A-to-I editing; C-to-U editing; MT: RNA/DNA editing; RNA editing; cardiovascular disease; methodology; neurodegenerative disease; neurovascular disease; Drug Discovery; Molecular Medicine
Abstract :
[en] RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.
Disciplines :
Cardiovascular & respiratory systems Neurology Biochemistry, biophysics & molecular biology
Author, co-author :
Karagianni, Korina; Department of Genetics, Development, and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Bibi, Alessia; Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097 Milan, Italy ; Department of Biosciences, University of Milan, Milan, Italy
Madé, Alisia; Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097 Milan, Italy
ACHARYA, Shubhra ; University of Luxembourg ; Cardiovascular Research Unit, Luxembourg Institute of Health, Strassen, Luxembourg
Parkkonen, Mikko; Research Unit of Biomedicine and Internal Medicine, Department of Pharmacology and Toxicology, University of Oulu, Oulu, Finland
Barbalata, Teodora; Lipidomics Department, Institute of Cellular Biology and Pathology "Nicolae Simionescu" of the Romanian Academy, 8, B. P. Hasdeu Street, 050568 Bucharest, Romania
Srivastava, Prashant K; National Heart & Lung Institute, Imperial College London, London, UK
de Gonzalo-Calvo, David; Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain ; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain
Emanueli, Constanza; National Heart & Lung Institute, Imperial College London, London, UK
Martelli, Fabio; Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097 Milan, Italy
DEVAUX, Yvan ; University of Luxembourg ; Cardiovascular Research Unit, Luxembourg Institute of Health, Strassen, Luxembourg
Dafou, Dimitra; Department of Genetics, Development, and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Nossent, A Yaël; Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands ; Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Copenhagen, Denmark
FNR14566210 - Long Noncoding Rnas In Tissue Repair, 2020 (01/08/2020-31/07/2023) - Shubhra Acharya
Funders :
Instituto de Salud Carlos III Hellenic Foundation for Research and Innovation Italian Society of Cardiology AFM-Telethon Horizon 2020 British Heart Foundation Telethon Foundation Nederlandse Organisatie voor Wetenschappelijk Onderzoek Fonds National de la Recherche
Benne, R., Van den Burg, J., Brakenhoff, J.P., Sloof, P., Van Boom, J.H., Tromp, M.C., Major transcript of the frameshifted coxII gene from trypanosome mitochondria contains four nucleotides that are not encoded in the DNA. Cell 46 (1986), 819–826.
Wagner, R.W., Smith, J.E., Cooperman, B.S., Nishikura, K., A double-stranded RNA unwinding activity introduces structural alterations by means of adenosine to inosine conversions in mammalian cells and Xenopus eggs. Proc. Natl. Acad. Sci. USA 86 (1989), 2647–2651.
Powell, L.M., Wallis, S.C., Pease, R.J., Edwards, Y.H., Knott, T.J., Scott, J., A novel form of tissue-specific RNA processing produces apolipoprotein-B48 in intestine. Cell 50 (1987), 831–840.
Chen, S.H., Habib, G., Yang, C.Y., Gu, Z.W., Lee, B.R., Weng, S.A., Silberman, S.R., Cai, S.J., Deslypere, J.P., Rosseneu, M., et al. Apolipoprotein B-48 is the product of a messenger RNA with an organ-specific in-frame stop codon. Science 238 (1987), 363–366.
Nishikura, K., A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17 (2016), 83–96.
Melcher, T., Maas, S., Herb, A., Sprengel, R., Seeburg, P.H., Higuchi, M., A mammalian RNA editing enzyme. Nature 379 (1996), 460–464.
Tan, M.H., Li, Q., Shanmugam, R., Piskol, R., Kohler, J., Young, A.N., Liu, K.I., Zhang, R., Ramaswami, G., Ariyoshi, K., et al. Dynamic landscape and regulation of RNA editing in mammals. Nature 550 (2017), 249–254.
Raghava Kurup, R., Oakes, E.K., Manning, A.C., Mukherjee, P., Vadlamani, P., Hundley, H.A., RNA binding by ADAR3 inhibits adenosine-to-inosine editing and promotes expression of immune response protein MAVS. J. Biol. Chem., 298, 2022, 102267.
Fossat, N., Tourle, K., Radziewic, T., Barratt, K., Liebhold, D., Studdert, J.B., Power, M., Jones, V., Loebel, D.A.F., Tam, P.P.L., C to U RNA editing mediated by APOBEC1 requires RNA-binding protein RBM47. EMBO Rep. 15 (2014), 903–910.
Wang, I.X., So, E., Devlin, J.L., Zhao, Y., Wu, M., Cheung, V.G., ADAR regulates RNA editing, transcript stability, and gene expression. Cell Rep. 5 (2013), 849–860.
Stellos, K., Gatsiou, A., Stamatelopoulos, K., Perisic Matic, L., John, D., Lunella, F.F., Jaé, N., Rossbach, O., Amrhein, C., Sigala, F., et al. Adenosine-to-inosine RNA editing controls cathepsin S expression in atherosclerosis by enabling HuR-mediated post-transcriptional regulation. Nat. Med. 22 (2016), 1140–1150.
Kawahara, Y., Megraw, M., Kreider, E., Iizasa, H., Valente, L., Hatzigeorgiou, A.G., Nishikura, K., Frequency and fate of microRNA editing in human brain. Nucleic Acids Res. 36 (2008), 5270–5280.
