Ribosomal RNA (rRNA) comprises at least 90% of total RNA extracted from mammalian tissue or cell line samples. Informative transcriptional profiling using massively parallel sequencing technologies requires either enrichment of mature poly-adenylated transcripts or targeted depletion of the rRNA fraction. The latter method is of particular interest because it is compatible with degraded samples such as those extracted from FFPE and also captures transcripts that are not poly-adenylated such as some non-coding RNAs. Here we provide a cross-site study that evaluates the performance of ribosomal RNA removal kits from Illumina, Takara/Clontech, Kapa Biosystems, Lexogen, New England Biolabs and Qiagen on intact and degraded RNA samples.We find that all of the kits are capable of performing significant ribosomal depletion, though there are differences in their ease of use. All kits were able to remove ribosomal RNA to below 20% with intact RNA and identify ~ 14,000 protein coding genes from the Universal Human Reference RNA sample at >1FPKM. Analysis of differentially detected genes between kits suggests that transcript length may be a key factor in library production efficiency.These results provide a roadmap for labs on the strengths of each of these methods and how best to utilize them.The online version of this article (10.1186/s12864-018-4585-1) contains supplementary material, which is available to authorized users. Ribosomal depletion is a critical method in transcriptomics that allows for efficient detection of functionally relevant coding as well as non-coding transcripts through removal of highly abundant rRNA species. Use of oligo dT primer to capture the polyadenylated 3′ end of the transcripts and isolate mRNA is routine in many RNA sequencing preparations; however this method lacks the ability to handle degraded samples where most of the RNA is separated from the 3′ tail, or to isolate non-polyadenylated transcripts such as lncRNAs. Ribosomal removal methods address these issues by directly depleting the rRNA while leaving other transcripts intact. This technique is widely utilized and is a basic component of many large datasets –3.The current generation of rRNA removal kits employs three distinct strategies to deplete these transcripts. In the first method, rRNA is captured by complimentary oligonucleotides that are coupled to paramagnetic beads, after which the bound rRNA is precipitated and removed from the reaction. Kits utilizing this method include Illumina’s RiboZero, Qiagen GeneRead rRNA depletion, and Lexogen RiboCop. The second method uses an alternative strategy, hybridizing the rRNA to DNA oligos and degrading the RNA:DNA hybrids using RNAseH. These kits include NEBNext rRNA depletion, Kapa RiboErase, and Takara/Clontech’s RiboGone. A third method that is specifically aimed at low-input samples using the Takara/Clontech SMARTer Pico kit, targets the ribosomal RNA sequences after conversion to cDNA and library prep using the ZapR enzyme. Additional high abundance transcripts such as globin and mitochondrial RNA (mtRNA) can be targeted by each method. Despite the availability of many different kits utilizing these methods, the efficiency of rRNA removal and possible off-target effects of these different methodologies on the resulting RNAseq data remains unclear.With an increasing number of ribosomal RNA depletion kits available, understanding the relative strengths of these methods is critical for improving experimental design. To address this challenge, we have conducted a cross-site study comparing seven rRNA depletion kits against a standard sample both as intact and degraded RNA. We find that about half of the kits are likely to require significant care in implementation and note that the Lexogen RiboCop and Qiagen GeneRead kits worked poorly with heavily degraded samples. The different kits also appear to be affected by relative lengths of the transcripts as well as the degradation of the input RNA. These results suggest that different methodologies may be appropriate depending on the experimental question and quality of input material.To better understand the strengths of the different ribosomal depletion methodologies available, we utilized a set of controlled samples that could be broadly distributed in order to provide a consistent biological background for each kit and site Fig. . All expThe study tested seven rRNA depletion kits Fig. , each teThe different methodologies were first evaluated for their ability to perform their primary objective, the removal of rRNA from the samples before sequencing. rRNA reads in each sample were identified by aligning to known rRNA sequences using BWA. A cutoff of 50% nuclear rRNA was chosen to indicate ribosomal depletion failure. Illumina’s RiboZero Gold kit showed ~ 5% rRNA with the intact sample at all sites (excluding a single point failure) but slightly higher rRNA fractions for the degraded sample. This kit was used as a baseline for the other kits due to its long-standing reputation in the sequencing community Fig. . The othBy comparison, the kits that degraded the rRNAs by either RNase H treatment or using ZapR showed more consistent results. Excluding single sites that failed with the NEBNext Ultra rRNA and SMARTer Pico kits, those two kits as well as the Takara/Clontech RiboGone and Kapa RiboErase kits performed very well with no differences observed between intact and degraded RNA. The RNaseH methods all showed very low rRNA fractions overall with the noted exception to the NEB kit. The SMARTer Pico kit had a slightly higher rRNA level, similar to that observed with Illumina Ribozero Gold degraded samples.For those samples with successful rRNA depletion, we next ascertained the quality of the RNA sequencing data. Samples with greater than 50% rRNA were excluded from further analysis to eliminate artifacts that may be caused by improper implementation of the protocol. All the kits showed strong strand bias as expected by the protocols Fig. . NotablyLooking at the non-rRNA reads in each sample, the vast majority align to protein coding genes based on the ENSEMBL annotation. All samples show > 60% protein coding with most over 80% and the Clontech RiboGone kit having the largest fraction mapping Fig. . Most ofp-values < 0.001, Fig. While the total number of protein coding genes detected was quite similar, many genes appear to be detected at significantly different rates. To better understand this observation, we performed differential gene expression analysis on the intact RNA samples that passed our QC metrics, comparing each preparation back to Illumina RiboZero Gold Fig. . The QiaRibosomal depletion is a key methodology used in studying noncoding RNAs as many of these transcripts lack polyadenylation sites . We focuWhile comparison of different detection rates of mRNAs in UHR can point to possible differences between the kits’ chemistries, the spike-in controls provide an absolute metric to evaluate their effectiveness. Both SIRV (co-degraded with the RNA) and ERCC (not degraded) control spike-ins were added to the sample before library preparation and should give an unbiased look at the behavior of the different kits. As an initial test, we examined the ratio between the two spike-in types to model the impact of degradation on efficiency of library formation. Importantly, the kits do treat degraded RNA differently in their protocols and the not degraded ERCCs in the degraded samples were processed along the path proscribed for the bulk RNA, suggesting they are under-fragmented relative to the bulk population. Examining the ratios between the ERCC and SIRVs, we find that all the intact samples show ~ 60% SIRV reads Fig. . By compth of the other kits), and some transcripts (e.g. purple at 1/4×) show loss of signal in the degraded sample. By comparison, the ERCC spike-ins showed significantly more variability across sites, even within the intact RNA samples  was diluted to 500 ng/ μL in 200 μl of RNase-free water and 3.94 μl of the Spike-in RNA Variant Control E2 Mix (Lexogen) were added. The sample was split into two aliquots, one of which was then heated at 94° C on an Eppendorf™Thermomixer for 1 h and 27 min. 1 μl of ERCC RNA Spike-In Mix 1 (ThermoFisher Scientific) was added to both the intact and degraded samples before running on a Bioanalyzer 2100 RNA Nano chip (Agilent)  and SMARTer Pico (CZ) kit to minimize shipping costs. Similarly, Illumina RiboZero (RZ) sites were selected to minimize shipping of the donated reagents. Sites were not selected based on prior experience with specific chemistries. Each chemistry was tested by four sites, named 1-4. The same library creation site will not necessary have the same site number for different chemistries. Indices were assigned by the group to prevent overlapping among libraries. Note the Qiagen chemistry is no longer available for purchase.For each chemistry, a conference call was arranged for each of the participating sites and a vendor representative to review the protocol in detail prior to library preparation. During the course of the phone call, the protocol was reviewed in detail and any required modifications or variables  were discussed and agreed on by all users. These details are reported in the Additional file Each site performed rRNA depletion and subsequent library prep following the vendor protocols  were reported for each gene. Genes with BH < 0.001 and absolute fold-changes greater than 2 were considered for downstream analyses  using Bioconductor’s DESeq2 package on the protein-coding genes only . Datasetses Fig. -d.http://bioinfogp.cnb.csic.es/tools/venny/).After removing lincRNAs that were not assigned to chromosomes, 7251 lincRNAs were identified from the ENSEMBL annotation for further analysis. The lincRNAs with mean RPM > =0.01 were retained for each kit. To examine the distribution of different lincRNAs in each of the samples, the percentage RPM was calculated. Except the top 4 lincRNAs, rest of the lincRNAs were classified as ‘Others’ category. The figures were generated using a custom python script. Based on the ribo-depletion methods, the seven kits were grouped into three sets: RLQ , NCK  and CZ. The lincRNAs detected in three sets were compared using an Euler diagram created using Venny  and TreeView Fig. -f.Additional file 1:Figure S1. BioAnalyzer traces for samples used in the study. Left: Intact UHR, Right: Heat degraded UHR RNA. (PPTX 127 kb)Additional file 2:Figure S1. – BioAnalyzer traces for samples used in the study. Left: Intact UHR, Right: Heat degraded UHR RNA. Figure S2. – Clustering of differentially detected genes. Top 50 most differentially detected genes, as measured by variance of log2RPKM across all samples, were clustered based on their differential expression. TOP: Hierarchical tree of clustering based on a complete linkage function using Euclidean distance. 