please refer to detailed instructions and videos available within the Trips-Viz platform  to serve as an example for genuine translation. As an example of a ncRNA whose ORF translation is unlikely we chose RPPH1 that encodes for the RNA component of RNase P. We further explored ribosome footprints aligned to SNHG8, ZFAS1, and XIST. The translation of all three lncRNAs has been reported previously . However, there appears to be no RNA-seq coverage for most of the annotated sequence up to ∼1500 nucleotides (nt) downstream of the annotated transcript 5′ end. For reference, we have also included a Trips-Viz visualization with ambiguously mapped reads enabled (MTLN CDS (in blue), there are also peptides whose masses matches products of conceptual translation of other reading frames (green and red), they may represent false positives. enabled . When am enabled . Consist enabled  and Phil enabled . SupplemMTLN locus on GWIPS-viz . Like in the previous case, only part of the annotated transcript is supported by RNA-seq data as visualized in the GWIPS-viz browser , a lncRNA located on human chromosome 4q26. This lncRNA hosts the H/ACA-box small nucleolar RNA (snoRNA), SNORA24. Non-coding genes that host snoRNAs were found to have short, poorly conserved ORFs and were believed to serve little function outside of carrying snoRNAs in their introns ; yet, elongating ribosome footprints would not fully encompass the ORF situated at this locus. The PhyloP tracks reveals high nucleotide conservation at SNORA24 indicating its important functional role.Examination of 00602819 . The firBased on the features seen on GWIPS-viz, we first examined transcript ENST00000602414 on Trips-Viz . FootpriFor the ORF at the eighth ATG noted on GWIPS-viz, we examined transcript ENST00000602483 in Trips-Viz . RibosomSNORA24 in Trips-Viz , a lncRNA located on human chromosome 20q13.13. It is positioned at the antisense strand of the 5′ end of the protein coding ZNFX1 gene. ZFAS1 also hosts three C/D-box snoRNAs namely SNORD12C, SNORD12B, and SNORD12 in sequential introns . To explore whether this is potentially due to mapping artifacts, we enabled the track “Multi-read mappability with 24mers.” The Umap track represents the probability that a randomly selected read of k-length (24 base pairs is the default) that overlaps a given position in the unconverted genome is uniquely mappable (SNORD12C (ENST00000386307). There also appears to be high peak of elongating ribosomes at this locus. As expected for a snoRNA SNORD12C sequence is highly conserved as can be seen in the PhyloP track.In mappable . AccordiThe third high footprint peak of initiating ribosomes matches the eighth ATG site. Multiple transcript isoforms appear to contain this ORF, which is outlined in a blue rectangle . Poor nuSNORD12C using Trips-Viz  on Trips-viz that match the first high footprint peak of initiating ribosomes seen in GWIPS-viz . Howeverrips-Viz . The disrips-Viz .XIST), a nuclear lncRNA with over 19,000 nucleotides  in humans and located on the q arm of the X chromosome. Previous work proposed that XIST evolved in eutherians from the pseudogenization of a protein coding gene  may be expressed in some cells, however, translation of such mRNA is unlikely to produce MTLN proteoforms since its start codon cannot be reached by scanning preinitiation complex.Here we used examples of lncRNAs with reported translated ORFs to guide in the manual examination of publicly available ribosome profiling data using Trips-viz and GWIPS-viz. CodAlignView was then used for detailed examination of codon substitution patterns as evidence for evolutionary selection acting on potential protein coding sequences. We used specific , therefoRPPH1 was used as a negative example to demonstrate the patterns that are inconsistent with translation and protein evolution. Finally, we examined the data available for other lncRNAs with reported translated ORFs, i.e., SNHG8 and ZFAS1 and XIST and concluded that they contain multiple short ORFs that are likely translated even though they do not exhibit signatures of protein coding evolution. We can only speculate on the biological significance of translation of these short ORFs. We do not know if they code any stable and biologically active peptides, as there is no support for their evolutionary selection. Yet it is possible that they could be used by the immune system as antigens for self-recognition. Additionally the translation of these ORFs may influence processing, stability, localization and structural folding of the corresponding lncRNAs irrespective of biological significance of the products of this translation.RNA component of RNase P encoded by Because of the complexity of translation and of ribosome profiling data, it is very difficult to design automatic tools for translation detections that are highly accurate. Thus, we hope that manual examination of individual cases using the tools described here, will benefit researchers in examining translation status of individual ORFs in non-coding RNAs.https://trips.ucc.ie. Source code for Trips-viz is available at https://github.com/skiniry/Trips-viz. Gwips-viz is freely available at https://gwips.ucc.ie/. CodAlignView is freely available at https://data.broadinstitute.org/compbio1/cav.php.Publicly available datasets were analyzed in this study. This data can be found in Trips-viz which is freely available at OZ: data curation, visualization, and writing. SK: software, data curation, and writing. PB: conceptualization, supervision, writing, project administration, and funding acquisition. KD: supervision, writing, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.PB is a founder of Ribomaps Ltd., a company that provides ribosome profiling as a service. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