Karagianni, K., Pettas, S., Christoforidou, G., Kanata, E., Bekas, N., Xanthopoulos, K., Dafou, D., Sklaviadis, T., A Systematic Review of Common and Brain-Disease-Specific RNA Editing Alterations Providing Novel Insights into Neurological and Neurodegenerative Disease Manifestations. Biomolecules, 12, 2022, 465.
Nossent, A.Y., The Epitranscriptome: RNA Modifications in Vascular Remodelling. 2022 Atherosclerosis.
Srivastava, P.K., Bagnati, M., Delahaye-Duriez, A., Ko, J.H., Rotival, M., Langley, S.R., Shkura, K., Mazzuferi, M., Danis, B., van Eyll, J., et al. Genome-wide analysis of differential RNA editing in epilepsy. Genome Res. 27 (2017), 440–450.
Streit, A.K., Derst, C., Wegner, S., Heinemann, U., Zahn, R.K., Decher, N., RNA editing of Kv1.1 channels may account for reduced ictogenic potential of 4-aminopyridine in chronic epileptic rats. Epilepsia 52 (2011), 645–648.
Higuchi, M., Single, F.N., Köhler, M., Sommer, B., Sprengel, R., Seeburg, P.H., RNA editing of AMPA receptor subunit GluR-B: a base-paired intron-exon structure determines position and efficiency. Cell 75 (1993), 1361–1370.
Daniel, C., Wahlstedt, H., Ohlson, J., Björk, P., Ohman, M., Adenosine-to-inosine RNA editing affects trafficking of the gamma-aminobutyric acid type A (GABA(A)) receptor. J. Biol. Chem. 286 (2011), 2031–2040.
Levanon, E.Y., Hallegger, M., Kinar, Y., Shemesh, R., Djinovic-Carugo, K., Rechavi, G., Jantsch, M.F., Eisenberg, E., Evolutionarily conserved human targets of adenosine to inosine RNA editing. Nucleic Acids Res. 33 (2005), 1162–1168.
Levanon, E.Y., Eisenberg, E., Yelin, R., Nemzer, S., Hallegger, M., Shemesh, R., Fligelman, Z.Y., Shoshan, A., Pollock, S.R., Sztybel, D., et al. Systematic identification of abundant A-to-I editing sites in the human transcriptome. Nat. Biotechnol. 22 (2004), 1001–1005.
Athanasiadis, A., Rich, A., Maas, S., Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biol., 2, 2004, e391.
Gu, T., Buaas, F.W., Simons, A.K., Ackert-Bicknell, C.L., Braun, R.E., Hibbs, M.A., Canonical A-to-I and C-to-U RNA editing is enriched at 3'UTRs and microRNA target sites in multiple mouse tissues. PLoS One, 7, 2012, e33720.
Uchida, S., Jones, S.P., RNA Editing: Unexplored Opportunities in the Cardiovascular System. Circ. Res. 122 (2018), 399–401.
Yang, W., Chendrimada, T.P., Wang, Q., Higuchi, M., Seeburg, P.H., Shiekhattar, R., Nishikura, K., Modulation of microRNA processing and expression through RNA editing by ADAR deaminases. Nat. Struct. Mol. Biol. 13 (2006), 13–21.
Kawahara, Y., Zinshteyn, B., Chendrimada, T.P., Shiekhattar, R., Nishikura, K., RNA editing of the microRNA-151 precursor blocks cleavage by the Dicer-TRBP complex. EMBO Rep. 8 (2007), 763–769.
van der Kwast, R.V.C.T., Parma, L., van der Bent, M.L., van Ingen, E., Baganha, F., Peters, H.A.B., Goossens, E.A.C., Simons, K.H., Palmen, M., de Vries, M.R., et al. Adenosine-to-Inosine Editing of Vasoactive MicroRNAs Alters Their Targetome and Function in Ischemia. Mol. Ther. Nucleic Acids 21 (2020), 932–953.
van der Kwast, R.V.C.T., van Ingen, E., Parma, L., Peters, H.A.B., Quax, P.H.A., Nossent, A.Y., Adenosine-to-Inosine Editing of MicroRNA-487b Alters Target Gene Selection After Ischemia and Promotes Neovascularization. Circ. Res. 122 (2018), 444–456.
Kume, H., Hino, K., Galipon, J., Ui-Tei, K., A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency. Nucleic Acids Res. 42 (2014), 10050–10060.
Liang, H., Landweber, L.F., Hypothesis: RNA editing of microRNA target sites in humans?. RNA 13 (2007), 463–467.
Nigita, G., Veneziano, D., Ferro, A., A-to-I RNA Editing: Current Knowledge Sources and Computational Approaches with Special Emphasis on Non-Coding RNA Molecules. Front. Bioeng. Biotechnol., 3, 2015, 37.
Ivanov, A., Memczak, S., Wyler, E., Torti, F., Porath, H.T., Orejuela, M.R., Piechotta, M., Levanon, E.Y., Landthaler, M., Dieterich, C., Rajewsky, N., Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals. Cell Rep. 10 (2015), 170–177.
Breen, M.S., Dobbyn, A., Li, Q., Roussos, P., Hoffman, G.E., Stahl, E., Chess, A., Sklar, P., Li, J.B., Devlin, B., et al. Global landscape and genetic regulation of RNA editing in cortical samples from individuals with schizophrenia. Nat. Neurosci. 22 (2019), 1402–1412.