2ND LINE: Intact/Degraded status is shown. Intact samples are indicated in white while degraded samples are indicated in grey. 3RD LINE: Kit. Dark Blue = RZ|RiboZero Gold, Yellow = LX|Lexogen RiboCop, Aqua = NE|NEBNext rRNA Depletion, Green = Q|Qiagen, Grey = K|Kapa RiboErase, Blue = CR|Clontech Ribogone, Orange = CZ|SMARTer Pico total RNA. HEAT MAP: Red indicate higher level of absolute expression. Scale shown to right. White lines indicate the highest branches within the hierarchical tree. Figure S3. – Insert size distribution for RNAseq libraries from intact RNA. The insert size for each library passing the 50% rRNA filter was calculated for reads with convergent reads that were separated by < 1000 bp. Kit abbreviations: RZ = RiboZero Gold, LX = Lexogen RiboCop, NE = NEBNext rRNA Depletion, K=Kapa RiboErase, CR = Clontech Ribogone, CZ = SMARTer Pico total RNA. Top: length of the 90th percentile of inserts reads. Middle: length of the median insert read. Bottom: length of the 10th percentile of inserts read. Figure S4. - Relative ratio of reads mapping to ERCCs. Fraction of reads mapping to each ERCC mRNA is shown for each replicate. Light horizontal lines show 2-fold changes in fraction observed . Each expected concentration is shown in a different color. Data sets ordered by intact/degraded status followed by site within each kit left to right. Table S1. Catalog numbers and manual versions for protocols used. Table S2. Comparison of RNA library preparation chemistry variables. (DOCX 21 kb)Contains the following: Describes normalization of kit protocols and any individual site deviations from this normalization. Additional file 3:Figure S2. Clustering of differentially detected genes. Top 50 most differentially detected genes, as measured by variance of log2RPKM across all samples, were clustered based on their differential expression. TOP: Hierarchical tree of clustering based on a complete linkage function using Euclidean distance. 2ND LINE: Intact/Degraded status is shown. Intact samples are indicated in white while degraded samples are indicated in grey. 3RD LINE: Kit. Dark Blue = RZ|RiboZero Gold, Yellow = LX|Lexogen RiboCop, Aqua = NE|NEBNext rRNA Depletion, Green = Q|Qiagen, Grey = K|Kapa RiboErase, Blue = CR|Clontech Ribogone, Orange = CZ|SMARTer Pico total RNA. HEAT MAP: Red indicate higher level of absolute expression. Scale shown to right. White lines indicate the highest branches within the hierarchical tree. (PPTX 179 kb)Additional file 4:Figure S3. Insert size distribution for RNAseq libraries from intact RNA. The insert size for each library passing the 50% rRNA filter was calculated for reads with convergent reads that were separated by < 1000 bp. Kit abbreviations: RZ = RiboZero Gold, LX = Lexogen RiboCop, NE = NEBNext rRNA Depletion, K=Kapa RiboErase, CR = Clontech Ribogone, CZ = SMARTer Pico total RNA. Top: length of the 90th percentile of inserts reads. Middle: length of the median insert read. Bottom: length of the 10th percentile of inserts read. (PPTX 82 kb)Additional file 5:Figure S4. Fraction of reads mapping to each ERCC mRNA is shown for each replicate. Light horizontal lines show 2-fold changes in fraction observed . Each expected concentration is shown in a different color. Data sets ordered by intact/degraded status followed by site within each kit left to right. (PPTX 165 kb)


The RIG-I like receptors (RLRs) RIG-I and MDA5 are cytosolic RNA helicases best characterized as restriction factors for RNA viruses. However, evidence suggests RLRs participate in innate immune recognition of other pathogens, including DNA viruses. Kaposi’s sarcoma-associated herpesvirus (KSHV) is a human gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and primary effusion lymphoma (PEL). Here, we demonstrate that RLRs restrict KSHV lytic reactivation and we demonstrate that restriction is facilitated by the recognition of host-derived RNAs. Misprocessed noncoding RNAs represent an abundant class of RIG-I substrates, and biochemical characterizations reveal that an infection-dependent reduction in the cellular triphosphatase DUSP11 results in an accumulation of select triphosphorylated noncoding RNAs, enabling their recognition by RIG-I. These findings reveal an intricate relationship between RNA processing and innate immunity, and demonstrate that an antiviral innate immune response can be elicited by the sensing of misprocessed cellular RNAs. RIG-I and MDA5, are cytosolic restriction factors and receptors for RNA viruses. Here the authors show during KSHV lytic infection that substrates for recognition by RIG-I and MDA5 are misprocessed RNAs derived from noncoding host RNA molecules, suggesting antiviral immunity can be engaged by sensing of misprocessed cellular RNAs. The RLRs share similar domain structures, including a central DExD/H-box helicase core, and a C-terminal domain that contributes to ligand discrimination. Additionally, within the N-terminus of RIG-I and MDA5 are two tandem caspase activation and recruitment domains (CARDs) that facilitate interactions with downstream adapter proteins. Upon ligand recognition, RIG-I and MDA5 undergo a series of conformational changes as well as post-translational modifications, which enables the oligomerization of CARDs4. RLR oligmerization facilitates their interactions with their common adapter mitochondrial antiviral-signaling protein (MAVS), leading to activation of the transcription factors interferon regulatory factor 3 (IRF3), IRF7, and an interferon gene-expression response4.The RIG-I-like receptor (RLR) family of PRRs is a group of cytosolic RNA helicases capable of discriminating self vs. nonself RNA. To date, three RLR members have been identified, retinoic acid-inducible gene-I , melanoma-differentiation-associated gene 5 (MDA5), and laboratory of genetics and physiology 2 (LGP2)5. In contrast, RIG-I senses short 5′ tri- and diphosphorylated dsRNA in which the terminal 5′- and 3′- nucleotides are base-paired9. Importantly, these features are generally absent from the host transcriptome. For instance, post-transcriptional adenosine-to-inosine editing in long-dsRNA structures disrupts long RNA secondary structure helices, thereby preventing immune activation14. Additionally, although RNAs are synthesized using triphosphates, the 5′ nucleotides are capped, or processed to remove the gamma- and beta-phosphate moieties, generating 5′-monophosphorylated RNAs15.MDA5 and RIG-I recognize distinct chemical and structural features of RNAs. The optimal substrate of MDA5 is long double stranded RNA (dsRNA) without unpaired bulged nucleotides24. Interestingly, several DNA viruses have also been reported to activate the RLR pathway. For example, Herpes simplex virus 1 (HSV-1) is recognized by both RIG-I and MDA526. Interestingly, in HSV-1 infected HEK293T cells, host-derived 5S ribosomal RNA (rRNA) pseudogene RNA was defined as a prominent RIG-I ligand27. During Epstein–Barr virus (EBV) infection, EBV-encoded small RNAs are recognized by RIG-I30. Furthermore, poly dA:dT DNA sequences within some DNA viral genomes recruit cytoplasmic RNA polymerase (RNAP) III to facilitate transcription of short triphosphorylated noncoding RNAs which are recognized by RIG-I32.As RNA sensors, restriction of RNA viruses, elicited by either viral genome or replicative intermediates, has been most extensively characterized34. RIG-I and MAVS were previously reported to restrict KSHV lytic reactivation, and the virus deploys multiple viral proteins to disrupt RIG-I activity during lytic infection37. For example, KSHV de novo infection deposits teguments proteins into the cytoplasm, of which ORF75 has been show to promote the deamidation of RIG-I partially disrupting its RNA-binding potential37. However, the role of MDA5 in KSHV lytic reactivation is not known. In addition, the in vivo substrates that elicit RLR activation have not been identified.Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and an AIDS-associated pathogen. KSHV is the etiological agent of KS and the B cell lymphoma, primary effusion lymphoma (PEL)Here, we define the relevance of RIG-I and MDA5 in KSHV lytic reactivation. We demonstrate that both RLRs are capable of restricting KSHV lytic reactivation in an epithelial-cell line as well as in patient-derived PEL cells. Strikingly, MDA5 is a more robust restriction factor than RIG-I, as depletion of MDA5 promotes a more significant increase in viral gene expression and production of infectious virions than RIG-I depletion. Additionally, using in vivo formaldehyde crosslinking RNA immunoprecipitation (IP) sequencing (fRIP-seq) we identified the in vivo substrates of RIG-I and MDA5 during lytic reactivation in PEL cells. Remarkably, only host-derived RNAs were significantly enriched by RIG-I and MDA5 fRIP-seq, indicating that the cell-intrinsic immune response against KSHV is initiated by the sensing of host RNAs. We further identified a defect in cellular noncoding RNA 5′-end processing that enables the accumulation of 5′-triphosphorylated (5′-ppp) noncoding RNAs resulting in their recognition by RIG-I. This study defines the in vivo substrates of RIG-I and MDA5 in an oncogenic DNA virus infection, and unexpectedly, demonstrates that recognition of misprocessed host noncoding RNAs activates the cell-intrinsic immune response. Thus, there is an intricate relationship between cellular RNA processing and cell-intrinsic immunity that the host is capable of leveraging during infection.36. We hypothesized that both RIG-I and MDA5 restrict KSHV lytic reactivation and do so by recognizing distinct RNAs. To test our hypothesis, we first determined the contribution of each RLR sensor by depleting RIG-I, MDA5, or their adapter MAVS, individually in the KSHV-positive cell line iSLK.219. iSLK.219 cells contain a latent version of the KSHV genome expressing a constitutive green fluorescent protein (GFP) marker, and a doxycycline (Dox)-inducible version of the major viral lytic transactivator protein, RTA, to enable entry into the lytic cycle. The viral genome also contains a red fluorescent protein (RFP) marker driven by a lytic cycle-specific promoter, which can be used to monitor lytic reactivated cells. siRNA knockdown of both MDA5 and MAVS resulted in a striking increase in RFP positive cells 48 h postreactivation , MDA5 (F-MDA5), or a control empty cassette (Con), thus the addition of dox to the cell culture media reactivates KSHV as well as induces expression of F-RIG-I and F-MDA5. Reactivation of iSLK.219 cells transduced with F-RIG-I and F-MDA5 resulted in fewer RFP positive cells compared to cells transduced with the empty cassette Fig. . QuantifNext, using the established supernatant transfer assay, we monitored the production of infectious virions in the media of F-RIG-I, F-MDA5, and control cells 72 h postreactivation. There was a striking reduction in the number of GFP-positive cells when media from F-RIG-I and F-MDA5 was used to infect target HEK293T cells relative to media from control cells Fig. . FurtherKSHV is the etiological agent of PEL, an aggressive HIV-associated non-Hodgkin’s B cell lymphoma. We investigated the impact of RIG-I, MDA5, and MAVS on KSHV lytic reactivation in BC-3 cells, a patient-derived PEL cell line. We transfected BC-3 cells with shRNA expression