C9ORF72 hexanucleotide GGGGCC repeat expansion is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Repeat-containing RNA mediates toxicity through nuclear granules and dipeptide repeat (DPR) proteins produced by repeat-associated non-AUG translation. However, it remains unclear how the intron-localized repeats are exported and translated in the cytoplasm. We use single molecule imaging approach to examine the molecular identity and spatiotemporal dynamics of the repeat RNA. We demonstrate that the spliced intron with G-rich repeats is stabilized in a circular form due to defective lariat debranching. The spliced circular intron, instead of pre-mRNA, serves as the translation template. The NXF1-NXT1 pathway plays an important role in the nuclear export of the circular intron and modulates toxic DPR production. This study reveals an uncharacterized disease-causing RNA species mediated by repeat expansion and demonstrates the importance of RNA spatial localization to understand disease etiology. C9ORF72 gene can cause amyotrophic lateral sclerosis (ALS) and frontal temporal dementia (FTD). Here the authors use single molecule imaging to show nuclear export and translation of circular repeat-containing C9ORF72 intronic RNA.Hexanucleotide repeat expansion in the intron 1 of the  The mechanisms of disease pathogenesis depend on the repeat sequence, the expansion size, the gene context, and the location. In particular, the diversity of repeat expansion sequence and length is most prominent in introns. Compared to the repeats located in the mRNA, the intronic expansions likely involve more complicated RNA processing pathways. However, most studies focused on pure repeats and rarely considered the influence of gene context and spatiotemporal localization of RNA molecules in vivo.Expansions of short nucleotide sequence repeats account for more than 40 neurological or neuromuscular diseasesC9ORF72 gene is the most common genetic cause of both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD)3. RNA-mediated gain of toxicity is implicated in the pathogenicity, including the formation of nuclear RNA granules which likely sequester RNA binding proteins and disrupt RNA processing5, and production of toxic dipeptide repeat (DPR) proteins by repeat-associated non-AUG (RAN) translation12. However, the molecular identity and the spatial distribution of these repeat-containing RNAs remain elusive. The GGGGCCexp is located inside the first intron of the C9ORF72 gene. Intron containing species, namely unspliced pre-mRNA and spliced intron, are normally degraded rapidly within the nucleus, and excluded from cytoplasm. It is an important aspect of ALS/FTD etiology to determine which RNA species are exported and subjected to RAN translation, and which are retained in the nucleus and form granules. Traditional biochemical or genetic approaches require breaking up cells and the spatiotemporal information is lost. We applied single-molecule imaging approaches to visualize the C9ORF72 repetitive RNAs directly in cells to probe the molecular and biophysical properties of RNA granules and DPR biogenesis.Hexanucleotide (GGGGCC) repeat expansion (HRE) in the intron 1 of the C9ORF72 flanked by the first two exons with the native exon–intron junction elements  and PP7 coat proteins (PCP)14. We inserted 24×MBS15 after the GGGGCCexp within the intron, and 24×PBS in the second exon , all the full-length reads containing PBS showed correct splicing to exon 1  repeats in the first intron of nts Fig. . To visuxon Fig. . An idenPBS Fig. .Fig. 1C917 to quantify the spatial distribution of various RNA species in different cellular compartments. We used separate probe sets labeled with distinct colors to target MBS  and PBS , respectively. In nucleus, we observed single RNAs as well as large RNA granules  les Fig. , as repoals Fig. . We quanals Fig. . The purlls Fig. , smallerlls Fig. . In addiols Fig. . This wa70 cells but not in controls ols Fig. . Over 95rol Fig. , consistrol Fig. . Second,rol Fig. , indicatrol Fig. . This fu15, while intron by stdMCP-Halotag labeled with JF646 Halo dyes20. We tracked single RNAs (Supplementary Movie 2/s) and mRNAs (0.25 µm2/s) 25 and examined whether the RNA reduction can be recapitulated by smFISH. Compared to control cells, both the intron and exon RNA numbers per cell decreased in C9-ASO transfected cells 70 splicing reporter cells, all the exons and nuclear introns were significantly reduced  to form a looped structure, known as a lariat, followed by 3′ splice site cleavage and exon–exon joining27 Fig. . Most innes Fig. . We devenes Fig. , -associated CCTGexp in ZNF9 and spinocerebellar ataxia type 10 (SCA10)-associated ATTCTexp in ATXN10 as representatives for intronic GC-rich and AT-rich repeats, respectively. We also tested the C9ORF72 antisense CCCCGGexp and FMR1 CGGexp in fragile X-associated tremor/ataxia syndrome (FXTAS). These two expansions were not originally found in introns but used in the C9-splicing context here as C-rich and G-rich repeat elements, respectively. We replaced the (GGGGCC)70 in the C9ORF72 splicing reporter with (CCTG)240, (ATTCT)73, (CCCCGG)70, or (CGG)98 and stably expressed them in separate U-2 OS cell lines 98 introns in the cytoplasm were predominantly spliced, not colocalized with exons 240 and (CCCCGG)70, were able to induce the intron to be exported to the cytoplasm 73 in their original gene context by constructing ZNF9- and ATXN10-splicing reporters, which showed no significant cytosolic spliced introns either  were widely present in both nucleus and cytoplasm for all the reporter cells, significant amount of cytosolic introns was only found in the (CGG)lls Fig. . Similarons Fig. . Neitherasm Fig. . We alsoles Fig. . Altoget33. We placed 24× SunTag epitopes34 between the (GGGGCC)70 and MBS in the splicing reporter  detected by SunTag signals colocalized with single-intron RNA molecules  technologyter Fig. . As the 21, probably due to the inefficient cap-independent translation initiation of circular RNA. To further confirm that RAN translation of GGGGCC repeats can happen independent of 5′-cap, we performed in vitro translation assay using the in vitro transcribed bicistronic reporter RNAs. The first open reading frame (ORF) at the 5′ end is translated by the canonical cap- and AUG-dependent translation and terminated by multiple stop codons. The second cistron, Nanoluc luciferase (NLuc), did not have start codon ATG and was fused downstream of the (GGGGCC)70 to monitor RAN translation , we identified 120 TLS out of 4768 cytoplasmic intron molecules, with no colocalization with exons Fig. . SimilarWe also measured the translation in response to the oxidative stress induced by sodium arsenite via live cell imaging. The stress enhanced translation as early as 7 min, increasing both the number of translating intron RNAs and the intensity of translation sites Fig. . After 3n RAN translation reporter in which the repeat is located in the mRNA35. We now tested whether this pathway can also mediate the export of (GGGGCC)n-containing spliced introns. Knockdown of NXF1 by siRNA 22. The Nanoluc Luciferase is generated through RAN translation of C9ORF72 (GGGGCC)n repeats (C9R-NLuc in GA- or GP-frame) located in the first intron of C9ORF72, and the Firefly Luciferase (FLuc) in exon 2 is produced by canonical translation (AUG-FLuc)  Fig. 22. Usingmes Fig. , suggestmes Fig. . This suThe finding of this work is summarized in a working model Fig. . After t30. The intron-retaining C9ORF72 transcripts were found to increase in patient postmortem brain and proposed to be the source of both nuclear granules and RAN