Park, E., Jiang, Y., Hao, L., Hui, J., Xing, Y., Genetic variation and microRNA targeting of A-to-I RNA editing fine tune human tissue transcriptomes. Genome Biol., 22, 2021, 77.
Quinones-Valdez, G., Tran, S.S., Jun, H.I., Bahn, J.H., Yang, E.W., Zhan, L., Brümmer, A., Wei, X., Van Nostrand, E.L., Pratt, G.A., et al. Regulation of RNA editing by RNA-binding proteins in human cells. Commun. Biol., 2, 2019, 19.
Cai, D., Behrmann, O., Hufert, F., Dame, G., Urban, G., Direct DNA and RNA detection from large volumes of whole human blood. Sci. Rep., 8, 2018, 3410.
Kondratov, K., Kurapeev, D., Popov, M., Sidorova, M., Minasian, S., Galagudza, M., Kostareva, A., Fedorov, A., Heparinase treatment of heparin-contaminated plasma from coronary artery bypass grafting patients enables reliable quantification of microRNAs. Biomol. Detect. Quantif. 8 (2016), 9–14.
Kirschner, M.B., Edelman, J.J.B., Kao, S.C.H., Vallely, M.P., van Zandwijk, N., Reid, G., The Impact of Hemolysis on Cell-Free microRNA Biomarkers. Front. Genet., 4, 2013, 94.
Stojkovic, S., Nossent, A.Y., Haller, P., Jäger, B., Vargas, K.G., Wojta, J., Huber, K., MicroRNAs as Regulators and Biomarkers of Platelet Function and Activity in Coronary Artery Disease. Thromb. Haemostasis 119 (2019), 1563–1572.
Evers, D.L., Fowler, C.B., Cunningham, B.R., Mason, J.T., O'Leary, T.J., The effect of formaldehyde fixation on RNA: optimization of formaldehyde adduct removal. J. Mol. Diagn. 13 (2011), 282–288.
Phan, H.V., van Gent, M., Drayman, N., Basu, A., Gack, M.U., Tay, S., High-throughput RNA sequencing of paraformaldehyde-fixed single cells. Nat. Commun., 12, 2021, 5636.
Vilades, D., Martínez-Camblor, P., Ferrero-Gregori, A., Bär, C., Lu, D., Xiao, K., Vea, À., Nasarre, L., Sanchez Vega, J., Leta, R., et al. Plasma circular RNA hsa_circ_0001445 and coronary artery disease: Performance as a biomarker. Faseb. J. 34 (2020), 4403–4414.
Lakkisto, P., Dalgaard, L.T., Belmonte, T., Pinto-Sietsma, S.J., Devaux, Y., de Gonzalo-Calvo, D., EU-CardioRNA COST Action CA17129. Development of circulating microRNA-based biomarkers for medical decision-making: a friendly reminder of what should NOT be done. Crit. Rev. Clin. Lab Sci. 60 (2023), 141–152.
Görgens, A., Corso, G., Hagey, D.W., Jawad Wiklander, R., Gustafsson, M.O., Felldin, U., Lee, Y., Bostancioglu, R.B., Sork, H., Liang, X., et al. Identification of storage conditions stabilizing extracellular vesicles preparations. J. Extracell. Vesicles, 11, 2022, e12238.
Chomczynski, P., Sacchi, N., Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 162 (1987), 156–159.
Chomczynski, P., Sacchi, N., The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nat. Protoc. 1 (2006), 581–585.
Mutiu, A.I., Brandl, C.J., RNA isolation from yeast using silica matrices. J. Biomol. Tech. 16 (2005), 316–317.
Berensmeier, S., Magnetic particles for the separation and purification of nucleic acids. Appl. Microbiol. Biotechnol. 73 (2006), 495–504.
Rodriguez-Molina, J.B., Turtola, M., Birth of a poly(A) tail: mechanisms and control of mRNA polyadenylation. FEBS Open Bio 13 (2022), 1140–1153.
Zhang, Y., Yang, L., Chen, L.L., Life without A tail: new formats of long noncoding RNAs. Int. J. Biochem. Cell Biol. 54 (2014), 338–349.
Hrdlickova, R., Toloue, M., Tian, B., RNA-seq Methods for Transcriptome Analysis. 2017, Wiley Interdiscip Rev RNA, 8.
Kraus, A.J., Brink, B.G., Siegel, T.N., Efficient and specific oligo-based depletion of rRNA. Sci. Rep., 9, 2019, 12281.
Archer, S.K., Shirokikh, N.E., Preiss, T., Probe-Directed Degradation (PDD) for Flexible Removal of Unwanted cDNA Sequences from RNA-Seq Libraries. Curr. Protoc. Hum. Genet. 85 (2015), 11.15.1–11.15.36.
Nicholson, A.W., Ribonuclease III Mechanisms of Double-Stranded RNA Cleavage, 5, 2014, Wiley Interdiscip Rev RNA, 31–48.
Zhao, S., Zhang, Y., Gordon, W., Quan, J., Xi, H., Du, S., von Schack, D., Zhang, B., Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap. BMC Genom., 16, 2015, 675.
Liao, J., Wang, J., Liu, Y., Li, J., Duan, L., Transcriptome sequencing of lncRNA, miRNA, mRNA and interaction network constructing in coronary heart disease. BMC Med. Genom., 12, 2019, 124.
Yang, I.S., Kim, S., Analysis of Whole Transcriptome Sequencing Data: Workflow and Software. Genomics Inform. 13 (2015), 119–125.