constructs targeting RIG-I, MDA5, MAVS, or a control scramble sequence, and quantified their expression by RT-qPCR. All gene-targeting shRNAs significantly reduced their target gene expression by ~50% Fig. . To quanWe reasoned that expression of RIG-I and MDA5 should restrict KSHV reactivation in PEL cells, and thus we established Dox-inducible F-RIG-I, F-MDA5, and control BC-3 cells. Reactivation of these cells with NaB, TPA, and Dox resulted the expression of Flag-tagged RIG-I and MDA5 Fig. . PAN RNARIG-I and MDA5 restrict KSHV lytic reactivation in PEL cells and thus we reasoned that during KSHV lytic reactivation dsRNAs must be present. To test whether immunostimulatory dsRNAs are present in lytic BC-3 cells, we isolated total RNA and subjected it to IP using the dsRNA-specific antibody J2 or control IgG. Immunoprecipitated RNAs were then transfected into a human HCT116 colorectal carcinoma cell line containing a stably integrated interferon stimulated gene 54 (ISG54)-inducible luciferase reporter, and luciferase levels were determined 24 h posttransfection Fig. . J2-enriGiven that both RIG-I and MDA5 restrict KSHV reactivation in PEL, BC-3 cells provide an ideal system to define and compare the in vivo ligands of both RLRs in a physiologically relevant cell type. To identify the in vivo RNAs that are recognized by RIG-I and MDA5 and prevent postlysis RNA–protein interactions, lytic F-RIG-I, F-MDA5, and control BC-3 cells were formaldehyde cross-linked. Cell lysates were then subjected to a stringent purification protocol including washes in 1 M urea, prior to cross-link reversal and isolation of bound RNAs, and RNA sequencing (fRIP-seq). Remarkably, despite observing robust viral gene expression in the input sequencing libraries, our bioinformatics analyses revealed only the presence of host-derived RNAs among the significantly enriched F-RIG-I and F-MDA5 RNAs Fig. . We idenThe substrate specificity of RIG-I and MDA5 is best characterized in vitro and it has been determined that they recognize distinct chemical and structural features of dsRNA. We thus determined the similarity amongst F-RIG-I and F-MDA5 bound RNAs by principal component analysis (PCA). Indeed, consistent with expectations derived from in vitro binding experiments, PCA demonstrated F-RIG-I and F-MDA5 enriched distinct RNAs from in vivo Fig. . Further38. To verify that vtRNAs are indeed RIG-I substrates we performed fRIP RT-qPCR. Consistent with the fRIP-seq data, vtRNAs were selectively enriched in RIG-I eluates compared to eluates from MDA5 and control cell IPs ncy Fig. . This efncy Fig. .Next, we tested whether vtRNAs are triphosphorylated during lytic reactivation. To test this, we developed a splint-ligation assay that leverages the ability of T4 DNA ligase to use a monophosphorylated RNA in a ligation reaction, while discriminating against a triphosphorylated substrate 27. However, RNA5SP141 is unlikely to be a universal activator of the RIG-I pathway following viral infection. In fact, RNA5SP141 is not expressed in PEL, or present in RIG-I or MDA5 fRIP-seq data from PEL cells. Interestingly, however, we do detect pseudogene transcripts derived from other RNAs, including spliceosomal U6 small nuclear RNA, an RNAP III transcribed RNA. Pseudogenes are known to participate in the posttranscriptional regulation of gene expression by functioning as competing endogenous RNAs46, and it is interesting to speculate that pseudogenes have also been co-opted into condition-specific triggers of interferon responses.Our findings demonstrating that both RIG-I and MDA5 sense host-encoded RNAs during KSHV reactivation is remarkable, and indicates that antiviral immunity can be triggered through the sensing of host RNAs. This is consistent with recent data demonstrating RNase L cleavage products and exosomal 7SL RNA can serve as RIG-I ligands45. While the RNA-binding proteins of vtRNAs are not well-defined, in fact only ~5% of vtRNAs are present within the vault complex for which they are named, it is unlikely that RNA unshielding is the only contributor to RIG-I activation during KSHV reactivation. This is perhaps best exemplified by our finding that Y RNAs, which are components of Ro60 ribonucleoprotein particles, are recognized by RIG-I during reactivation, yet there is no decrease in the expression of Ro60 at the RNA or protein level. Furthermore, the levels of Y RNAs are relatively constant throughout reactivation. Thus, the recognition of RNA by RIG-I in the context of KSHV reactivation is likely the result of multiple mechanisms, and dependent on the precise molecular steps involved in the biogenesis of a given RNA, with a deficiency in 5′-end processing one potential mechanism.RNA unshielding, loosely defined as an alternation in the stoichiometry of a triphosphorylated RNA and its given RNA-binding proteins, is implicated in the RIG-I sensing of exosomal 7SL RNA and RNA5SP14139. In fact, Alu’s, a family of noncoding retrotransposons transcribed by RNAP III that are DUSP11 substrates, are present in RIG-I fRIPs. This suggests that retrotransposon-derived RNAs are capable of being sensed by RLR-receptors and triggering an immune response. This would be consistent with reports demonstrating a role for these retrotransposon sequences in activation of immune responses in systemic lupus erythematosus, age related macular degeneration, and murine gammaherpesvirus 68 infection50. Given the role of DUSP11 in preventing the accumulation of endogenous RIG-I ligands it will be necessary to determine whether it is associated with autoimmune disease. In contrast, if molecules can be identified that reduce DUSP11 catalytic activity, promoting the activation of RIG-I, they may hold therapeutic promise for the treatment of some infectious diseases or even cancer.RNA processing is an essential step in gene expression and here we demonstrate a failure to properly process 5′-ends of vtRNAs contributes to their recognition by RIG-I. The extent to which defects in other RNA processing steps contribute to RIG-I activation is unclear, and identification of these molecular steps and defective processing intermediates may shed light into the molecular basis of disease, as well as represent a therapeutic target that can be leveraged. DUSP11 is an RNA triphosphatase that acts on RNAP III transcribed noncoding RNAs, thus it is likely that other DUSP11 substrate RNAs are also sensed32. The model for RNAP III triggered innate responses is that poly dA:dT DNA sequences within some DNA viral genomes are able to recruit cytoplasmic RNAP III, resulting in the transcription of short triphosphorylated noncoding RNAs which are then detected by RIG-I. We do not observe any significant enrichment of fRIP-seq reads that map to the KSHV genome and thus it is likely that RNAP III does not directly sense the KSHV genome. However, our identification of RNAP III transcribed host RNAs in RIG-I fRIP-seq data demonstrate RNAP III does participates in the innate immune response to KSHV by generating RNAs that can be sensed. Thus, RNAP III is a critical component of the cell-intrinsic immune response even in the absence of direct pathogen recognition.RNAP III participates in cell-intrinsic innate defense to variety of pathogens, including DNA viruses53. Some of these SNPs increase the sensitivity of the RLR-receptors, and thus they engage cellular RNAs leading to an interferon gene expression response. The in vivo ligands that are sensed and drive the interferon responses are just beginning to be identified54. Given all RLR-sensed ligands during KSHV infection are host-derived, these results should provide insight into potential substrates and mechanisms of RLR activation for several pathologies, not just KSHV infection.Genome-wide association studies have revealed the association of single nucleotide polymorphisms (SNPs) in RIG-I and MDA5 with the risk of many autoimmune diseases, including systemic lupus erythematosus, Aicardi–Goutieres syndrome, and Singleton–Merten syndrome55 , iSLK.219 control, iSLK.219 FLAG-RIG-I, iSLK.219 FLAG-MDA5, and HEK293T (ATCC) were maintained in Dulbecco’s modified Eagle medium  supplemented with 10% fetal bovine serum . BC-356 , BC-3 control, BC-3 FLAG-RIG-I, and BC-3 FLAG-MDA5 PEL cells were grown in RPMI 1640 medium (Invitrogen) supplemented with 10% FBS (Invitrogen) and 2 mM l-glutamine (Invitrogen). All cells were maintained with 100 U of penicillin/ml and 100 μg of streptomycin/ml (Invitrogen) at 37 °C under 5% CO2. iSLK.219 and derivatives were reactivated with 1 μg/ml of doxycycline (Fisher Scientific), while BC-3 cells were reactivated using TPA  and NaB .iSLK.219RIG-I and MDA5 were PCR amplified from pEF-BOS-RIG-I and pEF-BOS-MDA5 and cloned into pDONOR221, and subsequently pLenti-CMVtight-FL-HA-DEST-Blast (Addgene) using Gateway cloning (Invitrogen). Lentivirus was prepared in HEK293T cells. Cells were transfected at 50–60% confluency with RIG-I, MDA5, or Empty destination vectors, psPAX2 (Addgene), and pMD2.G (Addgene) using polyjet (SignaGen). After 72 h posttransfection the supernatant was collected, adjusted to 8 μg/ml polybrene (Millipore), and target cells were spinfected at 1000 g for 1 h at room temperature. Cells were selected for 2 weeks in media containing 5 μg/ml blasticidin (Invivogen).iSLK, iSLK.219, and its derivative cells were fixed with 2% paraformaldehyde and then analyzed on BD LSR Fortessa or Canto II instrument. Data were analyzed with FlowJo X software (TreeStar). The gating strategy is shown in Supplementary Fig. 57. Briefly, latent or reactivated BC-3 and derivative cells were fixed in 4% (vol/vol) paraformaldehyde and permeablized with 1× PBS containing 0.2% (vol/vol) Tween-20. The permeabilized cells were then hybridized with Fluorescein-12-dUTP labeled PAN antisense oligos in HB 10% dx buffer (10% (wt/vol) dextran sulfate, 2× saline-sodium citrate (SSC), 10% (vol/vol) formamide, 1 mg/ml tRNA and 0.2 mg/ml BSA) at 37 °C overnight. After extensive washing with HBW buffer  formamide and 0.2 mg/ml RNase-free BSA), cells were analyzed on BD Canto II instrument. Data were analyzed with FlowJo X software (TreeStar). The gating strategy is shown in Supplementary Fig. FISH-FlOW of PAN RNA in BC-3 cells was done as previously describediSLK.219 cells were transfected at 60–80% confluency with 40 nM siRNA (sequences in Supplementary Table MISSION shRNA for MAVS (TRCN0000149206), RIG-I (TRCN0000152922), MDA5 (TRCN0000050852), and nontargeting shRNA (SHC016) were obtained from Sigma Aldrich. BC-3 cells were microporated with shRNA expression vectors using the Neon transfection system (Invitrogen) at 1400v, 10 ms pulse width, and 3 pulses. After 24 h postmicroporation cells were reactivated as described above.iSLK.219 and derivatives cells were reactivated with doxycycline for 72 h, after which the supernatant was collected, adjusted to 8 μg/ml polybrene, and HEK293T cells were spinfected at 1000 g for 1 h at room temperature. The infection media was replaced with fresh media and incubated for 72 h, followed by analysis.Total RNA was isolated with