translation substrates in cytoplasm26. Traditional biochemical methods to separate nuclear and cytoplasmic fractions cannot achieve absolute purity, and PCR amplification of intronic fragment does not distinguish whether the intron is spliced out or in the unspliced pre-mRNA. We now used single-molecule imaging approach to directly visualize and precisely quantify different RNA species and their spatiotemporal localization. Although GGGGCCexp modestly inhibits splicing, consistent with previous findings, the more drastic effect is the stabilization of spliced introns. While both unspliced pre-mRNA and spliced intron are found in nuclear granules, only the spliced intron is exported to cytoplasm and subjected to translation.Microsatellite repeat expansions are linked to ~50 neurological and neuromuscular diseases. While the expansions occur in both coding and noncoding regions of host genes, the repeats located in the intron have more diverse sequences. It was reported that GC-rich intronic expansions can inhibit splicing and trigger host intron retentionC9ORF72 introns mainly existed in circular form. Recently, thousands of circular RNAs have been identified through high-throughput RNA sequencing. Two types of circular RNAs36 were recognized: exon-derived circRNAs formed by back-splicing; and intron-derived ones formed by either from escaping debranching (the predominant class)29, or recircularization of full-length intron through an unknown post debranching event29. Compared with linear RNAs, circular RNAs are generally more stable, likely due to their resistance to RNA exonucleases37. They are also found to be highly abundant and conserved in nervous systems, and dynamically modulated during differentiation and aging40. The C9ORF72 GGGGCCexp is located near the 5′ splice site of the first intron. The repeat sequence is close to the branch site during splicing reaction, and likely inhibits the debranching process and stabilizes the intron in the circular form. Analysis of other repeat expansion sequences showed that only the G-rich repeats CGGexp, but not the C-rich or AT-rich repeats, enhances the cytosolic accumulation of spliced introns. It is noted that the tetrapeptides produced from CCUGexp located in intron has been reported in DM2 patient postmortem tissues41. It is possible that other intronic elements can promote nuclear export, but were not included in our reporter, or the low level of cytosolic intron RNA is sufficient to produce the observed polypeptides. It is therefore important to consider the genetic context for the driving toxicities of different repeat expansion diseases. We also reanalyzed the circular intron sequences reported previously29. The transcriptome-wide assessment further revealed that the cytoplasmic circular introns tend to have higher G content and are more structured than the average introns  and TREX-2, is the predominant pathway for mRNA export49. A subset of mRNAs, rRNAs as well as U snRNAs are exported in a Crm1/Xpo1-dependent manner50. Trafficking of circular RNAs, however, is still poorly understood. Recent studies suggest that DDX39B and DDX39A, components of TREX, regulate the exonic circRNA export51, and cytoplasmic circular RNAs are exported depending on the NXF1/NXT1 system29. For repeat-containing circular introns, we have shown that upon knocking down NXF1-NXT1 pathway, both cytoplasmic intron number and the corresponding RAN translation product are reduced significantly more than the control exonic mRNAs transcribed from the same gene  via various pathwaysene Fig. . This stThis study demonstrated that the sequence and/or structure of repeat expansion and the location of the repeats could govern the post-transcriptional processing and the subsequent toxic pathways. The molecular identity of the repeat-containing RNAs need to be carefully examined in order to understand how the expansion contributes to the disease pathogenesis. The single-molecule approach developed here provides an important tool to distinguish the circular RNA from the linear version in situ to delineate their spatiotemporal dynamics. Understanding molecular features of pathological RNA species at the single-molecule level may reveal novel insights into the disease mechanisms and may open new opportunities for therapeutic target design.C9ORF72 exon 1a, 50nt of exon2, and around 200nt intron 1 from each exon–intron junction were synthesized (Genewiz) and cloned into pCAG vector via HindIII (DNA blunting at the HindIII end) and XhoI. We included HindIII and BamHI sites inside the synthesized sequence close to the repeat expansion location for downstream cloning of repeat sequences. Next, the first 300nt of the Fluc coding sequence was inserted into pCR4-24×PP7SL14 (Addgene Plasmid #31864) via SpeI and BamHI. The fused FLucN-24×PBS was then cut out by XhoI and BglII (Blunt) and inserted downstream of C9ORF72 exon2 via XhoI and NsiI (Blunt) to generate pCAG-C9splicing-exFLucN-24×PBS. To make the pCAG-(GGGGCC)70-24×MBS mRNA expression plasmid, 24×MBSV5 was PCR amplified from puc57-24×MBSV5 template15 and inserted downstream of (GGGGCC)70 via NsiI and SacI in the pCAG-(GGGGCC)70 vector35. The FLAG-24×SuntagV4 was cut out from pUbC-FLAG-24×SuntagV4-oxEBFP-AID-baUTR1-24×MBSV5-Wpre21 (Addgene Plasmid #84561) by NotI and FspI and inserted into pCAG-(GGGGCC)70-24×MBS via NotI and NsiI (Blunt) between (GGGGCC)70 and 24×MBS. The ATG start codon was removed and a frame shift was introduced by replacing the region between NotI and AflII with a linker sequence AGATTACAAGGACGACGACGATAAGGGCGGACCGGGTGGATCTGGAGGTGGAGGTTCTGGAGGAGAAGAACTTTTGAGCAAGAATTATCATCTTGAGAACGAAGTGGCTCGT for GA frame and GATTACAAGGACGACGACGATAAGGGCGGACCGGGTGGATCTGGAGGTGGAGGTTCTGGAGGAGAAGAACTTTTGAGCAAGAATTATCATCTTGAGAACGAAGTGGCTCGT for GR frame). The (GGGGCC)70-24×Suntag-24×MBS fragment was subsequently cut out by HindIII and BglII and inserted into the intron of pCAG-C9splicing-exFLucN-24×PBS vector at the HindIII and BamHI sites to generate pCAG-C9splicing-in(GGGGCC)70-24×Suntag-24×MBS-exFLucN-24×pbs (Suntag in GA or GR frame). We removed the 24×Suntag by cutting the above construct with NotI and NheI, blunting the ends and self-ligating to generate the C9ORF72 splicing reporter pCAG-C9splicing-in(GGGGCC)70-24×MBS-exFLucN-24×PBS; or removed (GGGGCC)70-24×Suntag by cutting with HindIII and NheI, blunting the ends and self-ligating to generate the no-repeat control C9ORF72 splicing reporter pCAG-C9splicing-in24×MBS-exFLucN-24×PBS. To delete the 5′ss, the entire fragment containing Intron-(GGGGCC)70-24XMBS-Intron-exFLucN-24XPBS was cut out from the C9ORF72 splicing reporter by HindIII and SacI, and cloned into pCAG vector.For the C9ORF72 splicing reporter, the