An organism’s diet is a major route of exposure to both beneficial nutrients and toxic environmental chemicals and natural products. The uptake of dietary xenobiotics in the intestine is prevented by transporters of the Solute Carrier (SLC) and ATP Binding Cassette (ABC) family. Several environmental chemicals and natural toxins have been identified to induce expression of these defense transporters in fish and aquatic invertebrates, indicating that they are substrates and can be eliminated. However, certain environmental chemicals, termed Transporter-Interfering Chemicals or TICs, have recently been shown to bind to and inhibit fish and mammalian P-glycoprotein (ABCB1), thereby sensitizing cells to toxic chemical accumulation. If and to what extent other xenobiotic defense or nutrient uptake transporters can also be inhibited by dietary TICs is still unknown. To date, most chemical-transporter interaction studies in aquatic organisms have focused on ABC-type transporters, while molecular interactions of xenobiotics with SLC-type transporters are poorly understood. In this perspective, we summarize current advances in the identification, localization, and functional analysis of protective MXR transporters and nutrient uptake systems in the digestive system of fish and aquatic invertebrates. We collate the existing literature data on chemically induced transporter gene expression and summarize the molecular interactions of xenobiotics with these transport systems. Our review emphasizes the need for standardized assays in a broader panel of commercially important fish and seafood species to better evaluate the effects of TIC and other xenobiotic interactions with physiological substrates and MXR transporters across the aquatic ecosystem and predict possible transfer to humans through consumption. Fish and other aquatic animals can be exposed to water-soluble environmental chemicals via uptake through the gills and hydrophobic xenobiotics through ingestion of contaminated food. Gills are commonly considered a part of the respiratory system of aquatic animals . In addiIn fish and mammals, most nutrients as well as water and ions in the diet are typically absorbed through the epithelial cells of the small and large intestines . TherebyYet, despite the similarities in intestinal macro- and micro-anatomy between terrestrial and aquatic organisms, little is known about the molecular composition and levels of both nutrient uptake and xenobiotic defense systems along the digestive tract of fish and other aquatic organisms. In addition, there is limited data available on the molecular interactions of aqueous and food-borne contaminants with these transport systems and how these interactions could affect nutrient homeostasis and toxic contaminant bioaccumulation.One of the key functions of the digestive system is to absorb nutrients from the diet that can be metabolized to provide energy for growth and development. At the same time, the intestinal tract serves as a major environmental barrier to prevent toxic xenobiotic uptake and accumulation. In the gills and intestine of fish and other aquatic organisms, several members of both the solute carrier (SLC) and ATP-Binding Cassette (ABC) family of transporters have been identified to participate in essential physiological functions such as nutrient uptake, ion flux, cell signaling processes, and toxic metabolite and xenobiotic efflux.Transepithelial transport of nutrients in the gastrointestinal tract is typically mediated by secondary active transporters of the solute carrier (SLC) family. In humans, SLCs represent the largest group of secondary active membrane transporters . SLCs arOncorhynchus mykiss) gills (Danio rerio) intestines (Oreochromis niloticus)  gills . Furthertestines . High inloticus) . In Zebrloticus) . Howeverloticus) . Intestiloticus) . Deficieloticus) .Members of both the ABC and SLC-type families have been shown to be part of a highly conserved defense mechanism against chemical insults, often referred to as multidrug-resistance (MDR) transporters . The mamIn the early 90s, a mechanism similar to MDR was reported in aquatic organisms and since then referred to as multixenobiotic-resistance or MXR . The MXRPleuronectes americanus using Southern blot techniques represented the first set of P-glycoprotein orthologs reported in lower vertebrates (The ABC-type transporter P-glycoprotein (aka MDR1 or ABCB1) is one the best characterized mammalian MDR/MXR transporters that shows high expression in biological barriers such as the liver, brain, kidney and the intestine . The idetebrates . Since ttebrates , carp  in the liver, brain, gonads, kidney, gills, and intestine of rainbow trout revealed similar expression patterns to mammalian tissues  and head (gills) of early larvae  and organic anion transporting polypeptide 2 (oatp2) genes were detected in yolk sac epithelium, while the mRNA levels of the organic anion transporting polypeptide 1d gene (oatp1d) were high in abdominal viscera  was shown to significantly decrease mRNA levels of the SLC15 peptide transporter gene family, but mRNA levels recovered upon refeeding (Fundulus heteroclitus), where short-term fasting increased, long-term fasting decreased, and re-feeding restored SLC15 mRNA levels  was shown to increase intestinal peptide transporter 1 (PepT1/SLC15A1) (Dicentrarchus labrax) diet was shown to increase PepT1 gene expression but not affect the expression of the monocarboxylate transporter SLC6A19  fed diets supplemented with saponins and pea protein were shown to have several metabolic systems greatly altered, including differential expression of several SLC proteins  . Similar SLC6A19 . Atlantiproteins . Studiesproteins .As a result of climate change and associated alterations in salinity, pH, and temperature of aquatic environments, the bioavailability and toxicity of environmental chemicals can increase . Recent Xenopus laevis oocytes  was shown to cause a decrease in copper absorption in rainbow trout gut sacs as well as a decrease in iron absorption by SLC11 proteins isolated from rainbow trout gills and expressed in  oocytes . The pH  oocytes . Zebrafi oocytes .Chionodraco hamatus) represents an extreme example, with structural changes to its PEPT1 protein facilitating in part its ability to function at very cold (−1.9°C) temperatures (Hypomesus transpacificus), the expression of the ion exchange transporter SLC8B1 was shown to have non-linear responses to increasing temperature, with decreased expression at 20°C and 25°C compared to the control (14°C) and re-elevated expression at 27°C , a linear increase in transport rate with increasing temperature has been demonstrated . The autMultixenobiotic Resistance transporters represent a critical line of defense in preventing xenobiotic chemicals from accumulating and harming an aquatic organism. Initial investigations identifying MXR transporters as molecular targets of xenobiotics focused on chemical exposure experiments and subsequent evaluation of transporter mRNA and/or protein expression levels . Therebyabcb1a and abcb1b, was significantly increased upon exposure to the antifungal agent clotrimazole (pgp (ABCB4) and abcc2 expression in zebrafish embryos , the abcb1 and abcc2 genes are induced in the liver upon exposure to cadmium ions  the expression levels of peptide uptake transporters SLC15a1a, SLC15a1b, SLC15a2, and SLC15a5 decreased after fasting (Ctenopharyngodon idella), the cotton derived phenol gossypol was shown to down-regulate several peptide and amino acid SLC transporters in the intestine, leading to intestinal damage and reduced growth  the ABCB1 homolog P-gp1 was shown to be inhibited by a series of pharmaceuticals, with sildenafil and simvastatin showing highest inhibition potency , moderately hydrophobic pesticides and PFAS compounds have been shown to inhibit gill tissue efflux of Rhodamine B, a known P-glycoprotein substrate (Mytilus coruscus), gill tissue pre-exposure to benzo(α)pyrene (BaP) reduced the inhibitory effect of ABCB1- and ABCC1-specific inhibitors reversin 205 and MK572 on Calcein-AM efflux pyrene led to increased deformities, impaired growth, and increased toxicant accumulation. For instance, in the Californian mussel  were exposed to combinations of the toxic MXR transporter substrates vinblastine and triclosan (TCS) or P-85 nanoparticles, the toxicity increased by a factor of 2-8 (Dreissena polymorpha) were exposed to a combination of vinblastine and model ABC transporter inhibitors, the toxicity increased super-additively  suggest that microcystin-LR may act as a substrate and competitive inhibitor of ABCC-like proteins in intestinal tissues  is capable of inhibiting ABCC-like transporters when absorbed by intestinal epithelial cells , ABCC2 activity was shown to be inhibited by the vasoconstrictor endothelin-1 via the ETB receptor and protein kinase C signaling . Transporter localization, gene expression levels and endogenous and xenobiotic substrate/inhibitor identification has mainly focused on two model organisms: zebrafish and rainbow trout. Furthermore, there have been only a few studies conducted on fish feed and dietary levels of contaminants and transporter-mediated uptake and bioaccumulation in commercial fish and other human-relevant aquaculture species. Finally, the extent to which other mutable environmental factors, such as salinity, temperature, and pH, may impair the levels and function of MXR and other nutrient transporters remains elusive. This perspective further highlights the urgent need to identify novel TICs, to determine their environmental levels in the natural diets and feeds of fish and other aquatic invertebrates, and to specify the transporter targets that regulate their uptake and disposition in aquatic organisms. Special emphasis should be placed on determining the interactions of TICs with protective MXR efflux transporters and essential nutrient uptake transporters lining the gills and intestinal epithelia to better predict toxic chemical accumulation and/or nutrient deprivation. The key strategies to mitigate toxicant and toxin accumulation in aquatic organisms include targeted chemical analysis of fish feed in aquaculture operations, a continuous biomonitoring of TIC levels in lower trophic level organisms that serve as prey or feed for commercial fish species and the development of regulatory guidelines that inform industrial chemical and agricultural pesticide management strategies to reduce or eliminate the use of environmentally persistent TIC compounds.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.Both 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. 