TRIzol (Invitrogen) in accordance with the manufacturer’s instructions. RNA was DNase I (NEB) treated at 37 °C for 20 min, and inactivated with EDTA at 70 °C for 10 min. cDNA was synthesized from DNase-treated RNA with random 9-mer (Integrated DNA Technologies) and M-MLV RT (Promega). qPCR was performed using the PowerUp SYBR Green qPCR kit (Thermo Scientific) with appropriate primers (Supplementary Table http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) using default parameters.Secondary structure analysis of vtRNAs was performed using RNAfold . Totally, 40pmol of each 5′-TEG biotinylated  and quantified by Bradford assay (BioRad). Equivalent amounts of each sample were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis, electrotransferred to polyvinylidene difluoride membrane (Millipore), and blotted for the indicated proteins. Antibodies: GAPDH , β-actin , vIRF1  ORF50 and ORF59 , ORF57 , and bZIP . IRF3 , Phospho-IRF3 S386 , FLAG , MAVS , DDX58 , MDA5 , DUSP11 . Primary antibodies were followed by AlexaFluor 680-conjugated secondary antibodies , and visualized by Odyssey CLx imaging system (LI-COR). The full size immunoblots scans are presented in Supplementary Fig. 58.vtRNA in vitro transcription templates were generated by overlapping PCR of two DNA oligonucleotides  were transfected with RNA using RNAiMAX (Invitrogen). Totally, 12–24 h posttransfection cell supernatants were collected and used to measure secreted Lucia luciferase activity using QUANTI-Luc (Invivogen) on a GLOMAX 20/20 Luminometer (Promega).dsRNA was immunoprecipitated using the dsRNA-specific J2 antibody. Briefly, 5 μg of J2 antibody or control IgG were added to total RNA and rotated overnight at 4 °C, whereupon 50 μl of prewashed protein G magnetic beads were added and rotated for an additional 2 h. Antibody-bead complexes were then isolated on a magnetic stand and washed three times with high-salt lysis buffer containing 500 mM NaCl. RNA was eluted by TRIzol extraction.59. For ligation, all reactions consist of 100fmol bridge oligonucleotide, 200fmol radiolabeled ligation oligonucleotide, 5 μg RNA, 8% PEG 8000, 1× T4 DNA ligase reaction buffer (NEB), and 10 units T4 DNA ligase (NEB). Before adding T4 DNA ligase, the reaction mixture was denatured at 95 °C for 1 min, cooled to 65 °C for 5 min, and 37 °C for 10 min, the ligase was added to the reaction mixture and incubated at 30 °C for 4 h. Reactions were terminated by heat inactivation at 75 °C for 10 min and subsequently separated using denaturing 8 M urea 10% polyacrylamide gels and imaged using a PhosphorImager.For total vtRNA measurements, 50 μg of total RNA was treated with calf intestinal phosphatase (CIP) (NEB) for 1 h at 37 °C according to manufactures recommendation. RNA was extracted with phenol:chloroform:isoamyl alcohol [25:24:1 (vol/vol)] followed by ethanol precipitation. A 5 μg of CIP-treated RNA was phosphorylated with ATP and T4 polynucleotide kinase prior to a biospin 6 (BioRad) clean up. Ligations were performed similar to as previously described60. Briefly, BC-3 cells were cross-linked with 1% formaldehyde for 10 min in PBS, and unreacted formaldehyde was neutralized with 0.3 M glycine for 5 min. Cells were washed 2× with PBS, then resuspended in RIPA buffer  and kept on ice for 10 min. Soluble cell extracts were incubated with anti-FLAG M2 magnetic resin (Sigma) at 4 °C for 2 h. Resin was washed three times for 10 min and then two times for 5 min at room temperature in RIPA buffer containing 0.1% SDS, 1 M NaCl, and 1 M urea. Resin was eluted with 1×FLAG peptide in RIPA buffer for 45 min at 4 °C. Protein–RNA cross-links were reversed by adding 100 mM Tris, pH 8.0, 10 mM EDTA, 1% SDS, and 2 mM DTT to eluted samples and heating to 70 °C for 45 min. RNA was recovered by extraction with TRIzol and then again with phenol:chloroform:isoamyl alcohol [25:24:1 (vol/vol)] followed by ethanol precipitation. Paired-end RNA-sequencing libraries were prepared from the recovered RNA using the NEBNext Ultra II Directional RNA Library Prep Kit (NEB) according to the manufacture recommendations. Libraries were then subjected to paired-end sequencing on a HiSeq3000 with 150 cycles at the Vanderbilt Technologies for Advanced Genomics (VANTAGE).fRIP-seq was performed as previously described with minor modificationswww.bioinformatics. babraham.ac.uk/projects/fastqc). Reads were aligned to the human reference genome (gencode GRCh38.p10) and KSHV genome (GQ994935.1) using Spliced Transcripts Alignment to a Reference (STAR) software61. We estimated transcript and gene abundances, as well as depletion/enrichment significance using cufflinks and cuffdiff 262, and summarized to biotypes annotated in GENCODE database (gencode.v27s), remaining unannotated reads were further annotated using RepeatMasker annotation library hg38.fa.out.gz (http://www.repeatmasker.org). Statistical analyses and virtualization were performed using R packages: principle component analysis was performed using the PCA function in package FactoMineR63 and presented with factoextra, genome coverages were presented with Gviz64, the gene ontology overrepresentation analysis was performed with clusterprofiler on GO biological process65.Raw read quality in fastq files were accessed by FastQC (Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


With the replicase protein, which lacks proofreading activity, as well as its short replication time, and high population size, Qβ phage has attractive features for in vitro evolution. The A1 protein gene shares the same initiation codon with the coat protein gene and is produced during translation when the coat protein’s UGA stop codon triplet (about 400 nucleotides from the initiation) is suppressed by a low level of ribosome misincorporation of tryptophan. Thus, A1 is termed the read-through protein. This RNA phage platform technology not only serves to display foreign peptides but is also exceptionally suited to address questions about in vitro evolution. The C-terminus of A1 protein confers to this RNA phage platform an exceptional feature of not only a linker for foreign peptide to be displayed also a model for evolution. This platform was used to present a peptide library of the G-H loop of the capsid region P1 of the foot-and-mouth disease virus (FMDV) called VP1 protein. The library was exposed on the exterior surface of Qβ phages, evolved and selected with the monoclonal antibodies (mAbs) SD6 of the FMDV. These hybrid phages could principally be good candidates for FMDV vaccine development. Separately, the membrane proximal external region (MPER) of human immunodeficiency virus type 1 (HIV-1) epitopes was fused with the A1 proteins and exposed on the Qβ phage exterior surface. The engineered phages with MPER epitopes were recognized by anti-MPER specific antibodies. This system could be used to overcome the challenge of effective presentation of MPER to the immune system. A key portion of this linear epitope could be randomized and evolved with the Qβ system. Overall, antigens and epitopes of RNA viruses relevant to public health can be randomized, evolved and selected in pools using the proposed Qβ model to overcome their plasticity and the challenge of vaccine development. Major epitopes of a particular virus can be engineered or displayed on the Qβ phage surface and used for vaccine efficacy evaluation, thus avoiding the use of live viruses.Qβ is a positive (+) single-stranded RNA bacteriophage covered by a 25 nm icosahedral shell. Qβ belongs to the family of Leviviridae and is found throughout the world . The genome of Qβ is about 4.2 kb, coding for four proteins. This genome is surrounded by 180 copies of coat proteins (capsomers) each comprised of 132 residues of amino acids. The other proteins, the subunit II (β) of a replicase, the maturation protein (A Viruses with RNA genomes are major causes of emerging deadly infectious diseases and represent the most ever-present and everlasting changing cellular parasites known. There are many different alterations of genetic elements occurring during RNA genome replication ,2. This The mechanisms of viral infection and prevention are the most difficult investigations in virology research. Virus tropism and neutralizing antibodies are the major crucial steps in virus life cycles and vaccine development respectively. To better understand the molecular pathways of infection and prevention, we must utilize synthetic biology. Synthetic biologists would simplify and engineer complex artificial biological systems that investigate natural biological phenomena ,8.The current generation of synthetic vaccines, diagnostic reagents , peptide antigens, oligonucleotides, etc.) and therapeutics may be very adequate to prevent, detect and cure infections due to DNA viruses. Unfortunately, these fail to address RNA viruses. This is largely due to the vast number of circulating quasispecies associated with RNA pathogens. The dynamics of viral quasispecies mandates careful consideration. In reality, prevention and therapy of infection due to an RNA virus should rely on multicomponent vaccines and antiviral agents that address the complexity of the RNA quasispecies mutant spectra. This can be accomplished using the RNA Qβ coliphage display peptide library.Phage display technology has been developed, applied extensively and proven to have success in exposing functional peptides on the exterior surface of phage . A novelThe desire of the molecular biologist to engineer natural biological entities that address future evolution are growing significantly. This includes the use of Darwin’s postulates as they relate to in vitro molecular evolution.The major success of in vitro evolution is best achieved with a system bearing a direct linkage between the genetic information and the trait or outlook representing the dichotomy prone by Ronald Fisher and Gregor Mendel . The RNAIn a quest to give insight to the concern of RNA virus replication in a host cellular DNA replication system, Sol Spiegelman and his colleagues presented the first in vitro Darwinian evolution experiment. RNA virus replication, particularly RNA phage was found to be the perfect model system for evolutionary investigation . The lifIn this theory of quasispecies, the living RNA molecules are in equilibrium, which involved a master genetic sequence and other closely related copies. The master sequence always represents the important portion (in concentration) of all favored sequences and not just the consensus sequence although it may represent it sometime. To be claimed perfect, a copy has to be much closer to the master. For RNA viruses, the term population was assigned to wild type and not limited to a single genome with defined sequence . The steEscherichia coli HB101 The subsequent construction of the vector could be used to insert foreign functional protein genes. The stable recombinant plasmid would be expressed in order to display a pool (library) of hybrid phages exposing the foreign proteins on the phage surface. The most adapted variant phages can then be concentrated and selected by