sequences including C9ORF72 gene context, the (GGGGCC)70 repeats was replaced by a fragment containing multiple cloning sites (GCTAGCCAATTGAAGCTTATGCATGCGGCCGCGCTGAGGTGTACA) through Gibson assembly . (CGG)98 was cut from pGLACTE-PseudoHT51 (Addgene Plasmid #99257) by HindIII and NheI (blunt) and inserted into the multiple cloning sites of the splicing reporter by HindIII and NotI (blunt). (CCCCGG)70 was inserted into the HindIII and NsiI sites. (CCTG)240 was cut from DT240 (Addgene Plasmid #80414) by SalI (Blunt) and HindIII and inserted into the NheI(blunt) and HindIII sites. (ATTCT)73 was synthesized (Genewiz) and cloned into the splicing reporter via HindIII and NotI.For splicing reporters with other repeats in the 240-splicing reporter in the genetic context of ZNF9 itself, the sequences including 100nt ZNF9 exon 1, 49nt of exon2 and around 200nt intron 1 from both 5′ and 3′ exon–intron junction were synthesized (Genewiz) and cloned into pCAG vector via HindIII and NsiI. The FLucN-24×PBS was cut from pCAG-C9splicing-in24×MBS-exFLucN-24×PBS and inserted after the exon2 by XhoI + SacI. The (CCTG)240 was cut from DT240 by SalI (Blunt) and BamHI and inserted into the the above construct by EcoRV and BamHI. At last, PCR amplified 24×MBS was inserted via AgeI and AvrII.For the ZNF9-(CCTG)73-splicing reporter in the genetic context of ATXN10 itself, the sequences including 69nt ATXN10 exon9, 45nt of exon10 and around 200nt intron9 from both 5′ and 3′ exon–intron junction were synthesized (Genewiz) and cloned into pCAG vector via BbvcI and NsiI. The FLucN-24×PBS was cut from pCAG-C9splicing-in24×MBS-exFLucN-24×PBS and inserted downstream of exon2 by XhoI + SacI. (ATTCT)73-24×MBS was cut from the previous C9-(ATTCT)73-splicing reporter and inserted in the ATXN10 reporter by NheI and AvrII.For the ATXN10-(ATTCT)52, cut at NotI and ClaI. 24×MBSV5 was inserted with AgeI and ClaI sites after the stop codon and before the WPRE element.For the mCherry-24xMBS plasmid, we used PCR fragment of mCherry with appended AgeI site to replace the DsRed-IRES-GFP in the phage-ubc-RIG lentiviral vector70-NLuc constructs (both frame-GA and frame-GP)22. A 600 bp random nonstructured sequence was inserted between the 200 bp ORF and NLuc by EcoRV and NotI to generate the pcDNA3.1-ORF1-Neg-NLuc. For IRES-positive controls, the EMCV or CrPV IRES was PCR amplified from templates53 and cloned between ORF1 and NLuc to generate pcDNA3.1-ORF1-EMCV/CrPV IRES-NLuc.To generate the bicistronic reporters for in vitro transcription, a 200 bp ORF was PCR amplified from the 3′ end of the FLuc sequence containing multiple stop codons in all three reading frames. The ORF was inserted before NLuc or C9R-NLuc in the pcDNA3.1-NLuc or pcDNA3.1-(GGGGCC)15 for splicing reporter, U2PA stably expressing stdMCP-Halotag and scFv-sfGFP21 for Suntag translation reporter) were transfected with linearized reporter constructs by nucleofection , followed by 200 µg/ml hygromycin selection for 20 days. HEK293T cells were transfected by TransIT®-LT1 Transfection Reagent (Mirus Bio). ALS patient-derived fibroblast cell lines  and healthy control fibroblast cell lines  are gifts from Dr. John Ravits25 were grown in DMEM supplemented with 20% (v/v) FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 2 mM Glutamine and 1% (v/v) nonessential amino acids. Patient lymphoblast cells were obtained from Coriell Institute and grown in RPMI1640 supplemented with 10% (v/v) FBS, 2 mM L-Glutamine, 100 U/ml penicillin, and 100 µg/ml streptomycin. The cell line information is listed in Supplementary Table U-2 OS (American Type Culture Collection HTB-96) and HEK293T (American Type Culture Collection CRL-1573) cells were grown in DMEM supplemented with 10% (v/v) FBS, 100 U/ml penicillin, and 100 µg/ml streptomycin (Sigma–Aldrich). To generate polyclonal stable cell lines, U-2 OS cells  was used to transfect siRNAs and ASOs. ON-TARGETplus pooled siRNAs of NXF1 , NXT1 (L-017194-00-0005), DDX39B (L-003805-00-0005), ENY2 (L-018808-01-0005), THOC1 (M-019911-01-0005), THOC7 (M-014575-01-0005), and nontargeting control (D-001810-10-05) were transfected at 25 nM and cells were harvested after 3 days. Nontargeting ASO (CCTTCCCTGAAGGTTCCTCC) and the C9ORF72-targeting ASO (GCCTTACTCTAGGACCAAGA)25 were transfected at 25 nM, and cells were collected after 2 days.LipofectamineFor immunoblotting, goat anti-mouse or anti-rabbit IgG HRP-conjugated  was used along with chemiluminescent detection reagents (Thermo Scientific). The primary antibodies included NXF1 , β-actin .http://neurolincs.org/pdf/diMN-protocol.pdf)55. At day 12 post-differentiation, iPSNs were dissociated and seeded in 24-well plate. 100 µL S3 medium containing 1 µL 100 µM siRNA and 3 µL Lipofectamine® RNAiMAX (Invitrogen) was incubated at room temperature for 10 min before adding onto iPSNs on day 16. S3 medium was half-changes every 3 days till day 32. The cell pellet was lysed in ELISA lysis buffer  and the protein concentration was quantified by BCA assay (Pierce). Duplicates of each sample were used for GP ELISA by a technician blinded to the experimental groups as described35.iPSC lines were obtained from Answer ALS and listed in Supplementary Table GAPDH mRNA. The endogenous C9ORF72 intron 1 and mRNA levels were detected by TaqMan gene expression assay using TaqMan fast advanced master mix , and normalized to β-actin mRNA. Custom primer sequences are listed in Supplementary Table ACTB primer set (Hs.PT.39a.22214847) was purchased from Integrated DNA technologies.RNA was extracted from cells or tissues by Trizol  and treated with RQ1 DNase I (Promega). The patient postmortem sample information is listed in Supplementary Table For RNase R treatment, 10 µg of DNase I digested total RNA was incubated at 37 °C for 15 min with 15U RNase R  to degrade liner RNA, and subsequently purified by Quick-RNA Microprep Kit . One microgram of RNA before or after RNase R treatment was used as the template for cDNA synthesis.70-NLuc construct (both frame-GA and frame-GP) were linearized with XhoI digestion, and served as the DNA template to synthesize RNA using MEGAscript T7 transcription kit . In vitro transcribed RNAs were purified by Trizol . One microgram of RNA was used for in vitro translation using Flexi Rabbit Reticulocyte Lysate System . Five microliters of completed translation reaction were used to measure NLuc activity by Nano-Glo dual-luciferase reporter assay system .The pcDNA3.1-ORF1-Neg-NLuc, pcDNA3.1-ORF1-IRES-NLuc, and pcDNA3.1-ORF1-(GGGGCC)70-splicing reporter cells, the poly(A) mRNA was purified from total RNA by NEBNext® Poly(A) mRNA Magnetic Isolation Module (E7490L). The sequencing samples were prepared using the Direct RNA Sequencing Kit . ~500 