Xiao, M.S., Wilusz, J.E., An improved method for circular RNA purification using RNase R that efficiently removes linear RNAs containing G-quadruplexes or structured 3' ends. Nucleic Acids Res. 47 (2019), 8755–8769.
Benesova, S., Kubista, M., Valihrach, L., Small RNA-Sequencing: Approaches and Considerations for miRNA Analysis. Diagnostics, 11, 2021, 964.
Fu, Y., Wu, P.H., Beane, T., Zamore, P.D., Weng, Z., Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genom., 19, 2018, 531.
Fuchs, R.T., Sun, Z., Zhuang, F., Robb, G.B., Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PLoS One, 10, 2015, e0126049.
Wright, C., Rajpurohit, A., Burke, E.E., Williams, C., Collado-Torres, L., Kimos, M., Brandon, N.J., Cross, A.J., Jaffe, A.E., Weinberger, D.R., Shin, J.H., Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods. BMC Genom., 20, 2019, 513.
Halvardson, J., Zaghlool, A., Feuk, L., Exome RNA sequencing reveals rare and novel alternative transcripts. Nucleic Acids Res., 41, 2013, e6.
Cieslik, M., Chugh, R., Wu, Y.M., Wu, M., Brennan, C., Lonigro, R., Su, F., Wang, R., Siddiqui, J., Mehra, R., et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. 25 (2015), 1372–1381.
Samorodnitsky, E., Jewell, B.M., Hagopian, R., Miya, J., Wing, M.R., Lyon, E., Damodaran, S., Bhatt, D., Reeser, J.W., Datta, J., Roychowdhury, S., Evaluation of Hybridization Capture Versus Amplicon-Based Methods for Whole-Exome Sequencing. Hum. Mutat. 36 (2015), 903–914.
Zhang, R., Li, X., Ramaswami, G., Smith, K.S., Turecki, G., Montgomery, S.B., Li, J.B., Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing. Nat. Methods 11 (2014), 51–54.
Zaidan, H., Ramaswami, G., Barak, M., Li, J.B., Gaisler-Salomon, I., Pre-reproductive stress and fluoxetine treatment in rats affect offspring A-to-I RNA editing, gene expression and social behavior. Environ. Epigenet., 4, 2018, dvy021.
Wehrens, M., de Leeuw, A.E., Wright-Clark, M., Eding, J.E.C., Boogerd, C.J., Molenaar, B., van der Kraak, P.H., Kuster, D.W.D., van der Velden, J., Michels, M., et al. Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy. Cell Rep., 39, 2022, 110809.
Bonacina, F., Di Costanzo, A., Genkel, V., Kong, X.Y., Kroon, J., Stimjanin, E., Tsiantoulas, D., Grootaert, M.O., The heterogeneous cellular landscape of atherosclerosis: Implications for future research and therapies. A collaborative review from the EAS young fellows. Atherosclerosis 372 (2023), 48–56.
Pettas, S., Karagianni, K., Kanata, E., Chatziefstathiou, A., Christoudia, N., Xanthopoulos, K., Sklaviadis, T., Dafou, D., Profiling Microglia through Single-Cell RNA Sequencing over the Course of Development, Aging, and Disease. Cells, 11, 2022, 2383.
Zhao, L., Huang, W., Yi, S., Cellular complexity of the peripheral nervous system: Insights from single-cell resolution. Front. Neurosci., 17, 2023, 1098612.
Gal-Mark, N., Shallev, L., Sweetat, S., Barak, M., Billy Li, J., Levanon, E.Y., Eisenberg, E., Behar, O., Abnormalities in A-to-I RNA editing patterns in CNS injuries correlate with dynamic changes in cell type composition. Sci. Rep., 7, 2017, 43421.
Lundin, E., Wu, C., Widmark, A., Behm, M., Hjerling-Leffler, J., Daniel, C., Öhman, M., Nilsson, M., Spatiotemporal mapping of RNA editing in the developing mouse brain using in situ sequencing reveals regional and cell-type-specific regulation. BMC Biol., 18, 2020, 6.
Sapiro, A.L., Shmueli, A., Henry, G.L., Li, Q., Shalit, T., Yaron, O., Paas, Y., Billy Li, J., Shohat-Ophir, G., Illuminating spatial A-to-I RNA editing signatures within the Drosophila brain. Proc. Natl. Acad. Sci. USA 116 (2019), 2318–2327.
Cuddleston, W.H., Li, J., Fan, X., Kozenkov, A., Lalli, M., Khalique, S., Dracheva, S., Mukamel, E.A., Breen, M.S., Cellular and genetic drivers of RNA editing variation in the human brain. Nat. Commun., 13, 2022, 2997.
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., Linnarsson, S., Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11 (2014), 163–166.
Deng, Q., Ramsköld, D., Reinius, B., Sandberg, R., Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343 (2014), 193–196.
Kowalczyk, M.S., Tirosh, I., Heckl, D., Rao, T.N., Dixit, A., Haas, B.J., Schneider, R.K., Wagers, A.J., Ebert, B.L., Regev, A., Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25 (2015), 1860–1872.
Wu, Y., Hao, S., Xu, X., Dong, G., Ouyang, W., Liu, C., Sun, H.X., A novel computational method enables RNA editome profiling during human hematopoiesis from scRNA-seq data. Sci. Rep., 13, 2023, 10335.
Adewale, B.A., Will long-read sequencing technologies replace short-read sequencing technologies in the next 10 years?. Afr. J. Lab. Med., 9, 2020, 1340.