panning which is an artificial selection against a known target [The fact that RNA populations continuously vary during replication make them more adaptive than their DNA counterpart and/or organisms. The variation rate increases with the reduced genome size . The adan target . In our E. coli [2 and the read-through or minor coat protein A1 [1 protein shares the same initiation codon with the coat protein and is produced during translation when the coat protein’s UGA stop codon triplet (about 400 nucleotides from the initiation) is suppressed by a low level of ribosome misincorporation of tryptophan [The phage display technique has mostly been applied using the M13 phage which contains a single-stranded circular DNA genome with 6407 nucleotides ,21. The E. coli ,24. Eachotein A1 ,29. The otein A1 . Each caotein A1 ,30. Thesotein A1 . The A1 yptophan ,21  protein is a read-through protein and is important for the formation of infectious phage particles [The RNA bacteriophage Qβ encodes genes essential for the production of proteins such as those that compose its outer shell termed the coat and minor coat proteins. Howbeit, we will focus on the minor coat protein Aarticles . Translaarticles . This re1 protein has a 15-residue-long polyproline type II helix PPII. The A1 protein motif suggests its relevance and complete functionality as these PPII motifs are known to foster protein–protein and protein–nucleic acid interactions that are essential in protein assembly, signal transduction, serve as binding sites and bacterial and viral pathogenesis [1 display sites per Qβ phage were between 3–7% in total [1 protein has twelve vertices that are evenly displayed across the surface of the Qβ phage [1 protein without negatively affecting the functionality or efficacy of the phages level of infectivity [The read-through domain of Qβ Aogenesis . It was in total . HoweverQβ phage . A previectivity .1 protein, current studies investigating the structure of Qβ phage and its constituents indirectly suggest the potential for the exploitation of the A1 protein’s motif capacity for detection assays, drug delivery, and vaccines.Although there is minimal research in the literature regarding the functionality of AE. coli, the α ribosomal protein , and two host translation elongation factors, γ , and δ . The β-subunit consists of a finger, thumb, and palm domain. The finger and thumb domains interact via hydrophobic interactions with elongation factors subunits γ and δ (EF-Tu and EF-Ts) allowing them to bind the subunit tightly [The Qβ replicase is an RNA-dependent RNA polymerase (RdRp). It differs from the reverse transcriptase found in retroviruses, which are RNA-dependent DNA polymerases (RdDp), in that it catalyzes the transcription of RNA from an RNA template, rather than RNA from a DNA template. This protein is encoded in all positive-stranded RNA viruses. The replicase is a holoenzyme consisting of four subunits: the β subunit (65 kDa) that is encoded by the phage itself, as well as three other subunits encoded for by the natural host of Qβ,  tightly . This in tightly .The S1-γ subunit is part of the bacterial 70S ribosome and is one of 21 proteins that comprises the smaller 30Ss subunit and is responsible for translation of mRNA within the cell after transcription by assisting mRNA in binding to the 30S ribosome subunit . S1 has + inorganic phosphate. It also plays a role in the accuracy of translation. After hydrolysis of GTP to GDP, the GDP-bound EF-Tu is then released from the ribosome. Elongation factor subunit δ is a second elongation factor in E. coli that allows EF-Tu to hydrolyze GTP into GDP. Elongation factor subunit γ then releases the bound GDP where it can be recycled and allows for EF-Tu to bind a new GTP to catalyze another aatRNA. Upon infection of the host cell by the Qβ virus, the β subunit hijacks these three subunits of the bacteria creating the replicase holoenzyme using the S1 subunit, EF-Tu, and EF-Ts as its coenzymes with the replicase becoming the β subunit, and the S1, EF-Tu, and EF-Ts becoming the α, γ, and δ subunits, respectively. It is the replicase core complex  that possesses polymerization activity [Elongation factor subunit γ is a translation elongation factor that binds to aminoacylated tRNAs (aatRNA) and assists them in moving into the correct position within the ribosome in the A-site via the formation of a ternary complex that consists of EF-Tu:GTP:aatRNA . It doesactivity ,42.E. coli bacteria encoded for by the hfq gene, is also required for initiation of replication and initiation will not occur in the absence of the enzyme [Host factor (HF1) is a 102 amino acid protein provided by the e enzyme . The rate enzyme ,23,38. Oe enzyme . The rep4 in less than an hour, it is able to distinguish between its own RNA and a vast amount of host RNA and only replicate its own RNA, as well as do both of these things without the use of endogenous primers [S1 protein and the EF-Tu [Qβ’s replicase has a unique set of features that not many other replicases possess. In addition, to amplifying its genome more than 10 primers . The temhe EF-Tu . The mothe EF-Tu .Functionally, polymerization occurs once the 3′ bound end of the template enters into the initiation site of the replicase . Additio2 protein, the RNA molecules are being injected through the tubular sex pili (6–8 nm in diameter) leaving behind the empty capsid [The Qβ phages infect bacteria with the F-pili, making them male dependent viruses. The Qβ infection cycle has been intensively investigated and simplified . To stary capsid . At thisIn the host cytoplasm, with its high level of RNA secondary structure, only the start region of the coat protein RNA is accessible to the ribosome. The binding of the ribosome to the start of the coat RNA region frees the replicase start gene region and allows the beginning of the early translation of this β subunit of the RNA-dependent RNA polymerase or the replicase . The β s2 start region of the newly synthesized RNA on the plus strand, not completed, would be attractive to ribosomes. The temporary availability of the A2 start gene to ribosomes, would produce a small amount of A2 proteins. The openness of the A2 protein gene to the ribosome is the result of a short relaxation (level free energy) of the RNA template secondary structure, which always prevents this accessibility. The plus strand produced by replication would serve as template for dual purpose: (i) replication and synthesis of progeny RNA molecules; and (ii) translation and production of more replicase enzymes and coat proteins for viral shells [2 protein and MurA, the mechanistic pathway that leads to host cell lysis during Qβ infection is now clear [After assemblage and formation of the active replicase complex, it competes with ribosomes for the coat protein start region. This competition results in a repression of the translation due to ribosome removal. Replication occurs after the complete repression of translation in the absence of the ribosome. The minus strand of the RNA genome is the first product of this replication that serves as the template for the synthesis of the plus strand. The Al shells . The furl shells . With thow clear .Thermus aquaticus DNA polymerase (Taq). The entire process can occur at 37 °C. As mentioned, Qβ’s replicase has high specificity for its template, a fact that can be overcome by the addition of Mg2+ to the solution, allowing the replicase to replicate non-native templates [Potential applications for Qβ’s replicase include usage in novel approaches to polymerase chain reaction (PCR). Isolation and use in place of traditional polymerases in PCR reactions. Unlike conventional PCR, where a short target sequence is amplified, Qβ’s replicase can mass produce using midivariant RNAs (MDV-1). Unlike most polymerases which amplify from the template strand only, Qβ’s replicase can use both the template and the product strands as templates during each round of amplification . These Memplates .A1 gene, we used bioinformatic analysis to make sure the secondary structures of the Qβ phage RNA are conserved. Specifically, the recombinant A1 gene sequence is always fused to a foreign protein DNA or cDNA, would be optimized by selecting the most adequate codons for insertion into the cassette.In our research work, plasmids harboring the full cDNA of bacteriophage Qβ were used . By clon1 protein.Unlike DNA phages, the use of an RNA phage display technique for in vitro evolution is poorly understood. The Qβ phage is also an important tool to map the library of a virus to determine its antigenic properties which can lead to easy detection of the virus both in the field and clinical settings. Recently, we have successfully constructed and exposed a 5-mer library of FMDV VP1 G-H loop on the external surface of Qβ . The tanA 50-mer representing the consensus sequence of the MPER region of HIV-1 was engineered and displayed of the surface of Qβ. MPER was designed for broadly neutralizing antibodies but was poorly accessible. We have demonstrated that recombinant Qβ phage framed with gp41 MPER of HIV can be used either alone or in combination with other strategies for the production and monitoring of HIV-1 gp41 MPER-specific immune responses . The resOther applications include use of nanotags as a biosensor for peptide detection. Nano-tags can be used as part of the peptide library for easy identification and tracking to establish binding of recombinant phage to the right antibody. This method was very useful in maintaining the structural conformation of the peptide as shown with FMDV, HIV-1, and severe acute respiratory syndrome coronavirus or SARS-CoV ,11. MoreFor the past hundred years, phages have provided insight into molecular biology, pathology, immunology, structural biology and medical technology. The quasispecies nature of RNA viruses has compromised numerous antiviral therapeutic agents. The RNA phage display system methodology described here is simple, low in cost and risk, and is still in its infancy and remains a powerful tool for in vitro evolution and application to the medical field. The RNA display can serve as platform for RNA virus vaccine development with its quasispecies aspect. The RNA phage can display antigens for a particular pathogen allowing it to serve as a point of care by facilitating detection and concentration of pathogen specific. This system can be further developed to play a role of biosensor for detection of disease in developing countries where sophisticated equipment is not available. Live RNA viruses are deadly and should be avoided in vaccine evaluation. Live viruses could be substituted or represented by this RNA phage display system, avoiding biohazards usually seen during live virus laboratory manipulations. With the volume of reports and papers on RNA viruses’ replication, mutation, resistance to antiviral therapy, Red Queen Hypothesis, and escaping immune molecules, the struggle and difficulties to develop vaccines against these viruses can be alleviated in the coming century using RNA phage display system.