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Genomic repeats have been intensely studied as regulatory elements controlling gene transcription, splicing and genome architecture. Our understanding of the role of the repetitive RNA such as the RNA coming from genomic repeats, or repetitive sequences embedded in mRNA/lncRNAs, in nuclear and cellular functions is instead still limited. In this review we discuss evidence supporting the multifaceted roles of repetitive RNA and RNA binding proteins in nuclear organization, gene regulation, and in the formation of dynamic membrane-less aggregates. We hope that our review will further stimulate research in the consolidating field of repetitive RNA biology. The eukaryotic nucleus is a very complex organelle containing large amounts of DNA, RNA, proteins  and nuclThe mechanisms behind the differential expression of protein coding and non-coding genes in time, space and in response to external stimuli, is the object of intensive studies. It is well known that, transcriptionally, chromatin exists in one of two main states: euchromatin, actively transcribed and not condensed, and heterochromatin, relatively compacted, gene-poor and transcriptionally silent , and thaWhile the role of repetitive genomic elements has been intensely studied, the role of repetitive or repeat-containing RNA has only relatively recently gained some attention. It is now becoming increasingly clear that mRNAs and nuclear non-coding RNAs are centrally involved in the regulation of chromatin state and therefore gene expression. For example, it has been known for decades that RNA can be intimately associated to chromatin . This tyDue to recent excellent reviews on the topic of lncRNA and RNA in general nuclear organization , this reRepetitive caRNAs are transcribed from repetitive DNA sequences such as DNA-containing genomic repeats, fragments of genomic repeats or repeated motifs such as simple repeats . It is bAlu. Together they make up around 30% of the human genome. Most of these TEs are however silenced as a possible cellular mechanism of defense, but a relatively small number of them (labeled as retrotransposition-competent LINE-1 or RC-L1) is responsible for a large amount of nuclear transcription  . These hcription . Noticeand ncRNA , and accnd ncRNA .Alu repeats were identified as a nuclear localization motif for RNA  in the establishment and maintenance of the two heterochromatin and the euchromatin states.Constitutive Heterochromatin is the transcriptionally silent, permanently condensed form of chromatin . It is aCentromeres are the sites of kinetochore attachment during mitosis and have obvious critical importance in cell survival . They vaS. pombe the silencing occurs via an RNA-dependent RNA polymerase (RdRP) acting on transcribed repeat RNA and forming dsRNA which on one hand triggers the Argonaute protein binding and subsequent transcriptional silencing and, on the other hand, recruits an RNA-induced transcriptional silencing (RITS) complex able to bind the histone methyltransferase Clr4 (homolog of human SUV39) to deposit the required H3K9Me marks. A feed-forward mechanism sees these methylated H3 recognized by another protein  , common to all constitutive HC forms. This marker is deposited by histone methyltransferase SUV39 and recognized by HP1 (Heterochromatin Protein 1); however, RNA, and specifically the repetitive RNA proceeding from the centromeric region, is necessary for histone methylation to take place and silencing to be maintained . In the man HP1) .While this mechanism has been very well studied in yeast, it has been ruled out in mammals by the lack of a mammalian RdRP . An alteTelomeres are found at the terminal sequences of every eukaryotic chromosome and their main function is to preserve chromosome integrity during each round of DNA replication. Critically, they prevent the recognition of chromosome ends as DNA breaks and also limit somatic cellular proliferation by inducing senescence when shortened below a critical length . TelomerThis term describes chromosomal regions which are silenced in a conditional way, being transcribed or silenced when circumstances, such as a certain stage of embryonic development or tissue differentiation, require it . FacultaIn this review, we will focus our attention to two of the most studied fHC events: XCI and genomic imprinting .X chromosome inactivation is a universal event in female mammalian development. As females have two X chromosomes, the simultaneous expression of both would result in abnormal protein dosage and lethal consequences (Xist (X inactive specific transcript)  with genes in the proximity of these loci being silenced first. It is of note that the XES lies in 3D proximity to the Xist locus (Xist spread onto the X chromosome (Xist is no longer required for long term maintenance of the Xi state after this period (equences . As a coequences . XCI is nscript) . This trnscript) . Repeat nscript) . This binscript) . The Xisnscript)  to provinscript) . It is tnscript) . This monscript) . Repeat nscript)  (3D-SIM)nscript) . Repeat nscript) . Howevernscript) , probablnscript) . The shonscript) . The spast locus , suggestromosome . A remars period . It is bs period , or stabs period   repeats of SAF-A, in some cell lines but not others, are sufficient to disrupt the bond . This haImprinting is the process by which some genes  are exclusively expressed in a monoallelic fashion, i.e., by only one of the parental alleles, the other one being permanently silenced . One exacis silencing of the cluster protein coding genes of the PoO and consequent exclusive expression of the opposite parent’s.These regions always express at least one lncRNA, whose expression PoO generally inversely correlates with the parent of origin of the expressed allele of the neighboring genes, i.e., expression of the paternal lncRNA will result in the expression of the maternal cluster protein coding genes . The expkcnq1ot1, a lncRNA located at the distal position of chromosome 7  but truncation studies have shown that the minimal length required for silencing is 1.7 kb with a crucial internal 860 bp 3′ stretch (Kcnq1ot1 contains five repeat sequences of 30 bp (MD1 repeats) which, however, do not seem to be strictly required for silencing , Lamin Associated domains (LADs)  and phasXist RNA (Two of the major protein partners of RNA in the regulation of chromatin state are the SAF (Scaffold Associated Factor) proteins, SAF-A and B . SAF-A iXist RNA . It appeXist RNA .SAF-B is a cognate protein of SAF-A, sharing with it its SAP  and RGG domains. It likewise binds RNA to modulate chromatin condensation. However, its role appears to be distinctly repressive . It is aXist E repeats have been recently studied in order to understand lncRNA spatial localization and function within the nucleus  of EZH2, previously encountered as a histone methyltransferase. Moreover, in this context, its activity is not a transcriptional repressor but an activating protein  in all somatic cells . NADs arUnder heat shock and acidosis conditions, the expression of ribosomal intergenic spacer long non-coding RNA (IGS lncRNA) is induced and these transcripts are essential and sufficient for the immobilization of proteins that contain a nucleolar detention signal (NoDS) within the nucleolus. The mature IGS transcripts then tether the molecular complexes to their sites of expression on the rDNA cassette. These ncRNAs contribute to dissolving the characteristic tripartite organization of the nucleolus , giving place to the “protein detention center” (DC), which is spatially, dynamically, and biochemically distinct. Upon removal of the environmental stressor, the ncRNAs are repressed, the DC is dissolved, and tripartite nucleolar organization is re-established . This foAlu repeats proceeding from intron splicing events in transcription hubs  are remarkable examples and are amongst the most conserved lncRNAs during vertebrate evolution although containing minimal repetitive elements.Nuclear RNAs and in particular, lncRNAs, have been shown to be involved in the formation of these sub-nuclear structures . This isNEAT1 lncRNA (NEAT1.2) appears to play a critical role in organizing a type of nuclear compartment called paraspeckles, containing various mRNAs and RBPs. Targeted degradation of NEAT1 disruptes the structure of these clusters n sequences (Hsr omega (heat-shock RNA-omega) arcRNA from Drosophila melanogaster contains tandem repeats of 280 nt in a stretch of ∼10 kb that contribute to the recruitment of various RNA-binding proteins to omega speckles   .An excellent recent review summarizing the current knowledge about cell organization and membrane-less compartments was published recently by It has been suggested that nuclear RNAs are an essential component of interphase chromosomes  but it rin cis) from where they are transcribed or elsewhere in the genome (in trans)  have particularly emerged as major regulators and can regulate transcription at the same locus (n trans) . Collectn trans) . Remarkan trans) . Considen trans) .In mammalian cells, the nuclear lamina is thought to be the key organizer of the radial arrangement of chromatin in interphase nuclei, by creating a large nuclear compartment where most of the inactive chromatin clusters in the form of lamina-associated domains (LADs) . LADs arXist lncRNA is the clearest example, and its interaction with lamin B receptor (LBR) is required for tethering the entire X-chromosome to the nuclear lamina during XCI  leads to the relocation of L1-enriched chromosomal segments from inactive domains to the nuclear interior and derepression of L1-associated genes. Moreover, L1 RNA seems to facilitate the interaction of L1 DNA to proteins such as nucleolin to target it to NADs/LADs in mouse ESCs , which are typically transcribed from telomeric regions and play a pivotal role in the establishment and maintenance of telomeric chromatin in the germline, facilitating loading of HP1 and H3K9me3 at different telomeric regions. In the fly germline, the loss of some piRNAs decreased perinuclear telomere positioning and lowered the local enrichment of HP1 and H3K9me3, resulting in telomere dysfunction .ThymoD ncRNA transcription promoted demethylation at CTCF bound sites and activated Cohesin-dependent looping to reposition the Bcl11b enhancer from the lamina to the nuclear interior and to juxtapose the Bcl11b enhancer and promoter into a single-loop domain. As expected, these large-scale changes in nuclear architecture were associated with the deposition of activating epigenetic