Lucas, M.C., Novoa, E.M., Long-read sequencing in the era of epigenomics and epitranscriptomics. Nat. Methods 20 (2023), 25–29.
Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., et al. Real-time DNA sequencing from single polymerase molecules. Science 323 (2009), 133–138.
Garalde, D.R., Snell, E.A., Jachimowicz, D., Sipos, B., Lloyd, J.H., Bruce, M., Pantic, N., Admassu, T., James, P., Warland, A., et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15 (2018), 201–206.
Furlan, M., Delgado-Tejedor, A., Mulroney, L., Pelizzola, M., Novoa, E.M., Leonardi, T., Computational methods for RNA modification detection from nanopore direct RNA sequencing data. RNA Biol. 18 (2021), 31–40.
Nguyen, T.A., Heng, J.W.J., Kaewsapsak, P., Kok, E.P.L., Stanojević, D., Liu, H., Cardilla, A., Praditya, A., Yi, Z., Lin, M., et al. Direct identification of A-to-I editing sites with nanopore native RNA sequencing. Nat. Methods 19 (2022), 833–844.
Deep learning identifies A-to-I RNA edits using nanopore sequencing data. Nat. Methods 19 (2022), 797–798.
Chen, L., Ou, L., Jing, X., Kong, Y., Xie, B., Zhang, N., Shi, H., Qin, H., Li, X., Hao, P., DeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using nanopore direct RNA sequencing. Genome Biol., 24, 2023, 75.
Diroma, M.A., Ciaccia, L., Pesole, G., Picardi, E., Elucidating the editome: bioinformatics approaches for RNA editing detection. Brief. Bioinform. 20 (2019), 436–447.
Lee, J.H., Ang, J.K., Xiao, X., Analysis and design of RNA sequencing experiments for identifying RNA editing and other single-nucleotide variants. RNA 19 (2013), 725–732.
Bahn, J.H., Lee, J.H., Li, G., Greer, C., Peng, G., Xiao, X., Accurate identification of A-to-I RNA editing in human by transcriptome sequencing. Genome Res. 22 (2012), 142–150.
Light, D., Haas, R., Yazbak, M., Elfand, T., Blau, T., Lamm, A.T., RESIC: A Tool for Comprehensive Adenosine to Inosine RNA Editing Site Identification and Classification. Front. Genet., 12, 2021, 686851.
Picardi, E., Pesole, G., REDItools: high-throughput RNA editing detection made easy. Bioinformatics 29 (2013), 1813–1814.
John, D., Weirick, T., Dimmeler, S., Uchida, S., RNAEditor: easy detection of RNA editing events and the introduction of editing islands. Brief. Bioinform. 18 (2017), 993–1001.
Li, H., Durbin, R., Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25 (2009), 1754–1760.
Sander, J., Ester, M., Kriegel, H.-P., Xu, X., Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Min. Knowl. Discov. 2 (1998), 169–194.
Wang, Z., Lian, J., Li, Q., Zhang, P., Zhou, Y., Zhan, X., Zhang, G., RES-Scanner: a software package for genome-wide identification of RNA-editing sites. GigaScience, 5, 2016, 37.
Alon, S., Eisenberg, E., Identifying RNA editing sites in miRNAs by deep sequencing. Methods Mol. Biol. 1038 (2013), 159–170.
Xiong, H., Liu, D., Li, Q., Lei, M., Xu, L., Wu, L., Wang, Z., Ren, S., Li, W., Xia, M., et al. RED-ML: a novel, effective RNA editing detection method based on machine learning. GigaScience 6 (2017), 1–8.
Zhang, F., Lu, Y., Yan, S., Xing, Q., Tian, W., SPRINT: an SNP-free toolkit for identifying RNA editing sites. Bioinformatics 33 (2017), 3538–3548.
Kim, D.D.Y., Kim, T.T.Y., Walsh, T., Kobayashi, Y., Matise, T.C., Buyske, S., Gabriel, A., Widespread RNA editing of embedded alu elements in the human transcriptome. Genome Res. 14 (2004), 1719–1725.
Barak, M., Levanon, E.Y., Eisenberg, E., Paz, N., Rechavi, G., Church, G.M., Mehr, R., Evidence for large diversity in the human transcriptome created by Alu RNA editing. Nucleic Acids Res. 37 (2009), 6905–6915.
Blow, M., Futreal, P.A., Wooster, R., Stratton, M.R., A survey of RNA editing in human brain. Genome Res. 14 (2004), 2379–2387.
Carmi, S., Borukhov, I., Levanon, E.Y., Identification of widespread ultra-edited human RNAs. PLoS Genet., 7, 2011, e1002317.
Quiles-Jiménez, A., Gregersen, I., Mittelstedt Leal de Sousa, M., Abbas, A., Kong, X.Y., Alseth, I., Holm, S., Dahl, T.B., Skagen, K., Skjelland, M., et al. N6-methyladenosine in RNA of atherosclerotic plaques: An epitranscriptomic signature of human carotid atherosclerosis. Biochem. Biophys. Res. Commun. 533 (2020), 631–637.
Alon, S., Erew, M., Eisenberg, E., DREAM: a webserver for the identification of editing sites in mature miRNAs using deep sequencing data. Bioinformatics 31 (2015), 2568–2570.
Yao, L., Wang, H., Song, Y., Dai, Z., Yu, H., Yin, M., Wang, D., Yang, X., Wang, J., Wang, T., et al. Large-scale prediction of ADAR-mediated effective human A-to-I RNA editing. Brief. Bioinform. 20 (2019), 102–109.