Rice mutant lines containing defective membrane trafficking Rab5 and Rab5-GEF mislocalize glutelin RNAs intracellularly, and export specific sets of RNAs that share functions and/or intracellular locations to extracellular paramural bodies. glup6, glutelin RNAs are specifically mislocalized from their normal location on the cisternal endoplasmic reticulum (ER) to the protein body-ER, and are also apparently translocated to the PMBs. We substantiated the association of mRNAs with this aborted endosome complex by RNA-seq of PMBs purified by flow cytometry. Two PMB-associated groups of RNA were readily resolved: those that were specifically enriched in this aborted complex and those that were highly expressed in the cytoplasm. Examination of the PMB-enriched RNAs indicated that they were not a random sampling of the glup6 transcriptome but, instead, encompassed only a few functional mRNA classes. Although specific autophagy is also an alternative mechanism, our results support the view that RNA localization may co-opt membrane vesicular trafficking, and that many RNAs that share function or intracellular location are co-transported in developing rice seeds.The transport of rice glutelin storage proteins to the storage vacuoles requires the Rab5 GTPase and its related guanine nucleotide exchange factor (Rab5-GEF). Loss of function of these membrane vesicular trafficking factors results in the initial secretion of storage proteins and later their partial engulfment by the plasma membrane to form an extracellular paramural body (PMB), an aborted endosome complex. Here, we show that in the rice Rab5-GEF mutant  RNA localization is a ubiquitous and effective strategy employed by micro-organisms, animal, and plant cells in sorting proteins to specific intracellular locations . Such a The transport and localization of the storage protein RNAs to the ER subdomains requires specific cis-sequences (zipcodes) located in the coding sequence and 3′-untranslated region , 2014. Cglup4 and glup6 (glutelin precursor) mutant lines, proglutelins and α-globulins are secreted to the extracellular space and deposited as small, electron-dense granules during early endosperm development  glup6 mutant alleles EM939 and EM1327 were used for RNAseq analysis of purified PMBs and microscopy studies, respectively -treated water.Total RNA was extracted using a protocol modified from The concentration and quality of each RNA sample were determined using a ThermoFisher Qubit RNA HS Kit and an Agilent Bioanalyzer 6000 Pico Chip, respectively. Polyadenylated (poly-A) RNA was enriched from the tRNA using a NEBNext Poly(A) mRNA Magnetic Isolation Module. The concentration of the poly-A-enriched RNA was obtained using the Qubit RNA HS Assay and an aliquot of 50 ng was fragmented and converted to cDNA. Adapter sequences and indexes were then added to the ends of the fragments to generate libraries for sequencing using an Illumina NextSeq 500. The resulting sequencing library for each sample was denatured to a final concentration of 1.5 pM before being loaded onto a NextSeq Reagent Cartridge for a paired-end 151-bp sequencing run.q-score of 15, and a minimum trimmed read length of >29 bp were kept for mapping and analysis. Quality-filtered reads were mapped using Bowtie2  with default settings, according to the manual. These included end-to-end alignment, default alignment scores (+/–0.6 × read length), and the default mode of searching for multiple alignments and then reporting the best one, or choosing randomly among equally good alignments. Abundance estimation  was calculated using RSEM . Transcripts that were differentially expressed >2-fold with a P-value <0.001 were considered statistically significant.The recovered forward and reverse 2 × 151-bp Nextseq reads were concatenated into a single file and mapped as single-end reads for analysis . All reaing RSEM . DiffereJXB online). The two halves of the seed were then opened up and the soft, bulky endosperm tissue removed to leave the tegmentum, the aleurone cell layer, and 3–5 layers of subaleurone intact. These seed peels were then stained overnight with 1:1000 diluted fluorescein-labeled anti-rabbit secondary antibody at 4 °C. The stained seed peels were washed three times with cold 1× PBS  for 30 min to remove excess staining. Stained dilated structures (the PMBs), which are located in the subaleurone layers, were released from membranes by sonication: two stained aleurone peels were submerged in 500 µl of 1× PBS in a 2-ml microfuge tube and sonicated three times with 5 W for 20 s. The supernatant, now containing the released PMBs, was pooled and then subjected to sorting by flow cytometry.One hundred developing seeds (mainly at 10–12 DAF) were freshly collected and dehulled. In order to prepare ‘seed peels’, the pericarp was removed using tweezers and then the seeds were sliced longitudinally using a thin razor blade, leaving 1 mm of tissue uncut . A 10-ml fraction of size- and fluorescence-selected PMB particles was concentrated to 1 ml final volume using a Centricon 100 kDa centrifugal filter. Overall, three biological replicates were prepared over a 10-d period.The Becton Dickinson  FACS Vantage with Diva software operating system was used to isolate fluorescent PMBs. A selective electronic gate with side and forward light scattering was used to select for particle size and a second gate was used to select for fluorescence. A purified PMB fraction was readily collected by comparing the particle-size fluorescence spectra generated by resolved preparations from g for 10 min. The supernatant was extracted with 100 µl of chloroform and centrifuged for 10 min. To improve RNA quality, 400 µl of the upper aqueous phase was mixed with 400 µl of 70% EtOH and then further purified using a RNeasy Mini Purification Kit (Qiagen). The integrity and purity of total RNA were assessed using an Agilent Bioanalyzer and OD260/280 with a Nanodrop spectrophotometer. Using 100 ng of total RNA, 1–2 μg of cDNA was generated using a Clontech Smart cDNA Synthesis Kit . cDNA was fragmented using a sonicator , profiled using an Agilent Bioanalyzer, and subjected to Illumina library preparation using NEBNext reagents (New England Biolabs).Half of the purified PMB fraction was incubated with 2 units of RNase free DNase I (Promega) for 30 min at 37 ºC to remove potential contamination by genomic DNA. RNA was isolated from purified PMBs by mixing with 500 µl of TRIzol reagent (Invitrogen) followed by centrifugation at 13 400 g at 4 °C for 15 min. The upper phase was transferred to a new tube and an equal volume of chloroform was added before centrifuging again at 4 °C for 15 min. The upper aqueous phase was then collected and total RNA precipitated by incubation with an equal volume of 4 M LiCl at 4 °C overnight. The RNA was pelleted by centrifugation at 4 °C for 30 min, resuspended in DEPC-treated water, and then precipitated in the presence of 0.3 M NaOAC (pH 5.2) and 2.5 volumes of 95% ethanol. The mixture was frozen in liquid nitrogen for 1 min and centrifuged at 4 °C for 30 min. The RNA pellet was washed in 70% ethanol after discarding the supernatant and, after air-drying, was then dissolved in 10 μl DEPC-treated water. A 10-ng sample of total RNA was subjected to double-strand cDNA amplification. The cDNA library was loaded on a HiSeq 2000 instrument and paired-end sequencing with 100 base reads was performed. The processing of fluorescent images into sequences, base-calling, and quality value calculations were performed using the Illumina data processing pipeline (version 1.8). Read sequences were aligned and mapped to the MSU v.7.0 cDNA reference  using CLC Genomics Workbench . To profile transcript expression levels for each mapped gene, RPKM was calculated for the exons of the transcripts in the respective MSU v.7.0 annotation. We only selected valuable transcript species for which cDNA species possessed an RPKM value ≥1 in both wild-type and sorted samples.For control transcripts, total RNA was extracted from seed peels isolated from developing wild-type TC65 seeds (10–15 DAF) using the RNA extraction method described above. In detail, five seed peels were ground to a fine powder under liquid nitrogen and then transferred to a tube containing 500 ul Extraction Buffer . An equal volume of buffer-saturated phenol was added, mixed well, and centrifuged at 13 400 in situ RT-PCR was as described previously by In situ RT-PCR was performed in the presence of 200 µM each of dATP, dCTP, dGTP, 190 µM dTTP, and 20 µM Alexa Fluor 488–dUTP, with basic PCR ingredients including 3 mM MgCl2, 0.5 mM MnSO4, 5 mM dithiothreitol, 2.5 U Tth DNA polymerase , and 1× PCR Enhancer. The primers used for cDNA synthesis are listed in + phosphate, 2.7 mM KCl, pH 7.4) to remove excess staining. Samples were observed using a Zeiss 510 series laser-scanning confocal microscope . Image processing was performed using Adobe Photoshop, NIH Image , or Microsoft PowerPoint.The procedure used for cryosectioning, fixation, and washing of sections of 10–15-d-old developing rice seed prior to glup4 plant line, developing glup6 seeds exhibited the same defects in storage-protein accumulation and endosomal trafficking. This mutant line, defective in the Rab5-dependent GEF, over-accumulated the 57-kD proglutelin precursor due to a defect in its transport to the protein storage vacuoles (PSVs), where it would normally be processed to acidic and basic subunits . At the TEM level, the ER is organized as successive layers of membrane at the cortex of the cell in wild-type endosperm cells. In glup6, the ER membranes were distributed in a more disordered manner near the PMBs and were far less abundant , they were clearly distinct from each other (R2=0.49). In general, glup6 seeds exhibited higher transcriptional activity than wild-type seeds. Considering RNAs having a RPKM≥1.0, seeds of glup6 had on average 11784 RNAs at this abundance while the wild-type had a significantly lower number at 6676  were also significantly up-regulated . The expression of a single TF, NAC domain-containing protein, was silenced in glup6.Substantially fewer genes  were down-regulated in ild-type . In genein situ RT-PCR reaction of endosperm sections, we set out to purify the PMBs. Having observed abnormally high background binding of fluorescein-labeled secondary antibodies to the PMBs, the ‘sticky’ nature of these structures was exploited to allow their rapid and efficient purification by fluorescence-activated cell sorting (FACS) and, in turn, to permit the identification and quantification of PMB-associated mRNA species. The starting materials chosen for this procedure were ‘aleurone/subaleurone peels’ isolated from mid-developing seeds of both the wild-type (TC65) and glup6 , the two PMB cDNA libraries showed poor correlation (R2=0.15) of gene expression with the glup6 transcriptome, indicating that the PMB libraries were considerably distinct from glup6. This diversity was also reflected in the number of gene sequences with RPKM values ≥1.0. While the glup6 transcriptome had 11784 gene sequences with a RPKM ≥1.0, a number close to the reported rice endosperm transcriptome (RNA samples (~10 ng each) from two purified PMB samples were reverse-transcribed and the resulting cDNA was amplified using Ovation RNA-seq System V2 to yield 5.9 ± 1.6 μg. We obtained a total of 29 million reads (19456436 and 9624850) for criptome . One concriptome . The preglup6 transcriptome (PMB/glup6) by at least 5-fold or greater. Based on these criteria, the differentially expressed PMB transcriptome contained 1245 RNAs, the majority consisting of expressed proteins (446), hypothetical proteins (149), retrotransposons (331), and transposons (149) (a aproprotein (4), and two PSI assembly proteins, ycf3 and ycf4 (a apoprotein (4 genes), and reaction the center proteins psbC and psbK. Four PMB-enriched RNAs coded for proteins that transfer electrons from PSII to PSI. These included those for the cytochrome b6-f complex including cytochrome b6 (3 genes) and the cytochrome b6-f