marks across the loop domain -derived ncRNAs might play a role in chromatin compartmentalization. Enhancer lncRNAs (elncRNAs), often originating from repetitive sequences/fragments derived from transposable elements  may reinp domain .in situ digestion before sequencing the GPSeq sample. Specifically, YFISH uses a Y-shaped adapter that will ligate the cuts introduced in situ by a restriction enzyme, and then the use of complementary fluorescently labeled oligos will help with the detection. In 2016 in vivo interference of formaldehyde crosslinking  and “B-type” (heterochromatin) genomic compartments . At the inter−TAD interactions  have been proposed to create TADs to regulate gene expression. TADs boundaries are enriched for the insulator binding protein CTCF, housekeeping genes, transfer RNAs, and short interspersed element (SINE) retrotransposons, indicating that these factors may have a role in establishing the topological domain structure of the genome . Cohesinractions . In thisractions , a phenoractions  as well ractions  than CTCractions . As alreractions  and as Eractions .There are also a few studies supporting the role of ncRNAs in facilitating long-distance chromatin interactions through Cohesin-binding. In mammalian cells, for instance, not all the CTCF sites are co-occupied by Cohesin, suggesting that additional factors could dictate Cohesin binding at CTCF sites . In factin vitro in a similar fashion  such as well-characterized species from imprinted loci and previously unannotated transcripts from intergenic space . CTCF is fashion . Remarka fashion .in situ to help establish chromatin and regulatory domains in the human genome (TEs-derived ncRNAs have been frequently described as involved in the establishment and maintenance of insulator boundaries between TADs. Transposable elements (TEs) are responsible for genomic instability, epigenetic silencing and are intrinsically linked to 3D organization as several studies described how they shape genome organization from demarcating TAD boundaries to harboring binding sites for architectural proteins . Mammalin genome . The prin genome . The Murn genome .In spite of the previously mentioned findings, recent studies suggest that the pool of RNA in the cell appears to be largely dispensable for the maintenance of TADs . As the XIST and FIRRE , colocalize with Xi and determine trans-chromosomal interactions , a 156-bp repeating sequence (FIRRE around its site of transcription in the nucleus but it can also localize any RNA containing it (FIRRE forms a punctate compartment in the nucleus where its locus on the X chromosome and several specific loci on mouse chromosomes 2, 9, 15, and 17 colocalize with it. FIRRE is then required for these inter-chromosomal interactions. Both genetic deletion of the Firre locus and knockdown of SAF-A resulted in loss of colocalization of these trans-chromosomal interacting loci (Linearly non-contiguous TADs can also contact each other, defining long-range interactions that can vary between cell types and during differentiation . For insractions . It is sX4 locus . These msequence , contribining it . In mousing loci .Epichloe festucae, these repeat-rich blocks mediate genome folding within the nucleus and help to divide the genome into distinct regions that have similar gene expression profiles, modulating in this way the expression of genes that are strongly differentially expressed (Similarly, TEs may be involved in the establishment of conserved long-range chromosomal interactions in different organisms and some of these interactions appear to be important in gene regulation. For example, in the fungus xpressed .Drosophila melanogaster (bona fide nuclear matrix proteins and have been proposed to play important structural roles in the maintenance of the nuclear and nuclear architecture and regulate gene expression.Within the nuclear matrix, novel classes of repetitive RNAs have been identified as major players of the nuclear architecture and associated genome regulation. A class of repeat-containing lncRNA from the AAGAG satellite DNA repeat have emerged as a crucial component of the nuclear architecture in nogaster . Reductinogaster . In 2010Early in the human genome project it became clear that most our DNA is made of non-coding, repetitive sequences. Lacking any obvious function and carrying no protein coding information, these vast stretches of the genome were soon labeled “junk DNA” and were thought to be remnants of viruses that had lost replicative competence and merely multiplied inside our DNA, or even aberrant products of DNA replication errors.It is now becoming more and more clear instead that, far from being genetic “deadwood” these repetitive expanses are actively and deliberately transcribed into non-coding RNAs which play a major role in regulating gene expression and silencing, organizing nuclear architecture, compartmentalizing the nucleus, and modulating protein function. We can now state with confidence that the study of repetitive RNA role and mechanism of action will open a new frontier in cell biology. We hope that our review will further stimulate research in the consolidating field of repetitive RNA biology.AC conceived the idea of this review. AC, NB, and GT wrote the manuscript and selected representative examples. All authors contributed to the article and approved the submitted version.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.


Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here, we present RNA Editing Sites Identification and Classification (RESIC), an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides (1) the detection of editing sites in both repetitive and non-repetitive genomic regions; (2) the identification of hyper-edited regions; and (3) optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable. The conversion of adenosine to inosine (A-to-I) in double-stranded RNA regions, by adenosine deaminases (ADARs) enzymes, is the most common form of RNA editing in metazoans . This tyIn recent years, many efforts have been invested in developing computational approaches to detect A-to-I RNA editing sites from sequencing data . Since iSeveral tools developed to detect A-to-I RNA editing sites are based on comparison between RNA-seq reads and DNA reference genome. Among these tools are REDItools, which suggest simple comparison using samtools , and GIRHyper-editing by ADAR enzymes, which is defined as multiple A-to-I editing sites in a proximity, is a widespread phenomenon. Since most tools designed to identify editing sites are based on the detection of a limited number of mismatches in read alignments , hyper-editing events, which result in multiple mismatches in a single read (SR), are usually unexposed. Therefore, several recent methods were specially oriented to track hyper-editing sites. Despite the efforts to develop computational tools for A-to-I RNA editing site detection from sequencing data, to date there is not a single platform enabling robust detection of editing sites of different classes and their classification. Here, we present RNA Editing Sites Identification and Classification (RESIC), which enables detection and classification of A-to-I RNA editing sites of different types in a single tool. We expanded the pipeline we previously applied to identify editing sites in repetitive and non-repetitive regions  and adopSince aberrant IFN and cytokine responses were observed in COVID-19 patients  and ADARRNA Editing Sites Identification and Classification enables the user to supply DNA or RNA datasets that should exhibit the desired editing phenomena and DNA or RNA sequencing datasets that should not exhibit the desired editing phenomena. The latter group is used to exclude changes deriving from SNPs. Since nucleotide changes in the former sequencing datasets correspond to positive evidence of that sites undergoing editing and the latter datasets correspond to negative evidence, we term these sets of datasets as positive and negative datasets. RESIC is completely reference agnostic. The users provide whichever reference file they wish to use for the alignment as well.For ambiguous read filtering, we adopted the method of S =  where A is an alignment algorithm, p is a list of alignment parameters for A, f_1 is a preprocessing function of the raw datasets and f_2 is a postprocessing function for aligned and misaligned reads. These seemingly verbose definitions enable RESIC to decouple the choice of alignment algorithm from the rest of the modules in RESIC.We define an alignment scheme to be a 4-tuple S be an alignment scheme, L be a sequencing dataset and R be a genome reference, we define PS,L,R and NS,L,Rto be the aligned and misaligned read fractions resulting from running S on L and R. We define S = .Let f_1 and f_2 are identity functions in said scheme. Pseudo code for calculating S:We say that a scheme is normal if def S:L′,R′=f_1 # preprocessing thesequencingdatasetsP′,N′=A # Alighning the sequencingdatasetsP,N = f_2 # post processing resultsreturn P,NG =  where nodes in V are alignment schemes, L a sequencing dataset and R a reference we define new alignment schemes Gv for each v ∈ V to be defined as follows:Given a directed acyclic graph G to be a set of aligned sequencing datasets {PGv,L,R|v ∈ V}. Simply put, we perform the alignment scheme of node v on all read fragments that were misaligned in any of v’s ancestors.We define X and Y be two distinct nucleotides. To be able mapping hyper editing sites, we apply the 3nt alignment scheme by which each appearance of either X or Y is transformed into X in both the sequencing datasets (reads) and the reference genomes. That was similarly done by others  and to the reference sense strand. Next, in order to identify hyper editing sites on the antisense strand, for each X and Y nucleotides pairs, we create the complement reference genome, based on the original reference, and reverse the reads that were unmapped in the previous step, to achieve the 3′–5′ direction, same as the created reference. Then we reapply the 3nt alignment scheme while considering the manipulation of the reference genome and reads when recording the mapped reads as aligned to the antisense.Let y others . Howevervia custom python scripts. In each step, after mapping the reads, aligned and unaligned reads are reverted to their original sequence l, k1, k2, u, r, and c) are user defined.After performing the graph alignment for each of the given sequencing datasets, samtools  is used l reads are discarded. We discarded sites from the positive datasets if those same sites appeared in any negative dataset with a