Nigita, G., Alaimo, S., Ferro, A., Giugno, R., Pulvirenti, A., Knowledge in the Investigation of A-to-I RNA Editing Signals. Front. Bioeng. Biotechnol., 3, 2015, 18.
Niu, G., Zou, D., Li, M., Zhang, Y., Sang, J., Xia, L., Li, M., Liu, L., Cao, J., Zhang, Y., et al. Editome Disease Knowledgebase (EDK): a curated knowledgebase of editome-disease associations in human. Nucleic Acids Res. 47 (2019), D78–D83.
Zhu, H., Huang, L., Liu, S., Dai, Z., Songyang, Z., Weng, Z., Xiong, Y., REIA: A database for cancer A-to-I RNA editing with interactive analysis. Int. J. Biol. Sci. 18 (2022), 2472–2483.
Picardi, E., D'Erchia, A.M., Lo Giudice, C., Pesole, G., REDIportal: a comprehensive database of A-to-I RNA editing events in humans. Nucleic Acids Res. 45 (2017), D750–D757.
Lin, C.H., Chen, S.C.C., The Cancer Editome Atlas: A Resource for Exploratory Analysis of the Adenosine-to-Inosine RNA Editome in Cancer. Cancer Res. 79 (2019), 3001–3006.
Stephens, Z.D., Lee, S.Y., Faghri, F., Campbell, R.H., Zhai, C., Efron, M.J., Iyer, R., Schatz, M.C., Sinha, S., Robinson, G.E., Big Data: Astronomical or Genomical?. PLoS Biol., 13, 2015, e1002195.
Gong, J., Liu, C., Liu, W., Xiang, Y., Diao, L., Guo, A.Y., Han, L., LNCediting: a database for functional effects of RNA editing in lncRNAs. Nucleic Acids Res. 45 (2017), D79–D84.
Liu, X., Jian, X., Boerwinkle, E., dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. 32 (2011), 894–899.
Kennedy, B., Kronenberg, Z., Hu, H., Moore, B., Flygare, S., Reese, M.G., Jorde, L.B., Yandell, M., Huff, C., Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data. Curr. Protoc. Hum. Genet. 81 (2014), 6.14.1–6.14.25.
Coonrod, E.M., Margraf, R.L., Russell, A., Voelkerding, K.V., Reese, M.G., Clinical analysis of genome next-generation sequencing data using the Omicia platform. Expert Rev. Mol. Diagn. 13 (2013), 529–540.
Vandeweyer, G., Van Laer, L., Loeys, B., Van den Bulcke, T., Kooy, R.F., VariantDB: a flexible annotation and filtering portal for next generation sequencing data. Genome Med., 6, 2014, 74.
Yandell, M., Huff, C., Hu, H., Singleton, M., Moore, B., Xing, J., Jorde, L.B., Reese, M.G., A probabilistic disease-gene finder for personal genomes. Genome Res. 21 (2011), 1529–1542.
Wang, K., Li, M., Hakonarson, H., ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res., 38, 2010, e164.
Cingolani, P., Platts, A., Wang, L.L., Coon, M., Nguyen, T., Wang, L., Land, S.J., Lu, X., Ruden, D.M., A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6 (2012), 80–92.
Pagel, K.A., Kim, R., Moad, K., Busby, B., Zheng, L., Tokheim, C., Ryan, M., Karchin, R., Integrated Informatics Analysis of Cancer-Related Variants. JCO Clin. Cancer Inform. 4 (2020), 310–317.
McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R.S., Thormann, A., Flicek, P., Cunningham, F., The Ensembl Variant Effect Predictor. Genome Biol., 17, 2016, 122.
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13 (2003), 2498–2504.
Reimand, J., Kull, M., Peterson, H., Hansen, J., Vilo, J., g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35 (2007), W193–W200.
Kuleshov, M.V., Jones, M.R., Rouillard, A.D., Fernandez, N.F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S.L., Jagodnik, K.M., Lachmann, A., et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44 (2016), W90–W97.
Huang, D.W., Sherman, B.T., Lempicki, R.A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4 (2009), 44–57.
Chen, J., Bardes, E.E., Aronow, B.J., Jegga, A.G., ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37 (2009), W305–W311.
Mi, H., Muruganujan, A., Thomas, P.D., PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41 (2013), D377–D386.
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.P., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102 (2005), 15545–15550.
Merico, D., Isserlin, R., Stueker, O., Emili, A., Bader, G.D., Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One, 5, 2010, e13984.
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A.H., Tanaseichuk, O., Benner, C., Chanda, S.K., Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun., 10, 2019, 1523.
Xie, Z., Bailey, A., Kuleshov, M.V., Clarke, D.J.B., Evangelista, J.E., Jenkins, S.L., Lachmann, A., Wojciechowicz, M.L., Kropiwnicki, E., Jagodnik, K.M., et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc., 1, 2021, e90.
Garcia-Moreno, A., Carmona-Saez, P., Computational Methods and Software Tools for Functional Analysis of miRNA Data. Biomolecules, 10, 2020.
Kozomara, A., Birgaoanu, M., Griffiths-Jones, S., miRBase: from microRNA sequences to function. Nucleic Acids Res. 47 (2019), D155–D162.
Vlachos, I.S., Paraskevopoulou, M.D., Karagkouni, D., Georgakilas, G., Vergoulis, T., Kanellos, I., Anastasopoulos, I.L., Maniou, S., Karathanou, K., Kalfakakou, D., et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 43 (2015), D153–D159.