complex subunit 4, and for plastocyanin. Lastly, RNAs for NADPH-dependent oxidoreductase (7) and the CO2-fixing enzyme Rubisco large subunit (2) were also enriched on the PMBs.To identify mRNA sequences that were enriched in the PMBs, we selected PMB-localized RNA sequences present with an RPKM value ≥1.0 and enriched over the total ns (149) . The remns (149) , 3. The and ycf4 . Additio1 and F0 complexes of ATPase synthase. These included the A, B, and C subunits of the F0 membrane proton channel and two (α and β) of the five subunits of the F1 catalytic core. Two RNAs coded for proteins in cytochrome C oxidase formation and two coded for subunits of the NADH-ubiquinone oxidoreductase. Of the 17 RNAs in the energy-respiration class, 13 coded for various subunits of the Fglup6 compared to the wild-type but it was enriched more than 33-fold in the PMBs (Transcripts in the DNA-binding class (17) coded for proteins involved in RNA transcription and DNA replication and repair, although many RNAs in this class coded for DNA-binding proteins of unknown functions. Two RNA-binding classes were evident: one coded for ribosomal proteins associated with cytoplasmic (3), mitochondrial (4), and plastid (9) ribosomes, and the second included RNA-binding proteins. One notable RNA-binding protein RBP-D (LOC_Os06g37000) recognizes the prolamine zipcode and therefore probably participates in the transport and localization of prolamine RNAs to the PB-ER. The expression of RBP-D was only slightly activated (1.4-fold) in the PMBs . A seconglup6 and specifically found only in the PMBs. The membrane-related class consisted mainly of transporters for small molecules (e.g. aquaporin) or polypeptides (tat-like protein). The gene activation of cell wall- and membrane-related proteins was consistent with the significant changes at the cell surface due to the formation of PMBs.Two other classes enriched on the PMBs were cell wall-related and membrane-related proteins. The cell wall-related class consisted of eight glycine-rich cell wall structural proteins, cellulose synthase-like protein, and lignin-degrading laccase-9 precursor. Six of the RNAs for glycine-rich cell wall structural proteins were activated in Other RNAs coded for antimicrobial peptides (8), enzymes (6), cytoskeleton (1), protein turnover-modification (4), signal transduction (3), and a large class of miscellaneous RNAs (37). Many of the RNAs in this latter class coded for proteins with defined peptide domains . Sixteen were transcripts for latency-associated nuclear antigens.in situ RT-PCR, they were not enriched relative to their glup6 levels. Examination of the RNA-seq data, however, indicated that eight glutelin RNAs were among the first 40 most-abundant RNAs on the PMBs  in the PMBs  to a low of ~5 for another glutelin RNA (LOC_Os02g15070). Other significant differences are observed for ABA stress- and cell wall-related RNAs. Six prominent RNAs that coded for heat-shock proteins and other stress-related proteins had prominent RPKM values in the PMBs, although they encompassed only 1–6% of the total transcriptome levels in Other than the changes observed for the aforementioned classes of RNAs, many of the other RNA classes were relatively unaffected by the two criteria used in in situ RT-PCR. These RNAs varied in abundance on the PMBs from RPKM values >150 for RBP-D to <13 for DNA-directed RNA polymerase  is the reduction in intracellular membranes. This was readily apparent for the ER, which under normal conditions was arranged in multiple layers, and ran parallel to the plasma membrane , and for terminal NADPH-oxidoreductase.The composition of RNAs enriched on the PMBs was clearly non-random and was not simply a proportional sampling of the glup6 compared to the wild-type. While there were RNAs that were not transcribed in wild-type developing seeds but activated in glup6, others showed relatively minor changes in gene expression compared with the wild-type  and were not present on the PMBs, the presence of these storage-protein RNAs was probably an artifact of PMB purification. One plausible explanation is that the partially intact prolamine PBs, which contained peripherally bound prolamine and α-globulin RNAs, become associated with PMBs during their purification due to the hydrophobic nature of the exposed prolamine polypeptides. Whatever the explanation, our microscopic studies of rice endosperm sections  in glup6 seeds based on their RPKM values  and LOC_Os12g39890 (coding for a transposon) were expressed in both glup6 and the wild-type at RPKM values of ≤0.1.In view of their close biochemical relationship, loss of Rab5 and GEF activities would be expected to result in similar, if not identical changes in gene expression. In order to assess the degree of overlap between differentially regulated transcripts in developing seeds of M values . In contglup4 and glup6 mutants. One factor likely to be important is that microarrays typically are not able to provide an accurate measure of fold-changes, especially for low-expression genes. This limitation comes from differences in hybridization efficiency as well as differences in cross-hybridization backgrounds among millions of array probes, while RNA-seq technology produces discrete, digital sequencing read counts and can quantify expression across a larger dynamic range (>105 for RNA-seq versus 103 for arrays)   that were up-regulated in glup4 but silenced in glup6.A second, and likely compounding factor, is that rice contains a total of four Rab5 genes, two of which are conventional Rab5s (GLUP4/Rab5a and Rab5c) and two which are plant-specific (Rab5b and Rab5d), which are expressed during rice seed development. In addition, rice seeds also express two GEFs, GLUP6/GEF and Rab5-GEF2, , glup6 compared to the wild-type.Table S2. Transcriptome RPKM data for genes that were up-regulated in glup6 compared to the wild-type.Table S3. Transcriptome RPKM data for genes that were down-regulated in Table S4. Alignment of LOC_Os09g00999 and LOC_Os09g01000 cDNA sequences to various plant rRNA sequences.Table S5. List of expressed proteins, hypothetical proteins, retrotransposons, and transposons enriched in PMBs.Table S6. RNA classes enriched in PMBs with RPKM ratio ≥5.0.Table S7. RNAs present in purified PMBs at RPKM ≥5.0.glup4 and glup6.Table S8. Comparison of up-regulated and down-regulated genes in in situ RT-PCR.Table S9. List of primers used for Fig. S1. Preparation of aleurone/subaleurone peels from developing rice seeds.in situ RT-PCR.Fig. S2. Localization of selected RNAs on prolamine PBs and PMBs as assessed by Supplementary Table S1Click here for additional data file.Supplementary Table S2Click here for additional data file.Supplementary Table S3Click here for additional data file.Supplementary Table S4Click here for additional data file.Supplementary Table S5Click here for additional data file.Supplementary Table S6Click here for additional data file.Supplementary Tables S7-S8 and Figures S1-S2Click here for additional data file.glup6 and TC65 (wild-type) rice lines and the two biological repeats each for purified PMBs and seed peels are available at Dryad Digital Repository. doi:10.5061/dryad.sn878g4 (Raw transcriptome data for the three biological repeats each for


RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design.RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call  The range of functions performed by ribonucleic acid (RNA) molecules in cellular processes—from protein synthesis  to gene With the growing number of RNA structures solved experimentally, computational modeling of RNA structures for analysis, prediction, and design becomes increasingly important . A commoRNA 2D structures were first represented as graphs by Waterman , followeThe tree graph representation is intuitive and convenient in many applications, most recently in structure prediction  and desiblocks. A block is a subgraph whose vertices cannot be removed to produce disconnected graphs or a graph composed of a single vertex. The dual graph partitioning algorithm helps identify all blocks while keeping all RNA junctions as well as pseudoknots intact.Graph theory offers natural ways to divide RNA 2D structure modeled as graphs. Previously, we have applied a mathematical graph partitioning approach to segment tree graphs . We alsoHere, we report an RNA submotif library of RNA substructures represented as dual graphs by partitioning the 2D structures of a representative set of 2280 RNA structures  RNA website, see RAG-3Dual. The dataset contains a total of 5332 atomic fragments corresponding to the 56 subgraph blocks (between 2–9 vertices), and 48 atomic fragments for dual graph blocks with ≥10 vertices. RAG-3Dual serves as a first step in using dual graphs and associated atomic components to analyze and search substructures among RNAs, and for 3D structure prediction and design of RNAs by fragment assembly .As an application of graph partitioning, we analyze the distribution of submotifs within ribosomal RNAs (rRNAs) of various prokaryotic and eukaryotic species in our representative dataset. Our analysis reveals subgraphs that are present in all species we analyzed and subgraphs that are common to a subset of species (and may relate to evolution), as well as subgraph blocks that are exclusively present in rRNA structures. These findings suggest that graph partitioning may be useful for evolutionary analysis and that some submotifs may be unique to rRNAs. Further applications of our dual graph work for motif search and design can be envisioned.In this section, we describe the methods and data we use for the extraction, partitioning, and analysis of RNA submotifs using dual graphs.stems or helices, and the single-stranded regions connecting the helices are called loops . The secondary (2D) structure describes the connectivity between various stems and loops in an RNA chain. A common 2D structural motif found in RNA chains is a pseudoknot, which is composed of intertwined base-pairing regions that are not well nested (RNA molecules are chains made up of four nucleotides: adenine (A), guanine (G), cytosine (C), and uracil (U). RNA chains can fold onto themselves to form canonical base pairs . These double-stranded regions are called l nested .An RNA stem (or helix) with at least two canonical base pairs is considered as a vertex.Each loop strand between two helices is denoted as an edge. Single-residue bulges and internal loops with only one nucleotide in each strand are ignored.Uninterrupted hairpin loops  are represented as self loops.Unpaired bases or helical ends at the 5Our RAG resource represents the 2D structure of RNAs as dual graphs using the following rules ,29:An RNV is the set of vertices and E is the set of edges, the Laplacian matrix, a mathematical representation of graph/network connectivity, is defined as D is the degree matrix and A is the adjacency matrix. D specifies the number of edges at each vertex, i.e., degree of a vertex as diagonal entries A indicates the number of edges between each pair of vertices, i.e., i and j, 0 if i and j are not connected; i, 0 otherwise. By construction, the diagonal element D is equal to the sum of all elements in row i of matrix A. Self loops are ignored when calculating the Laplacian.The usage of graphs to represent RNA 2D structures reduces the degrees of freedom and provides a quantitive way to study RNA topology. For a given graph L is a measure of graph connectivity, and can help us to classify RNAs based on our graph representations. For a dual or tree graph of n nodes, the n eigenvalues of L are non-negative real numbers, as L is symmetric. The first eigenvalue, The eigenvalue spectrum of topology . Graphs pologies ,29. In a done in .V vertices, and are connected by randomly selected number of edges . Because the previous vertex is always selected from the connected graph (except for the first step), all enumerated graphs are inherently connected. Dual graphs with the same Laplacian spectrum are removed. The complexity and number of dual graphs increase