nucleotide change.First, sites with no nucleotide changes and sites covered by less than k_1 percent or more than k_2 percent of the reads mapped to that site, (2) whose other nucleotide changes constitute over u percent of the reads mapped to that site, and (3) whose most abundant nucleotide change is in at least r reads. We term: k_1, the minimal editing percent threshold, k_2, the maximal editing percent threshold, r, the editing read threshold, and u, the editing noise threshold.For each positive sequencing dataset, we filter out any site: (1) whose most abundant nucleotide change constitutes less than We filter all sites that were defined as editing sites at the previous step, under more than one editing category , if they represented more than one type of nucleotide change .Deriving from our method, it may be possible that under the hyper editing categories, a non-hyper editing site would be recorded. Namely, for each pair of nucleotides X and Y that we perform the 3nt genome scheme, other nucleotide mismatches than hyper X to Y or Y to X may be noted, enabled by the new conditions created by the 3nt scheme. Therefore, we filter the hyper editing files to include only X to Y or Y to X changes  that were sequenced at 75 SR, were downloaded from SRA. FastQC was used to control the read quality and trimming was performed accordingly. Reads were further collapsed and then taken for a RESIC run. For the underlying sequencing algorithm, we used Bowtie  was used. RNA was treated with turbo DNase I (Invitrogene) and then a reverse transcriptase reaction was performed with Maxima First Strand cDNA Synthesis Kit (Thermo Scientific), using primers that surrounded the candidate editing sites . The amplification products were directly sequenced by Sanger sequencing.l = 2 to be the coverage per site threshold, k1=30 and k2=99 for the editing minimal and maximal percent threshold, respectively, u = 3 for the noise thresholds, and r = 2 for the editing read threshold. The consensus module was run with c = 0.5. We then filtered the site lists obtained to have only A-to-I sites. Since the RNA library preparation strategy was stranded , we filtered the files obtained to include only actual A-to-G sites and not T-to-C. To test the difference in the numbers of editing sites, under the non-repetitive and hyper-non-repetitive classes, between all SARS-CoV-2 and mock samples, we performed Two-tailed T-test with equal variances . For this test, we included the normalized  numbers of editing sites of six repeats for each sample type, originated from 3 biological replicas that were evaluated twice for each strand separately.Raw RNA-seq data of Calu-3 human Lung adenocarcinoma cells infected with SARS-CoV-2 virus or mock, were downloaded from SRA, BioProject PRJNA615032. FastQC was used to control the read quality and trimming was performed accordingly. Reads were collapsed and first aligned to the SARS-CoV-2 reference genome version NC_045512.2 using bowtie. Alignment to the SARS-CoV-2 genome was made to exclude reads that are originated from the virus for further analysis, and to validate that in contrast to the mock samples, the SARS-CoV-2 samples are infected with the virus. Indeed, few thousands of reads were mapped to the SARS-CoV-2 genome, only for the SARS-CoV-2 infected samples. We applied RESIC separately on the unaligned reads of the mock and SARS-CoV-2 infected samples  to identify changes in RNA editing events upon coronavirus infection. For the underline sequencing algorithm, we used Bowtie  alignmenTo perform differential expression analysis (DEA), we mapped the same unaligned reads that were used for RESIC analysis before, to the human transcriptome version GRCh37 (hg19) using bowtie. Gene expression levels were evaluated by read counts. We then compared our created gene counts to the already processed counts downloaded from GEO: GSE147507. Although read count values were not identical, as expected due to the use of different alignment tools, the trend was the same.We performed DEA, using DESeq2 , with lf1.RNA Editing Sites Identification and Classification is an open source available at our GitHub repositoryTo have a complete identification of RNA editing sites, which include sites in non-repetitive regions, sites in repetitive regions, and sites in hyper-editing regions, we generated a novel tool termed “RESIC.” RESIC composed of an enhanced alignment graph model to identify and classify editing sites by their type, multiple-step filtering process to increase result reliability in a flexible manner  and plotTo use RESIC, the user should supply positive datasets, i.e., RNA-/DNA sequencing datasets that should exhibit the desired editing phenomena, and a reference genome. The user may also supply negative datasets, DNA or RNA sequencing datasets that should not exhibit the desired editing phenomena, or a SNP database, to contradict editing-site existence.All datasets are first processed according to the graph alignment . The graFollowing alignment, the candidate editing sites that were identified are going through strict multi-stage filtering process . The filOur motivation was to build one tool that envelopes several algorithms and enables prediction of all classes of editing sites with as much flexibility as possible. Most of the current bioinformatics tools as described in the introduction focus on one class of editing sites identification  that were sequenced at 75 SR. These datasets were previously screened for editing sites by others  to eliminate changes that are not originated from A-to-I RNA editing, but from genomic polymorphism. All datasets were processed according to the graph alignment and went through all filtration steps . Since each of the 16 samples is originated from a different tissue, and editing sites may be tissue specific , we defiof RESIC  to the cof RESIC . It is iof RESIC  that oveof RESIC  and toolof RESIC  are lessvia RESIC, the “non-repetitive” class yielded the largest overlap . Overall, the number of hyper editing sites upon SARS-CoV-2 infection was 36.45% greater than in mock . Overall, the number of non-repetitive sites upon SARS-CoV-2 infection was 28.62% greater than in mock . Following RESIC run, we assessed the numbers of the most prevalent classes of A-to-I RNA editing types: non-repetitive, and hyper non-repetitive. We compared between SARS-CoV-2 and mock editing site numbers for each class following normalization, relying on the total read processed in each node. A-to-I non-repetitive hyper editing was significantly more frequent in SARS-CoV-2 infected cells compared to mock  cells that were infected with SARS-CoV-2, using a different human cell line. For that aim, we used RNA-seq data from human adenocarcinomic alveolar basal epithelial (A549) that were infected with SARS-CoV-2 virus  and searAnother goal was to validate the novel editing sites found by us, using the BodyMap2 dataset, that include seven different tissues: adipose, adrenal, brain, breast, colon, kidney, and heart. For that purpose, we searched for the new identified sites  in mock,To understand whether the higher editing activity upon SRAS-CoV-2 infection is manifested by a larger number of sites in the same genes as in mock, or additional sites located in new genes, we characterized the editing landscape with respect to site locations and gene annotation.We first tested the ratio of shared sites between SARS-CoV-2 and mock samples. For both non-repetitive and hyper non-repetitive classes, almost ∼70% of the sites were unique among samples infected with SARS-CoV-2, and about 60% of the sites were unique for mock samples . We nextInterestingly, the gene APOBEC3C became hyper edited following SARS-CoV-2 infection , while iP-adjusted values of related pathways in the range of 1.91E-03–3.50E-04; P-adjusted values of related pathways in the range of 1.25E-06–1.03E-12; To test if the sets of unique edited genes display shared biological processes, we examined their biological process enrichment, using the web-based tool GeneMANIA . SubmittGSE147507. Although read count values were not identical, as expected due to the use of different alignment tools, the trend was the same.Given these observations, we reasoned that interrogating the differences in ADAR RNA expression levels between SARS-CoV-2 and mock treated samples, would help to complete the picture. Therefore, we performed DEA, on the same data of P-value = 2.2e-16) in SARS-CoV-2 Calu3 infected cells. In comparison, the expression of ADAR2 did not differ between SARS-CoV-2 and mock samples . We also found that IFIH1 (NM_022168.4), that encodes for MDA5 is significantly up-regulated in the SARS-CoV-2 infected samples (P-adjusted = 7.46e-137). This is in line with findings indicating that the IFN response upon SARS-CoV-2 infection is primarily regulated by MDA5  the number of hyper editing sites is increased; and (2) ADAR1 activity is enhanced. The combination between these two observations goes together with the finding that ADAR1 is the enzyme mostly catalyzing hyper editing sites .in vitro SARS-CoV-2 infected cell types created in the same study. For that purpose, we downloaded from GEO the already processed gene count data, for A549 and NHBE cells infected with SARS-CoV-2 high-multiplicity of infection (MOI). We chose downloading the already processed gene count data after validating for Calu3 cells that the gene count values created by us and the downloaded gene count from GEO (accession number GSE147507) are of the same trend (see “Methods and Definitions”). For A549 cells, with a vector expressing human ACE2, indeed ADAR1, but not ADAR2, was significantly upregulated following SARS-CoV-2 infection, corroborating our previous results for Calu3 cells. However, for NHBE cells, both ADAR1 and ADAR2 were not significantly changed after SARS-CoV-2 infection (We tested if these results hold true for more nfection . The nonTaken together, our results suggest that the catalyzation of hyper editing sites by ADAR1 is enhanced following SARS-CoV-2 infection. These results are intriguing in the context of ADAR1’s role to block the IFN response, and particularly the role of hyper editing events to suppress the IFN induction . We hypoThe original contributions presented in the study are included in the article/DL, RH, and ATL conceived and designed the study and wrote the manuscript with input from all authors. DL, RH, MY, TE, and TB implemented and developed RESIC. RH analyzed the data. ATL supervised the work. All authors contributed to the article and approved the submitted version.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.


Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non–poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints.TMPRSS2-ERG–positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq.We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA–minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 By using the full potential of non–poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects. TMPRSS2-ERG fusions in prostate adenocarcinoma (PCa) ” was significantly more frequently hit (P = 1.6 × 10−4) within PCa due to TMPRSS2, ERG, ETV1, H3FA3, SLC45A3, and ELK4. Within BrCa, pathways ETF and E2F were significantly enriched  in ER+ BrCa and “Proteoglycans in cancer” in ER− BrCa (P = 1.4 × 10−5). Genes that were recurrently hit in glioma were found more often in pathways “Rap1 signaling pathway” (P = 3.2 × 10−4), “Glioma” (P = 5.9 × 10−3), and “Ras Signaling” (P = 2.6 × 10−3)  .TMPRSS2-ERG in the sense that they were larger, had no consistent fusion partners, and often contained multiple hot spot junctions per sample. If these hot spot region rearrangements are responsible for consistent changes at the transcriptional level, they may provide a selective advantage. In both the BrCa and glioma datasets, transcriptional effects of the hot spot rearrangements were investigated by performing differential gene expression analysis between samples with  and without a hot spot rearrangement .Hot spot regions (20−30 Mb) enriched with RNA-seq−detected junctions were observed in the BrCa (chr11) and glioma (chr12) datasets. These hot spots differed from focal events such as SHANK2 and TENM4, both located in the hot spot region, were the most frequently hit genes , yet were not among the strongest up-regulated genes of the overall region. Instead, genes with a strong increase in logFC were FGF4 and CCND1, the cluster KCTD21, ALG8 and GAB2, and genes downstream of TENM4. Up-regulation of the overall region indicated amplifications of CCND1 and/or the gene cluster, which is in concordance with previous reports [SHANK2 is influenced by being adjacent to CTTN, a gene containing an enhancer often co-amplified with CCND1 [SHANK2 (785 kb) and TENM4 (788 kb) suggests that they are “collateral damage” of the amplifications, a hypothesis that has been described in glioma [BrCa samples having a chr11 hot spot rearrangement were characterized by a large stretch of significant up-regulated genes within the respective hot spot region Fig . The lar reports . We presth CCND1 . The hign glioma . This hyCDK4, MDM2, and neighbouring genes. Both CDK4 and MDM2 are known to be hyper-amplified in glioblastoma [CDK4  [TMPRSS2-driven up-regulation of ERG. In 32 of the 51 samples Dr. Disco identified mRNA fusion transcripts of TMPRSS2-ERG, including genomic breakpoints in 27 of 32 samples  exonic junctions in 3 samples , indicating that breakpoint-spanning reads were present in all 32 TMPRSS2-ERG–positive RNA-seq samples.From previous analyses it emerged that atients) , resultiles Fig. . These fERG-flanking genomic breakpoint located in an intergenic region upstream to ERG’s first exon . In the same 2 samples, ERG had additional deletions, removing exon 2  [TMPRSS2-ERG in VCaP was analysed using both rRNA-minus and poly(A)+ RNA-seq data.PCa cell line VCaP has been subjected to intensive research, revealing that it is point B) , 39. TMP+ RNA-seq shows that only the first exon of TMPRSS2 splices to ERG, even though the genomic breakpoint to ERG is located in the fifth intron . The rRNA-minus RNA-seq data not only revealed both intronic and exonic junctions but also showed the complex downstream effects on transcription and splicing.Poly(A)h intron . The rRNinverted . The smaHead-to-tail aligned reads  are mark+ RNA-seq + RNA-seq, and fusion gene detection algorithms are in particular focused on annotated exons or splice junctions. It has become common practice to sequence ribosome-depleted total RNA (rRNA-minus) , especia RNA-seq , which p RNA-seq , we inteTMPRSS2-ERG fusion gene . Because the algorithm was conservative in detecting mRNA fusions, it is likely that genomic breakpoints were missed and the actual percentage is somewhat higher. Conversely, estimation of this percentage implies that WGS results offer the ground truth, but these are also affected by noise, coverage, and filter cut-offs, indicating that this percentage is an approximation. The results commonly included intergenic junctions. For instance, 3 TMPRSS2-ERG fusions had their breakpoint located before ERG, supported by cryptic intergenic splice junctions and intergenic pre-mRNA coverage + RNA genomic breakpoints are observed less frequently, with lower confidence, and often in long untranslated regions. The VCaP TMPRSS2-ERG analysis underlined the differences: on the basis of rRNA-minus RNA-seq, each genomic rearrangement with resulting splice variants and their strand as well as the order of events could be deduced, while both the poly(A)+ and WGS alone were insufficient.That both Arriba and Dr. Disco make use of STAR and that Arriba finds a higher number of true-positive results indicates that improving the filtering is an important future step, but care must be taken not to compromise Dr. Disco’s true-positive ratio. The large search space combined with graph analysis was an effective solution as shown by providing a unique view on 1,275 transcriptomes. Whereas our initial hypothesis was that only rRNA-minus RNA-seq would reveal genomic breakpoints, this also accounts for poly(A)CircRNAs are a relatively new group of non-polyadenylated transcripts with >90,000 different human circRNAs identified so far , 42. TheThe number of RNA-seq–detected intronic junctions representing genomic breakpoints varied widely between the 4 different cancer types . This variation is in line with the omics-reported number of structural variants—low in colorectal cancer  while hiTMPRSS2-ERG. Known exceptions that can be considered driver events include promoter and enhancer rearrangements such as known for AR and FOXP1 [While only a fraction of all genomic rearrangements is transcribed, an even smaller fraction is causal for fusion genes, which are currently of high interest in RNA research. Transcribed genomic breakpoints more often involve driver events than non-transcribed genomic breakpoints, as seen with nd FOXP1 , but alsnd FOXP1 , 49. TheTMPRSS2-ERG fusion events.Although WGS depth surpasses 40× coverage, Dr. Disco showed that 26% and 48% of all RNA-seq intronic junctions in PCa and BrCa, respectively, were not identified by WGS. While these will contain false-positive results for sure, 100 of the 357 were found in 8 of the 207 samples all characterized by high number of genomic breakpoints. We suspect that this discrepancy is partially due to actual genomic breakpoints missed by WGS because of the following factors: high RNA-seq coverage of highly expressed genes (up to 1000×), clonality because this difference was in particular high for a small subset of samples, low local coverage in WGS, and selection criteria in software such as cut-offs and read mapping rulings. In the PCMM-FFPE dataset, samples with low insert sizes or short read lengths often resulted in insufficient split-reads whilst resulting in many false positive read-pairs in full transcriptome analysis. However, FFPE data could still be used effectively in identifying the targeted, highly expressed, TMPRSS2-ERG to be analysed in-depth, revealing additional cryptic and intergenic exons including TMPRSS2 exon 0 [ERG that combined with cryptic intergenic exons co-exist with exon 2 deletions, and a tumour that harbours TMPRSS2-ERG that originated from a reciprocal translocation rather than a deletion. In VCaP, stranded RNA-seq provided an advantage in deciphering the chronological order of complex genomic events. Moreover, this underlined the importance of automatic resolution of complex genomic rearrangements or poly-fusions. The current implementation does not offer such top-level integration for poly-fusions, while techniques exist with this purpose in mind [The large number of PCa samples allowed 2 exon 0 , a detai in mind , 50. IntCCND1 amplifications, despite the frequent events in sizeable genes TEMN4 and SHANK2 of which their fusion transcripts are not driving cancer.In both BrCa and glioma, RNA-seq data alone revealed hot spot regions of junctions with subsequent up-regulation of known amplified oncogenes and neighbouring genes within these regions. The inconsistent transcriptional direction of the junctions combined with the lack of consistent acceptor/donor genes provides additional context that distinguishes these events from focal fusion genes. In BrCa, this combined analysis indicated that the events are related to Chromothripsis-derived junctions matching WGS-detected genomic breakpoints were present at the RNA level, in VCaP and 3 BrCa samples. As with most genomic rearrangements, the majority of the chromothripsis rearrangements were not detected on the RNA level. Solely on the basis of RNA-seq data, it will be difficult to prove the presence of chromothripsis because not all parameters that define this specific process can readily be extracted  , 52. HowTMPRSS2-ERG exon-0, intergenic splice variants are merged with gene-spanning splice variants and will be considered in-gene as-whole. Intergenic mRNA fusions are characterized by incorporation of  cryptic exons. But cryptic exons are not limited to intergenic events, as we reported that cryptic exons in fusion transcripts transcribed in the anti-sense direction of a gene are common.While our preliminary aim was to study to which extent genomic breakpoints are present in RNA, we were surprised by how common both intergenic events and cryptic exons are. That ∼35% of the junctions in rRNA-minus datasets were full or partial intergenic events is an under-representation because, as for example with Cryptic exons are of importance because they may encode nonsense proteins with completely novel neo-antigens that are more divergent than point mutation–based neo-antigens and could therefore be more immunogenic . We wantFacilitated by Dr. Disco, we set out to extract both intronic and exonic junctions from comprehensive rRNA-minus RNA-seq datasets and identified novel genomic breakpoints, circRNAs, novel gene and intergenic fusions, cryptic exons, and chromothripsis events and were able to link expressed rearrangements to transcriptional outcome. Discovering both genomic breakpoints and exonic junctions from only RNA-seq data requires an analysis strategy keeping these 2 levels of information separated. The number of breakpoints detected is limited to ∼7% because most are not within expressed regions. These results indicate that this analysis is not a replacement for WGS, but performing analysis like this will result in considerably more cancer-specific transcripts than interrogating classical fusion genes only. This holds in particular for rRNA-minus RNA-seq, which harbours more intronic reads. Furthermore, combined WGS and RNA analysis showed that RNA can function as informative