ng of poly(A) RNA was reverse transcribed with SuperScript IV (ThermoFisher), ligated to the 1D sequencing adapter, and loaded onto the GridION flow cell . The sequencing was run for 72 h using the MinKNOW software (ver. 20.06.9) with default parameters . The reads were base-called in real time on the GridION instrument using Guppy with a minimum q score of 7 (ver. 4.0.11), which yielded a final count of 1.52 million reads. The base-called reads were then aligned to the reporter pre-mRNA sequence (including a flanking 100 bp vector sequence upstream and downstream of the transgene) using minimap2  in the nanopore direct RNA sequencing splice alignment mode (-ax splice) with a 14-nt seeding window (-k14), a forced directionality (-uf), a 10,000-nt minimizer window cutoff (-g10000), and a lowered penalty of 5 for noncanonical splice junctions (-C5). Only primary mapping reads with a MAPQ higher than 12 were used for analyzing the presence of various molecules in the experiments, which identified 171 reads on the reporter transgene in stable U-2-OS cell line and 6838 reads in transfected HEK293T cells. Due to the repetitive sequences in 24×PBS, some reads were mapped incompletely and showed nonexisting gaps in the alignment. We therefore used the EMBOSS Needle tool (https://www.ebi.ac.uk/Tools/psa/emboss_needle/) as an alternative method, which partially improved the alignments.For nanopore sequencing of poly(A) RNA from (GGGGCC)C9ORF72 circular intron and splice junctions of exon 1-2. For circular intron, first-strand cDNA was produced by TGIRT-III reverse transcriptase  using 1ug of RNase R-treated RNA. Library was prepared by two rounds of PCR amplification of the fragment across the potential 5′ splice site-branch site junction. For mRNA, first-strand cDNA was synthesized by High-capacity cDNA reverse transcription kit . Library was also prepared by two rounds of PCR amplification of mRNA exon 1-2. All primer sequences are listed in Supplementary Table C9ORF72 reference sequences (with candidate mRNA isoforms and circular RNAs) using BWA-MEM for mRNA and Minimap 2 (ver. 2.17) for circular intron. The reads that do not cross the splice site-branch site junction were removed by SAMTools.We generated MiSeq libraries to sequence the 56 and described in detail56. In brief, DNA oligoes were ordered from Integrated DNA Technology and labeled in house with Cy3, Cy5, or Cy7 NHS ester 56. Cells were seeded on 18 mm coverslips coated with 30 µg/mL rat tail collagen I , and cultured overnight. After fixation with 4% paraformaldehyde and permeabilization with 0.1% of Triton, cells were incubated with 50–100 nM probes in hybridization buffer for 3 h at 37 °C. The unbound probes were washed away with 10% formamide and the coverslips were mounted on microscope slide using ProLong Diamond Antifade Mountant containing DAPI  for nucleus staining. For patient fibroblast, the cells were treated with 70% of ethanol overnight after fixation and before permealization. The RNA FISH probe sequences are listed in Supplementary Data The RNA single-molecule FISH (smFISH) using 20mer DNA oligo probes was adapted from refs. 57. Briefly, cells were fixed with 4% paraformaldehyde for 10 min and blocked with 50 mM Glycine in 1× phosphate buffer saline (PBS) at room temperature. After permeablization with 0.1% Triton at room temperature, cells were incubated with 100 nM probes targeting RNA of interest and primary antibody targeting sfGFP  at 37 °C for 3 h. After washing away unbound primary antibody and probes, cells were washed twice with secondary antibody (Goat anti-Chicken IgY (H+L) Alexa Fluor 647, Thermo Fisher Scientific, A21449) at 37 °C for 20 min. Finally, the coverslip was washed with PBS before mounting on microscope slide as mentioned in smFISH protocol.Cells used for single-molecule translation assay were treated with 500 µM IAA overnight. The protocol for smFISH immunofluorescence (IF) experiments was detailed in ref. 2 for 5 min and subsequently proceeded the same as normal smFISH protocol, with 50 nM probes targeting POLR2A (Cy3), MBSV5 (Cy5), and PBS (Cy7).For smFISH with RNase R treatment, cells were fixed with Methanol-Acetic Acid (Fisher A38-212) mixture (4:1) for 20 min at −20 °C after a quick wash with ice cold PBS. It is critical not to use paraformaldehyde for fixation because it may crosslink protein and RNAs. Next, cells were washed with 1×PBS, incubated with 1× RNase R buffer for 5 min at room temperature, and treated with RNase R (1 U/50uL in 1× RNAse R buffer) at 37 °C for 15 min. Cells were washed with 1× PBS supplemented with 5 mM MgClThe fixed samples were imaged on an automated inverted Nikon Ti-2 wide-field microscope equipped with ×60 1.4NA oil immersion objective lens (Nikon), Spectra X LED light engine (Lumencor), and Orca 4.0 v2 scMOS camera (Hamamatsu). The live cell experiments were performed on a custom microscope built around Nikon Ti-E stand. The excitation was through HTIRF (Nikon) with an LU-n4 four laser unit (Nikon) with solid state lasers with wavelengths 405, 488, 561, and 640 nm. The main dichroic was a quad band dichroic mirror . The imaging was done through the ×100 1.49NA oil immersion objective (Nikon). To achieve simultaneous 3-color imaging, we used a TriCam light splitter into three separate EMCCD cameras (Andor iXon Ultra 897) with ultraflat 2 mm thick imaging splitting dichroic mirrors . A band pass emission filter was placed in front of each camera, respectively . The microscope was also equipped with an automated XY-stage with extra fine lead-screw pitch of 0.635 mm and 10 nm linear encoder resolution and a Piezo-Z stage (Applied Scientific Instrumentation) for fast Z-acquisition. The whole microscope was under the control of Nikon Elements for automation.20 and washed for 1 h in the incubator. The media was changed to Leibovitz’s L-15 media supplemented with 10% FBS and the sample was kept at 37 °C with humidity control with a Tokai Hit stage top incubator during imaging.Cells were seeded in a 35 mm dish with 20 mm micro well #1.5 cover glass bottom  and grown overnight. Two hours prior to imaging, cells were incubated for 1 h with 100 nM Halo dye (JF646)For translation imaging, the pCAG-C9splicing-in(GGGGCC)70-24×Suntag-24×MBS-exFLucN-24×PBS reporter was transiently transfected into U2PA-stdMCP-halo-CAAX cell line 24 h before imaging. For stress response, the sample was initially imaged every 30 s for 3 min, then treated with 2 uM arsenite and imaged every 30 s for 40 min to capture changes in translation. For translation inhibition, the sample was initially imaged every 30 s for 3 min, then treated with 100 µg/mL puromycin for 10 min and imaged afterwards.58. In brief, single-molecule spots for