rapidly with vertex number, and hence it may not be possible to exhaustively enumerate all possibilities for larger values of V [Our dual graph definitions as described in  library ,34. In eues of V .V is the number of vertices and n is a unique integer (based on the Laplacian spectrum) which distinguishes among the dual graphs with the same number of vertices. For vertex numbers V = 2, 3, 4, 5, 6, 7, 8, and 9, our enumerated dual graph library contains 3, 8, 30, 108, 494, 2388, 12,184, and 38,595 graphs, respectively [The dual graphs in our library are cataloged based on the number of vertices and the eigenvalue spectrum of the corresponding Laplacian. Specifically, each dual graph ID has the form ectively . The annectively ; dual grectively . AlthougTo assign a dual graph ID to a given RNA structure, we convert the 2D structure to a dual graph using the above rules , calculav is said to be an articulation point if block. A block corresponds to a pseudoknot if it contains a vertex v of degree at least three i.e., three or more edges are incident at v, not counting self loops [Simplified graph representations of RNAs provide a systematic and efficient approach for partitioning them into topologically and biologically meaningful submotifs. Our recent partitioning method for dual graphs maintains pseudoknots and junctions intact ,48. The lf loops ,48. The lf loops .In The representative set of RNA structures obtained from the BGSU RNA site available online in  is used RNA 3D structures listed in the representative set were downloaded from the PDB (including multiple PDB format files for large structures in mmCIF format), and RNA chains listed in the set were extracted. Duplicate entries within a single equivalence class (duplicate chains and multiple NMR models) were removed, along with water and ligand molecules. Modified bases were retained, and the residues were renumbered if the PDB file contained insertion codes (residue numbers containing letters). Chains from a single PDB file separated into different equivalence classes were not combined. The RNAView program  was usedWe determined dual graphs for 863 2D structures in the representative list of RNA 3D structures. Of these, the dual graphs for 108 structures contained ≥ 10 vertices. For these 108 structures, there are no dual graph IDs in our library since we have enumerated only dual graphs between two to nine vertices. For the remaining 755 structures, we identified 94 unique dual graph topologies. These include four new topologies with nine vertices that correspond to RNA structures but did not exist in our enumerated dual graph library . They arNext, we updated dual graph topologies classified as existing topologies  in the 2011 update to our RAG resource . PreviouComparing the new set of 94 dual graph topologies to the 71 we classified as existing in 2011, we find that 34 are common. Among the 37 of 71 that are missing, 24 topologies corresponded to RNA 2D structures from the Rfam, Pseudobase, or the SRP database; and 13 were from the PDB but with duplicate chains, chains from different RNA molecules separated in the representative dataset, or segments of RNA molecules part of larger RNA structures in the representative dataset. Therefore, these 37 dual graph topologies  by applying dual graph partitioning algorithm . The ate online ,41,46  are responsible for many of the functional activities and structural properties of the ribosome. Numerous tertiary interactions in rRNAs, including pseudoknots, dictate the RNA chain folding onto its 3D structure . The 2D Not surprisingly, some of the most common submotifs found in the entire dataset are also the most common ones found in rRNAs also see  are not large subunit rRNAs of a few representative species, with interesting subgraphs highlighted. The subgraph 4_21 (3-way junction with a pseudoknot shown in green) is common to all prokaryotic and eukaryotic large subunit rRNAs and occurs once in every structure. The subgraph 7_2389 (7-way junction shown in blue) is also present in all large subunit rRNAs, except that of Homo Sapiens. Instead, the 28S/5.8S rRNA of Homo sapiens contains a larger 21-vertex subgraph (shown in light brown). The subgraph 4_23  is present in all bacterial 23S rRNAs but not in any archaea, and all eukaryotic 25-28S/5.8S rRNAs except 1 of the protozoa Trichomonas vaginalis and the fungus Thermomyces lanuginosus. Instead, the archaea 23S rRNAs contain the subgraph 5_46 (shown in light purple), and so does the 25S/5.8S rRNA of the protozoan. The subgraph 6_253 (shown in dark purple) is present in all archaea and three bacteria . A smaller pseudoknot subgraph 4_27 (shown in orange) is present in all eukaryotes except the protozoan Plasmodium falciparum, the fungus Thermomyces lanuginosus, and the yeast Kluyveromyces lactis. Interestingly, the subgraph 5_107 (shown in light blue) is present in all species that do not contain either the 6_253 or the 4_27 subgraph, except the fungus.small subunit rRNAs of a few representative species, with interesting subgraphs highlighted. The subgraph 5_2 (5-way junction shown in light blue) is present twice in all prokaryotic 16S rRNAs, and subgraph 5_18 (4-way junction with pseudoknot shown in green) is present once. The seven occurrences of 5_2 and 4 occurrence of 5_18 in eukaryotic 18S rRNAs are distributed over four structures  and are absent in all the yeast, fly, and plant structures. The subgraph 7_814 (with two junctions and a pseudoknot shown in pink) is present in all but one of the 16S rRNAs (bacterium Lactococcus lactis that contains the subgraph 6_28 instead (shown in light purple)), and only in three 18S rRNAs ; two of the protozoan 18S rRNAs, P. falciparum and T. vaginalis, contain the subgraph 8_3258 instead (shown in hotpink). A smaller subgraph with a 3-way junction and a pseudoknot, 4_27 (shown in orange), is present in 11 of the 12 18S rRNAs , and 7 of the 10 16S rRNAs. Interestingly, the 16S rRNAs that do not contain 4_27  contain a smaller pseudoknot subgraph 3_7 (shown in yellow). All 16S rRNAs and five of the 18S rRNAs consist of only subgraphs with 2–8 vertices . Interestingly, these five 18S rRNAs are the ones mentioned above that stand out from other 18S rRNAs.Representing RNAs using a coarse-grained approach and characterizing RNA modular units can help shed insights into structure/function relationships among RNAs. Partitioning RNAs can similarly help analyze recurring 2D submotifs and suggest functional relationships. Application of our partitioning method to large RNA structures such as rRNAs could help detect evolutionary pathways. Because the partitioning is coarse-grained, it provides a clear visualization of submotifs even for very large and complex structures like ribosomes. Our graph representations also provide additional flexibility in terms of RNA size as they focus on the overall topology and connectivity of RNA 2D structural elements, rather than the exact number of residues in stems and loops that can change even in related structures. This flexibility also allows us to ignore small variations in base pairing in RNA 3D structures as detected by different 2D structure annotation methods. As long as the number of stems and their connectivity remains the same, the dual graph topology will be the same. However, differences or errors in base pair annotations that lead to fewer or more stems and loops  may lead to a different dual graph.One main advantage of using dual graphs for RNA 2D structures is their capability of representing pseudoknots. Combined with our dual graph partitioning algorithm that keeps pseudoknots intact, our method offers an effective tool to study RNA structures as well as submotifs and substructures containing pseudoknots. Although the partitioning of structures in the representative RNA dataset reveals many submotifs with pseudoknots, only a few of them occur frequently. In addition, almost all subgraph blocks contain 2–9 vertices (5332 of the 5380 subgraph blocks). Increasing our dual graph library for more than nine vertices and applying dual graph partitioning on more RNAs may certainly increase this submotif library. However, our results suggest that RNAs adopt a combination of only a limited number of submotifs of all theoretically possible motifs, which may simplify the study of RNA substructures and design.Various extensions can be envisioned for our work with dual graph representations. Similar to our work with tree graphs , extendiThe RAG-3Dual database (available online in ) we haveWe have presented an efficient and systematic way of identifying modular units of RNA 2D structures based on dual graph partitioning. Using our dual graph representation and partitioning algorithm on a representative set of RNA structures, we identify 94 dual graph topologies (between two and nine vertices) that correspond to existing RNA structures, and 56 dual graph topologies (between two and nine vertices) that emerge as non-separable subgraph blocks . Of the References


Betanodavirus were obtained from Australian barramundi . Phylogenetic analyses revealed that the sequences have closest homology to the red spotted grouper nervous necrosis virus (RGNNV) species and share between 91 and 98% homology with the other two published complete/near-complete sequences of isolates from Australian fish.The complete RNA-1 and RNA-2 genome sequences of  Betanodavirus were obtained from Australian barramundi . Phylogenetic analyses revealed that the sequences have closest homology to the red spotted grouper nervous necrosis virus (RGNNV) species and share between 91 and 98% homology with the other two published complete/near-complete sequences of isolates from Australian fish.The complete RNA-1 and RNA-2 genome sequences of  Betanodavirus cause the disease viral nervous necrosis (VNN), which is synonymous with vacuolating/viral encephalopathy and retinopathy (VER) (www.ncbi.nlm.nih.gov) contains over 1,200 nucleotide accessions of betanodavirus sourced from over 220 fish species and over 30 countries. VNN has been recognized in Australia since 1988, when it first caused significant mortalities in hatchery-reared larval barramundi, Lates calcarifer (Members of the genus hy (VER) . The dislcarifer .L. calcarifer juveniles were collected from an aquaculture hatchery in Queensland, Australia. The fish displayed mass mortality and clinical signs typical of VNN, including loss of appetite, darkened color, erratic and spiral swimming, and hyperinflation of the swim bladder. Betanodavirus was detected using reverse transcriptase quantitative PCR (RT-qPCR) analysis on eye and brain tissues (Diseased  tissues . Further tissues . PCR amp tissues  in NCBI. tissues .Betanodavirus RNA-1 genomes. The RNA-2 sequence contained the complete open reading frame (ORF) encoding the viral coat protein. Alignment of the sequence against type sequences of the four currently recognized Betanodavirus taxa indicated that the sequences have highest homology to the RGNNV type species.The RNA-1 sequence contained the mRNA encoding RNA-dependent RNA polymerase and B1 and B2 protein motifs characteristic of GQ402010 and GQ402012).Comparative analysis at the nucleotide level indicated that the RNA-1 sequence was 95 to 99% homologous to the sequences within the RGNNV genotype and 96 to 98% homologous to the only two complete RNA-1 sequences obtained from Australian strains (NCBI accession numbers AY744705) and 91 to 98% homology to the only 3 complete/near-complete RNA-2 sequences from Australian strains . This report adds to the genome record of betanodavirus strains endemic to Australia and continues to demonstrate the remarkable trend of conservation of the RGNNV genome sequence across time, host species, and geographic location.Comparative analysis at the nucleotide level of RNA-2 indicated 99% homology to the sequence obtained from the original RGNNV isolate from China in 2000 (NCBI accession number MH181161 (RNA-1) and MH017207 (RNA-2).The gene sequences have been deposited in GenBank under the accession numbers