supplement to WGS analysis because of stranding, expression, and resolution of the fusion gene structure(s). rRNA-minus RNA-seq provides more unique and complete information on non-polyadenylated and aberrant transcripts and, if the pre-mRNA is sequenced, the genomic breakpoints that underlie transcriptional changes.Thus, RNA-seq data can reveal genomic breakpoints, (cryptic and/or intergenic) splicing, and gene expression information, which together can reveal consequences and their selective advantage for cancer development and progression and be a useful supplement to DNA-seq.The sequencing datasets used are given in Table Of the PCa-LINES samples, each sample was WGS DNA sequenced and processed using the Complete Genomics platform , 57 ler/gost ), using U test in R.Plots were made with base R (3.6.3), ggplot2 (3.3.5), plotrix (3.8.1), and circlize (0.4.13), and illustrations, with Inkscape (0.92.4). Differential gene expression analysis was performed using edgeR (3.28.1) . AssociaFor the Venn diagrams describing overlap across intronic, exonic, and WGS junctions Fig. , both siChromosomal differential expression plots Fig.  were madSnapshots of discordant alignments were made in IGV (2.8.0) using the split view and with "Color alignments by" set to "read group."Supporting data, including reference data, final results tables, and chimeric alignments, and an archival copy of the code are available in the GigaDB database . The resProject name: Dr. Discohttps://github.com/yhoogstrate/dr-discoProject home page: https://bio.tools/dr_discobio.tools: RRID:SCR_021739Operating system(s): GNU/LinuxProgramming language: PythonOther requirements: STAR License: GNU GPL 3.0Project name: bam-lorenz-coveragehttps://github.com/yhoogstrate/bam-lorenz-coverageProject home page: https://bio.tools/bam-lorenz-coveragebio.tools: RRID: SCR_021837Operating system(s): GNU/LinuxProgramming language: PythonOther requirements: Python libraries: numpy, pysam, tqdm, matplotlib & clickSupplementaryDr. Disco Technical SpecificationSupplementary Figure S1. Exonic and intronic junctionsSupplementary Figure S2. Snapshot of fusion using cryptic exons in ENCODE MCF-7 datasetSupplementary Figure S3. IGV Screenshot of in-exon genomic breakpointSupplementary Figure S4. Junctions in PCa-LINES datasetSupplementary Figure S5. rRNA-minus RNA-seq and WGS data intersection BASIS datasetSupplementary Figure S6. Correlation binned density WGS breakpoints and rRNA-minus junctionsSupplementary Figure S7. Intrachromosomal junctions in BASIS datasetSupplementary Figure S8. Intrerchromosomal junctions in BASIS datasetSupplementary Figure S9. Intrachromosomal junctions in NGS-ProToCol prostate datasetSupplementary Figure S10. Intrachromosomal junctions in NGS-ProToCol colon datasetSupplementary Figure S11. Intrachromosomal junctions in CGGA datasetSupplementary Figure S12. Correlation RNA-seq depth, WGS breakpoints, and RNA-seq junctions in BASIS datasetSupplementary Figure S13. Snapshot cryptic fusion in BrCa sample PR9608aSupplementary Figure S14. Snapshot of intergenic breakpoints in PCa sample 7046-004-134Supplementary Figure S15. Chromosomal differential expression plot of chr11 in BASIS datasetSupplementary Figure S16. Chromosomal differential expression plot of EGFR in CGGA datasetSupplementary Figure S17. Chromothripsis on chr5q in VCaPSupplementary Figure S18. Catastrophic events on chr17 in BASIS datasetSupplementary Figure S19. Snapshot of intergenic breakpoints in TMPRSS2-ERGSupplementary Figure S20. Detailed overview of junctions in ERGSupplementary Figure S21. Detailed overview of junctions in TMPRSS2Supplementary Figure S22. Schematic representation of TMPRSS2-ERG as reciprocal translocationSupplementary Figure S23. TMPRSS2-ERG in VCaPSupplementary Figure S24. Schematic overview discordant read orientationsSupplementary Figure S25. Overlap head-to-tail junctions and circBase in PCa-LINES datasetSupplementary Figure S26. Lorenz and coverage plots of rRNA-minus, poly(A)+, and WGS dataSupplementary Figure S27. (Inter)genic events in WGSSupplementary MethodsSupplementaryTable S1.Results SRR534293Supplementary Table S2.gProfilerSupplementaryTableS3.SHANK2 and TENM4 related junctionsSupplementary Table S4.TMPRSS2-ERG and related junctions+: estrogen receptor–positive; FFPE: formalin-fixed paraffin-embedded; IGV: Integrative Genomics Viewer; logFC: logarithmic fold change; Mb: megabase pairs; mRNA: messenger RNA ; PCa: prostate cancer; poly(A)+: polyadenylated; pre-mRNA: RNA that is actively being transcribed by polymerase (not polyadenylated); rRNA-minus RNA-seq: RNA-seq prepared such that there is no specific positive selection for poly(A)-tails while reducing the amount of ribosomal RNA; poly(A)+ RNA-seq: RNA-seq prepared with a positive selection for poly(A)-tails; STAR: Spliced Transcripts Alignment to a Reference; WGS: whole-genome sequencing.bp: base pairs; BrCa: breast cancer; circRNA: circular RNA; edgeR: empirical analysis of DGE in R; ERhttps://www.lygature.org/ctmm-portfolio), 51 prostate cancers from the Erasmus MC were snap-frozen and stored in liquid nitrogen as previously described [For the CTMM NGS-ProToCol study ; escribed . Use of The authors declare that they have no competing interests.This study was performed within the framework of the CTMM  research program; NGS-ProToCol (grant 03O-402); PCMM (grant 03O-203-1); Translational Research IT (TraIT); the Complete Genomics Inc. grant (EMC GL 083111); the FP7 Marie Curie Initial Training Network PRO-NEST (grant No. 238278) and Support for the Cancer Computational Biology Center was provided by the Daniel den Hoed Foundation. Funding for open access charge: NGS-ProToCol (grant 03O-402).Y.H. and G.W.J. designed most of the experiments. Y.H. carried out most of the experiments and analysis and wrote the manuscript and software. M.A.K., R.B., J.v.R., H.J.G.v.d.W., R.H., A.P.S., and G.W.J. contributed to the methodology. N.D., D.S., D.v.d.M., F.P., C.H.B., G.J.L.H.v.L., M.S., J.W.M.M., W.v.W., B.J., E.C., M.d.J., G.A.M., E.v.d.B, A.S.B, R.H., R.J.A.F., and G.W.J. acquired data. H.JG.v.d.W, J.v.R, S.v.L., C.R., R.B., M.S., A.P.S., and M.A.K. analysed and prepared data. P.J.F., R.B., M.S., G.W.J., B.J., R.J.A.F., and P.J.v.d.S. made large contributions to writing the manuscript. G.J., A.P.S., P.J.F., C.H.B., R.J.A.F., and P.J.v.d.S. acquired funding.giab080_GIGA-D-21-00236_Original_SubmissionClick here for additional data file.giab080_GIGA-D-21-00236_Revision_1Click here for additional data file.giab080_GIGA-D-21-00236_Revision_2Click here for additional data file.giab080_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab080_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab080_Reviewer_1_Report_Original_SubmissionWencke Walter, Ph.D. -- 8/30/2021 ReviewedClick here for additional data file.giab080_Reviewer_1_Report_Revision_1Wencke Walter, Ph.D. -- 10/11/2021 ReviewedClick here for additional data file.giab080_Reviewer_2_Report_Original_SubmissionDaniel Nicorici -- 9/7/2021 ReviewedClick here for additional data file.giab080_Reviewer_3_Report_Original_SubmissionBo Li, Ph.D. -- 9/12/2021 ReviewedClick here for additional data file.giab080_Supplemental_FilesClick here for additional data file.


The nucleoprotein (NP) of Marburg virus (MARV), a close relative of Ebola virus (EBOV), encapsidates the single-stranded, negative-sense viral genomic RNA (vRNA) to form the helical NP–RNA complex. The NP–RNA complex constitutes the core structure for the assembly of the nucleocapsid that is responsible for viral RNA synthesis. Although appropriate interactions among NPs and RNA are required for the formation of nucleocapsid, the structural basis of the helical assembly remains largely elusive. Here, we show the structure of the MARV NP–RNA complex determined using cryo-electron microscopy at a resolution of 3.1 Å. The structures of the asymmetric unit, a complex of an NP and six RNA nucleotides, was very similar to that of EBOV, suggesting that both viruses share common mechanisms for the nucleocapsid formation. Structure-based mutational analysis of both MARV and EBOV NPs identified key residues for helical assembly and subsequent viral RNA synthesis. Importantly, most of the residues identified were conserved in both viruses. These findings provide a structural basis for understanding the nucleocapsid formation and contribute to the development of novel antivirals against MARV and EBOV. Marburg virus causes severe hemorrhagic fever in humans. Here, the authors determine the structure of Marburg virus nucleoprotein–RNA complex by cryo-electron microscopy and provide mechanistic insight into the helical assembly of the nucleocapsid. Filoviridae and possesses a 19.1 kb non-segmented, single-stranded, negative-sense RNA genome (vRNA)1. Similar to EBOV, it causes severe hemorrhagic fever in humans and non-human primates with a high mortality rate, which continuously poses a threat to public health. However, no vaccine or antivirals against MARV diseases have yet been licensed. The nucleoprotein (NP) of MARV consisting of 695 amino acids encapsidates the vRNA to form a left-handed helical complex2. The helical NP–RNA complex acts as a scaffold for the formation of a nucleocapsid, which is responsible for the transcription and replication of the vRNA4. Since the NP–RNA complex is the structural unit that constitutes the helical core structure of the nucleocapsid, for which appropriate interactions between NP molecules and RNA nucleotides are required, solving the NP–RNA unit structure is essential for understanding the mechanisms of the nucleocapsid formation.Marburg virus (MARV), a close relative of the Ebola virus (EBOV), belongs to the family 6, which is similar to that of the EBOV NP core structure8. These two lobes are connected by a flexible hinge region and are considered to clamp the RNA strand between the two lobes by electrostatic interactions. The overall three-dimensional architecture of the NP–RNA complex, in which the cellular RNA was coated with truncated NP comprising its N-terminal 390 residues, was visualized using cryo-electron tomography (cryo-ET), showing that the helical NP–RNA complex constitutes the innermost layer of the MARV nucleocapsid9. However, since the resolution was limited to the scale of a few nanometers, the structural basis for the assembly of NP and RNA into the nucleocapsid core largely remains unknown.The core structure of RNA-free monomeric MARV NP, spanning residues 21–373, features a typical bilobed fold consisting of N-terminal and C-terminal lobes, as determined using X-ray crystallography at 2.9 Å resolution10. Here, we employed the same technique to determine the cryo-EM structure of the MARV NP–RNA complex and identified key residues important for helical NP–RNA assembly and subsequent viral RNA synthesis from the nucleocapsid using structure-based mutagenesis.Using single-particle cryo-electron microscopy (cryo-EM), we recently determined the structure of EBOV NP–RNA complex at 3.6 Å resolution9. Purified complexes were imaged using cryo-EM  is