RNA in cytoplasm and nucleus were fitted to a 3D Gaussian function after background subtraction to extract the centers and amplitudes of the fluorescent spots. To correct chromatic aberration, we imaged 0.1 µm TetraSpec beads  and calibrated an affine transformation matrix to register beads positions in different channels59. The position of RNA and protein was adjusted with the transformation matrix for the colocalization analysis between intron and exon or between RNA and translation site. A threshold of 200 nm was applied as a criterion for colocalization. Next, we determined the brightest IF spot within the distance threshold of an intron. If its intensity was brighter than a single mature SunTag protein, we deemed it as a translation site. Finally, the distance between the identified translation sites and the closest exons were measured and compared with the threshold (300 nm) for colocalization criterion.To determine the coordinates and fluorescence amplitudes of smFISH and translation site signals, images were analyzed using the Matlab software FISH QuantThe positions and intensities of RNA foci were determined with the “Transcription Site” modality in the FISH Quant software. We applied a threshold of six times of single-RNA intensity to be considered as foci. The number of single RNAs in each focus was determined by dividing its integrated intensity by the corresponding single RNA one.60 and the particle positions between frames were tracked by u-track61. Tracks longer than five frames were further analyzed. The diffusion coefficient for each track was calculated using mean-square-displacement (MSD) with the first seven displacements. RNAs with diffusion coefficient larger than 0.01 µm2 /s were classified as freely diffusing.The live imaging analysis was performed using existing packages or custom build programs in Matlab (MathWorks). Single particles of RNA or translation sites were identified with Airlocalize29. The genome-wide intron sequences were extracted with the R BioConductor GenomicFeatures package (https://bioconductor.org/packages/release/bioc/html/GenomicFeatures.html) using the RefSeq database . Only constitutive introns that contain no alternative exons were selected for analysis. Intron G content was calculated using a custom built script in Matlab (MathWorks). In brief, for each circular intron, the number of G nucleotide was divided by the full length of the sequence. For genome-wide introns, we removed the last 30 nucleotides from the 3′ splice site (the average distance between branch site and 3′ splice site) and selected the rest sequences longer than 25nt. To calculate the percentage of G at the single nucleotide level, we aligned all introns starting from the 5′ or from 3′ splice sites, the total number of G nucleotide in each position is divided by the total number of sequences considered. For genome-wide introns, we selected ones that full length was longer than 25nt for this calculation.The circular intron sequences in HeLa cells were obtained from ref. RNA secondary structure was predicted by the “RNAfold” function from ViennaRNA package. The “gquad” modifier was used to incorporate G-Quadruplex formation into the structure prediction algorithm to calculate the minimum free energy (ΔG) of the RNA secondary structure. The ΔG value was divided by the length of the RNA sequence to calculate the minimum thermodynamic free energy (−ΔG /nt). For genome-wide intron secondary structure prediction, introns between 100nt and 600nt were selected and the last 30nt from the 3′ splice site (the average distance between branch site and 3′ splice site) were trimmed.Further information on research design is available in the Supplementary informationPeer Review FileMovie S1Movie S2Movie S3Description of additional supplementary filesReporting Summary


As a comprehensive database, REIA curates not only 8,447,588 RESs from 9,895 patients across 34 cancers, where 33 are from TCGA and 1 from GEO, but also 13 different types of multi-omic data for the cancers. As an interactive server, REIA provides various options for the user to specify the interested sites, to browse their annotation/editing level/profile in cancer, and to compare the difference in multi-omic features between editing and non-editing groups. From the editing profiles, REIA further detects 658 peptides that are supported by mass spectrum data but not yet covered in any prior works.Epitranscriptomic changes caused by adenosine-to-inosine (A-to-I) RNA editing contribute to the pathogenesis of human cancers; however, only a small fraction of the millions editing sites detected so far has clear functionality. To facilitate more in-depth studies on the editing, this paper offers REIA ( RNA editing is one of the most conservative features in RNA evolution. It alters the primary RNA transcripts via insertion, deletion, or base substitution of nucleotides Despite the importance of these functions, the research community is still lacking a comprehensive understanding on the general functionalities of A-to-I RESs, thus calling for further studies. However, prior works based on the experimental approach often suffer from the loss of genomic information and the risk in off-target edits and delivery http://bioinfo-sysu.com/reia. The architecture of REIA is shown in Figure In this paper, we provide a new web server for such analysis. The server is termed REIA, a database for A-to-I RNA editing in cancers with interactive analysis, which is publicly available at Summing up, the web server REIA offers a new RNA editing database operating on the per cancer basis and opens up a new avenue for the research in association of A-to-I RNA editing with cancer multi-omics features.Data Collection: The overall process is shown in Figure http://srv00.recas.ba.infn.it/atlas/index.html) and merged it with the 8,972,972 sites from TCEA http://tcea.tmu.edu.tw). After removing the duplicates, we obtained a candidate set of 15,680,513 sites, which covers most  editing sites ever detected in human samples, either normal or cancerous. Together we have 9,895 samples, in which 9,697 RNA-seq bam files (V21.0) across 33 cancer types included in TCGA are downloaded from Genomic Data Commons , while the remaining 198 files are from GEO  on nasopharyngeal carcinoma (NPC). For somatic mutation, we download the data from GDC and for Germline single nucleotide poly-morphisms (SNPs) from GDC and dbSNP .1) RES Detection: The 15,680,513 sites collected above serves as a candidate set of our RES detection. For mapped reads, we apply REDItools (REDItoolKnown.py) under the same setting as Picardi et al. http://sprint.tianlan.cn), a SNP-free toolkit; however, unlike most previous studies which