Georgakilas, G., Vlachos, I.S., Zagganas, K., Vergoulis, T., Paraskevopoulou, M.D., Kanellos, I., Tsanakas, P., Dellis, D., Fevgas, A., Dalamagas, T., Hatzigeorgiou, A.G., DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators. Nucleic Acids Res. 44 (2016), D190–D195.
Backes, C., Fehlmann, T., Kern, F., Kehl, T., Lenhof, H.P., Meese, E., Keller, A., miRCarta: a central repository for collecting miRNA candidates. Nucleic Acids Res. 46 (2018), D160–D167.
Fromm, B., Domanska, D., Høye, E., Ovchinnikov, V., Kang, W., Aparicio-Puerta, E., Johansen, M., Flatmark, K., Mathelier, A., Hovig, E., et al. MirGeneDB 2.0: the metazoan microRNA complement. Nucleic Acids Res. 48 (2020), D132–D141.
Paraskevopoulou, M.D., Georgakilas, G., Kostoulas, N., Vlachos, I.S., Vergoulis, T., Reczko, M., Filippidis, C., Dalamagas, T., Hatzigeorgiou, A.G., DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 41 (2013), W169–W173.
Sticht, C., De La Torre, C., Parveen, A., Gretz, N., miRWalk: An online resource for prediction of microRNA binding sites. PLoS One, 13, 2018, e0206239.
Vlachos, I.S., Zagganas, K., Paraskevopoulou, M.D., Georgakilas, G., Karagkouni, D., Vergoulis, T., Dalamagas, T., Hatzigeorgiou, A.G., DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 43 (2015), W460–W466.
Ding, J., Li, X., Hu, H., TarPmiR: a new approach for microRNA target site prediction. Bioinformatics 32 (2016), 2768–2775.
Volders, P.J., Verheggen, K., Menschaert, G., Vandepoele, K., Martens, L., Vandesompele, J., Mestdagh, P., An update on LNCipedia: a database for annotated human lncRNA sequences. Nucleic Acids Res. 43 (2015), 4363–4364.
Bhartiya, D., Pal, K., Ghosh, S., Kapoor, S., Jalali, S., Panwar, B., Jain, S., Sati, S., Sengupta, S., Sachidanandan, C., et al. lncRNome: A Comprehensive Knowledgebase of Human Long Noncoding RNAs. Database, 2013, 2013, bat034.
Paraskevopoulou, M.D., Vlachos, I.S., Karagkouni, D., Georgakilas, G., Kanellos, I., Vergoulis, T., Zagganas, K., Tsanakas, P., Floros, E., Dalamagas, T., Hatzigeorgiou, A.G., DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res. 44 (2016), D231–D238.
Lerner, T., Kluesner, M., Tasakis, R.N., Moriarity, B.S., Papavasiliou, F.N., Pecori, R., C-to-U RNA Editing: From Computational Detection to Experimental Validation. Methods Mol. Biol. 2181 (2021), 51–67.
Androvic, P., Valihrach, L., Elling, J., Sjoback, R., Kubista, M., Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification. Nucleic Acids Res., 45, 2017, e144.
Voss, G., Ceder, Y., Two-Tailed RT-qPCR for the Quantification of A-to-I-Edited microRNA Isoforms. Curr. Protoc., 3, 2023, e645.
Bhakta, S., Sakari, M., Tsukahara, T., RNA editing of BFP, a point mutant of GFP, using artificial APOBEC1 deaminase to restore the genetic code. Sci. Rep., 10, 2020, 17304.
Dick, A.L.W., Khermesh, K., Paul, E., Stamp, F., Levanon, E.Y., Chen, A., Adenosine-to-Inosine RNA Editing Within Corticolimbic Brain Regions Is Regulated in Response to Chronic Social Defeat Stress in Mice. Front. Psychiatry, 10, 2019, 277.
Paul, D., Sinha, A.N., Ray, A., Lal, M., Nayak, S., Sharma, A., Mehani, B., Mukherjee, D., Laddha, S.V., Suri, A., et al. A-to-I editing in human miRNAs is enriched in seed sequence, influenced by sequence contexts and significantly hypoedited in glioblastoma multiforme. Sci. Rep., 7, 2017, 2466.
Jain, M., Weber, A., Maly, K., Manjaly, G., Deek, J., Tsvyetkova, O., Stulić, M., Toca-Herrera, J.L., Jantsch, M.F., A-to-I RNA editing of Filamin A regulates cellular adhesion, migration and mechanical properties. FEBS J. 289 (2022), 4580–4601.
Kokot, K.E., Kneuer, J.M., John, D., Rebs, S., Möbius-Winkler, M.N., Erbe, S., Müller, M., Andritschke, M., Gaul, S., Sheikh, B.N., et al. Reduction of A-to-I RNA editing in the failing human heart regulates formation of circular RNAs. Basic Res. Cardiol., 117, 2022, 32.
Tian, N., Li, X., Luo, Y., Han, Z., Li, Z., Fan, C., Curcumin regulates the metabolism of low density lipoproteins by improving the C-to-U RNA editing efficiency of apolipoprotein B in primary rat hepatocytes. Mol. Med. Rep. 9 (2014), 132–136.
Mukherjee, P., Raghava Kurup, R., Hundley, H.A., RNA immunoprecipitation to identify in vivo targets of RNA editing and modifying enzymes. Methods Enzymol. 658 (2021), 137–160.