associated with an adjacent NPn+1 inside the helical complex -RNA complex, the MARV NP (1–395)-RNA complex purified from NP-expressing cells reproducibly constituted double-helical structures . The MD simulation followed by binding free energy analysis revealed that the energy contribution of RNA backbone to NP-binding was higher than that of the RNA bases. These results highlighted the importance of the interactions between RNA backbone and NP for RNA binding, which probably assists in the incorporation of viral RNA in a sequence-independent manner to form the helical NP–RNA complex.It is believed that, in many RNA viruses, viral nucleoproteins bind to viral genomic RNA in a sequence-independent manner22  likely play more important roles in functional nucleocapsid formation, compared to K230 and H292 in the C-terminal lobe. Interestingly, paramyxovirus and pneumovirus nucleoproteins, which encapsidate vRNA in the positively charged cleft composed between the N- and C-terminal lobes, contain more basic amino acid residues on the RNA-binding cleft of N-terminal lobe than that of C-terminal lobeFiloviridae  cells were maintained in the Dulbecco’s Modified Eagle Medium  supplemented with 10% fetal bovine serum .Human embryonic kidney 293 Freestyle (HEK293F) cells were maintained in Expi293 Expression Medium , at 37 °C in an 8% CORenilla luciferase (p3M-5M-Luc), and T7 DNA-dependent RNA polymerase (pCAGGS-T7), are described elsewhere23. The pCAGGS-NP (1–395) that expresses residues 1-395 of MARV NP was constructed from pCAGGS-NP by PCR using primers with digestion enzyme sites. Then, the PCR product was cloned into digested pCAGGS vector with a Kozak sequence present upstream of the initiation codon. The NP as well as NP (1–395) mutant constructs were generated by PCR-based site-directed mutagenesis. The primers used in this study are listed in Supplementary Table All MARV  plasmids encoding wild-type proteins , T7-driven MARV minigenome encoding 6 cells/ml) were transfected with 6 µg of pCAGGS-NP (1–395) using Polyethyleneimine MAX . Three days post-transfection, the cells were collected and lysed with 0.1% Nonidet P-40 substitute  in Tris-HCl buffer (10 mM Tris-HCl (pH 8.0), 150 mM NaCl, 1 mM EDTA) including a cOmplete Protease Inhibitor  and 10 mM Ribonucleoside-Vanadyl Complex  on ice. The lysate was centrifuged at 10,000 g at 4 °C for 10 min to remove insoluble substances, and the supernatant was subjected to a discontinuous CsCl gradient ultracentrifugation at 246,100 g at 4 °C for 2 h. Then, fractions containing the NP–RNA complex were collected and ultracentrifuged at 246,100 g at 4 °C for 15 min. The pellet was suspended in Tris-HCl buffer.For MARV, HEK293F cells grown in 10 ml of medium (3.0 × 105 cells/ml) were transfected with 10 µg of pCAGGS-NP (1–450) using TransIT-293 Reagent . Three days post-transfection, NP–RNA complexes were purified as described above and suspended in Tris-HCl buffer.For EBOV, HEK293T cells grown in 10 ml of medium (3.0 × 1025.Five microliters of sample were applied to a glow-discharged copper grid coated with carbon and negatively stained with 2% uranyl acetate. Images were obtained using transmission electron microscopy (HT-7700 (system software v.02.22.15.15), Hitachi High Technology, Tokyo, Japan) operating at 80 kV with an XR81-B CCD camera. The length distribution was measured for about 60 helices by using ImageJ softwareThe 2.5 µl sample of purified NP–RNA complex solution was applied onto glow-discharged 200-mesh R1.2/1.3 Cu grids . The grids were blotted and rapidly frozen in liquid ethane using a Vitrobot Mark IV .Images were acquired on a Titan Krios cryo-EM  at 300 kV equipped with a Cs corrector , which was installed in the Institute for Protein Research, Osaka University. Data were acquired automatically with EPU software as movies on a Falcon 3EC detector . The detailed imaging condition is described in Supplementary Table 26 for the processing steps. The movie frames were motion-corrected using relion_motioncorrection with 5  × 5 patches. Next, GCTF27 software was utilized for contrast transfer function (CTF) estimation of all micrographs. A total of 2469 micrographs were subjected to subsequent helical image processing. Helices were manually traced. A total of 30,668 segments were extracted and two rounds of 2D classification were performed. Classes, in which the detailed structure of helices was confirmed, were selected for further analysis. The 3D initial model, a featureless cylinder, was made by the relion_helix_toolbox command. Then, the featureless cylinder was refined via Refine3D and used as the initial model for the following 3D classification. The 3D classification was performed with local symmetry search around expected helical parameters from 2D class images . A total of 23,545 segments from the best resolved 3D class were subjected to 3D auto-refinement . Finally, we ran the CTF refinement job and the Bayesian polishing job to improve the resolution and the final map was calculated from 3D auto-refinement. The resulting EM map has a nominal resolution of 3.1 Å estimated from the Fourier Shell Correlation and was normalized with PyMOL. Local resolution was estimated with RELION and the locally sharpened map was used for the following atomic model building. The detailed image processing conditions are listed in Supplementary Table We used the software RELION3.110 served as the initial template. First, we performed a rigid body fitting of the atomic model of EBOV NP (1–450) into the locally sharpened EM map of MARV NP (1–395)-RNA complex using the software UCSF Chimera28. Next, the software COOT29 was used to replace each amino acid of EBOV NPs with the amino acid sequence of MARV NPs; for amino acid residues from 389 to 391, which have no homologous sites in the atomic model of EBOV NPs, the models were constructed de novo. The atomic model was refined with phenix.real_space_refine30. To precisely refine the atomic models located in the intermolecular region, surrounding eight molecules of the atomic models were fitted to our map and refined with constraints on the protein secondary structure estimated by phenix_secondary_structure_restraints. The model validity was assessed by the software Phenix30 and MolProbity31. The detailed results of the atomic model construction and its evaluation are shown in Supplementary Table 32 was used for displaying the EM map and the atomic models.Atomic modeling of NP (1–395) was performed by template-based and de novo structure modeling. An atomic model of EBOV NP 1–450)–RNA complex (PDB-ID: 5Z9W)50–RNA coRenilla and firefly luciferase activities were measured using GloMax®-Multi+ Detection System  with an Renilla-Juice kit and a FireFly-Juice kit  according to the manufacturer’s protocol. The Renilla luciferase activity was normalized to the firefly luciferase activity.For MARV, each well of HEK 293T cells grown in a 12-well plate was transfected with the plasmids expressing MARV nucleocapsid components , 200 ng of T7 polymerase (pCAGGS-T7), and 20 ng of firefly luciferase (pGL) by using 4 µl of TransIT 293  and 140 µl of OPTI-MEM . At 48 h post-transfection, the cells were lysed using lysis buffer , and the For EBOV, HEK293T cells were grown in a 24-well plate and each well was transfected with the plasmids expressing EBOV nucleocapsid components , 200 ng of T7 polymerase (pCAGGS-T7), and 10 ng of Renilla luciferase (pTK r.luc) by using 4 µl of TransIT 293  and 100 µl of OPTI-MEM . At 48 h post-transfection, the cells were lysed using Passive Lysis buffer , and the firefly and Renilla luciferase activities were measured using GloMax®-Multi+ Detection System  with a Luciferase Assay Reagent II and a Stop & Glo® Reagent  according to the manufacturer’s protocol. The firefly luciferase activity was normalized to the Renilla luciferase activity.HEK293T cells, transfected with plasmids expressing MARV or EBOV nucleocapsid components, were dissolved with 2× Tris-Glycine SDS Sample Buffer , boiled for 5 min, and subjected to SDS-PAGE. Proteins were electroblotted onto membranes, and labeled with antibodies against MARV NP , EBOV NP , and β-actin .33. The AMBER ff14SB34 and RNA.OL336 force fields were used for the simulation. The protonation states of the ionizable residues were assigned a pH of 7.0 using the PDB2PQR web server37. The system was solvated in a truncated octahedral box of TIP3P water molecules with a distance of at least 10 Å around the proteins, and was then energy-minimized in four steps: first, only hydrogen atoms; second, water and ions; third, the side chains; and finally, all atoms. After energy minimization, the system was gradually heated from 0 to 310 K over 300 ps with harmonic restraints . Two additional rounds of MD (50 ps each at 310 K) were performed with decreasing the restraint weight from 0.5 to 0.1 kcal/mol Å2. Next, 500 ns of unrestrained production run was performed in the NPT ensemble at a pressure of 1.0 atm and a temperature of 300 K. All bond lengths including hydrogen atoms were constrained using the SHAKE algorithm38, and a time step of 2 fs was used. A cut-off radius of 10 Å was set for the nonbonded interactions. Long-range electrostatic interactions were treated using the particle mesh Ewald method39. The stability of the trajectory was assessed by monitoring the RMSD of the backbone Cα atoms from the initial structure of MD simulation. After confirming that RMSD in the system reached equilibrium within 200 ns, the trajectory extracted from the last 300 ns  was used for subsequent per-residue energy analysis. Binding free energies were calculated using the script of the molecular mechanics-generalized Born surface area (MM-GBSA) method in AmberTools18. The conformational entropy was not considered due to the high computational cost and low prediction accuracy. The salt concentration was set to 0.20 M.MD simulation was performed for three consecutive units of MARV NP–RNA complex Fig. , each cot test using RStudio software. Data are presented as the mean ± s.d. A p value < 0.01 was considered statistically significant. The data from minigenome assay and negative staining analysis of MARV and EBOV NP mutants were repeated in three independent experiments.Statistically significant differences in minigenome assay were evaluated using ANOVA-Dunnett’s test to correct for multiple hypothesis testing with RStudio software. Statistically significant differences in the length of MARV NP variants were evaluated by unpaired Student Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Caulimoviridae, are typical of retroelements with reverse transcriptase and share a common origin with retroviruses and LTR retrotransposons, presumably dating back 1.6 billion years and illustrating the transition from an RNA to a DNA world. After transcription of the viral genome in the host nucleus, viral DNA synthesis occurs in the cytoplasm on the generated terminally redundant RNA including inter- and intra-molecule recombination steps rather than relying on nuclear DNA replication. RNA recombination events between