considered only hyper RESs 2) RES Annotation: To annotate the RESs, we used ANNOVAR https://annovar.openbioinformatics.org/en/latest) together with Gencode (v34), Refseq and UCSC for gene structure, dbSNP (v151), ExAC, GnomAD (v30) for variant, COSMIC (v92) and ClinVar for disease, RepeatMask for repetitive elements, PhyloP and PhastCons from UCSC (https://genome.ucsc.edu) for conservation score across 100 vertebrates. Annotated sites are then divided either into 8 types by their locations in the genomic region, or into 3 types by the repeat region. The 8 genomic- region types include exonic, intronic, intergenic, UTR3, UTR5, ncRNA_intronic, ncRNA_exonic, and splicing, while the 3 repeat-region types are ALU, REP, and NONREP.3) We provide 13 types of multi-omic data to relate the detected editing events with molecular features of 33 cancers in TCGA and its follow-up studies. From PanCanAtlas https://www.gsea-msigdb.org/gsea/msigdb). Throughout the paper, we use patient ID as our unique index for all data used in the interactive analysis.Particularly for gene expression, previous studies had demonstrated gene pathways are essential to the development of cancers, with some being prognostic markers We also apply a sample-customized search strategy, as shown in Figure Construct a set of variation-associated peptides: First, we generate a variant call format (VCF) file according to the RNA editing sites obtained by using SAMtools and R scripts. Then, we download an mRNA sequence file (GRCh38) and a gene annotation file from UCSC (http://genome.ucsc.edu/cgi-bin/hgTables?command=start). Also, we download the external cross reference file, xref, from MartView (http://biomart.intogen.org/biomart/martview/). Finally, we use the R package of sapFinder https://academic.oup.com/bioinformatics/article/30/21/3136/2422150) to construct a data set for peptides associated with then RNA editing. A list of all potential peptides resulting from RNA editing events is then obtained and labeled as “peptide set A”.1) Select peptides supported by cancer mass spectrum: In this step, the peptides are validated via mass spectrum (MS) data. We download the mzML format data about 9 cancers from Clinical Proteomic Tumor Analysis Consortium (CPTAC) 2)  Identify novel peptides: Finally, we identify peptides listed in the above “set B” but not yet covered in any of the following databases: Uniprot https://asia.ensembl.org/index.html). To this end, we apply the Basic Local Alignment Search Tool of Protein Database (BLASTP) to search for peptides that contain at least one unmatching with the peptides of the above databases. We obtain a list of 658 such unmatched peptides and label them as “novel peptides”.3)We develop REIA, a new web server for the RNA A-to-I editing interactive analysis, using the MVC framework. MVC is a state-of-the-art design pattern that implements data, user interface, and controlling logic of a software in a divide-and-conquer fashion. Three components are included in MVC: the Model for managing data and business logic, the View for handling layout and display, and the Controller for routing commands to the former two. In Figure The web site of REIA has 6 tabs, namely, Home, Analysis, Statistics, About, Help, and Download, where 1) Home: overview of the server, search and display of interested RESs; 2) Analysis: association between RESs and multi-omic molecular features, identification of novel peptides; 3) Statistics: statistical information of the database; 4) About: introduction to the server; 5) Help: usage and examples; 6) Download: data of the database, see Figure COPA I164V (chr1_160332454) as our example throughout this section. We use COPA I164V simply due to its great generality in cancers and its tight connection with other omics features, e.g., patient survival, protein diversity, and gene expression COPA I164V as our target position and move on to the “Analysis” page to divide the patients into two groups (edited or not) and further carry out their interactive analysis with 13 multi-omic features. More details will be elaborated immediately in the two subsections that follow.To better demonstrate the usage of “Home” and “Analysis”, we take REIA has collected 8,447,588 editing sites from 34 cancer types for search and display. Among the 34 cancers, STAD, NPC, and ESCA have the highest number of editing sites, while LGG, OV, and BRCA are the second highest  “RESs Browser” for inquiry via RESs, including chromosome and editing site coordinate; ii) “Gene Browser” for inquiry via genes, including gene name, gene region, repeat type, and amino acids change; and, iii) “Cancer Browser” for inquiry via cancers, including cancer type and number of edited samples. Across the three, all items could be combined arbitrarily. To display the search result, REIA provides a table for the annotation and the distribution of each editing site, which contains coordinates, strand, genomic position, reference nucleotide, edited nucleotide, region of cytoband, gene name, gene region, repeated element (if any), potential amino acid change, disease-specific sites, PhyloP and PhastCons conservation score across 100 vertebrates, databases  reporting the RES, and number of edited samples per cancer type. Following each item of the table is a button of “Plot”, which plots the distribution of editing level (defined as the number of edited G reads over the number of A+G reads) at the selected position in each cancer type. Yet another button is “Add”, which adds the current position into the input list of a multi-omic analysis to be detailed in next tab “Analysis”. For the aforementioned example of In the “Analysis” tab, REIA provides, for 33 TCGA cancers, 13 types of interactive analysis at 4 different levels, including DNA, transcriptome, protein, and clinicopathology. The site position(s) of interest can be either imported from the search result (in last tab) or customized at will. Also, all interactive analysis supports the selection of any cancer(s) within the 33. For each selection, the samples are divided into two groups, an “editing” group and a “non-editing”, according to their status at the site position(s). In the analysis that follows, sample ID is used as the unique index for all data. In the same tab, REIA further provides 658 cancer-specific peptides that are derived from A-to-I RNA editing and supported by mass spectrum, but not reported in the existing databases. For each of the analysis functions, the selected index is then plotted for comparison between the two groups. Wilcoxon rank-sum test is adopted in all significance analysis throughout this paper. The p-values here are two-tailed, and Benjamini and Hochberg (BH) FDR is used as a correction