Thomas, J.M., Beal, P.A., How do ADARs bind RNA? New protein-RNA structures illuminate substrate recognition by the RNA editing ADARs. Bioessays, 39, 2017.
Dafou, D., Kanata, E., Pettas, S., Bekas, N., Dimitriadis, A., Kempapidou, G., Lagoudaki, R., Theotokis, P., Touloumi, O., Delivanoglou, N., et al. RNA Editing Alterations Define Disease Manifestations in the Progression of Experimental Autoimmune Encephalomyelitis (EAE). 2022 Cells 11.
Katrekar, D., Chen, G., Meluzzi, D., Ganesh, A., Worlikar, A., Shih, Y.R., Varghese, S., Mali, P., In vivo RNA editing of point mutations via RNA-guided adenosine deaminases. Nat. Methods 16 (2019), 239–242.
Yi, Z., Qu, L., Tang, H., Liu, Z., Liu, Y., Tian, F., Wang, C., Zhang, X., Feng, Z., Yu, Y., et al. Engineered circular ADAR-recruiting RNAs increase the efficiency and fidelity of RNA editing in vitro and in vivo. Nat. Biotechnol. 40 (2022), 946–955.
Jain, M., Manjaly, G., Maly, K., de Vries, M.R., Janisiw, M., König, L., Nossent, A.Y., Jantsch, M.F., Filamin A pre-mRNA editing modulates vascularization and tumor growth. Mol. Ther. Nucleic Acids 30 (2022), 522–534.
Khermesh, K., D'Erchia, A.M., Barak, M., Annese, A., Wachtel, C., Levanon, E.Y., Picardi, E., Eisenberg, E., Reduced levels of protein recoding by A-to-I RNA editing in Alzheimer's disease. RNA 22 (2016), 290–302.
Vlachogiannis, N.I., Sachse, M., Georgiopoulos, G., Zormpas, E., Bampatsias, D., Delialis, D., Bonini, F., Galyfos, G., Sigala, F., Stamatelopoulos, K., et al. Adenosine-to-inosine Alu RNA editing controls the stability of the pro-inflammatory long noncoding RNA NEAT1 in atherosclerotic cardiovascular disease. J. Mol. Cell. Cardiol. 160 (2021), 111–120.
Altaf, F., Vesely, C., Sheikh, A.M., Munir, R., Shah, S.T.A., Tariq, A., Modulation of ADAR mRNA expression in patients with congenital heart defects. PLoS One, 14, 2019, e0200968.
Ma, Y., Dammer, E.B., Felsky, D., Duong, D.M., Klein, H.U., White, C.C., Zhou, M., Logsdon, B.A., McCabe, C., Xu, J., et al. Atlas of RNA editing events affecting protein expression in aged and Alzheimer's disease human brain tissue. Nat. Commun., 12, 2021, 7035.
Kanata, E., Llorens, F., Dafou, D., Dimitriadis, A., Thüne, K., Xanthopoulos, K., Bekas, N., Espinosa, J.C., Schmitz, M., Marín-Moreno, A., et al. RNA editing alterations define manifestation of prion diseases. Proc. Natl. Acad. Sci. USA 116 (2019), 19727–19735.
Hosaka, T., Tsuji, H., Kwak, S., RNA Editing: A New Therapeutic Target in Amyotrophic Lateral Sclerosis and Other Neurological Diseases. Int. J. Mol. Sci., 22, 2021, 10958.
Decher, N., Streit, A.K., Rapedius, M., Netter, M.F., Marzian, S., Ehling, P., Schlichthörl, G., Craan, T., Renigunta, V., Köhler, A., et al. RNA editing modulates the binding of drugs and highly unsaturated fatty acids to the open pore of Kv potassium channels. EMBO J. 29 (2010), 2101–2113.
Mandrekar, J.N., Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 5 (2010), 1315–1316.
Salvetat, N., Checa-Robles, F.J., Patel, V., Cayzac, C., Dubuc, B., Chimienti, F., Abraham, J.D., Dupré, P., Vetter, D., Méreuze, S., et al. A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers. Transl. Psychiatry, 12, 2022, 182.
Sinnamon, J.R., Kim, S.Y., Corson, G.M., Song, Z., Nakai, H., Adelman, J.P., Mandel, G., Site-directed RNA repair of endogenous Mecp2 RNA in neurons. Proc. Natl. Acad. Sci. USA 114 (2017), E9395–E9402.
Wu, X., Wang, L., Wang, K., Li, J., Chen, R., Wu, X., Ni, G., Liu, C., Das, S., Sluijter, J.P.G., et al. ADAR2 increases in exercised heart and protects against myocardial infarction and doxorubicin-induced cardiotoxicity. Mol. Ther. 30 (2022), 400–414.
Goossens, E.A.C., de Vries, M.R., Simons, K.H., Putter, H., Quax, P.H.A., Nossent, A.Y., miRMap: Profiling 14q32 microRNA Expression and DNA Methylation Throughout the Human Vasculature. Front. Cardiovasc. Med., 6, 2019, 113.
Rogg, E.M., Abplanalp, W.T., Bischof, C., John, D., Schulz, M.H., Krishnan, J., Fischer, A., Poluzzi, C., Schaefer, L., Bonauer, A., et al. Analysis of Cell Type-Specific Effects of MicroRNA-92a Provides Novel Insights Into Target Regulation and Mechanism of Action. Circulation 138 (2018), 2545–2558.