an ancestral genomic retroelement with exogenous RNA viruses were seminal in pararetrovirus evolution resulting in horizontal transmission and episomal replication. Instead of active integration, pararetroviruses use the host DNA repair machinery to prevail in genomes of angiosperms, gymnosperms and ferns. Pararetrovirus integration – leading to Endogenous ParaRetroViruses, EPRVs – by illegitimate recombination can happen if their sequences instead of homologous host genomic sequences on the sister chromatid (during mitosis) or homologous chromosome (during meiosis) are used as template. Multiple layers of RNA interference exist regulating episomal and chromosomal forms of the pararetrovirus. Pararetroviruses have evolved suppressors against this plant defense in the arms race during co-evolution which can result in deregulation of plant genes. Small RNAs serve as signaling molecules for Transcriptional and Post-Transcriptional Gene Silencing  pathways. Different populations of small RNAs comprising 21–24 nt and 18–30 nt in length have been reported for Citrus, Fritillaria, Musa, Petunia, Solanum and Beta. Recombination and RNA interference are driving forces for evolution and regulation of EPRVs.Pararetroviruses, taxon  Metaviridae and Pseudoviridae also known as Ty3/Gypsy- and Ty1/Copia-elements, respectively. In plants, LTR retrotransposons are present. True retroviruses are lacking but plants have infective pararetroviruses (PRV), family Caulimoviridae. They encapsidate circular dsDNA that, after release and transport to the nucleus, forms a minichromosome allowing synthesis of terminal redundant viral RNA (Retroelements (class I transposable elements) can be considered to lie at the transition from an RNA to a DNA world. They are genomic DNA elements within all kingdoms, but employ reverse transcriptase for DNA synthesis using an RNA intermediate template . Besidesiral RNA . TherefoHigh throughput DNA and RNA sequencing, metagenomics, bioinformatics and palaeovirology have revealed the impact of viral retroelements on eukaryotic genomes and become important for understanding the origin of viruses. In cells, DNA polymerases secure the amplification of large genomes based on linear dsDNA molecules. However, relics of the “RNA world” can still be encountered such as ribosomal RNA, RNA splicing, telomerases and RNA-dependent RNA polymerases RDR; . SpecialCaulimoviridae, Metaviridae, and Pseudoviridae occupy distinct niches in plant genomes  identified also retrotransposon RNA in 0.4–1.3% of sequences isolated from virions (Hordeum vulgare BARE-1 virus (Pseudoviridae) comprises 10% of the barley genome  ssRNA viruses  to enable viruses to move between cells via plasmodesmata in the cell wall. Encounter of a LTR retrotransposon transcript with a + ssRNA virus encoding a 30 k MP in the cytoplasm most likely through recombination, formed chimeric molecules that gave rise to an ancestral form of Caulimoviridae  overhang is generated by strand resection that then attracts recombinases to search for homologous sequences . This sst nuclei , and mayOryza sp.: Beta vulgaris: It is necessary that the DNA within the chromosome is accessible and able to unwind during repair and recombination . ChromosBeta vulgaris: Often EPRVs form clusters and can occupy large parts of the genome  endoribonucleases  MIR-loci  from perfect dsRNA templates, respectively, each with minor distinct catalytic profiles to ensure specificity -siRNAs and is thought to regulate the biogenesis of 22-nt vsiRNAs -siRNA precursors  to its (near) complementary target sequence after being loaded into an ARGONAUTE (AGO) protein (Endogenous PRVs co-exist with exogenous virus(es) and viroid(s) in the same host. Like all viruses and viroid’s, EPRVs possess an RNA phase during their replication cycle . Activates DCLs; . DCL1 fuMIR-loci . The rolMIR-loci  or regulMIR-loci . DCL3 ancificity . Overlapcificity . The 24-cificity , while tcificity . DCL2 sy vsiRNAs . In dcl4ecursors . The speecursors . Recentlecursors , which mall RNAs . Each of protein . Upon arFritillaria imperialis (Fri)EPRV, Solanum species show CHH methylation  counteracted the PTGS of CHS-A as seen in a changed color pattern of the flowers. With more viral transcripts available also derived 21-/22-nt and 24-nt siRNAs increased, reinitiating TGS and PTGS to terminate transcription from chromosomal as well as episomal PVCV DNA  which cells carry active EPRVs within tissues?; (v) what are the initial landing sites of EPRVs within chromosomes and how do they spread?; and (vi) what pathways are involved in EPRV activation and silencing?New methodologies allow sequence analysis of ever smaller units such as virions or single cells. Thus, we can address: (i) which molecules are encapsidated?; (ii) are virions similar in their content?; (iii) what replicative and recombinogenic interactions of It will be essential to characterize the presence and consequences of EPRVs across all plant species, as is happening with endogenous retroviruses in animals. Solanaceae, and in particular Petunia, with a moderate diploid genome size of 1.4 Gb comprising mobile genetic elements such as DNA transposons, LTR retrotransposons and EPRVs, easy cultivation, seed or vegetative propagation, tissue culture and transformation, along with genomic and genetic resources, is an appropriate model to help answer the questions above. Genome editing using CRISPR/Cas will be useful to elucidate the functionality of EPRVs beyond a viral context.The diversity in EPRV structure and in co-evolution with its host requires investigation of all pools of small RNA populations present in small RNA sequencing data. Bioinformatics will be an important tool to examine the arms race and evolution of associated regulatory mechanisms in the virus and host.KR-P in discussion with TS and KV concepted the review. KR-P wrote the abstract, introduction, outlook, and contributed to the other parts. KR-P, GC, and OA wrote the sections on PRV and EPRV evolution (1–3). TS, OA, and JH-H wrote the sections on recombination and prevalence of EPRVs in the genome . KV wrote the section on RNAi with contributions of GC (6). TS, JH-H, and KV designed the figures. KR-P, JH-H, TS, KV, and GC edited the final manuscript. All authors contributed to writing, editing the manuscript including figures, and approved the version to be published.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.


An essential challenge in the lifecycle of RNA viruses is identifying and replicating the viral genome amongst all the RNAs present in the host cell cytoplasm. Yet, how the viral polymerase selectively recognizes and copies the viral RNA genome is poorly understood. In flaviviruses, the 5′-end of the viral RNA genome contains a 70 nucleotide-long stem-loop, called stem-loop A (SLA), which functions as a promoter for genome replication. During replication, flaviviral polymerase NS5 specifically recognizes SLA to both initiate viral RNA synthesis and to methylate the 5′ guanine cap of the nascent RNA. While the sequences of this region vary between different flaviviruses, the three-way junction arrangement of secondary structures is conserved in SLA, suggesting that viruses recognize a common structural feature to replicate the viral genome rather than a particular sequence. To better understand the molecular basis of genome recognition by flaviviruses, we recently determined the crystal structures of flavivirus SLAs from dengue virus (DENV) and Zika virus (ZIKV). In this review, I will provide an overview of (1) flaviviral genome replication; (2) structures of viral SLA promoters and NS5 polymerases; and (3) and describe our current model of how NS5 polymerases specifically recognize the SLA at the 5′ terminus of the viral genome to initiate RNA synthesis at the 3′ terminus. Flaviviruses are important human pathogens that are transmitted by mosquitoes or ticks. Members of flavivirus include dengue (DEN), Zika (ZIK), West Nile (WN), Japanese encephalitis (JE), yellow fever (YF) and tick-borne encephalitis viruses (TBEV), all of which cause wide-spread death and disease throughout the world. For example, dengue virus (DENV) causes diseases ranging from mild dengue fever with non-specific flu-like symptoms to fatal dengue hemorrhagic fever and dengue shock syndrome . The incm7GpppNm-RNA, where the guanosine cap and first nucleotide of the RNA are methylated), while the 3′ terminus lacks a poly(A) tail. The 5′ UTR spans approximately 100 nt, and the 3′ UTR ranges from 400 to 700 nt in length, depending on the virus species . The. The4].  species ,7. The s species . The 3′  species ,10,11.N-7 guanine and 2′-O ribose methyltransferase activities (N7-MTase and 2′O-MTase) that transfer a methyl group to capped RNA to form a type 1 cap structure  uses the genomic positive-sense RNA as a template and synthesizes a complementary negative-sense RNA. NS5 recognizes a conserved RNA stem-loop structure called stem-loop A (SLA) within the 5′ UTR as a promoter, yet initiates negative-strand synthesis at the 3′-end of the genome , step 1 m7GpppNm-RNA, mGpppRNA → mGpppNm-RNA, During viral replication, the positive-strand RNA needs to be modified with a type 1 cap structure  in the bottom stem and the ‘AG’ sequence in the apical loop of the top stem-loop were shown to be essential for viral replication ,33. BothDENV2 containing the ZIKV SLA is shown to replicate as well as the wild-type DENV2, suggesting that DENV2 NS5 polymerase can recognize both DENV2 and ZIKV SLA as a promoter to initiate RNA synthesis . This raNS5 catalyzes both negative- and positive-strand RNA synthesis. During virus replication, 10-50-fold higher amounts of positive-strand RNA are synthesized compared to negative-strand RNA , and thuin trans [The identity of the promoter for positive-strand RNA synthesis is also unclear. Following separation of the dsRNA intermediate, both SLA and its complementary SLA(-) are exposed in the positive and negative strand, respectively. It is not clear whether NS5 binds to SLA or SLA(-) to synthesize positive-strand RNA. Currently, there is no widely accepted mechanism for positive-strand RNA synthesis in flavivirus, and biochemical data exist to support both SLA and SLA(-) as the promoters for positive-strand synthesis. For example, WNV NS5 is able to use the 3′-terminal 230 nt of the negative-strand to synthesize RNA . This tein trans ,20. For The identification of SLA as an RNA promoter for flavivirus polymerase NS5, and the structural studies of the SLA promoter and NS5 have provided insight into how flavivirus selectively replicate the viral genome using specific interactions between SLA and NS5. Future studies will be geared toward understanding how the NS5-SLA complex carries out individual steps of RNA replication involving both negative- and positive-strand RNA synthesis, and how the complex coordinates positive RNA synthesis and type 1 cap formation. Future studies on the structure and function SLA(-) would also be needed to determine whether SLA(-) has a role in viral replication.