for multiple comparisons.COPA I164V (chr1_160332454) in breast cancer (BRCA) as our running example. Previous study has shown that the edited COPA I164V not only enhances cell viability, wounding healing, migration and invasion significantly, but also make a notable contribution to the tumor development For better illustration of REIA, we take the editing site of As RNA editing affects a variety of molecular features, REIA here investigates 6 major ones on the genome level, i.e., mutation, CNV, telomere length, DNA methylation, MSI, tumor purity and ploidy. COPA I164V (chr1_160332454). For each somatic mutation, one can further check information like its overlapped gene, genome coordinate position, and the variant classification n hexameric DNA repeats of telomeres) is critical to cancer progression, as it ensures both the stability of chromosomes and the integrity of genome: the shorter a telomere, the higher its risk in cancer For DNA methylation, REIA calculates the β values therein. As the prior work demonstrated COPA I164V example, the MSI in the editing group is seen to be significantly higher than the non-editing group , REIA plots the distribution of its event number. MSI, as a major carcinogenetic pathway For tumor purity and ploidy, REIA computes the two values to investigate their (possible) association with the A-to-I editing. Tumor purity and ploidy have a great impact on cancer genomic evolution and tumor heterogeneity, thus affecting severely cancer progression and patient survival RNA editing in cancers may affect many features on the transcript level COPA I164V example, the expression level of IGF-1 (insulin-like growth factors) in the P53 signaling pathway of the editing group can be seen to be significantly higher than that of the non-editing group . Alternative splicing, a key factor in prognostic analysis, also affects the individual changes in regulatory binding sites and the alterations to protein-coding sequences COPA I164V example is given in Figure REIA has collected 2,455 miRNA expressions from 9,406 patients. As high-throughput detection suggests, the A-to- I RNA editing occurs usually in non-coding RNAs, especially in microRNAs (miRNAs). RNA editing is often found in the binding domain of miRNAs and mRNA COPA , S367G in AZIN1, and I635V in COG3, is the root cause of protein expression dysregulation and proteomic diversity COPA I164V, the volcano plot presents a series of proteins differentially expressed between the editing and the non-editing groups  REIA provides 197 protein expressions of the TCGA patients. For cancers, A-to-I RNA editing in certain positions, such as I164V in 2) REIA also identifies 658 novel peptides, which were derived from the A-to-I RNA editing and supported by MS data from related literature. Although they are cancer specific, these peptides are not yet included in any protein database currently available. For these peptides, REIA offers the RES coordinates, sequence, gene name, mutant and wild-type bases on amino acids, codons, and distribution in cancers, which are essential to target therapy Figure H. It shoCOPA I164V example is shown in Figure 1) Patient Survival: REIA provides the overall survival and the progression-free survival data of 11,160 patients. As exemplified in COPA I164V, mRNAsi and EREG-mRNAsi stem indices can be seen to be significantly higher in the editing group, suggesting that the editing is associated with stemness  Stem Indices: REIA provides 6 stemness indices summarized from the 9,399 patients. Commonly found in the metastatic tumors, the stemness indices have a significantly higher level of dedifferentiation in cancer progression COPA I164V example, the result shows that 6 immune cells are significantly different  Tumor Immune Microenvironment: REIA has collected 26 indices of immune cell infiltration from 11,010 patients. A-to-I RNA editing is known to get evolved in the discrimination of self and nonself RNA; the human RNA editing enzyme ADAR1 prevents endogenous RNA from activating innate immune sensors , thus regulating the delicate balance between pathogen detection and protection versus autoinflammation and disease The A-to-I RNA editing was repeatedly found involved in cellular functions; however, the biological role of the editing in human cancers has not been fully elucidated. In literatures currently available, databases were mostly for the A- to-I RESs of healthy tissues, lacking in cancer-centric solutions. This paper provides one such solution, the REIA, a database for A-to-I RNA editing in cancers with interactive analysis on the association with multi-omic features. On one hand, REIA has detected 8,447,588 A-to-I RESs from the RNA-Seq data of 9,895 tumor patients across 34 cancers. REIA also collected 13 types of multi-omic data related to human cancers. In addition, it further identified 658 novel peptides from non-synonymous peptides supported by mass spectrum data, which paves the way for targeted therapies. On the other hand, REIA applied MVC to handle user interface, business logic, and data management in a divide-and-conquer fashion, making the interaction and maintenance much easier than prior works. In terms of interactive analysis, REIA not only provides a retrieval function for the editing sites detected, but also analyzes/visualizes their association with multi-omic features. In summary, REIA, as a cancer-centric database, opens up a new avenue for the study of associations between A-to-I RNA editing and multi-omic features.https://icgc.org). Fourth, a new analysis for ncRNA A-to-I RNA editing can be considered. Unlike the extensively studied recording RESs REIA could be further enhanced in the following aspects. First, genome sequence data can be added to evaluate more fully the influence of nucleic acid sequence alterations on cancers, as we all know editing is a root cause of sequence diversity in cancers http://bioinfo-sysu.com/reia), has been implemented, providing a comprehensive resource for the analysis of association between A-to-I RNA editing and human cancers.A new database, REIA (For REIA, 8,447,588 editing sites were curated from 9,895 patients across 34 cancer types, among which 33 were from TCGA and 1 from GEO.13 types of multi-omic data related to the cancers were curated manually to perform an association analysis with the editing sites in an interactive fashion.A user-friendly interface was implemented for REIA, where the users could browse, search, analyze and download the data of the editing sites and/or association analysis.658 novel peptides were also detected from the editing profiles, all of which were supported by mass spectrum data and thus could serve as clues for downstream experimental design.